diff --git a/assignment/06-lab/index.html b/assignment/06-lab/index.html
index 5fae91e..082dde7 100644
--- a/assignment/06-lab/index.html
+++ b/assignment/06-lab/index.html
@@ -569,7 +569,7 @@ <h3 id="read-in-medical-transcriptions">read in Medical Transcriptions</h3>
 <pre><code class="language-r">library(readr)
 library(dplyr)
 mt_samples &lt;- read_csv(&quot;https://raw.githubusercontent.com/USCbiostats/data-science-data/master/00_mtsamples/mtsamples.csv&quot;)
-mt_samples &lt;- mt_samples %&gt;%
+mt_samples &lt;- mt_samples |&gt;
   select(description, medical_specialty, transcription)
 
 head(mt_samples)
diff --git a/assignment/index.xml b/assignment/index.xml
index a848eb0..266ac40 100644
--- a/assignment/index.xml
+++ b/assignment/index.xml
@@ -1319,7 +1319,7 @@ If you don&amp;rsquo;t already have &lt;code&gt;tidytext&lt;/code&gt; then you c
 &lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;library(readr)
 library(dplyr)
 mt_samples &amp;lt;- read_csv(&amp;quot;https://raw.githubusercontent.com/USCbiostats/data-science-data/master/00_mtsamples/mtsamples.csv&amp;quot;)
-mt_samples &amp;lt;- mt_samples %&amp;gt;%
+mt_samples &amp;lt;- mt_samples |&amp;gt;
   select(description, medical_specialty, transcription)
 
 head(mt_samples)
diff --git a/index.json b/index.json
index f818f4a..1a2bcd6 100644
--- a/index.json
+++ b/index.json
@@ -1 +1 @@
-[{"authors":["EmilHvitfeldt"],"categories":null,"content":"Emil Hvitfeldt is a Research Programmer at University of Southern California.\n","date":-62135596800,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":-62135596800,"objectID":"9b5dd807a92ef9cf578125f1957a7629","permalink":"https://uscbiostats.github.io/PM566/authors/kjhealy/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/authors/kjhealy/","section":"authors","summary":"Emil Hvitfeldt is a Research Programmer at University of Southern California.","tags":null,"title":"","type":"authors"},{"authors":null,"categories":null,"content":"  I’ll post relevant materials (slides, example code, in-class activities, etc.) from each class session.\n","date":1701993600,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1701993600,"objectID":"108da05078d325a5a1f01a1ff2583053","permalink":"https://uscbiostats.github.io/PM566/class/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/class/","section":"class","summary":"I’ll post relevant materials (slides, example code, in-class activities, etc.) from each class session.","tags":null,"title":"Class details","type":"docs"},{"authors":null,"categories":null,"content":"  If I assign readings, you really should read them.\n","date":1669766400,"expirydate":-62135596800,"kind":"section","lang":"en","lastmod":1669766400,"objectID":"40fcd2da3bf2dc718a2fe044c31cdc56","permalink":"https://uscbiostats.github.io/PM566/reading/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/reading/","section":"reading","summary":"If I assign readings, you really should read them.","tags":null,"title":"Reading","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise To be announced\nSlides To be announced\nLinks To be announced\n","date":1701993600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1701993600,"objectID":"b19988fbc4a596745287cbd320c34ce7","permalink":"https://uscbiostats.github.io/PM566/class/14-class/","publishdate":"2023-12-08T00:00:00Z","relpermalink":"/PM566/class/14-class/","section":"class","summary":"Lab Exercise To be announced\nSlides To be announced\nLinks To be announced","tags":null,"title":"Final Project","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise To be announced\nSlides To be announced\nLinks To be announced\n","date":1701388800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1701388800,"objectID":"7892754aa83d7c3b826089f56279db6e","permalink":"https://uscbiostats.github.io/PM566/class/13-class/","publishdate":"2023-12-01T00:00:00Z","relpermalink":"/PM566/class/13-class/","section":"class","summary":"Lab Exercise To be announced\nSlides To be announced\nLinks To be announced","tags":null,"title":"Final project workshop: review project progress, preliminary presentations","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise Lab is on a website at this link\nTest\nSlides  link\n","date":1700179200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1700179200,"objectID":"850826af6bd1df6d36626b4f8cf5ca9b","permalink":"https://uscbiostats.github.io/PM566/class/12-class/","publishdate":"2023-11-17T00:00:00Z","relpermalink":"/PM566/class/12-class/","section":"class","summary":"Lab Exercise Lab is on a website at this link\nTest\nSlides  link","tags":null,"title":"Interactive visualization and effective data communication II","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise  link\nSlides  link\nLinks To be announced\n","date":1698969600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1698969600,"objectID":"e8bb9ff0e14e12dc0d25a632d1529969","permalink":"https://uscbiostats.github.io/PM566/class/11-class/","publishdate":"2023-11-03T00:00:00Z","relpermalink":"/PM566/class/11-class/","section":"class","summary":"Lab Exercise  link\nSlides  link\nLinks To be announced","tags":null,"title":"Interactive visualization and effective data communication I","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise  link\nSlides  link\nLinks  https://www.w3schools.com/sql/\n","date":1698364800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1698364800,"objectID":"1a77c377ec266054be4f99d55ca51959","permalink":"https://uscbiostats.github.io/PM566/class/10-class/","publishdate":"2023-10-27T00:00:00Z","relpermalink":"/PM566/class/10-class/","section":"class","summary":"Lab Exercise  link\nSlides  link\nLinks  https://www.w3schools.com/sql/","tags":null,"title":"SQL","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise  link\nSlides  link\nLinks  CRAN Task View on High-Performance Computing (link) USC’s Center for Advance Research Computing (CARC) (link)  ","date":1697760000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1697760000,"objectID":"ac13c8c9f9cbf2a9e59eb2af5882f72b","permalink":"https://uscbiostats.github.io/PM566/class/09-class/","publishdate":"2023-10-20T00:00:00Z","relpermalink":"/PM566/class/09-class/","section":"class","summary":"Lab Exercise  link\nSlides  link\nLinks  CRAN Task View on High-Performance Computing (link) USC’s Center for Advance Research Computing (CARC) (link)  ","tags":null,"title":"High performance computing, cloud computing","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise To be announced\nSlides To be announced\nLinks To be announced\n","date":1697155200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1697155200,"objectID":"242ff150a12ad6aee0a3ecc2f3a0beb0","permalink":"https://uscbiostats.github.io/PM566/class/08-class/","publishdate":"2023-10-13T00:00:00Z","relpermalink":"/PM566/class/08-class/","section":"class","summary":"Lab Exercise To be announced\nSlides To be announced\nLinks To be announced","tags":null,"title":"Midterm Exam","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise  link\nSlides  link\nLinks Here is an extended example of web scraping ( link) that will be featured in the class.\n","date":1696550400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1696550400,"objectID":"2a4adb088999c46e5830743b3c7848b5","permalink":"https://uscbiostats.github.io/PM566/class/07-class/","publishdate":"2023-10-06T00:00:00Z","relpermalink":"/PM566/class/07-class/","section":"class","summary":"Lab Exercise  link\nSlides  link\nLinks Here is an extended example of web scraping ( link) that will be featured in the class.","tags":null,"title":"Week 7: Scraping, APIs, and Regular Expressions","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise  link\nSlides  link\nLinks  R tidytext package  Text Mining with R  Supervised Machine Learning for Text Analysis in R  ","date":1695945600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1695945600,"objectID":"0689e6e444ea198dfcda1929fe27b954","permalink":"https://uscbiostats.github.io/PM566/class/06-class/","publishdate":"2023-09-29T00:00:00Z","relpermalink":"/PM566/class/06-class/","section":"class","summary":"Lab Exercise  link\nSlides  link\nLinks  R tidytext package  Text Mining with R  Supervised Machine Learning for Text Analysis in R  ","tags":null,"title":"Text mining","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise  link\nSlides  link\nLinks  R data.table python data.table  Python and Jupyter  ","date":1695340800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1695340800,"objectID":"20820e62b9f7b664343afa3154498474","permalink":"https://uscbiostats.github.io/PM566/class/05-class/","publishdate":"2023-09-22T00:00:00Z","relpermalink":"/PM566/class/05-class/","section":"class","summary":"Lab Exercise  link\nSlides  link\nLinks  R data.table python data.table  Python and Jupyter  ","tags":null,"title":"Data cleaning and wrangling","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise  link\nSlides  link\nLinks To be announced\n","date":1694736000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1694736000,"objectID":"4c5b16d8a3974a196ac2c16c240044f8","permalink":"https://uscbiostats.github.io/PM566/class/04-class/","publishdate":"2023-09-15T00:00:00Z","relpermalink":"/PM566/class/04-class/","section":"class","summary":"Lab Exercise  link\nSlides  link\nLinks To be announced","tags":null,"title":"Data visualization","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise  link\nSlides  link\nLinks To be announced\n","date":1694131200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1694131200,"objectID":"0ac6b606b4526ab8d78934908811a979","permalink":"https://uscbiostats.github.io/PM566/class/03-class/","publishdate":"2023-09-08T00:00:00Z","relpermalink":"/PM566/class/03-class/","section":"class","summary":"Lab Exercise  link\nSlides  link\nLinks To be announced","tags":null,"title":"Exploratory data analysis","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise  link\nSlides  link\nLinks   Git  GitHub  ","date":1693526400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1693526400,"objectID":"d8f16c438fb963107e4972dd50006da1","permalink":"https://uscbiostats.github.io/PM566/class/02-class/","publishdate":"2023-09-01T00:00:00Z","relpermalink":"/PM566/class/02-class/","section":"class","summary":"Lab Exercise  link\nSlides  link\nLinks   Git  GitHub  ","tags":null,"title":"Version control and reproducible research","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise  link\n Download Template (click the “download” icon on the right, near the “Raw” button)\nSlides  link\nLinks   The R Project for Statistical Computing  R Studio  ","date":1692921600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1692921600,"objectID":"cbd2b035124ee81c843cf99929c787d5","permalink":"https://uscbiostats.github.io/PM566/class/01-class/","publishdate":"2023-08-25T00:00:00Z","relpermalink":"/PM566/class/01-class/","section":"class","summary":"Lab Exercise  link\n Download Template (click the “download” icon on the right, near the “Raw” button)\nSlides  link\nLinks   The R Project for Statistical Computing  R Studio  ","tags":null,"title":"Look at Data / Up and Running in R and ggplot","type":"docs"},{"authors":null,"categories":null,"content":"Due date: Friday December 6th, 2024, by 11:59pm Pacific Time.\nLearning Objective: To apply the skills learned in PM 566 by analyzing and interpreting a dataset of your choice.\nNarrative: Through this project you will launch a portfolio of data science projects that will become seminal for your job hunt.\nUsing the dataset from your midterm, make sure you have formulated a clear and concise question to answer. You will apply the skills learned throughout the semester to answer this question.\nDeliverables  Written report (single spaced, 6-8 pages where 3-4 pages are tables and figures) Website (summarize your analysis, include interactive figures, provide a link to download the full written report)  Written report The actual analysis should be included in the PDF report. The PDF report can refer to interactive visualizations included in the website, but otherwise all figures and tables should be included in the PDF.\nThe report should have the following sections (elaborate from what was written in the midterm): Introduction (provide background on your dataset and formulated question), Methods (include how and where the data were acquired, how you cleaned and wrangled the data, what tools you used for data exploration), Results (provide final, publication ready tables and figures from your analysis, make separate subsections as needed), and a brief Conclusion and Summary where you describe your findings.\nIn your report, please do not include any code (so make sure echo = FALSE), unformatted output, or dataset summaries (e.g. output from head(), str(), etc.)\nWebsite Checklist See the checklist below for additional details about the website requirements:\n  Create a website (HTML document and all the required files, including figures). It should feature:\n  A brief description of the project,\n  Interactive visualizations, also with a description so that people can understand what they are looking at, and\n  A link to the PDF version of the actual report (i.e. a link to “Download the report.”).\n  Your home page should feature no more than five interactive tables and figures. If you want to include more to showcase your skills, we request you do so by adding extra pages to the website. We will only evaluate the homepage of the website.\n    Upload everything, source code, website files, and PDF report, to the GitHub repository.\n  Make sure that the website, which is to be hosted in GitHub pages, actually works, i.e., figures and interactive visualizations are properly rendered when visiting the website.\n  Have a README.md file in the upper level of the repository. This file provides general information about the project, like title, brief description, etc.\n  The README.md file should provide a link to the website, e.g. https://USERNAME.github.io/pm566-project.\n  Have a data folder with either the dataset or instructions about how to acquire it. You should provide the instructions in a README.md file within that folder.\n  ","date":1732233600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1732233600,"objectID":"915965f8a000fc393c5e18173dbf000b","permalink":"https://uscbiostats.github.io/PM566/assignment/14-final/","publishdate":"2024-11-22T00:00:00Z","relpermalink":"/PM566/assignment/14-final/","section":"assignment","summary":"Due date: Friday December 6th, 2024, by 11:59pm Pacific Time.\nLearning Objective: To apply the skills learned in PM 566 by analyzing and interpreting a dataset of your choice.\nNarrative: Through this project you will launch a portfolio of data science projects that will become seminal for your job hunt.\nUsing the dataset from your midterm, make sure you have formulated a clear and concise question to answer. You will apply the skills learned throughout the semester to answer this question.","tags":null,"title":"Final Project","type":"assignment"},{"authors":null,"categories":null,"content":"Due Date This assignment is due by 11:59pm Pacific Time, November 22nd, 2024.\nThe learning objectives are to write faster code for computational task requiring a loop and to implement some queries and basic data wrangling in SQL.\nHPC Make things run faster Rewrite the following R functions to make them faster. It is OK (and recommended) to take a look at StackOverflow and Google\n# Total row sums fun1 \u0026lt;- function(mat) { n \u0026lt;- nrow(mat) ans \u0026lt;- double(n) for (i in 1:n) { ans[i] \u0026lt;- sum(mat[i, ]) } ans } fun1alt \u0026lt;- function(mat) { # YOUR CODE HERE } # Cumulative sum by row fun2 \u0026lt;- function(mat) { n \u0026lt;- nrow(mat) k \u0026lt;- ncol(mat) ans \u0026lt;- mat for (i in 1:n) { for (j in 2:k) { ans[i,j] \u0026lt;- mat[i, j] + ans[i, j - 1] } } ans } fun2alt \u0026lt;- function(mat) { # YOUR CODE HERE }  Question 1 Using the dataset generated below (dat), check that the output of both of your new functions matches the output of the original functions. Then use microbenchmark to check that your version is actually faster.\n# Use the data with this code set.seed(2315) dat \u0026lt;- matrix(rnorm(200 * 100), nrow = 200) # Test for the first microbenchmark::microbenchmark( fun1(dat), fun1alt(dat), unit = \u0026quot;relative\u0026quot; ) # Test for the second microbenchmark::microbenchmark( fun2(dat), fun2alt(dat), unit = \u0026quot;relative\u0026quot; )  Make things run faster with parallel computing The following function allows simulating pi:\nsim_pi \u0026lt;- function(n = 1000, i = NULL) { p \u0026lt;- matrix(runif(n*2), ncol = 2) mean(rowSums(p^2) \u0026lt; 1) * 4 } # Here is an example of the run set.seed(156) sim_pi(1000) # 3.132  In order to get accurate estimates, we can run this function multiple times, with the following code:\n# This runs the simulation a 4,000 times, each with 10,000 points set.seed(1231) system.time({ ans \u0026lt;- unlist(lapply(1:4000, sim_pi, n = 10000)) print(mean(ans)) })  Question 2 Rewrite the previous code using parLapply() (or your parallelization method of choice) to parallelize it. Run the code once, using system.time(), to show that your version is faster.\n# YOUR CODE HERE system.time({ # YOUR CODE HERE ans \u0026lt;- # YOUR CODE HERE print(mean(ans)) # YOUR CODE HERE })  SQL Setup a temporary database by running the following chunk\n# install.packages(c(\u0026quot;RSQLite\u0026quot;, \u0026quot;DBI\u0026quot;)) library(RSQLite) library(DBI) # Initialize a temporary in memory database con \u0026lt;- dbConnect(SQLite(), \u0026quot;:memory:\u0026quot;) # Download tables film \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/film.csv\u0026quot;) film_category \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/film_category.csv\u0026quot;) category \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/category.csv\u0026quot;) # Copy data.frames to database dbWriteTable(con, \u0026quot;film\u0026quot;, film) dbWriteTable(con, \u0026quot;film_category\u0026quot;, film_category) dbWriteTable(con, \u0026quot;category\u0026quot;, category)  When you write a new chunk, remember to replace the r with sql, connection=con. Some of these questions will require you to use an inner join. Read more about them here https://www.w3schools.com/sql/sql_join_inner.asp\nQuestion 3 How many many movies are available in each rating category?\nQuestion 4 What is the average replacement cost and rental rate for each rating category?\nQuestion 5 Use table film_category together with film to find how many films there are with each category ID.\nQuestion 6 Incorporate the category table into the answer to the previous question to find the name of the most popular category.\n","date":1731024000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1731024000,"objectID":"bb9dff9b6c32caa35a8d0693f3d633ce","permalink":"https://uscbiostats.github.io/PM566/assignment/11-hw4/","publishdate":"2024-11-08T00:00:00Z","relpermalink":"/PM566/assignment/11-hw4/","section":"assignment","summary":"Due Date This assignment is due by 11:59pm Pacific Time, November 22nd, 2024.\nThe learning objectives are to write faster code for computational task requiring a loop and to implement some queries and basic data wrangling in SQL.\nHPC Make things run faster Rewrite the following R functions to make them faster. It is OK (and recommended) to take a look at StackOverflow and Google\n# Total row sums fun1 \u0026lt;- function(mat) { n \u0026lt;- nrow(mat) ans \u0026lt;- double(n) for (i in 1:n) { ans[i] \u0026lt;- sum(mat[i, ]) } ans } fun1alt \u0026lt;- function(mat) { # YOUR CODE HERE } # Cumulative sum by row fun2 \u0026lt;- function(mat) { n \u0026lt;- nrow(mat) k \u0026lt;- ncol(mat) ans \u0026lt;- mat for (i in 1:n) { for (j in 2:k) { ans[i,j] \u0026lt;- mat[i, j] + ans[i, j - 1] } } ans } fun2alt \u0026lt;- function(mat) { # YOUR CODE HERE }  Question 1 Using the dataset generated below (dat), check that the output of both of your new functions matches the output of the original functions.","tags":null,"title":"Assignment 04 - HPC and SQL","type":"assignment"},{"authors":null,"categories":null,"content":"Due Date This assignment is due by 11:59pm Pacific Time, November 8th, 2024.\nText Mining A new dataset has been added to the data science data repository https://github.com/USCbiostats/data-science-data/tree/master/03_pubmed. The dataset contains 3,241 abstracts from articles collected via 5 PubMed searches. The search terms are listed in the second column, term and these will serve as the \u0026ldquo;documents.\u0026rdquo; Your job is to analyse these abstracts to find interesting insights.\n Tokenize the abstracts and count the number of each token. Do you see anything interesting? Does removing stop words change what tokens appear as the most frequent? What are the 5 most common tokens for each search term after removing stopwords? Tokenize the abstracts into bigrams. Find the 10 most common bigrams and visualize them with ggplot2. Calculate the TF-IDF value for each word-search term combination (here you want the search term to be the \u0026ldquo;document\u0026rdquo;). What are the 5 tokens from each search term with the highest TF-IDF value? How are the results different from the answers you got in question 1?  ","date":1729814400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1729814400,"objectID":"c5b38245189a89adfe25370d23380195","permalink":"https://uscbiostats.github.io/PM566/assignment/09-hw3/","publishdate":"2024-10-25T00:00:00Z","relpermalink":"/PM566/assignment/09-hw3/","section":"assignment","summary":"Due Date This assignment is due by 11:59pm Pacific Time, November 8th, 2024.\nText Mining A new dataset has been added to the data science data repository https://github.com/USCbiostats/data-science-data/tree/master/03_pubmed. The dataset contains 3,241 abstracts from articles collected via 5 PubMed searches. The search terms are listed in the second column, term and these will serve as the \u0026ldquo;documents.\u0026rdquo; Your job is to analyse these abstracts to find interesting insights.\n Tokenize the abstracts and count the number of each token.","tags":null,"title":"Assignment 03 - Text Mining","type":"assignment"},{"authors":null,"categories":null,"content":"Due Sunday, October 27th, 2024 by 11:59pm Pacific Time.\nLearning Objective: To apply the skills learned in PM 566 (through Week 6) by analyzing and interpreting a dataset of your choice.\nNarrative: Through this project you will launch a portfolio of data science projects that will become seminal for your job hunt. This midterm is a stepping stone for the final project. The first step in any data analysis is to have a dataset for which you have formulated an interesting question. If you do not have a dataset to work with, you may choose one from our list of suggestions. With your dataset, formulate a clear and concise question to answer and conduct data wrangling, exploratory data analysis, and data visualization to explore/answer this question.\nDeliverable: A knitted Quarto/R markdown written report (HTML or PDF) with embedded tables and figures that is submitted to a project-specific github repository that you create. The report should have the following sections: Introduction (provide background on your dataset and formulated question), Methods (include how and where the data were acquired, how you cleaned and wrangled the data, what tools you used for data exploration), Preliminary Results (provide summary statistics in tabular form and publication-quality figures, take a look at the kable function from knitr to write nice tables in Rmarkdown), and a brief Conclusion about what you found in terms of the formulated question.\nIn your report, please do not include unformatted output or dataset summaries (e.g. output from head(), str(), etc.). You should summarize these aspects of your data within the text.\n","date":1728604800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1728604800,"objectID":"5606a4bc71acb3181d0674120a22b374","permalink":"https://uscbiostats.github.io/PM566/assignment/08-midterm/","publishdate":"2024-10-11T00:00:00Z","relpermalink":"/PM566/assignment/08-midterm/","section":"assignment","summary":"Due Sunday, October 27th, 2024 by 11:59pm Pacific Time.\nLearning Objective: To apply the skills learned in PM 566 (through Week 6) by analyzing and interpreting a dataset of your choice.\nNarrative: Through this project you will launch a portfolio of data science projects that will become seminal for your job hunt. This midterm is a stepping stone for the final project. The first step in any data analysis is to have a dataset for which you have formulated an interesting question.","tags":null,"title":"Midterm Exam/Project","type":"assignment"},{"authors":null,"categories":null,"content":"Due Date This assignment is due by 11:59pm Pacific Time, October 11th, 2024.\nFor this assignment, we will be analyzing data from USC’s Children’s Health Study. The learning objectives are to conduct data wrangling and visualize the data with key questions in mind.\nData Wrangling You will need to download two datasets from https://github.com/USCbiostats/data-science-data. The individual and regional CHS datasets in 01_chs. The individual data includes personal and health characteristics of children in 12 communities across Southern California. The regional data include air quality measurements at the community level. Once downloaded, you can merge these datasets using the location variable. Once combined, you will need to do the following:\n  After merging the data, make sure you don’t have any duplicates by counting the number of rows. Make sure it matches.\nIn the case of missing values, impute data using the average amongst individuals with the same values for the \u0026ldquo;male\u0026rdquo; and \u0026ldquo;hispanic\u0026rdquo; variables. For categorical variables, take the mode. If you are interested (and feel adventurous) in the theme of Data Imputation, take a look at this paper on \u0026ldquo;Multiple Imputation\u0026rdquo; using the Amelia R package here.\n  Create a new categorical variable named “obesity_level” using the BMI measurement (underweight BMI\u0026lt;14; normal BMI 14-22; overweight BMI 22-24; obese BMI\u0026gt;24). To make sure the variable is rightly coded, create a summary table that contains the minimum BMI, maximum BMI, and the total number of observations per category.\n  Create another categorical variable named \u0026ldquo;smoke_gas_exposure\u0026rdquo; that summarizes \u0026ldquo;Second Hand Smoke\u0026rdquo; and \u0026ldquo;Gas Stove.\u0026rdquo; The variable should have four categories in total.\n  Create four summary tables showing the average (or proportion, if binary) and sd of “Forced expiratory volume in 1 second (ml)” (an asthma indicator) by town, sex, obesity level, and \u0026ldquo;smoke_gas_exposure.\u0026rdquo;\n  Looking at the Data (EDA) The primary questions of interest are:\n What is the association between BMI and FEV (forced expiratory volume)? What is the association between smoke and gas exposure and FEV? What is the association between PM2.5 exposure and FEV?  Follow the EDA checklist from week 3 and the previous assignment. Be sure to focus on the key variables.\nVisualization Create the following figures and interpret them. Be sure to include easily understandable axes, titles, and legends.\n Facet plot showing scatterplots with regression lines of BMI vs FEV by “townname”. Stacked histograms of FEV by BMI category and FEV by smoke/gas exposure. Use different color schemes than the ggplot default. Barchart of BMI by smoke/gas exposure. Statistical summary graphs of FEV by BMI and FEV by smoke/gas exposure category. A leaflet map showing the concentrations of PM2.5 mass in each of the CHS communities. Choose a visualization to examine whether PM2.5 mass is associated with FEV.  ","date":1727395200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1727395200,"objectID":"9499827750596518aa6839e0f7343f77","permalink":"https://uscbiostats.github.io/PM566/assignment/05-hw2/","publishdate":"2024-09-27T00:00:00Z","relpermalink":"/PM566/assignment/05-hw2/","section":"assignment","summary":"Due Date This assignment is due by 11:59pm Pacific Time, October 11th, 2024.\nFor this assignment, we will be analyzing data from USC’s Children’s Health Study. The learning objectives are to conduct data wrangling and visualize the data with key questions in mind.\nData Wrangling You will need to download two datasets from https://github.com/USCbiostats/data-science-data. The individual and regional CHS datasets in 01_chs. The individual data includes personal and health characteristics of children in 12 communities across Southern California.","tags":null,"title":"Assignment 02 - Data Viz and Wrangling","type":"assignment"},{"authors":null,"categories":null,"content":"Due Date This assignment is due by 11:59pm Pacific Time, September 27th, 2024.\nLearning Goals  Download, read, and get familiar with an external dataset. Step through the EDA \u0026ldquo;checklist\u0026rdquo; presented in class Practice making exploratory plots  Assignment Description We will work with air pollution data from the U.S. Environmental Protection Agency (EPA). The EPA has a national monitoring network of air pollution sites that The primary question you will answer is whether daily concentrations of PM$_{2.5}$ (particulate matter air pollution with aerodynamic diameter less than 2.5 \\(\\mu\\)m) have decreased in California over the last 20 years (from 2002 to 2022).\nA primer on particulate matter air pollution can be found here.\nYour assignment should be completed in Quarto or R Markdown.\nSteps   Given the formulated question from the assignment description, you will now conduct EDA Checklist items 2-4. First, download 2002 and 2022 data for all sites in California from the EPA Air Quality Data website. Read in the data using data.table(). For each of the two datasets, check the dimensions, headers, footers, variable names and variable types. Check for any data issues, particularly in the key variable we are analyzing. Make sure you write up a summary of all of your findings.\n  Combine the two years of data into one data frame. Use the Date variable to create a new column for year, which will serve as an identifier. Change the names of the key variables so that they are easier to refer to in your code.\n  Create a basic map in leaflet() that shows the locations of the sites (make sure to use different colors for each year). Summarize the spatial distribution of the monitoring sites.\n  Check for any missing or implausible values of PM$_{2.5}$ in the combined dataset. Explore the proportions of each and provide a summary of any temporal patterns you see in these observations.\n  Explore the main question of interest at three different spatial levels. Create exploratory plots (e.g. boxplots, histograms, line plots) and summary statistics that best suit each level of data. Be sure to write up explanations of what you observe in these data.\n state county sites in Los Angeles     This homework has been adapted from the case study in Roger Peng\u0026rsquo;s Exploratory Data Analysis with R\n","date":1726185600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1726185600,"objectID":"590007c4654c7f5d368d9a80c65beeea","permalink":"https://uscbiostats.github.io/PM566/assignment/03-hw1/","publishdate":"2024-09-13T00:00:00Z","relpermalink":"/PM566/assignment/03-hw1/","section":"assignment","summary":"Due Date This assignment is due by 11:59pm Pacific Time, September 27th, 2024.\nLearning Goals  Download, read, and get familiar with an external dataset. Step through the EDA \u0026ldquo;checklist\u0026rdquo; presented in class Practice making exploratory plots  Assignment Description We will work with air pollution data from the U.S. Environmental Protection Agency (EPA). The EPA has a national monitoring network of air pollution sites that The primary question you will answer is whether daily concentrations of PM$_{2.","tags":null,"title":"Assignment 01 - Exploratory Data Analysis","type":"assignment"},{"authors":null,"categories":null,"content":"Due Date This assignment is due by midnight Pacific Time, December 1st, 2023.\nInteractive Visualization I and II   Using your midterm/final project data, create 3 different interactive visualizations using the tools learned in week 11 class/lab.\n  Include the visualizations created in (1) on your final project website hosted on GitHub Pages.\n  Rename your final project website by modifying the relevant text in the .yml configuration file and the YAML header in the index.Rmd file. You may also modify the name of your GitHub repo so that the website url also provides a descriptive name.\n  Extra Credit: Create content across 2 pages on your website. You can use the example content from Lab 12 as an example.\n","date":1700784000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1700784000,"objectID":"d52385349ae5b2e8c3f9a4325925001c","permalink":"https://uscbiostats.github.io/PM566/assignment/13-hw5/","publishdate":"2023-11-24T00:00:00Z","relpermalink":"/PM566/assignment/13-hw5/","section":"assignment","summary":"Due Date This assignment is due by midnight Pacific Time, December 1st, 2023.\nInteractive Visualization I and II   Using your midterm/final project data, create 3 different interactive visualizations using the tools learned in week 11 class/lab.\n  Include the visualizations created in (1) on your final project website hosted on GitHub Pages.\n  Rename your final project website by modifying the relevant text in the .yml configuration file and the YAML header in the index.","tags":null,"title":"Assignment 05 - Interactive Visualization I and II","type":"assignment"},{"authors":null,"categories":null,"content":"Required reading To be announced\nOptional reading To be announced\n","date":1669766400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1669766400,"objectID":"b99ea743a9cce93a77ae79561642096a","permalink":"https://uscbiostats.github.io/PM566/reading/13-reading/","publishdate":"2022-11-30T00:00:00Z","relpermalink":"/PM566/reading/13-reading/","section":"reading","summary":"Required reading To be announced\nOptional reading To be announced","tags":null,"title":"Final project workshop: review project progress, preliminary presentations","type":"reading"},{"authors":null,"categories":null,"content":"Required reading We will be developing a website using rmarkdown’s site generator. Please read:\n   R Markdown: The Definitive Guide, Yihui Xie, J. J. Allaire, Garrett Grolemund\n   Chapter 10.5: Websites in rmarkdown’s site generator\n  Optional reading   For a helpful tutorial on creating a website using rmarkdown, see: Creating websites in R, Emily C. Zabor\n  For more advanced static website creation using blogdown and the website generators Hugo and Jeckyll see\n   Creating websites with R Markdown, by Yihui Xie, Amber Thomas, Alison Presmanes Hill. Appendix B presents basic information about websites, such as HTML, CSS, and JavaScript.\n   Hugo – can explore themes and operate with them using blogdown.\n  ","date":1668556800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1668556800,"objectID":"995bf726bde285fc49dd3fa3422061de","permalink":"https://uscbiostats.github.io/PM566/reading/12-reading/","publishdate":"2022-11-16T00:00:00Z","relpermalink":"/PM566/reading/12-reading/","section":"reading","summary":"Required reading We will be developing a website using rmarkdown’s site generator. Please read:\n   R Markdown: The Definitive Guide, Yihui Xie, J. J. Allaire, Garrett Grolemund\n   Chapter 10.5: Websites in rmarkdown’s site generator\n  Optional reading   For a helpful tutorial on creating a website using rmarkdown, see: Creating websites in R, Emily C. Zabor\n  For more advanced static website creation using blogdown and the website generators Hugo and Jeckyll see","tags":null,"title":"Interactive visualization and effective data communication II","type":"reading"},{"authors":null,"categories":null,"content":"Required reading You are expected to have read these items before class. Most of these linked sections/chapters are quite short.\nPlotly Please read the following sections of Interactive web-based data visualization with R, plotly, and shiny, by Carson Sievert:\n Creating Views (Plotly features): Sections 1-3, 5-7 Publishing Views: Sections 9-12 Arranging and Animating Views: Sections 13-14 Linking Multiple Data Views: Sections 15 Server-side linking with Shiny: Section 17  Shiny   Introduction to Interactive Documents: provides a quick guide to getting started with Shiny.  Dashboards   Chapter 5: Dashboards (Layout, Components, Shiny) from R Markdown: The Definitive Guide, Yihui Xie, J. J. Allaire, Garrett Grolemund, provides a guide for using Shiny with flexdashboard to create interactive documents from R Markdown  Optional reading Interactive visualization  The official Shiny website includes extensive articles, tutorials, and examples.  Coursera course on developing data products, sessions on Shiny, GoogleVis, and Plotly  Data visualization best practices   Data Visualization: A Practical Introduction, Kieran Healy  Fundamentals of Data Visualization, Claus O. Wilke  ","date":1667952000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1667952000,"objectID":"8e0343f063c60970b2ee0db51d983550","permalink":"https://uscbiostats.github.io/PM566/reading/11-reading/","publishdate":"2022-11-09T00:00:00Z","relpermalink":"/PM566/reading/11-reading/","section":"reading","summary":"Required reading You are expected to have read these items before class. Most of these linked sections/chapters are quite short.\nPlotly Please read the following sections of Interactive web-based data visualization with R, plotly, and shiny, by Carson Sievert:\n Creating Views (Plotly features): Sections 1-3, 5-7 Publishing Views: Sections 9-12 Arranging and Animating Views: Sections 13-14 Linking Multiple Data Views: Sections 15 Server-side linking with Shiny: Section 17  Shiny   Introduction to Interactive Documents: provides a quick guide to getting started with Shiny.","tags":null,"title":"Interactive visualization and effective data communication I","type":"reading"},{"authors":null,"categories":null,"content":"Required reading You are expected to have read this when you show up for the session:\n  Take a look at the High Performance Computing CRAN task View ( link)\n  Chapter 22 of RD Peng’s R Programming for Data Science ( link)\n  Optional reading TBD\n","date":1666742400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1666742400,"objectID":"cb6e89c2506f4879aa8a94abb8ddd1c5","permalink":"https://uscbiostats.github.io/PM566/reading/09-reading/","publishdate":"2022-10-26T00:00:00Z","relpermalink":"/PM566/reading/09-reading/","section":"reading","summary":"Required reading You are expected to have read this when you show up for the session:\n  Take a look at the High Performance Computing CRAN task View ( link)\n  Chapter 22 of RD Peng’s R Programming for Data Science ( link)\n  Optional reading TBD","tags":null,"title":"High performance computing, cloud computing","type":"reading"},{"authors":null,"categories":null,"content":"Required reading   Chapter 17, Regular Expressions, from “R Programming for Data Science” ( link)\n  The vignette from the R package rvest: “Harvesting the web with rvest” ( link)\n  This post “HTTP: The Protocol Every Web Developer Must Know - Part 1”, excluding the section “Tools to View HTTP Traffic” link.\n  Optional reading  A Full course on APIs by Zapier ( link) R for Data Science chapter on Strings (includes a discussion of the stringr package, which we will be using) ( link)  ","date":1664928000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1664928000,"objectID":"5338e944486d33d9f0da8560c0f2991d","permalink":"https://uscbiostats.github.io/PM566/reading/07-reading/","publishdate":"2022-10-05T00:00:00Z","relpermalink":"/PM566/reading/07-reading/","section":"reading","summary":"Required reading   Chapter 17, Regular Expressions, from “R Programming for Data Science” ( link)\n  The vignette from the R package rvest: “Harvesting the web with rvest” ( link)\n  This post “HTTP: The Protocol Every Web Developer Must Know - Part 1”, excluding the section “Tools to View HTTP Traffic” link.\n  Optional reading  A Full course on APIs by Zapier ( link) R for Data Science chapter on Strings (includes a discussion of the stringr package, which we will be using) ( link)  ","tags":null,"title":"Scraping, APIs, and Regular Expressions","type":"reading"},{"authors":null,"categories":null,"content":"Required reading   Chapter 14 or R for Data Science by Garrett Grolemund and Hadley Wickham.  Chapter 1 of Text Mining with R by Julia Silge and David Robinson.  Optional reading If you want more indept information about what is happening with regards to using text as data, tokenization, stopwords etc. Then you can look at the first 4 chapters of Supervised Machine Learning for Text Analysis in R by Emil Hvitfeldt and Julia Silge.\n","date":1664323200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1664323200,"objectID":"df2711c67a6e0014f1ff7731e4b038b6","permalink":"https://uscbiostats.github.io/PM566/reading/06-reading/","publishdate":"2022-09-28T00:00:00Z","relpermalink":"/PM566/reading/06-reading/","section":"reading","summary":"Required reading   Chapter 14 or R for Data Science by Garrett Grolemund and Hadley Wickham.  Chapter 1 of Text Mining with R by Julia Silge and David Robinson.  Optional reading If you want more indept information about what is happening with regards to using text as data, tokenization, stopwords etc. Then you can look at the first 4 chapters of Supervised Machine Learning for Text Analysis in R by Emil Hvitfeldt and Julia Silge.","tags":null,"title":"Text Mining","type":"reading"},{"authors":null,"categories":null,"content":"Required reading You are expected to have read this when you show up for the session.\n  The Introduction to data.table vignette included in the data.table R package\n   “Chapter 5: Data Transformation” of the book R for Data Science\n  Optional reading   We recommend you going through the instructions to download Python here and installing jupyter here.\n  While we will not be using Pandas, here is a neat short intro to it.\n  ","date":1663718400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1663718400,"objectID":"25a92a1f3c48ccf25100c7abb1371dd0","permalink":"https://uscbiostats.github.io/PM566/reading/05-reading/","publishdate":"2022-09-21T00:00:00Z","relpermalink":"/PM566/reading/05-reading/","section":"reading","summary":"Required reading You are expected to have read this when you show up for the session.\n  The Introduction to data.table vignette included in the data.table R package\n   “Chapter 5: Data Transformation” of the book R for Data Science\n  Optional reading   We recommend you going through the instructions to download Python here and installing jupyter here.\n  While we will not be using Pandas, here is a neat short intro to it.","tags":null,"title":"Data cleaning and wrangling","type":"reading"},{"authors":null,"categories":null,"content":"Required reading Chapter 3 of R for Data Science\nOptional reading Chapter 15 of Exploratory Data Analysis with R\n","date":1663113600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1663113600,"objectID":"db2e5ee70039459f918f68ed6f964cf0","permalink":"https://uscbiostats.github.io/PM566/reading/04-reading/","publishdate":"2022-09-14T00:00:00Z","relpermalink":"/PM566/reading/04-reading/","section":"reading","summary":"Required reading Chapter 3 of R for Data Science\nOptional reading Chapter 15 of Exploratory Data Analysis with R","tags":null,"title":"Data visualization","type":"reading"},{"authors":null,"categories":null,"content":"Required Reading Exploratory Data Analysis  The main text we will use for EDA is another book by Roger Peng, Exploratory Data Analysis with R Please focus on Chapter 4, Chapter 5 and Chapter 6  Optional reading The following are recommended (but not expected) for you to read\n  Introduction to data.table  Chapter 7 of R for Data Science  ","date":1662508800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1662508800,"objectID":"68871e83f1fea259615904a0d5972d28","permalink":"https://uscbiostats.github.io/PM566/reading/03-reading/","publishdate":"2022-09-07T00:00:00Z","relpermalink":"/PM566/reading/03-reading/","section":"reading","summary":"Required Reading Exploratory Data Analysis  The main text we will use for EDA is another book by Roger Peng, Exploratory Data Analysis with R Please focus on Chapter 4, Chapter 5 and Chapter 6  Optional reading The following are recommended (but not expected) for you to read\n  Introduction to data.table  Chapter 7 of R for Data Science  ","tags":null,"title":"Exploratory data analysis","type":"reading"},{"authors":null,"categories":null,"content":"Required reading You are expected to have read this when you show up for the first day of class. The following items are all rather short and shouldn’t take too long. The “How to read an R help page” section stops once you reach “A.1.2 The basics of accessing and selecting things”.\n The Syllabus rmarkdown lessons 1, 2, 3 and 8  How to read an R help page  Additionally depending on your previous knowledge I would recommend you take a look at the programming basics primer.\nOptional reading The following articles are recommended (but not expected) for you to read if you want to get a glimpse into the world of data science and R as a language.\n  How R changed me as an analyst  Data scientists mostly just do arithmetic and that’s a good thing  How rOpenSci uses Code Review to Promote Reproducible Science  50 Years of Data Science  ","date":1661299200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1661299200,"objectID":"779a2db7f9aaaf0fb69f23ae1b1d9d0c","permalink":"https://uscbiostats.github.io/PM566/reading/01-reading/","publishdate":"2022-08-24T00:00:00Z","relpermalink":"/PM566/reading/01-reading/","section":"reading","summary":"Required reading You are expected to have read this when you show up for the first day of class. The following items are all rather short and shouldn’t take too long. The “How to read an R help page” section stops once you reach “A.1.2 The basics of accessing and selecting things”.\n The Syllabus rmarkdown lessons 1, 2, 3 and 8  How to read an R help page  Additionally depending on your previous knowledge I would recommend you take a look at the programming basics primer.","tags":null,"title":"Introduction to Data Science tools: R, R Markdown","type":"reading"},{"authors":null,"categories":null,"content":"Learning goals  Use the merge() function to join two datasets. Deal with missings and impute data. Identify relevant observations using quantile(). Practice your GitHub skills.  Lab description For this lab we will be, again, dealing with the meteorological dataset downloaded from the NOAA, the met. In this case, we will use data.table to answer some questions regarding the met dataset, while at the same time practice your Git+GitHub skills for this project.\nThis markdown document should be rendered using github_document document.\nPart 1: Setup the Git project and the GitHub repository   Go to your documents (or wherever you are planning to store the data) in your computer, and create a folder for this project, for example, \u0026ldquo;PM566-labs\u0026rdquo;\n  In that folder, save this template as \u0026ldquo;README.Rmd\u0026rdquo;. This will be the markdown file where all the magic will happen.\n  Go to your GitHub account and create a new repository, hopefully of the same name that this folder has, i.e., \u0026ldquo;PM566-labs\u0026rdquo;.\n  Initialize the Git project, add the \u0026ldquo;README.Rmd\u0026rdquo; file, and make your first commit.\n  Add the repo you just created on GitHub.com to the list of remotes, and push your commit to origin while setting the upstream.\n  Most of the steps can be done using command line:\n# Step 1 cd ~/Documents mkdir PM566-labs cd PM566-labs # Step 2 wget https://raw.githubusercontent.com/USCbiostats/PM566/master/website/content/assignment/05-lab.Rmd mv 05-lab.Rmd README.md # Step 3 # Happens on github # Step 4 git init git add README.Rmd git commit -m \u0026quot;First commit\u0026quot; # Step 5 git remote add origin git@github.com:[username]/PM566-labs git push -u origin master  You can also complete the steps in R (replace with your paths/username when needed)\n# Step 1 setwd(\u0026quot;~/Documents\u0026quot;) dir.create(\u0026quot;PM566-labs\u0026quot;) setwd(\u0026quot;PM566-labs\u0026quot;) # Step 2 download.file( \u0026quot;https://raw.githubusercontent.com/USCbiostats/PM566/master/website/content/assignment/05-lab.Rmd\u0026quot;, destfile = \u0026quot;README.Rmd\u0026quot; ) # Step 3: Happens on Github # Step 4 system(\u0026quot;git init \u0026amp;\u0026amp; git add README.Rmd\u0026quot;) system('git commit -m \u0026quot;First commit\u0026quot;') # Step 5 system(\u0026quot;git remote add origin git@github.com:[username]/PM566-labs\u0026quot;) system(\u0026quot;git push -u origin master\u0026quot;)  Once you are done setting up the project, you can now start working with the MET data.\nSetup in R   Load the data.table (and the dtplyr and dplyr packages if you plan to work with those).\n  Load the met data from https://raw.githubusercontent.com/USCbiostats/data-science-data/master/02_met/met_all.gz, and also the station data. For the later, you can use the code we used during lecture to pre-process the stations data:\n  # Download the data stations \u0026lt;- fread(\u0026quot;https://noaa-isd-pds.s3.amazonaws.com/isd-history.csv\u0026quot;) stations[, USAF := as.integer(USAF)] # Dealing with NAs and 999999 stations[, USAF := fifelse(USAF == 999999, NA_integer_, USAF)] stations[, CTRY := fifelse(CTRY == \u0026quot;\u0026quot;, NA_character_, CTRY)] stations[, STATE := fifelse(STATE == \u0026quot;\u0026quot;, NA_character_, STATE)] # Selecting the three relevant columns, and keeping unique records stations \u0026lt;- unique(stations[, list(USAF, CTRY, STATE)]) # Dropping NAs stations \u0026lt;- stations[!is.na(USAF)] # Removing duplicates stations[, n := 1:.N, by = .(USAF)] stations \u0026lt;- stations[n == 1,][, n := NULL]  Merge the data as we did during the lecture.  Question 1: Representative station for the US What is the median station in terms of temperature, wind speed, and atmospheric pressure? Look for the three weather stations that best represent continental US using the quantile() function. Do these three coincide?\nKnit the document, commit your changes, and push it to GitHub. Don\u0026rsquo;t forget to add README.md to the tree, the first time you render it.\nQuestion 2: Representative station per state Just like the previous question, you are asked to identify what is the most representative, the median, station per state. This time, instead of looking at one variable at a time, look at the euclidean distance. If multiple stations show in the median, select the one located at the lowest latitude.\nKnit the doc and save it on GitHub.\nQuestion 3: In the middle? For each state, identify what is the station that is closest to the mid-point of the state. Combining these with the stations you identified in the previous question, use leaflet() to visualize all ~100 points in the same figure, applying different colors for those identified in this question.\nKnit the doc and save it on GitHub.\nQuestion 4: Means of means Using the quantile() function, generate a summary table that shows the number of states included, average temperature, wind-speed, and atmospheric pressure by the variable \u0026ldquo;average temperature level,\u0026rdquo; which you\u0026rsquo;ll need to create.\nStart by computing the states' average temperature. Use that measurement to classify them according to the following criteria:\n low: temp \u0026lt; 20 Mid: temp \u0026gt;= 20 and temp \u0026lt; 25 High: temp \u0026gt;= 25  Once you are done with that, you can compute the following:\n Number of entries (records), Number of NA entries, Number of stations, Number of states included, and Mean temperature, wind-speed, and atmospheric pressure.  All by the levels described before.\nKnit the document, commit your changes, and push them to GitHub. Once you\u0026rsquo;re done, you can email me the link to your repository or, preferably, link to the Lab 5 Issue in your commit message, as follows:\ngit commit -a -m \u0026quot;Finalizing lab 5 https://github.com/USCbiostats/PM566/issues/68\u0026quot;  This will let me know which version of your repository to look at for grading purposes.\n","date":1632355200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1632355200,"objectID":"5f15b828c0bd14a8fe4dc38804fd31a4","permalink":"https://uscbiostats.github.io/PM566/assignment/05-lab/","publishdate":"2021-09-23T00:00:00Z","relpermalink":"/PM566/assignment/05-lab/","section":"assignment","summary":"Learning goals  Use the merge() function to join two datasets. Deal with missings and impute data. Identify relevant observations using quantile(). Practice your GitHub skills.  Lab description For this lab we will be, again, dealing with the meteorological dataset downloaded from the NOAA, the met. In this case, we will use data.table to answer some questions regarding the met dataset, while at the same time practice your Git+GitHub skills for this project.","tags":null,"title":"Lab 05 - Data Wrangling","type":"assignment"},{"authors":null,"categories":null,"content":"Required reading To be announced\nOptional reading To be announced\n","date":1605657600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1605657600,"objectID":"50371a905277d0453e50747202316409","permalink":"https://uscbiostats.github.io/PM566/reading/14-reading/","publishdate":"2020-11-18T00:00:00Z","relpermalink":"/PM566/reading/14-reading/","section":"reading","summary":"Required reading To be announced\nOptional reading To be announced","tags":null,"title":"Final Project","type":"reading"},{"authors":null,"categories":null,"content":"Required reading No Reading for todays class\nOptional reading ","date":1603238400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1603238400,"objectID":"9bb8bcf808ed7241aa731039ffa9b4ed","permalink":"https://uscbiostats.github.io/PM566/reading/10-reading/","publishdate":"2020-10-21T00:00:00Z","relpermalink":"/PM566/reading/10-reading/","section":"reading","summary":"Required reading No Reading for todays class\nOptional reading ","tags":null,"title":"SQL","type":"reading"},{"authors":null,"categories":null,"content":"Required reading To be announced\nOptional reading To be announced\n","date":1602028800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1602028800,"objectID":"89e63c8106614b5d2cd6c1f496355320","permalink":"https://uscbiostats.github.io/PM566/reading/08-reading/","publishdate":"2020-10-07T00:00:00Z","relpermalink":"/PM566/reading/08-reading/","section":"reading","summary":"Required reading To be announced\nOptional reading To be announced","tags":null,"title":"Midterm Exam","type":"reading"},{"authors":null,"categories":null,"content":"Learning goals  Get acquainted with R and RStudio, which we will be using throughout the course to analyze data. Appreciate the value of visualization in exploring the relationship between variables. Start using R for building plots and calculating summary statistics.  Terminology We\u0026rsquo;ve already thrown around a few new terms, so let\u0026rsquo;s define them before we proceed.\n R: Name of the programming language we will be using throughout the course. RStudio: An integrated development environment for R. In other words, a convenient interface for writing and running R code.  I like to think of R as the engine of the car, and RStudio is the dashboard.\nStarting slow As the labs progress, you are encouraged to explore beyond what the labs dictate; a willingness to experiment will make you a much better programmer. Before we get to that stage, however, you need to build some basic fluency in R. Today we begin with the fundamental building blocks of R and RStudio: the interface, reading in data, and basic commands.\nGetting started 1 Download R If you don\u0026rsquo;t have R installed. Go to the CRAN and download R, make sure you get the version that matches your operating system.\nIf you have R installed If you have R installed run the following code\nR.version  ## _ ## platform x86_64-apple-darwin20 ## arch x86_64 ## os darwin20 ## system x86_64, darwin20 ## status ## major 4 ## minor 3.3 ## year 2024 ## month 02 ## day 29 ## svn rev 86002 ## language R ## version.string R version 4.3.3 (2024-02-29) ## nickname Angel Food Cake  This should tell you what version of R you are currently using. If your R version is lower then 4.3.0, I would strongly recommend updating. In general, it is a good idea to keep your R version up to date, unless you have a project right now that depends on a specific version of R.\n1.2 Download RStudio We recommend using RStudio as your IDE if you don\u0026rsquo;t already have it installed. You can go to the RStudio website to download and install the software. Once it is installed, open RStudio and use it to complete the rest of this lab.\nHello RStudio! RStudio is comprised of four panes.\n  On the bottom left is the Console, this is where you can write code that will be evaluated. Try typing 2 + 2 here and hit enter, what do you get?\n  On the bottom right is the Files pane, as well as other panes that will come handy as we start our analysis.\n  If you click on a file, it will open in the editor, on the top left pane.\n  Finally, the top right pane shows your Environment. If you define a variable it would show up there. Try typing x \u0026lt;- 2 in the Console and hit enter, what do you get in the Environment pane?\n  Start a new Project RStudio Projects are a great way to stay organized and keep all of your work on a particular topic in one place. Project files keep track of things like the R objects you are using and which files you have open, so that you can quickly jump in and out of different work environments.\nWe\u0026rsquo;re going to start a new Project called PM566labs. If you want to keep all of your materials for this class in one place, you may want to open Finder (MacOS) or File Explorer (Windows) and create a new folder (directory) for this class.\nIn the top right of the RStudio window, you should see a drop-down menu that says \u0026ldquo;Project: (None)\u0026rdquo;. Click on this and then \u0026ldquo;New Project\u0026hellip;\u0026rdquo;, which will open up a dialogue box. If you had already created a PM566labs directory, you could choose the \u0026ldquo;Existing Directory\u0026rdquo; option to associate this Project with that directory. Since we haven\u0026rsquo;t done that, we\u0026rsquo;ll use the \u0026ldquo;New Directory\u0026rdquo; option, then select the generic Project type, \u0026ldquo;New Project\u0026rdquo;. Give your new directory the name PM566labs and use the \u0026ldquo;Browse\u0026rdquo; button to choose where you want to save it on your computer, then click \u0026ldquo;Create Project\u0026rdquo;. Now in the top right, you should see \u0026ldquo;PM566labs\u0026rdquo; next to the R Project logo.\nCreate a Quarto document We will use Quarto documents a lot in this course because they are fully reproducible and allow us to seamlessly integrate code and text. We expect you to use Quarto for all homework assignments and (almost) all labs.\nIn the top left, you will see a \u0026ldquo;New File\u0026rdquo; icon (a white \u0026ldquo;plus\u0026rdquo; sign in a green circle over a blank document) that leads to a drop-down menu. Click on this and then select \u0026ldquo;Quarto document\u0026hellip;\u0026rdquo;, which will open up a dialogue box. You can leave most of the settings on their defaults for now, just give your document a title (like \u0026ldquo;Lab 1\u0026rdquo;) and an author (yourself) and click \u0026ldquo;Create\u0026rdquo;. RStudio may ask you if you would like to install a package that is required, and if so, click \u0026ldquo;Install\u0026rdquo;.\nThis will open the default Quarto document, which already contains some example content. Read through it, then remove this content. Please do not hand in assignments that contain the default content, as I have seen it plenty of times! Set up new sections in your document titled \u0026ldquo;Question 1\u0026rdquo; through \u0026ldquo;Question 6\u0026rdquo;. This document will serve as the template for your responses to this lab. Under each section title, add an R code chunk via \u0026ldquo;Insert\u0026hellip;\u0026rdquo;, \u0026ldquo;Executable Cell\u0026rdquo;, \u0026ldquo;R\u0026rdquo;. Alternatively, you can switch to the \u0026ldquo;Source\u0026rdquo; editor (in the top left of the Editor pane) and add an R chunk by typing the following:\n```{r}\n```\nThis will create an R code chunk and any code you add inside of it will be executed when you Render the document.\nSave your Quarto markdown (qmd) file as lab-01.qmd and see what happens when you click \u0026ldquo;Render\u0026rdquo;.\nPackages R is an open-source language with a vibrant community of users and developers. Developers can contribute new functionality to R via packages. In this lab we will work with the datasauRus package, which contains a fun example dataset.\nYou can load this package with the following command:\nlibrary(datasauRus)  If you haven\u0026rsquo;t installed it yet and R complains, then you can install the package by running the following command (note that R package names are case-sensitive)\u0026quot;\ninstall.packages(\u0026quot;datasauRus\u0026quot;)  We encourage you to work interactively on this lab (and future assignments) by running code directly in your Console pane. Try breaking down complex or unfamiliar lines of code into smaller, executable pieces to get a sense for how the code works. You may also want to open a new tab in the Editor windown that is just an R Script, where you can play around with pieces of code before adding them to the final product that is your Quarto document.\nHowever, you should note that Quarto documents are Rendered in their own unique environment, separate from your interactive session. So while you should run library(datasauRus) in your Console, you will also need to include it inside an R code chunk if you want your Quarto document to use this package. Similarly, if you create a variable called var while running code in the Console, Quarto won\u0026rsquo;t have acess to it, so you will need to re-create that variable inside a code chunk.\nWarm up Before we introduce the data, let\u0026rsquo;s warm up with some simple exercises.\nYAML The top portion of your Quarto file (between the three dashed lines) is called YAML. It stands for \u0026ldquo;YAML Ain\u0026rsquo;t Markup Language\u0026rdquo;. It is a human-friendly data serialization standard for all programming languages. All you need to know is that this area is called the YAML (we will refer to it as such) and that it contains meta information about your document.\nOpen the Quarto (qmd) file in your project, make sure the author name is your name. You can add additional lines to the YAML section to control more aspects of your document. For example, you can set the output to be a PDF or HTML document by adding either format: pdf or format: html.\nRegardless of what output format you choose, add another line to the YAML header that reads embed-resources: true. This tells Quarto that you want to produce a \u0026ldquo;stand-alone\u0026rdquo; document that contains all images directly in the document. If you don\u0026rsquo;t do this, Quarto will often create a separate directory (called \u0026lt;document\u0026gt;_files) that contains these images and your output will depend on this outside directory.\nWhen you\u0026rsquo;re done, click \u0026ldquo;Render\u0026rdquo; to compile the document.\nData The data frame we will be working with today is called datasaurus_dozen and it\u0026rsquo;s in the datasauRus package. Actually, this single data frame contains 13 datasets, designed to show us why data visualization is important and how summary statistics alone can be misleading. The different datasets are marked by the dataset variable. The data is contained in an object called a data.frame. Type datasaurus_dozen into the R Console and hit Return to see a printout of the dataset. Alternatively, you can use View(datasaurus_dozen) to open a new tab with the full dataset (but be careful! This command will not work in a Quarto document, because they are not interactive).\nTo find out more about the dataset, type the following in your Console: ?datasaurus_dozen. A question mark before the name of an object will always bring up its help file. Similar to View, this command must be run in the Console.\nQuestion 1  Based on the help file, how many rows and how many columns does the datasaurus_dozen file have? What are the variables included in the data frame? Add your responses to your lab report, with relevant code in the associated R code chunk, and free-form text outside of the code chunk.  Let\u0026rsquo;s take a look at what these datasets are. To start, we can make a frequency table of the dataset variable:\ntable(datasaurus_dozen$dataset)  Here, we used the $ operator to access a specific column (variable) of the dataset. Then we used the table function to summarize that variable. table is great for quickly summarizing categorical variables, but it\u0026rsquo;s not very useful for summarizing continuous variables, where most unique values are only present once. For continuous variables, try the summary function.\nThe original Datasaurus (dino) was created by Alberto Cairo in this great blog post. The other Dozen were generated using simulated annealing and the process is described in the paper Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing by Justin Matejka and George Fitzmaurice. In the paper, the authors simulate a variety of datasets with the same summary statistics as the Datasaurus, but with very different distributions.\nData visualization and summary Question 2 Plot y vs. x for the dino dataset. Then, calculate the correlation coefficient between x and y for just this dataset.  Below is the code you will need to complete this exercise. Basically, the answer is already given, but you need to include relevant bits in your Quarto document and successfully Render it and view the results.\nWe\u0026rsquo;re going to start by subsetting our data down to just the dino dataset.\nSubsetting dino_data \u0026lt;- datasaurus_dozen[datasaurus_dozen$dataset == 'dino', ]  There is a lot going on here, so let\u0026rsquo;s slow down and unpack it a bit.\nThe first thing to note is the assignment operator: \u0026lt;-. This special symbol is used to create a new object or assign a new value to an existing one. In this case, we\u0026rsquo;re creating a new object called dino_data.\nThe second important feature is the set of square brackets: []. When these come immediately after the name of an object, they are used for subsetting that object, or taking a smaller piece of it. In this case, we want to subset the datasaurus_dozen object. Since this is a two-dimensional data.frame, we can subset it by either rows or columns, or both. The comma (,) separates our row subset from our column subset. In this case, we only want to subset by row and keep all the columns, so there is nothing after the comma.\nIn R (as in Python and many other programming languages), we can check whether or not two values are equal by using the == operator. Here, we again use the $ to access the dataset variable and we check if it is equal to the value 'dino'. If it is, this comparison will return TRUE and we will keep that row. If it isn\u0026rsquo;t, this comparison will return FALSE and we will not keep that row.\nPlotting Next, we need to visualize these data. We will use the plot function for this, which is R\u0026rsquo;s most basic plotting function. If you provide the plot function with two numeric vectors, it will plot them in a scatter plot. Let\u0026rsquo;s see what happens when we plot the x and y variables from our dino_data object:\nplot(dino_data$x, dino_data$y) # ggplot(data = dino_data, mapping = aes(x = x, y = y)) + # geom_point()  (I\u0026rsquo;ve also included code for producing similar plots using the ggplot function from the ggplot2 package. This is another widely-used plotting function that includes some more aesthetically pleasing defaults, but requires more complicated syntax)\nWe will talk a lot more about the philosophy of data visualization, how to choose the right plot type, and constructing visualizations in layers in the coming weeks. But for now, you can follow along with the code that is provided.\nFor the second part of these exercises, we need to calculate a summary statistic: the correlation coefficient. The correlation coefficient, often referred to as \\(r\\) in statistics, measures the linear association between two variables. You will see that some of the pairs of variables we plot do not have a linear relationship between them. This is exactly why we want to visualize first: visualize to assess the form of the relationship, and calculate \\(r\\) only if relevant. In this case, calculating a correlation coefficient really doesn\u0026rsquo;t make sense since the relationship between x and y is definitely not linear \u0026ndash; it\u0026rsquo;s dinosaurial!\nBut, for illustrative purposes, let\u0026rsquo;s calculate correlation coefficient between x and y. Like plot, the cor function takes two numeric variables and calculates their correlation coefficient:\ncor(dino_data$x, dino_data$y) # dino_data |\u0026gt; # summarize(r = cor(x, y))  (I\u0026rsquo;ve also included the \u0026ldquo;tidyverse\u0026rdquo; way of calculating this value. The \u0026ldquo;tidyverse\u0026rdquo; is a collection of packages that includes ggplot2 and is based on the concept of \u0026ldquo;tidy\u0026rdquo;, rectangular datasets. We will tend to focus on the \u0026ldquo;Base R\u0026rdquo; way of doing things, as learning the \u0026ldquo;tidyverse\u0026rdquo; coding style is almost like learning another language in addition to R)\nQuestion 3 Now try it on your own! Plot y vs. x for the star dataset, another one of the datasaurus_dozen. You can (and should) re-use code we introduced above, just replace the dataset name with the desired dataset. Then, calculate the correlation coefficient between x and y for this dataset. How does this value compare to the r of dino?  Question 4 Plot y vs. x for the circle dataset. You can (and should) reuse code we introduced above, just replace the dataset name with the desired dataset. Then, calculate the correlation coefficient between x and y for this dataset. How does this value compare to the r of dino?  Question 5 Now let\u0026rsquo;s plot all 13 datasets at once. In order to do this we will make use of the layout function. This function allows you to put multiple plots in the same plotting window. We will create a 4x4 matrix containing the values 1 through 16 and pass this to layout, which lets it know where we want each plot to go (we only have 13 datasets, so there will be a few empty spots at the end). This may be too many plots for a small viewing window, so if you get the error message Error in plot.new() : figure margins too large, try making your plotting window larger.\nThen we use a for loop to perform a set of actions over every unique value of the dataset variable. This creates a new object, called name, that takes on each of those unique values, but it only exists within the context (interior) of the loop. Then we subset and plot the data, as we have before.\nlayout(matrix(1:16, nrow=4, ncol=4)) for(name in unique(datasaurus_dozen$dataset)){ subset \u0026lt;- datasaurus_dozen[datasaurus_dozen$dataset == name, ] plot(subset$x, subset$y, main = name) } layout(1)  # ggplot(datasaurus_dozen, aes(x = x, y = y, color = dataset))+ # geom_point()+ # facet_wrap(~ dataset, ncol = 3) + # theme(legend.position = \u0026quot;none\u0026quot;)  The second call to layout will reset the plotting window, so that the next plot will take up the entire window, rather than 1/16th of it.\nOptional:If you really want to maximize the plotting area of this figure, you can also adjust the plotting margins via the par function (short for \u0026ldquo;graphical parameters\u0026rdquo;). The setting for margins is called mar and it takes a vector of length 4, specifying the bottom, left, top, and right margins, in order (the units are lines of text). You can set each margin to a value of 2 with par(mar = c(2,2,2,2)) and then reset to the default values with par(mar = c(5,4,4,2) + 0.1).\nQuestion 6 Finally, we want to calculate the correlation between the x and y variables for all 13 datasets. Like before, we will use a loop, but this time, since we want to return a specific value every time through the loop, we will use the sapply function. sapply is useful way to apply a function to every element of a vector. In this case, we provide the vector of unique dataset names (like before) and then our own custom function. This function subsets the data as before, and then returns the correlation coefficient as the output of the function.\nsapply(unique(datasaurus_dozen$dataset), function(name){ subset \u0026lt;- datasaurus_dozen[datasaurus_dozen$dataset == name, ] return(cor(subset$x, subset$y)) })  # datasaurus_dozen |\u0026gt; # group_by(dataset) |\u0026gt; # summarize(r = cor(x, y))  You\u0026rsquo;re now done with the data analysis exercises, but we\u0026rsquo;d like you to do two more things:\nResize your figures:\nAdd the fields fig-width and fig-height to the YAML header of your document. These will allow you to specify the size (in inches) of any figures generated by the code chunks in your report.\nYou can also use different figure sizes for different figures. If you are in the Visual editor mode, switch to Source mode. Notice that each R chunk starts and ends with three backticks. Click on the gear icon in the top right of a code chunk and select \u0026ldquo;Use custom figure size\u0026rdquo; in the pop-up menu. Set the height and width of the figures and hit Apply when done. Then, render your document and see how you like the new sizes. Try making the plot for Question 5 larger, until you are happy with its size. Note that changing the figure sizes added new options to these chunks: fig.width and fig.height. You can change them by defining different values directly in your Quarto document as well.\nChange the look of your report:\nIf you have time, you can explore the different ways you can add styling to your document. Try adding a theme field to the YAML header and see if you can find valid names of different themes.\nHere is a Quarto cheatsheet and a general markdown cheatsheet that shows some of the many cool features you can make use of in a Quarto document.\nSubmitting your lab Since we have not covered GitHub yet, for this week (and ONLY this week) you may email me a fully rendered PDF or HTML version of your completed lab at kelly(dot)street(at)usc(dot)edu.\n This set of lab exersices have been adopted from Mine Çetinkaya-Rundel\u0026rsquo;s class Introduction to Data Science.\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"ec913329e57aa90c7d5ae82a1e52ac76","permalink":"https://uscbiostats.github.io/PM566/assignment/01-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/01-lab/","section":"assignment","summary":"Learning goals  Get acquainted with R and RStudio, which we will be using throughout the course to analyze data. Appreciate the value of visualization in exploring the relationship between variables. Start using R for building plots and calculating summary statistics.  Terminology We\u0026rsquo;ve already thrown around a few new terms, so let\u0026rsquo;s define them before we proceed.\n R: Name of the programming language we will be using throughout the course.","tags":null,"title":"Lab 01 - Hello R!","type":"assignment"},{"authors":null,"categories":null,"content":"Learning goals In this lab, you are expected to learn/put in practice the following skills:\n Forking a repository on GitHub Git workflow clone/commit/push Using pull requests (PR)  Updating a single repo One of the most common tasks that people use git for is for collaborating. While in general team members organize such that there is no overlapping editing of the files, git is (usually) smart enough to avoid clashes when multiple edits are done in the same document1. To show this, we will do a collaborative edit of a file!\nWe will be working with the repository https://github.com/USCbiostats/PM566-whoami\nStep 1: Fork the project to create your own repo Not a term/command actually available in Git, forking is a feature available in Github (as in other services) that allows users to create copies of other people\u0026rsquo;s projects to propose changes (i.e. make pull requests, i.e. \u0026ldquo;I have this great update for your project! Would you like to add it by pulling it into your repo?\u0026quot;).\nTo start, you just need to use the Fork button available on the main page of the repository you would like to contribute to2:\nOnce you \u0026ldquo;Fork\u0026rdquo; a project, GitHub will:\n  Create a copy (using git clone) of that project in your account.\n  Set up a pipeline to generate pull requests for the original repository.\n  Once you have a copy of the project in your account, you can proceede by \u0026ldquo;downloading it\u0026rdquo; to your computer using the git clone command. For example, if your github user name is statsnerd and the original repository is PM566-whoami, you could use the following in your command line\ncd where/you/want/to/download/the/thing git clone https://github.com/statsnerd/PM566-whoami.git  And if you have your ssh credentials set up, you can do instead\ncd where/you/want/to/download/the/thing git clone git@github.com:statsnerd/PM566-whoami.git  This way you will get a copy of the repository in your local machine. Now, let\u0026rsquo;s see how can we update the project!\nStep 2: Modifying the corresponding line If you got the correct copy, you should find a very simple repository with only two files: CODE_OF_CONDUCT.md and README.md. The first file is a general code of conduct for the project, which we do not need to edit. The second file is the one that we will be playing with. The README file, which happens to be a Markdown file, contains, or at least will contain, your and your team members' biographies. Here is what you need to do:\n  Find the line with your name.\n  In that single line (i.e. not spanning multiple lines), write something about yourself, e.g. \u0026ldquo;I am from XYZ, I love doing ABC, \u0026hellip;\u0026rdquo;.\n  (optional) if you feel like it, add at the end of the line a picture of yourself (or avatar) using either html or markdown. This will require you to include the figure in the repo (if you are not linking a web fig).\n  Commit the changes and push the changes to your repo using git commit and git push, e.g.\n  git commit -a -m \u0026quot;[A short but meaningful message]\u0026quot; # git add [your-avatar.png] ... if you need to add a picture git push  You are now one step closer to make your first \u0026ldquo;pull request\u0026rdquo;. We will see how that happens in the next part.\nStep 3: Do the pull request This is the final step. Overall, pull requests (PR) are as complex as the proposed changes are. The PR that you are about to make should go smoothly, yet, any time that you make a new PR, the changes should be able to be merged in the original repository without conflicts. Conflicts may only appear if the proposed changes are out-dated with respect to the main repository, meaning that the main repository was modified after your fork and your proposed changes cannot be merged without generating conflicts3. For now, let\u0026rsquo;s just look at the simple case.\nTo create the PR, you just need to go to your online copy of the project and click on \u0026ldquo;Contribute\u0026rdquo; then \u0026ldquo;Open Pull Request\u0026rdquo;:\n You can submit pull requests to the original repo from your copy of it via the \"Contribute\" button.  This will create a PR in the original repository. GitHub will automatically analyze the PR and check whether merging the PR to the master branch will result in a conflict or not. If all is OK, then the owner/admin of the repository can merge the PR. Otherwise, if there\u0026rsquo;s a conflict, you can go back to your local repo, make the needed changes, commit the changes, and push the changes to your copy on GitHub. In this stage, the PR will automatically update to reflect the new changes you made in your copy of the project.\nFor more information, check out Creating a pull request from a fork  on GitHub.\n  Team-members could be working on the same file but editing different lines of code. If this is the case, after pull/push, git will integrate the changes without conflicts.\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n For more details, take a look at the Forking Projects article in GitHub guides.\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n More info about how to deal with conflicts in this very neat post on stackoverflow.com How to resolve merge conflicts in Git. GitHub also has a way to solve conflicts in PRs, but this is only available to the admins of target repo. More info here,\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n   ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"859a20e34ce01209178958c65cb80780","permalink":"https://uscbiostats.github.io/PM566/assignment/02-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/02-lab/","section":"assignment","summary":"Learning goals In this lab, you are expected to learn/put in practice the following skills:\n Forking a repository on GitHub Git workflow clone/commit/push Using pull requests (PR)  Updating a single repo One of the most common tasks that people use git for is for collaborating. While in general team members organize such that there is no overlapping editing of the files, git is (usually) smart enough to avoid clashes when multiple edits are done in the same document1.","tags":null,"title":"Lab 02 - GitHub","type":"assignment"},{"authors":null,"categories":null,"content":"        Learning Goals  Read in and get familiar with the meteorology dataset Step through the EDA “checklist” presented in the class slides Practice making exploratory graphs  As you do this, think about what questions you would like to ask regarding this data. What would you ask a collaborator who was more familiar with it?\nLab Description We will work with the meteorological data presented in lecture. Recall the dataset consists of weather station readings in the continental US.\nThe objectives of the lab are to find the weather station with the highest elevation and look at patterns in the time series of its wind speed and temperature. 1. Read in the data First download and then read in with data.table::fread(). This is slightly faster than some of the more common functions, such as read.table, but it produces a different type of object, which is why we need to convert it into a data.frame after reading it in.\ndownload.file( \u0026quot;https://raw.githubusercontent.com/USCbiostats/data-science-data/master/02_met/met_all.gz\u0026quot;, destfile = file.path(\u0026quot;~\u0026quot;, \u0026quot;Downloads\u0026quot;, \u0026quot;met_all.gz\u0026quot;), method = \u0026quot;libcurl\u0026quot;, timeout = 60 ) met \u0026lt;- data.table::fread(file.path(\u0026quot;~\u0026quot;, \u0026quot;Downloads\u0026quot;, \u0026quot;met_all.gz\u0026quot;)) met \u0026lt;- as.data.frame(met)  2. Check the dimensions, headers, footers. How many columns, rows are there? Some useful functions for this are dim, head, and tail. 3. Take a look at the variables. Show the type (class) of each variable (hint: try the str function). What are the names of the key variables related to our question of interest? 4. Take a closer look at the key variables. table(met$year)  ## ## 2019 ## 2377343  table(met$day)  ## ## 1 2 3 4 5 6 7 8 9 10 11 12 13 ## 75975 75923 76915 76594 76332 76734 77677 77766 75366 75450 76187 75052 76906 ## 14 15 16 17 18 19 20 21 22 23 24 25 26 ## 77852 76217 78015 78219 79191 76709 75527 75786 78312 77413 76965 76806 79114 ## 27 28 29 30 31 ## 79789 77059 71712 74931 74849  table(met$hour)  ## ## 0 1 2 3 4 5 6 7 8 9 10 ## 99434 93482 93770 96703 110504 112128 106235 101985 100310 102915 101880 ## 11 12 13 14 15 16 17 18 19 20 21 ## 100470 103605 97004 96507 97635 94942 94184 100179 94604 94928 96070 ## 22 23 ## 94046 93823  summary(met$temp)  ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## -40.00 19.60 23.50 23.59 27.80 56.00 60089  summary(met$elev)  ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## -13.0 101.0 252.0 415.8 400.0 9999.0  summary(met$wind.sp)  ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 0.00 0.00 2.10 2.46 3.60 36.00 79693  It looks like the elevation variable has observations with 9999.0, which is probably an indicator for missing. We should take a deeper look at the data dictionary to confirm. The wind speed variable is OK but there is a lot of missing data.\nAfter checking the data we should make the appropriate modifications. Replace elevations with 9999 as NA.\nmet[met$elev==9999.0, ] \u0026lt;- NA summary(met$elev)  ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## -13 101 252 413 400 4113 710  At what elevation is the highest weather station? We also have the issue of the minimum temperature being -40C, which seems implausible, so we should remove those observations.\nmet \u0026lt;- met[met$temp \u0026gt; -40, ] head(met[order(met$temp), ])  ## USAFID WBAN year month day hour min lat lon elev wind.dir ## 1203053 722817 3068 2019 8 1 0 56 38.767 -104.3 1838 190 ## 1203055 722817 3068 2019 8 1 1 56 38.767 -104.3 1838 180 ## 1203128 722817 3068 2019 8 3 11 56 38.767 -104.3 1838 NA ## 1203129 722817 3068 2019 8 3 12 56 38.767 -104.3 1838 NA ## 1203222 722817 3068 2019 8 6 21 56 38.767 -104.3 1838 280 ## 1203225 722817 3068 2019 8 6 22 56 38.767 -104.3 1838 240 ## wind.dir.qc wind.type.code wind.sp wind.sp.qc ceiling.ht ceiling.ht.qc ## 1203053 5 N 7.2 5 NA 9 ## 1203055 5 N 7.7 5 NA 9 ## 1203128 9 C 0.0 5 NA 9 ## 1203129 9 C 0.0 5 NA 9 ## 1203222 5 N 2.6 5 NA 9 ## 1203225 5 N 7.7 5 NA 9 ## ceiling.ht.method sky.cond vis.dist vis.dist.qc vis.var vis.var.qc ## 1203053 9 N NA 9 N 5 ## 1203055 9 N NA 9 N 5 ## 1203128 9 N NA 9 N 5 ## 1203129 9 N NA 9 N 5 ## 1203222 9 N NA 9 N 5 ## 1203225 9 N NA 9 N 5 ## temp temp.qc dew.point dew.point.qc atm.press atm.press.qc rh ## 1203053 -17.2 5 NA 9 NA 9 NA ## 1203055 -17.2 5 NA 9 NA 9 NA ## 1203128 -17.2 5 NA 9 NA 9 NA ## 1203129 -17.2 5 NA 9 NA 9 NA ## 1203222 -17.2 5 NA 9 NA 9 NA ## 1203225 -17.2 5 NA 9 NA 9 NA  There are still some suspiciously low values for temperature (-17.2C), but we will deal with those later.\nWe should also check the wind speed variable for any abnormalities.\nHow many missing values are there in the wind.sp variable? 5. Check the data against an external data source. We should check the suspicious temperature value (where is it located?) and validate that the range of elevations make sense (-13m to 4113m).\nGoogle is your friend here.\nFix any problems that arise in your checks.\nWhere was the location for the coldest temperature readings (-17.2C)? Do these seem reasonable in context? Does the range of values for elevation make sense? Why or why not? 6. Calculate summary statistics Remember to keep the initial question in mind. We want to pick out the weather station with maximum elevation and examine its wind speed and temperature.\nSome ideas: select the weather station with maximum elevation; look at the correlation between temperature and wind speed; look at the correlation between temperature and wind speed with hour and day of the month.\nelev \u0026lt;- met[which(met$elev == max(met$elev, na.rm = TRUE)), ] summary(elev)  ## USAFID WBAN year month day ## Min. :720385 Min. :419 Min. :2019 Min. :8 Min. : 1.0 ## 1st Qu.:720385 1st Qu.:419 1st Qu.:2019 1st Qu.:8 1st Qu.: 8.0 ## Median :720385 Median :419 Median :2019 Median :8 Median :16.0 ## Mean :720385 Mean :419 Mean :2019 Mean :8 Mean :16.1 ## 3rd Qu.:720385 3rd Qu.:419 3rd Qu.:2019 3rd Qu.:8 3rd Qu.:24.0 ## Max. :720385 Max. :419 Max. :2019 Max. :8 Max. :31.0 ## ## hour min lat lon elev ## Min. : 0.00 Min. : 6.00 Min. :39.8 Min. :-105.8 Min. :4113 ## 1st Qu.: 6.00 1st Qu.:13.00 1st Qu.:39.8 1st Qu.:-105.8 1st Qu.:4113 ## Median :12.00 Median :36.00 Median :39.8 Median :-105.8 Median :4113 ## Mean :11.66 Mean :34.38 Mean :39.8 Mean :-105.8 Mean :4113 ## 3rd Qu.:18.00 3rd Qu.:53.00 3rd Qu.:39.8 3rd Qu.:-105.8 3rd Qu.:4113 ## Max. :23.00 Max. :59.00 Max. :39.8 Max. :-105.8 Max. :4113 ## ## wind.dir wind.dir.qc wind.type.code wind.sp ## Min. : 10.0 Length:2117 Length:2117 Min. : 0.000 ## 1st Qu.:250.0 Class :character Class :character 1st Qu.: 4.100 ## Median :300.0 Mode :character Mode :character Median : 6.700 ## Mean :261.5 Mean : 7.245 ## 3rd Qu.:310.0 3rd Qu.: 9.800 ## Max. :360.0 Max. :21.100 ## NA's :237 NA's :168 ## wind.sp.qc ceiling.ht ceiling.ht.qc ceiling.ht.method ## Length:2117 Min. : 30 Min. :5.000 Length:2117 ## Class :character 1st Qu.: 2591 1st Qu.:5.000 Class :character ## Mode :character Median :22000 Median :5.000 Mode :character ## Mean :15145 Mean :5.008 ## 3rd Qu.:22000 3rd Qu.:5.000 ## Max. :22000 Max. :9.000 ## NA's :4 ## sky.cond vis.dist vis.dist.qc vis.var ## Length:2117 Min. : 0 Length:2117 Length:2117 ## Class :character 1st Qu.:16093 Class :character Class :character ## Mode :character Median :16093 Mode :character Mode :character ## Mean :15913 ## 3rd Qu.:16093 ## Max. :16093 ## NA's :683 ## vis.var.qc temp temp.qc dew.point ## Length:2117 Min. : 1.00 Length:2117 Min. :-6.0000 ## Class :character 1st Qu.: 6.00 Class :character 1st Qu.: 0.0000 ## Mode :character Median : 8.00 Mode :character Median : 0.0000 ## Mean : 8.13 Mean : 0.8729 ## 3rd Qu.:10.00 3rd Qu.: 2.0000 ## Max. :15.00 Max. : 7.0000 ## ## dew.point.qc atm.press atm.press.qc rh ## Length:2117 Min. : NA Min. :9 Min. :53.63 ## Class :character 1st Qu.: NA 1st Qu.:9 1st Qu.:58.10 ## Mode :character Median : NA Median :9 Median :61.39 ## Mean :NaN Mean :9 Mean :60.62 ## 3rd Qu.: NA 3rd Qu.:9 3rd Qu.:61.85 ## Max. : NA Max. :9 Max. :70.01 ## NA's :2117  Note that to find the maximum elevation, we had to add na.rm = TRUE, because the elevation variable contains missing values. This is an example of how missing values can quickly propagate throughout an analysis (as the “maximum” of 1, 2, and NA is NA, because it cannot be defined).\nAlso note that we used the which function to tell us which elements of the logical comparison are TRUE. We did this because some of them were NA, which can lead to issues when subsetting by a logical variable.\ncor(elev$temp, elev$wind.sp, use=\u0026quot;complete\u0026quot;)  ## [1] -0.09373843  cor(elev$temp, elev$hour, use=\u0026quot;complete\u0026quot;)  ## [1] 0.4397261  cor(elev$wind.sp, elev$day, use=\u0026quot;complete\u0026quot;)  ## [1] 0.3643079  cor(elev$wind.sp, elev$hour, use=\u0026quot;complete\u0026quot;)  ## [1] 0.08807315  cor(elev$temp, elev$day, use=\u0026quot;complete\u0026quot;)  ## [1] -0.003857766  The use=\u0026quot;complete\u0026quot; argument is another thing we added to avoid compounding NAs.\n7. Exploratory graphs We should look at the distributions of all of the key variables to make sure there are no remaining issues with the data.\nUse the hist function to make histograms of the elevation, temperature, and wind speed variables for the whole dataset One thing we should consider for later analyses is to log transform wind speed and elevation as they are very skewed.\nLook at where the weather station with highest elevation is located.\nleaflet(elev) %\u0026gt;% addProviderTiles('OpenStreetMap') %\u0026gt;% addCircles(lat=~lat,lng=~lon, opacity=1, fillOpacity=1, radius=100)   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Look at the time series of temperature and wind speed at this location. For this we will need to create a date-time variable for the x-axis.\nlibrary(lubridate) elev$date \u0026lt;- with(elev, ymd_h(paste(year, month, day, hour, sep= ' '))) summary(elev$date)  ## Min. 1st Qu. ## \u0026quot;2019-08-01 00:00:00.0000\u0026quot; \u0026quot;2019-08-08 11:00:00.0000\u0026quot; ## Median Mean ## \u0026quot;2019-08-16 22:00:00.0000\u0026quot; \u0026quot;2019-08-16 14:09:56.8823\u0026quot; ## 3rd Qu. Max. ## \u0026quot;2019-08-24 11:00:00.0000\u0026quot; \u0026quot;2019-08-31 22:00:00.0000\u0026quot;  elev \u0026lt;- elev[order(elev$date), ] head(elev)  ## USAFID WBAN year month day hour min lat lon elev wind.dir ## 221697 720385 419 2019 8 1 0 36 39.8 -105.766 4113 170 ## 221698 720385 419 2019 8 1 0 54 39.8 -105.766 4113 100 ## 221699 720385 419 2019 8 1 1 12 39.8 -105.766 4113 90 ## 221700 720385 419 2019 8 1 1 35 39.8 -105.766 4113 110 ## 221701 720385 419 2019 8 1 1 53 39.8 -105.766 4113 120 ## 221702 720385 419 2019 8 1 2 12 39.8 -105.766 4113 120 ## wind.dir.qc wind.type.code wind.sp wind.sp.qc ceiling.ht ceiling.ht.qc ## 221697 5 N 8.8 5 1372 5 ## 221698 5 N 2.6 5 1372 5 ## 221699 5 N 3.1 5 1981 5 ## 221700 5 N 4.1 5 2134 5 ## 221701 5 N 4.6 5 2134 5 ## 221702 5 N 6.2 5 22000 5 ## ceiling.ht.method sky.cond vis.dist vis.dist.qc vis.var vis.var.qc temp ## 221697 M N NA 9 N 5 9 ## 221698 M N NA 9 N 5 9 ## 221699 M N NA 9 N 5 9 ## 221700 M N NA 9 N 5 9 ## 221701 M N NA 9 N 5 9 ## 221702 9 N NA 9 N 5 9 ## temp.qc dew.point dew.point.qc atm.press atm.press.qc rh ## 221697 5 1 5 NA 9 57.61039 ## 221698 5 1 5 NA 9 57.61039 ## 221699 5 2 5 NA 9 61.85243 ## 221700 5 2 5 NA 9 61.85243 ## 221701 5 2 5 NA 9 61.85243 ## 221702 5 2 5 NA 9 61.85243 ## date ## 221697 2019-08-01 00:00:00 ## 221698 2019-08-01 00:00:00 ## 221699 2019-08-01 01:00:00 ## 221700 2019-08-01 01:00:00 ## 221701 2019-08-01 01:00:00 ## 221702 2019-08-01 02:00:00  With the date-time variable we can plot the time series of temperature and wind speed.\nUse the plot function to make line graphs of temperature vs. date and wind speed vs. date Summarize any trends that you see in these time series plots. 8. Ask questions By now, you might have some specific questions about how the data was gathered and what some of the different variables and values mean. Alternatively, maybe you have an idea for how some of the variable should be related and you want to explore that relationship. In a real-world analysis, these questions could potentially be answered by a collaborator, who may have been part of the team that collected the data.\nWhat questions do you have about the data? If you haven’t already, now would be a good time to look at the accompanying data dictionary for this dataset and see if it can answer any of your questions. If you have questions about the nature of the dataset and how it was gathered, this might be able to help.\nFor questions about variables in the dataset or relationships between them, try making some more exploratory plots. Do you see the patterns you would expect? There are many different types of summaries and visualization strategies that we have not discussed, but which could provide interesting perspectives on the data.\nSome other useful plotting functions include:\n pairs for making all pairwise scatter plots in a dataset with \u0026gt;2 dimensions. heatmap and/or corrplot (from the corrplot package) for visualizing matrices in general or correlation matrices in particular. image a low-level matrix visualization function barplot, especially with table, for visualizing frequencies of categorical variables.  ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"408129ada1d90a82b52f15f5d96d2d85","permalink":"https://uscbiostats.github.io/PM566/assignment/03-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/03-lab/","section":"assignment","summary":"Learning Goals  Read in and get familiar with the meteorology dataset Step through the EDA “checklist” presented in the class slides Practice making exploratory graphs  As you do this, think about what questions you would like to ask regarding this data. What would you ask a collaborator who was more familiar with it?\nLab Description We will work with the meteorological data presented in lecture.","tags":null,"title":"Lab 03 - Exploratory Data Analysis","type":"assignment"},{"authors":null,"categories":null,"content":"Learning Goals  Read in and prepare the meteorological dataset Create several graphs with different geoms() in ggplot2 Create a facet graph Conduct some customizations to the graphs Create a more detailed map using leaflet()  Lab Description We will again work with the meteorological data presented in lecture.\nThe objective of the lab is to examine the association between weekly average dew point and wind speed in four regions of the US and by elevation.\nPer Wikipedia: \u0026ldquo;The dew point of a given body of air is the temperature to which it must be cooled to become saturated with water vapor. This temperature depends on the pressure and water content of the air.\u0026rdquo;\nAgain, feel free to supplement your knowledge of this dataset by checking out the data dictionary.\nSteps 1. Read in the data First download and then read in with data.table::fread()\nif (!file.exists(\u0026quot;met_all.gz\u0026quot;)) download.file( url = \u0026quot;https://raw.githubusercontent.com/USCbiostats/data-science-data/master/02_met/met_all.gz\u0026quot;, destfile = \u0026quot;met_all.gz\u0026quot;, method = \u0026quot;libcurl\u0026quot;, timeout = 60 ) met \u0026lt;- data.table::fread(\u0026quot;met_all.gz\u0026quot;)  2. Prepare the data  Remove temperatures less than -17C Make sure there are no missing data in the key variables coded as 9999, 999, etc Generate a date variable using the functions as.Date() (hint: You will need the following to create a date paste(year, month, day, sep = \u0026quot;-\u0026quot;)). Using the data.table::week function, keep the observations of the first week of the month. Compute the mean by station of the variables temp, rh, wind.sp, vis.dist, dew.point, lat, lon, and elev. Create a region variable for NW, SW, NE, SE based on lon = -98.00 and lat = 39.71 degrees Create a categorical variable for elevation as in the lecture slides  3. Use geom_violin to examine the wind speed and dew point by region You saw how to use geom_boxplot in class. Try using geom_violin instead (take a look at the help). (hint: You will need to set the x aesthetic to 1)\n Use facets Make sure to deal with NAs Describe what you observe in the graph  4. Use geom_jitter with stat_smooth to examine the association between dew point and wind speed by region  Color points by region Make sure to deal with NAs Fit a linear regression line by region Describe what you observe in the graph  5. Use geom_bar to create barplots of the weather stations by elevation category colored by region  Bars by elevation category using position=\u0026quot;dodge\u0026quot; Change colors from the default. Color by region using scale_fill_brewer see this Create nice labels on the axes and add a title Describe what you observe in the graph Make sure to deal with NA values  6. Use stat_summary to examine mean dew point and wind speed by region with standard deviation error bars   Make sure to remove NAs\n  Use fun.data=\u0026quot;mean_sdl\u0026quot; in stat_summary\n  Add another layer of stats_summary but change the geom to \u0026quot;errorbar\u0026quot; (see the help).\n  Describe the graph and what you observe\n  Dew point is\u0026hellip;\n  Wind speed is\u0026hellip;\n  7. Make a map showing the spatial trend in relative humidity in the US   Make sure to remove NAs\n  Use leaflet()\n  Make a color palette with custom colors\n  Use addMarkers to include the top 10 places in relative humidity (hint: this will be useful rank(-rh) \u0026lt;= 10)\n  Add a legend\n  Describe the trend in RH across the US\n  8. Use a ggplot extension  Pick an extension (except cowplot) from here and make a plot of your choice using the met data (or met_avg) Might want to try examples that come with the extension first (e.g. ggtech, gganimate, ggforce)  ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"1ba8ca6f733939b9bce0616b9af32e16","permalink":"https://uscbiostats.github.io/PM566/assignment/04-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/04-lab/","section":"assignment","summary":"Learning Goals  Read in and prepare the meteorological dataset Create several graphs with different geoms() in ggplot2 Create a facet graph Conduct some customizations to the graphs Create a more detailed map using leaflet()  Lab Description We will again work with the meteorological data presented in lecture.\nThe objective of the lab is to examine the association between weekly average dew point and wind speed in four regions of the US and by elevation.","tags":null,"title":"Lab 04 - Data Visualization","type":"assignment"},{"authors":null,"categories":null,"content":"knitr::opts_chunk$set(eval = FALSE, include = TRUE)  Learning goals  Use unnest_tokens() and unnest_ngrams() to extract tokens and ngrams from text. Use dplyr and ggplot2 to analyze text data  Lab description For this lab we will be working with a new dataset. The dataset contains transcription samples from https://www.mtsamples.com/. And is loaded and \u0026ldquo;fairly\u0026rdquo; cleaned at https://raw.githubusercontent.com/USCbiostats/data-science-data/master/00_mtsamples/mtsamples.csv.\nSetup packages You should load in dplyr, (or data.table if you want to work that way), ggplot2 and tidytext. If you don\u0026rsquo;t already have tidytext then you can install with\ninstall.packages(\u0026quot;tidytext\u0026quot;)  read in Medical Transcriptions Loading in reference transcription samples from https://www.mtsamples.com/\nlibrary(readr) library(dplyr) mt_samples \u0026lt;- read_csv(\u0026quot;https://raw.githubusercontent.com/USCbiostats/data-science-data/master/00_mtsamples/mtsamples.csv\u0026quot;) mt_samples \u0026lt;- mt_samples %\u0026gt;% select(description, medical_specialty, transcription) head(mt_samples)   Question 1: What specialties do we have? We can use count() from dplyr to figure out how many different catagories do we have? Are these catagories related? overlapping? evenly distributed?\nmt_samples %\u0026gt;% count(___, sort = TRUE)   Question 2  Tokenize the the words in the transcription column Count the number of times each token appears Visualize the top 20 most frequent words  Explain what we see from this result. Does it makes sense? What insights (if any) do we get?\n Question 3  Redo visualization but remove stopwords before Bonus points if you remove numbers as well  What do we see know that we have removed stop words? Does it give us a better idea of what the text is about?\n Question 4 repeat question 2, but this time tokenize into bi-grams. how does the result change if you look at tri-grams?\n Question 5 Using the results you got from questions 4. Pick a word and count the words that appears after and before it.\n Question 6 Which words are most used in each of the specialties. you can use group_by() and top_n() from dplyr to have the calculations be done within each specialty. Remember to remove stopwords. How about the most 5 used words?\nQuestion 7 - extra Find your own insight in the data:\nIdeas:\n Interesting ngrams See if certain words are used more in some specialties then others  ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"f00132d85ef0ce8610dfed06d117afb4","permalink":"https://uscbiostats.github.io/PM566/assignment/06-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/06-lab/","section":"assignment","summary":"knitr::opts_chunk$set(eval = FALSE, include = TRUE)  Learning goals  Use unnest_tokens() and unnest_ngrams() to extract tokens and ngrams from text. Use dplyr and ggplot2 to analyze text data  Lab description For this lab we will be working with a new dataset. The dataset contains transcription samples from https://www.mtsamples.com/. And is loaded and \u0026ldquo;fairly\u0026rdquo; cleaned at https://raw.githubusercontent.com/USCbiostats/data-science-data/master/00_mtsamples/mtsamples.csv.\nSetup packages You should load in dplyr, (or data.table if you want to work that way), ggplot2 and tidytext.","tags":null,"title":"Lab 06 - Text Mining","type":"assignment"},{"authors":null,"categories":null,"content":"Learning goals  Use a real world API to make queries and process the data. Use regular expressions to parse the information. Practice your GitHub skills.  Lab description In this lab, we will be working with the NCBI API to make queries and extract information using XML and regular expressions. For this lab, we will be using the httr, xml2, and stringr R packages.\nQuestion 1: How many sars-cov-2 papers? Build an automatic counter of sars-cov-2 papers using PubMed. You will need to apply XPath as we did during the lecture to extract the number of results returned by PubMed in the following web address:\nhttps://pubmed.ncbi.nlm.nih.gov/?term=sars-cov-2  Complete the lines of code:\n# Downloading the website website \u0026lt;- xml2::read_html(\u0026quot;[URL]\u0026quot;) # Finding the counts counts \u0026lt;- xml2::xml_find_first(website, \u0026quot;[XPath]\u0026quot;) # Turning it into text counts \u0026lt;- as.character(counts) # Extracting the data using regex stringr::str_extract(counts, \u0026quot;[REGEX FOR NUMBERS WITH COMMAS/DOTS]\u0026quot;)  Question 2: Academic publications on COVID19 and Hawaii You need to query the following. The parameters passed to the query are documented here.\nUse the function httr::GET() to make the following query:\n  Baseline URL: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi\n  Query parameters:\n db: pubmed term: covid19 hawaii retmax: 1000    library(httr) query_ids \u0026lt;- GET( url = \u0026quot;BASELINE URL\u0026quot;, query = list(\u0026quot;QUERY PARAMETERS\u0026quot;) ) # Extracting the content of the response of GET ids \u0026lt;- httr::content(query_ids)  The query will return an XML object, we can turn it into a character list to analyze the text directly with as.character(). Another way of processing the data could be using lists with the function xml2::as_list(). We will skip the latter for now.\nTake a look at the data, and continue with the next question.\nQuestion 3: Get details about the articles The IDs are wrapped around text in the following way: \u0026lt;Id\u0026gt;... id number ...\u0026lt;/Id\u0026gt;. We can use a regular expression that extracts that information. Fill out the following lines of code:\n# Turn the result into a character vector ids \u0026lt;- as.character(ids) # Find all the ids ids \u0026lt;- stringr::str_extract_all(ids, \u0026quot;PATTERN\u0026quot;)[[1]] # Remove all the leading and trailing \u0026lt;Id\u0026gt; \u0026lt;/Id\u0026gt;. Make use of \u0026quot;|\u0026quot; ids \u0026lt;- stringr::str_remove_all(ids, \u0026quot;PATTERN\u0026quot;)  With the IDs in hand, we can now try to get the abstracts of the papers. As before, we will need to coerce the contents (results) to a list using:\n  Baseline url: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi\n  Query parameters:\n db: pubmed id: A character with all the ids separated by comma, e.g., \u0026ldquo;1232131,546464,13131\u0026rdquo; retmax: 1000 rettype: abstract    Pro-tip: If you want GET() to take some element literal, wrap it around I() (as you would do in a formula in R). For example, the text \u0026quot;123,456\u0026quot; is replaced with \u0026quot;123%2C456\u0026quot;. If you don\u0026rsquo;t want that behavior, you would need to do the following I(\u0026quot;123,456\u0026quot;).\npublications \u0026lt;- GET( url = \u0026quot;BASELINE URL HERE\u0026quot;, query = list( \u0026quot;PARAMETERS OF THE QUERY\u0026quot; ) ) # Turning the output into character vector publications \u0026lt;- httr::content(publications) publications_txt \u0026lt;- as.character(publications)  With this in hand, we can now analyze the data. This is also a good time for committing and pushing your work!\nQuestion 4: Distribution of universities, schools, and departments Using the function stringr::str_extract_all() applied on publications_txt, capture all the terms of the form:\n University of \u0026hellip; \u0026hellip; Institute of \u0026hellip;  Write a regular expression that captures all such instances\ninstitution \u0026lt;- str_extract_all( publications_txt, \u0026quot;[YOUR REGULAR EXPRESSION HERE]\u0026quot; ) institution \u0026lt;- unlist(institution) table(institution)  Repeat the exercise and this time focus on schools and departments in the form of\n School of \u0026hellip; Department of \u0026hellip;  And tabulate the results\nschools_and_deps \u0026lt;- str_extract_all( abstracts_txt, \u0026quot;[YOUR REGULAR EXPRESSION HERE]\u0026quot; ) table(schools_and_deps)  Question 5: Form a database We want to build a dataset which includes the title and the abstract of the paper. The title of all records is enclosed by the HTML tag ArticleTitle, and the abstract by Abstract.\nBefore applying the functions to extract text directly, it will help to process the XML a bit. We will use the xml2::xml_children() function to keep one element per ID. This way, if a paper is missing the abstract, or something else, we will be able to properly match PUBMED IDS with their corresponding records.\npub_char_list \u0026lt;- xml2::xml_children(publications) pub_char_list \u0026lt;- sapply(pub_char_list, as.character)  Now, extract the abstract and article title for each one of the elements of pub_char_list. You can either use sapply() as we just did, or simply take advantage of vectorization of stringr::str_extract\nabstracts \u0026lt;- str_extract(pub_char_list, \u0026quot;[YOUR REGULAR EXPRESSION]\u0026quot;) abstracts \u0026lt;- str_remove_all(abstracts, \u0026quot;[CLEAN ALL THE HTML TAGS]\u0026quot;) abstracts \u0026lt;- str_remove_all(abstracts, \u0026quot;[CLEAN ALL EXTRA WHITE SPACE AND NEW LINES]\u0026quot;)  How many of these don\u0026rsquo;t have an abstract? Now, the title\ntitles \u0026lt;- str_extract(pub_char_list, \u0026quot;[YOUR REGULAR EXPRESSION]\u0026quot;) titles \u0026lt;- str_remove_all(titles, \u0026quot;[CLEAN ALL THE HTML TAGS]\u0026quot;)  Finally, put everything together into a single data.frame and use knitr::kable to print the results\ndatabase \u0026lt;- data.frame( \u0026quot;[DATA TO CONCATENATE]\u0026quot; ) knitr::kable(database)  Done! Knit the document, commit, and push.\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"30eb5ee3cc44d8e2fba241e8f4996c51","permalink":"https://uscbiostats.github.io/PM566/assignment/07-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/07-lab/","section":"assignment","summary":"Learning goals  Use a real world API to make queries and process the data. Use regular expressions to parse the information. Practice your GitHub skills.  Lab description In this lab, we will be working with the NCBI API to make queries and extract information using XML and regular expressions. For this lab, we will be using the httr, xml2, and stringr R packages.\nQuestion 1: How many sars-cov-2 papers?","tags":null,"title":"Lab 07 - Web scraping and Regular Expressions","type":"assignment"},{"authors":null,"categories":null,"content":"Setup # install.packages(c(\u0026quot;RSQLite\u0026quot;, \u0026quot;DBI\u0026quot;)) library(RSQLite) library(DBI) # Initialize a temporary in memory database con \u0026lt;- dbConnect(SQLite(), \u0026quot;:memory:\u0026quot;) # Download tables actor \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/actor.csv\u0026quot;) rental \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/rental.csv\u0026quot;) customer \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/customer.csv\u0026quot;) payment \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/payment_p2007_01.csv\u0026quot;) # Copy data.frames to database dbWriteTable(con, \u0026quot;actor\u0026quot;, actor) dbWriteTable(con, \u0026quot;rental\u0026quot;, rental) dbWriteTable(con, \u0026quot;customer\u0026quot;, customer) dbWriteTable(con, \u0026quot;payment\u0026quot;, payment)  dbListTables(con)  TIP: You can use the following QUERY to see the structure of a table\nPRAGMA table_info(actor)  SQL references:\n https://www.w3schools.com/sql/\nExercise 1 Edit the code below to retrieve the actor ID, first name and last name for all actors using the actor table. Sort by last name and then by first name (note that the code chunk below is set up to run SQL code rather than R code).\nSELECT FROM ORDER by  Exercise 2 Retrieve the actor ID, first name, and last name for actors whose last name equals \u0026lsquo;WILLIAMS\u0026rsquo; or \u0026lsquo;DAVIS\u0026rsquo;.\nSELECT FROM WHERE ___ IN ('WILLIAMS', 'DAVIS')  Exercise 3 Write a query against the rental table that returns the IDs of the customers who rented a film on July 5, 2005 (use the rental.rental_date column, and you can use the date() function to ignore the time component). Include a single row for each distinct customer ID.\nSELECT DISTINCT FROM WHERE date(___) = '2005-07-05'  Exercise 4 Exercise 4.1 Construct a query that retrieves all rows from the payment table where the amount is either 1.99, 7.99, 9.99.\nSELECT * FROM ___ WHERE ___ IN (1.99, 7.99, 9.99)  Exercise 4.2 Construct a query that retrieves all rows from the payment table where the amount is greater then 5.\nSELECT * FROM WHERE  Exercise 4.2 Construct a query that retrieves all rows from the payment table where the amount is greater then 5 and less then 8.\nSELECT * FROM ___ WHERE ___ AND ___  Exercise 5 Retrieve all the payment IDs and their amounts from the customers whose last name is \u0026lsquo;DAVIS\u0026rsquo;.\nSELECT FROM INNER JOIN WHERE AND  Exercise 6 Exercise 6.1 Use COUNT(*) to count the number of rows in rental.\n Exercise 6.2 Use COUNT(*) and GROUP BY to count the number of rentals for each customer_id.\n Exercise 6.3 Repeat the previous query and sort by the count in descending order.\n Exercise 6.4 Repeat the previous query but use HAVING to only keep the groups with 40 or more.\n Exercise 7 Write a query that calculates a number of summary statistics for the payment table using MAX, MIN, AVG and SUM\n Exercise 7.1 Modify the above query to do those calculations for each customer_id.\n Exercise 7.2 Modify the above query to only keep the customer_ids that have more then 5 payments.\n Cleanup Run the following chunk to disconnect from the connection.\n# clean up dbDisconnect(con)  ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"3ef79e3e3bbf9328d3c80aafd06adf32","permalink":"https://uscbiostats.github.io/PM566/assignment/10-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/10-lab/","section":"assignment","summary":"Setup # install.packages(c(\u0026quot;RSQLite\u0026quot;, \u0026quot;DBI\u0026quot;)) library(RSQLite) library(DBI) # Initialize a temporary in memory database con \u0026lt;- dbConnect(SQLite(), \u0026quot;:memory:\u0026quot;) # Download tables actor \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/actor.csv\u0026quot;) rental \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/rental.csv\u0026quot;) customer \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/customer.csv\u0026quot;) payment \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/payment_p2007_01.csv\u0026quot;) # Copy data.frames to database dbWriteTable(con, \u0026quot;actor\u0026quot;, actor) dbWriteTable(con, \u0026quot;rental\u0026quot;, rental) dbWriteTable(con, \u0026quot;customer\u0026quot;, customer) dbWriteTable(con, \u0026quot;payment\u0026quot;, payment)  dbListTables(con)  TIP: You can use the following QUERY to see the structure of a table\nPRAGMA table_info(actor)  SQL references:","tags":null,"title":"Lab 10 - SQL","type":"assignment"},{"authors":null,"categories":null,"content":"                                                                        Learning Goals  Read in and process the COVID dataset from the New York Times GitHub repository Create interactive graphs of different types using plot_ly() and ggplotly() functions Customize the hoverinfo and other plot features Create a Choropleth map using plot_geo() Create an interactive table using DataTable  Lab Description We will work with the COVID data presented in lecture. Recall the dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic. We will explore cases, deaths, and their population normalized values over time to identify trends.\nSteps I. Reading and processing the New York Times (NYT) state-level COVID-19 data 1. Read in the data  Read in the COVID data with data.table:fread() from the NYT GitHub repository: “https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv” Read in the state population data with data.table:fread() from the repository: “https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv” Merge datasets  ## data extracted from New York Times state-level data from NYT Github repository # https://github.com/nytimes/covid-19-data ## state-level population information from us_census_data available on GitHub repository: # https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data # load COVID state-level data from NYT ### FINISH THE CODE HERE ### cv_states_readin \u0026lt;- as.data.frame(data.table::fread(\u0026quot;https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv\u0026quot;) ) # load state population data ### FINISH THE CODE HERE ### state_pops_readin \u0026lt;- as.data.frame(data.table::fread(\u0026quot;https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv\u0026quot;)) state_pops \u0026lt;- state_pops_readin state_pops$abb \u0026lt;- state_pops$state state_pops$state \u0026lt;- state_pops$state_name state_pops$state_name \u0026lt;- NULL ### FINISH THE CODE HERE ### cv_states \u0026lt;- merge( cv_states_readin, state_pops, by=\u0026quot;state\u0026quot;)  2. Look at the data  Inspect the dimensions, head, and tail of the data Inspect the structure of each variables. Are they in the correct format?  dim(cv_states)  ## [1] 58094 9  head(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 1 Alabama 2023-01-04 1 1587224 21263 1 4887871 96.50939 AL ## 2 Alabama 2020-04-25 1 6213 213 1 4887871 96.50939 AL ## 3 Alabama 2023-02-26 1 1638348 21400 1 4887871 96.50939 AL ## 4 Alabama 2022-12-03 1 1549285 21129 1 4887871 96.50939 AL ## 5 Alabama 2020-05-06 1 8691 343 1 4887871 96.50939 AL ## 6 Alabama 2021-04-21 1 524367 10807 1 4887871 96.50939 AL  tail(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 58089 Wyoming 2022-09-11 56 175290 1884 56 577737 5.950611 WY ## 58090 Wyoming 2022-08-21 56 173487 1871 56 577737 5.950611 WY ## 58091 Wyoming 2021-01-26 56 51152 596 56 577737 5.950611 WY ## 58092 Wyoming 2021-02-21 56 53795 662 56 577737 5.950611 WY ## 58093 Wyoming 2021-08-22 56 70671 809 56 577737 5.950611 WY ## 58094 Wyoming 2021-03-20 56 55581 693 56 577737 5.950611 WY  str(cv_states)  ## 'data.frame':\t58094 obs. of 9 variables: ## $ state : chr \u0026quot;Alabama\u0026quot; \u0026quot;Alabama\u0026quot; \u0026quot;Alabama\u0026quot; \u0026quot;Alabama\u0026quot; ... ## $ date : IDate, format: \u0026quot;2023-01-04\u0026quot; \u0026quot;2020-04-25\u0026quot; ... ## $ fips : int 1 1 1 1 1 1 1 1 1 1 ... ## $ cases : int 1587224 6213 1638348 1549285 8691 524367 1321892 1088370 1153149 814025 ... ## $ deaths : int 21263 213 21400 21129 343 10807 19676 16756 16826 15179 ... ## $ geo_id : int 1 1 1 1 1 1 1 1 1 1 ... ## $ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ... ## $ pop_density: num 96.5 96.5 96.5 96.5 96.5 ... ## $ abb : chr \u0026quot;AL\u0026quot; \u0026quot;AL\u0026quot; \u0026quot;AL\u0026quot; \u0026quot;AL\u0026quot; ...  3. Format the data  Make date into a date variable Make state and abb into a factor variable Order the data first by state, second by date Confirm the variables are now correctly formatted Inspect the range values for each variable. What is the date range? The range of cases and deaths?  # format the date cv_states$date \u0026lt;- as.Date(cv_states$date, format=\u0026quot;%Y-%m-%d\u0026quot;) # format the state variable state_list \u0026lt;- unique(cv_states$state) cv_states$state \u0026lt;- factor(cv_states$state, levels = state_list) # format the state abbreviation (abb) variable ### FINISH THE CODE HERE ### abb_list = unique(cv_states$abb) cv_states$abb = factor(cv_states$abb, levels = abb_list) # order the data first by state, second by date cv_states = cv_states[order(cv_states$state, cv_states$date),] # Confirm the variables are now correctly formatted str(cv_states)  ## 'data.frame':\t58094 obs. of 9 variables: ## $ state : Factor w/ 52 levels \u0026quot;Alabama\u0026quot;,\u0026quot;Alaska\u0026quot;,..: 1 1 1 1 1 1 1 1 1 1 ... ## $ date : Date, format: \u0026quot;2020-03-13\u0026quot; \u0026quot;2020-03-14\u0026quot; ... ## $ fips : int 1 1 1 1 1 1 1 1 1 1 ... ## $ cases : int 6 12 23 29 39 51 78 106 131 157 ... ## $ deaths : int 0 0 0 0 0 0 0 0 0 0 ... ## $ geo_id : int 1 1 1 1 1 1 1 1 1 1 ... ## $ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ... ## $ pop_density: num 96.5 96.5 96.5 96.5 96.5 ... ## $ abb : Factor w/ 52 levels \u0026quot;AL\u0026quot;,\u0026quot;AK\u0026quot;,\u0026quot;AZ\u0026quot;,..: 1 1 1 1 1 1 1 1 1 1 ...  head(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL ## 597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL ## 282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL ## 12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL ## 266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL ## 78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL  tail(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 57902 Wyoming 2023-03-18 56 185640 2009 56 577737 5.950611 WY ## 57916 Wyoming 2023-03-19 56 185640 2009 56 577737 5.950611 WY ## 57647 Wyoming 2023-03-20 56 185640 2009 56 577737 5.950611 WY ## 57867 Wyoming 2023-03-21 56 185800 2014 56 577737 5.950611 WY ## 58057 Wyoming 2023-03-22 56 185800 2014 56 577737 5.950611 WY ## 57812 Wyoming 2023-03-23 56 185800 2014 56 577737 5.950611 WY  # Inspect the range values for each variable. What is the date range? The range of cases and deaths? head(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL ## 597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL ## 282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL ## 12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL ## 266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL ## 78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL  summary(cv_states)  ## state date fips cases ## Washington : 1158 Min. :2020-01-21 Min. : 1.00 Min. : 1 ## Illinois : 1155 1st Qu.:2020-12-06 1st Qu.:16.00 1st Qu.: 112125 ## California : 1154 Median :2021-09-11 Median :29.00 Median : 418120 ## Arizona : 1153 Mean :2021-09-10 Mean :29.78 Mean : 947941 ## Massachusetts: 1147 3rd Qu.:2022-06-17 3rd Qu.:44.00 3rd Qu.: 1134318 ## Wisconsin : 1143 Max. :2023-03-23 Max. :72.00 Max. :12169158 ## (Other) :51184 ## deaths geo_id population pop_density ## Min. : 0 Min. : 1.00 Min. : 577737 Min. : 1.292 ## 1st Qu.: 1598 1st Qu.:16.00 1st Qu.: 1805832 1st Qu.: 43.659 ## Median : 5901 Median :29.00 Median : 4468402 Median : 107.860 ## Mean : 12553 Mean :29.78 Mean : 6397965 Mean : 423.031 ## 3rd Qu.: 15952 3rd Qu.:44.00 3rd Qu.: 7535591 3rd Qu.: 229.511 ## Max. :104277 Max. :72.00 Max. :39557045 Max. :11490.120 ## NA's :1106 ## abb ## WA : 1158 ## IL : 1155 ## CA : 1154 ## AZ : 1153 ## MA : 1147 ## WI : 1143 ## (Other):51184  min(cv_states$date)  ## [1] \u0026quot;2020-01-21\u0026quot;  max(cv_states$date)  ## [1] \u0026quot;2023-03-23\u0026quot;  4. Add new_cases and new_deaths and correct outliers   Add variables for new cases, new_cases, and new deaths, new_deaths:\n Hint: new_cases is equal to the difference between cases on date i and date i-1, starting on date i=2    Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?\n  Correct outliers: Set negative values for new_cases or new_deaths to 0\n  Recalculate cases and deaths as cumulative sum of updates new_cases and new_deaths\n  # Add variables for new_cases and new_deaths: for (i in 1:length(state_list)) { cv_subset = subset(cv_states, state == state_list[i]) cv_subset = cv_subset[order(cv_subset$date),] # add starting level for new cases and deaths cv_subset$new_cases = cv_subset$cases[1] cv_subset$new_deaths = cv_subset$deaths[1] #### FINISH THE CODE HERE ### for (j in 2:nrow(cv_subset)) { cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1] cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1] } # include in main dataset cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths } # Inspect outliers in new_cases and new_deaths using plotly ### FINISH THE CODE HERE ### p1\u0026lt;-ggplot(cv_states, aes( x=date, y=new_cases, color=state ) ) + geom_line() + geom_point(size = .5, alpha = 0.5) ggplotly(p1)   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(y) \",\"x\"],\"110d419b04a8b\":[\"function (y) \",\"x\"]},\"highlight\":{\"on\":\"plotly_click\",\"persistent\":false,\"dynamic\":false,\"selectize\":false,\"opacityDim\":0.20000000000000001,\"selected\":{\"opacity\":1},\"debounce\":0},\"shinyEvents\":[\"plotly_hover\",\"plotly_click\",\"plotly_selected\",\"plotly_relayout\",\"plotly_brushed\",\"plotly_brushing\",\"plotly_clickannotation\",\"plotly_doubleclick\",\"plotly_deselect\",\"plotly_afterplot\",\"plotly_sunburstclick\"],\"base_url\":\"https://plot.ly\"},\"evals\":[],\"jsHooks\":[]} p1\u0026lt;-NULL # to clear from workspace ### FINISH THE CODE HERE ### p2\u0026lt;-ggplot(cv_states, aes(x=date, y=new_deaths, color=state ) ) + geom_line() + geom_point(size = .5, alpha = 0.5) ggplotly(p2)   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2020-03-13\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-14\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-15\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-16\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-17\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-18\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-19\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-20\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-21\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-22\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-23\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-24\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-25\nnew_deaths: 1\nstate: Alabama\",\"date: 2020-03-26\nnew_deaths: 2\nstate: Alabama\",\"date: 2020-03-27\nnew_deaths: 1\nstate: Alabama\",\"date: 2020-03-28\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-29\nnew_deaths: 1\nstate: Alabama\",\"date: 2020-03-30\nnew_deaths: 6\nstate: Alabama\",\"date: 2020-03-31\nnew_deaths: 3\nstate: Alabama\",\"date: 2020-04-01\nnew_deaths: 14\nstate: Alabama\",\"date: 2020-04-02\nnew_deaths: 4\nstate: Alabama\",\"date: 2020-04-03\nnew_deaths: 6\nstate: Alabama\",\"date: 2020-04-04\nnew_deaths: 6\nstate: Alabama\",\"date: 2020-04-05\nnew_deaths: 1\nstate: Alabama\",\"date: 2020-04-06\nnew_deaths: 8\nstate: Alabama\",\"date: 2020-04-07\nnew_deaths: 11\nstate: Alabama\",\"date: 2020-04-08\nnew_deaths: 3\nstate: Alabama\",\"date: 2020-04-09\nnew_deaths: 11\nstate: Alabama\",\"date: 2020-04-10\nnew_deaths: 2\nstate: Alabama\",\"date: 2020-04-11\nnew_deaths: 13\nstate: Alabama\",\"date: 2020-04-12\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-04-13\nnew_deaths: 11\nstate: Alabama\",\"date: 2020-04-14\nnew_deaths: 10\nstate: Alabama\",\"date: 2020-04-15\nnew_deaths: 9\nstate: Alabama\",\"date: 2020-04-16\nnew_deaths: 14\nstate: Alabama\",\"date: 2020-04-17\nnew_deaths: 14\nstate: Alabama\",\"date: 2020-04-18\nnew_deaths: -4\nstate: Alabama\",\"date: 2020-04-19\nnew_deaths: 13\nstate: Alabama\",\"date: 2020-04-20\nnew_deaths: 4\nstate: Alabama\",\"date: 2020-04-21\nnew_deaths: 22\nstate: Alabama\",\"date: 2020-04-22\nnew_deaths: 15\nstate: Alabama\",\"date: 2020-04-23\nnew_deaths: -4\nstate: Alabama\",\"date: 2020-04-24\nnew_deaths: 12\nstate: Alabama\",\"date: 2020-04-25\nnew_deaths: 4\nstate: Alabama\",\"date: 2020-04-26\nnew_deaths: 6\nstate: Alabama\",\"date: 2020-04-27\nnew_deaths: 9\nstate: Alabama\",\"date: 2020-04-28\nnew_deaths: 14\nstate: Alabama\",\"date: 2020-04-29\nnew_deaths: 20\nstate: Alabama\",\"date: 2020-04-30\nnew_deaths: 10\nstate: Alabama\",\"date: 2020-05-01\nnew_deaths: 17\nstate: Alabama\",\"date: 2020-05-02\nnew_deaths: -1\nstate: Alabama\",\"date: 2020-05-03\nnew_deaths: 2\nstate: Alabama\",\"date: 2020-05-04\nnew_deaths: 8\nstate: Alabama\",\"date: 2020-05-05\nnew_deaths: 17\nstate: Alabama\",\"date: 2020-05-06\nnew_deaths: 28\nstate: Alabama\",\"date: 2020-05-07\nnew_deaths: 26\nstate: Alabama\",\"date: 2020-05-08\nnew_deaths: 14\nstate: Alabama\",\"date: 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(y) \",\"x\"],\"110d44f19d980\":[\"function (y) \",\"x\"]},\"highlight\":{\"on\":\"plotly_click\",\"persistent\":false,\"dynamic\":false,\"selectize\":false,\"opacityDim\":0.20000000000000001,\"selected\":{\"opacity\":1},\"debounce\":0},\"shinyEvents\":[\"plotly_hover\",\"plotly_click\",\"plotly_selected\",\"plotly_relayout\",\"plotly_brushed\",\"plotly_brushing\",\"plotly_clickannotation\",\"plotly_doubleclick\",\"plotly_deselect\",\"plotly_afterplot\",\"plotly_sunburstclick\"],\"base_url\":\"https://plot.ly\"},\"evals\":[],\"jsHooks\":[]} p2\u0026lt;-NULL # to clear from workspace # set negative new case or death counts to 0 cv_states$new_cases[cv_states$new_cases\u0026lt;0] = 0 cv_states$new_deaths[cv_states$new_deaths\u0026lt;0] = 0 # Recalculate `cases` and `deaths` as cumulative sum of updates `new_cases` and `new_deaths` for (i in 1:length(state_list)) { cv_subset = subset(cv_states, state == state_list[i]) # add starting level for new cases and deaths cv_subset$cases = cv_subset$cases[1] cv_subset$deaths = cv_subset$deaths[1] for (j in 2:nrow(cv_subset)) { cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1] cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1] } # include in main dataset cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths }  5. Add additional variables   Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:\n per100k = cases per 100,000 population newper100k= new cases per 100,000 deathsper100k = deaths per 100,000 newdeathsper100k = new deaths per 100,000    Add a “naive CFR” variable representing deaths / cases on each date for each state\n  Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture\n  # add population normalized (by 100,000) counts for each variable cv_states$per100k = as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1)) cv_states$newper100k = as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1)) cv_states$deathsper100k = as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1)) cv_states$newdeathsper100k = as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1)) # add a naive_CFR variable = deaths / cases cv_states = cv_states %\u0026gt;% mutate(naive_CFR = round((deaths*100/cases),2)) # create a `cv_states_today` variable ### FINISH THE CODE HERE ### max_date \u0026lt;- max(cv_states$date) cv_states_today = cv_states %\u0026gt;% filter(date==as.Date(max_date))  II. Interactive plots 6. Explore scatterplots using plot_ly()  Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)  Use hover to identify any outliers. Remove those outliers and replot.   Choose one plot. 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195.4\",\"type\":\"scatter\",\"name\":\"Alaska\",\"textfont\":{\"color\":\"rgba(135,188,156,1)\",\"size\":5.2663096276619381},\"error_y\":{\"color\":\"rgba(135,188,156,1)\",\"width\":5.2663096276619381},\"error_x\":{\"color\":\"rgba(135,188,156,1)\",\"width\":5.2663096276619381},\"line\":{\"color\":\"rgba(135,188,156,1)\",\"width\":5.2663096276619381},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[63.135855048000003],\"y\":[463.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(160,182,146,1)\",\"size\":[15.99568224761712],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(160,182,146,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Arizona:\nCases per 100k: 34178.7\nDeaths per 100k: 463.6\",\"type\":\"scatter\",\"name\":\"Arizona\",\"textfont\":{\"color\":\"rgba(160,182,146,1)\",\"size\":15.99568224761712},\"error_y\":{\"color\":\"rgba(160,182,146,1)\",\"width\":15.99568224761712},\"error_x\":{\"color\":\"rgba(160,182,146,1)\",\"width\":15.99568224761712},\"line\":{\"color\":\"rgba(160,182,146,1)\",\"width\":15.99568224761712},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[57.919684465000003],\"y\":[439.5],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(181,176,137,1)\",\"size\":[9.0623019782701117],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(181,176,137,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Arkansas:\nCases per 100k: 33469.2\nDeaths per 100k: 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266.1\",\"type\":\"scatter\",\"name\":\"California\",\"textfont\":{\"color\":\"rgba(200,169,128,1)\",\"size\":70},\"error_y\":{\"color\":\"rgba(200,169,128,1)\",\"width\":70},\"error_x\":{\"color\":\"rgba(200,169,128,1)\",\"width\":70},\"line\":{\"color\":\"rgba(200,169,128,1)\",\"width\":70},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[54.955977558999997],\"y\":[256.30000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(217,161,119,1)\",\"size\":[13.534239627855888],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(217,161,119,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Colorado:\nCases per 100k: 31270.9\nDeaths per 100k: 256.3\",\"type\":\"scatter\",\"name\":\"Colorado\",\"textfont\":{\"color\":\"rgba(217,161,119,1)\",\"size\":13.534239627855888},\"error_y\":{\"color\":\"rgba(217,161,119,1)\",\"width\":13.534239627855888},\"error_x\":{\"color\":\"rgba(217,161,119,1)\",\"width\":13.534239627855888},\"line\":{\"color\":\"rgba(217,161,119,1)\",\"width\":13.534239627855888},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[737.74459965000005],\"y\":[344.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(233,153,110,1)\",\"size\":[9.9941964080019083],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(233,153,110,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Connecticut:\nCases per 100k: 27388\nDeaths per 100k: 344.6\",\"type\":\"scatter\",\"name\":\"Connecticut\",\"textfont\":{\"color\":\"rgba(233,153,110,1)\",\"size\":9.9941964080019083},\"error_y\":{\"color\":\"rgba(233,153,110,1)\",\"width\":9.9941964080019083},\"error_x\":{\"color\":\"rgba(233,153,110,1)\",\"width\":9.9941964080019083},\"line\":{\"color\":\"rgba(233,153,110,1)\",\"width\":9.9941964080019083},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[496.43246049999999],\"y\":[346.69999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(248,144,101,1)\",\"size\":[5.6494012156398465],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(248,144,101,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Delaware:\nCases per 100k: 34553.4\nDeaths per 100k: 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266.7\",\"type\":\"scatter\",\"name\":\"Minnesota\",\"textfont\":{\"color\":\"rgba(224,152,179,1)\",\"size\":13.39352330215816},\"error_y\":{\"color\":\"rgba(224,152,179,1)\",\"width\":13.39352330215816},\"error_x\":{\"color\":\"rgba(224,152,179,1)\",\"width\":13.39352330215816},\"line\":{\"color\":\"rgba(224,152,179,1)\",\"width\":13.39352330215816},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[63.645625459000001],\"y\":[450.5],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(217,164,165,1)\",\"size\":[9.0167861625455217],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(217,164,165,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Mississippi:\nCases per 100k: 33250.5\nDeaths per 100k: 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348.9\",\"type\":\"scatter\",\"name\":\"Montana\",\"textfont\":{\"color\":\"rgba(201,186,137,1)\",\"size\":5.8080420514391893},\"error_y\":{\"color\":\"rgba(201,186,137,1)\",\"width\":5.8080420514391893},\"error_x\":{\"color\":\"rgba(201,186,137,1)\",\"width\":5.8080420514391893},\"line\":{\"color\":\"rgba(201,186,137,1)\",\"width\":5.8080420514391893},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[25.114922059000001],\"y\":[275.19999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(191,196,121,1)\",\"size\":[7.253747424146165],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(191,196,121,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Nebraska:\nCases per 100k: 29875.6\nDeaths per 100k: 275.2\",\"type\":\"scatter\",\"name\":\"Nebraska\",\"textfont\":{\"color\":\"rgba(191,196,121,1)\",\"size\":7.253747424146165},\"error_y\":{\"color\":\"rgba(191,196,121,1)\",\"width\":7.253747424146165},\"error_x\":{\"color\":\"rgba(191,196,121,1)\",\"width\":7.253747424146165},\"line\":{\"color\":\"rgba(191,196,121,1)\",\"width\":7.253747424146165},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[27.6406025],\"y\":[399],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(180,205,105,1)\",\"size\":[9.0965985081110219],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(180,205,105,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Nevada:\nCases per 100k: 29431.5\nDeaths per 100k: 399\",\"type\":\"scatter\",\"name\":\"Nevada\",\"textfont\":{\"color\":\"rgba(180,205,105,1)\",\"size\":9.0965985081110219},\"error_y\":{\"color\":\"rgba(180,205,105,1)\",\"width\":9.0965985081110219},\"error_x\":{\"color\":\"rgba(180,205,105,1)\",\"width\":9.0965985081110219},\"line\":{\"color\":\"rgba(180,205,105,1)\",\"width\":9.0965985081110219},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[151.50059716000001],\"y\":[222.5],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(168,215,87,1)\",\"size\":[6.298557301222484],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(168,215,87,1)\"}},\"hoverinfo\":\"text\",\"text\":\"New Hampshire:\nCases per 100k: 27991.2\nDeaths per 100k: 222.5\",\"type\":\"scatter\",\"name\":\"New 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364.9\",\"type\":\"scatter\",\"name\":\"Ohio\",\"textfont\":{\"color\":\"rgba(239,217,56,1)\",\"size\":23.529339335629047},\"error_y\":{\"color\":\"rgba(239,217,56,1)\",\"width\":23.529339335629047},\"error_x\":{\"color\":\"rgba(239,217,56,1)\",\"width\":23.529339335629047},\"line\":{\"color\":\"rgba(239,217,56,1)\",\"width\":23.529339335629047},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[57.482602245000002],\"y\":[419.80000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(250,217,50,1)\",\"size\":[10.611880795831471],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(250,217,50,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Oklahoma:\nCases per 100k: 32863.5\nDeaths per 100k: 419.8\",\"type\":\"scatter\",\"name\":\"Oklahoma\",\"textfont\":{\"color\":\"rgba(250,217,50,1)\",\"size\":10.611880795831471},\"error_y\":{\"color\":\"rgba(250,217,50,1)\",\"width\":10.611880795831471},\"error_x\":{\"color\":\"rgba(250,217,50,1)\",\"width\":10.611880795831471},\"line\":{\"color\":\"rgba(250,217,50,1)\",\"width\":10.611880795831471},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[43.658853835000002],\"y\":[225.59999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(253,215,59,1)\",\"size\":[11.024823221592339],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(253,215,59,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Oregon:\nCases per 100k: 23078.6\nDeaths per 100k: 225.6\",\"type\":\"scatter\",\"name\":\"Oregon\",\"textfont\":{\"color\":\"rgba(253,215,59,1)\",\"size\":11.024823221592339},\"error_y\":{\"color\":\"rgba(253,215,59,1)\",\"width\":11.024823221592339},\"error_x\":{\"color\":\"rgba(253,215,59,1)\",\"width\":11.024823221592339},\"line\":{\"color\":\"rgba(253,215,59,1)\",\"width\":11.024823221592339},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[286.23455445000002],\"y\":[396.30000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(250,212,76,1)\",\"size\":[25.393024806905242],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(250,212,76,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Pennsylvania:\nCases per 100k: 27668.6\nDeaths per 100k: 396.3\",\"type\":\"scatter\",\"name\":\"Pennsylvania\",\"textfont\":{\"color\":\"rgba(250,212,76,1)\",\"size\":25.393024806905242},\"error_y\":{\"color\":\"rgba(250,212,76,1)\",\"width\":25.393024806905242},\"error_x\":{\"color\":\"rgba(250,212,76,1)\",\"width\":25.393024806905242},\"line\":{\"color\":\"rgba(250,212,76,1)\",\"width\":25.393024806905242},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[1022.6505771],\"y\":[371.10000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(244,207,104,1)\",\"size\":[5.7997209699053665],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(244,207,104,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Rhode Island:\nCases per 100k: 43691.6\nDeaths per 100k: 371.1\",\"type\":\"scatter\",\"name\":\"Rhode 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329.6\",\"type\":\"scatter\",\"name\":\"Texas\",\"textfont\":{\"color\":\"rgba(227,195,149,1)\",\"size\":51.898395938686235},\"error_y\":{\"color\":\"rgba(227,195,149,1)\",\"width\":51.898395938686235},\"error_x\":{\"color\":\"rgba(227,195,149,1)\",\"width\":51.898395938686235},\"line\":{\"color\":\"rgba(227,195,149,1)\",\"width\":51.898395938686235},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[38.458214685000002],\"y\":[168.19999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(221,193,154,1)\",\"size\":[9.3078989498736107],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(221,193,154,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Utah:\nCases per 100k: 34579\nDeaths per 100k: 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149.9\",\"type\":\"scatter\",\"name\":\"Vermont\",\"textfont\":{\"color\":\"rgba(214,191,158,1)\",\"size\":5.0809796315522071},\"error_y\":{\"color\":\"rgba(214,191,158,1)\",\"width\":5.0809796315522071},\"error_x\":{\"color\":\"rgba(214,191,158,1)\",\"width\":5.0809796315522071},\"line\":{\"color\":\"rgba(214,191,158,1)\",\"width\":5.0809796315522071},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[215.73630392999999],\"y\":[280.69999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(208,188,162,1)\",\"size\":[18.24027147942185],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(208,188,162,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Virginia:\nCases per 100k: 26992\nDeaths per 100k: 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215\",\"type\":\"scatter\",\"name\":\"Washington\",\"textfont\":{\"color\":\"rgba(201,186,167,1)\",\"size\":16.602579245378088},\"error_y\":{\"color\":\"rgba(201,186,167,1)\",\"width\":16.602579245378088},\"error_x\":{\"color\":\"rgba(201,186,167,1)\",\"width\":16.602579245378088},\"line\":{\"color\":\"rgba(201,186,167,1)\",\"width\":16.602579245378088},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[75.113987850000001],\"y\":[466],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(194,184,171,1)\",\"size\":[7.0479115483527828],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(194,184,171,1)\"}},\"hoverinfo\":\"text\",\"text\":\"West Virginia:\nCases per 100k: 35756.9\nDeaths per 100k: 466\",\"type\":\"scatter\",\"name\":\"West Virginia\",\"textfont\":{\"color\":\"rgba(194,184,171,1)\",\"size\":7.0479115483527828},\"error_y\":{\"color\":\"rgba(194,184,171,1)\",\"width\":7.0479115483527828},\"error_x\":{\"color\":\"rgba(194,184,171,1)\",\"width\":7.0479115483527828},\"line\":{\"color\":\"rgba(194,184,171,1)\",\"width\":7.0479115483527828},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[107.32815663],\"y\":[284],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(186,181,175,1)\",\"size\":[13.731017364392411],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(186,181,175,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Wisconsin:\nCases per 100k: 34652.1\nDeaths per 100k: 284\",\"type\":\"scatter\",\"name\":\"Wisconsin\",\"textfont\":{\"color\":\"rgba(186,181,175,1)\",\"size\":13.731017364392411},\"error_y\":{\"color\":\"rgba(186,181,175,1)\",\"width\":13.731017364392411},\"error_x\":{\"color\":\"rgba(186,181,175,1)\",\"width\":13.731017364392411},\"line\":{\"color\":\"rgba(186,181,175,1)\",\"width\":13.731017364392411},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[5.9506106574000004],\"y\":[348.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(179,179,179,1)\",\"size\":[5],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(179,179,179,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Wyoming:\nCases per 100k: 32269.4\nDeaths per 100k: 348.6\",\"type\":\"scatter\",\"name\":\"Wyoming\",\"textfont\":{\"color\":\"rgba(179,179,179,1)\",\"size\":5},\"error_y\":{\"color\":\"rgba(179,179,179,1)\",\"width\":5},\"error_x\":{\"color\":\"rgba(179,179,179,1)\",\"width\":5},\"line\":{\"color\":\"rgba(179,179,179,1)\",\"width\":5},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null}],\"highlight\":{\"on\":\"plotly_click\",\"persistent\":false,\"dynamic\":false,\"selectize\":false,\"opacityDim\":0.20000000000000001,\"selected\":{\"opacity\":1},\"debounce\":0},\"shinyEvents\":[\"plotly_hover\",\"plotly_click\",\"plotly_selected\",\"plotly_relayout\",\"plotly_brushed\",\"plotly_brushing\",\"plotly_clickannotation\",\"plotly_doubleclick\",\"plotly_deselect\",\"plotly_afterplot\",\"plotly_sunburstclick\"],\"base_url\":\"https://plot.ly\"},\"evals\":[],\"jsHooks\":[]} 7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()  For pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly()  What’s the geom_*() we need here?   Explore the pattern between \\(x\\) and \\(y\\) using geom_smooth()  Explain what you see. Do you think pop_density is a correlate of newdeathsper100k?    ### FINISH THE CODE HERE ### p \u0026lt;- ggplot(cv_states_today_scatter, aes(x=pop_density, y=per100k, color=state, size=population)) + geom_point() + geom_smooth() ggplotly(p)   {\"x\":{\"data\":[{\"x\":[96.509388650000005],\"y\":[33724],\"text\":\"pop_density: 96.509389\nper100k: 33724.0\nstate: Alabama\npopulation: 4887871\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(248,118,109,1)\",\"opacity\":1,\"size\":10.063522757639795,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(248,118,109,1)\"}},\"hoveron\":\"points\",\"name\":\"Alabama\",\"legendgroup\":\"Alabama\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[1.2915230608999999],\"y\":[41888],\"text\":\"pop_density: 1.291523\nper100k: 41888.0\nstate: Alaska\npopulation: 737438\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(243,123,89,1)\",\"opacity\":1,\"size\":4.989134516452693,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(243,123,89,1)\"}},\"hoveron\":\"points\",\"name\":\"Alaska\",\"legendgroup\":\"Alaska\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[63.135855048000003],\"y\":[34178.699999999997],\"text\":\"pop_density: 63.135855\nper100k: 34178.7\nstate: Arizona\npopulation: 7171646\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(238,128,66,1)\",\"opacity\":1,\"size\":11.552049076298584,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(238,128,66,1)\"}},\"hoveron\":\"points\",\"name\":\"Arizona\",\"legendgroup\":\"Arizona\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[57.919684465000003],\"y\":[33469.199999999997],\"text\":\"pop_density: 57.919684\nper100k: 33469.2\nstate: Arkansas\npopulation: 3013825\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(231,133,30,1)\",\"opacity\":1,\"size\":8.5038218636115648,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(231,133,30,1)\"}},\"hoveron\":\"points\",\"name\":\"Arkansas\",\"legendgroup\":\"Arkansas\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[253.9065023],\"y\":[30767.599999999999],\"text\":\"pop_density: 253.906502\nper100k: 30767.6\nstate: California\npopulation: 39557045\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(224,138,0,1)\",\"opacity\":1,\"size\":22.677165354330711,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(224,138,0,1)\"}},\"hoveron\":\"points\",\"name\":\"California\",\"legendgroup\":\"California\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[54.955977558999997],\"y\":[31270.900000000001],\"text\":\"pop_density: 54.955978\nper100k: 31270.9\nstate: Colorado\npopulation: 5695564\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(217,143,0,1)\",\"opacity\":1,\"size\":10.627046763294087,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(217,143,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Colorado\",\"legendgroup\":\"Colorado\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[737.74459965000005],\"y\":[27388],\"text\":\"pop_density: 737.744600\nper100k: 27388.0\nstate: Connecticut\npopulation: 3572665\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(208,148,0,1)\",\"opacity\":1,\"size\":9.017746558042079,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(208,148,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Connecticut\",\"legendgroup\":\"Connecticut\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[496.43246049999999],\"y\":[34553.400000000001],\"text\":\"pop_density: 496.432460\nper100k: 34553.4\nstate: Delaware\npopulation: 967171\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(199,152,0,1)\",\"opacity\":1,\"size\":5.6684207064933991,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(199,152,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Delaware\",\"legendgroup\":\"Delaware\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[397.01575444999997],\"y\":[35603.900000000001],\"text\":\"pop_density: 397.015754\nper100k: 35603.9\nstate: Florida\npopulation: 21299325\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(189,157,0,1)\",\"opacity\":1,\"size\":17.558028671037185,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(189,157,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Florida\",\"legendgroup\":\"Florida\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[182.26478911000001],\"y\":[28379.299999999999],\"text\":\"pop_density: 182.264789\nper100k: 28379.3\nstate: Georgia\npopulation: 10519475\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(178,161,0,1)\",\"opacity\":1,\"size\":13.323333834114047,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(178,161,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Georgia\",\"legendgroup\":\"Georgia\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[221.17691551999999],\"y\":[26217.200000000001],\"text\":\"pop_density: 221.176916\nper100k: 26217.2\nstate: Hawaii\npopulation: 1420491\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(165,165,0,1)\",\"opacity\":1,\"size\":6.5582224950863113,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(165,165,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Hawaii\",\"legendgroup\":\"Hawaii\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[21.225798535999999],\"y\":[29809.5],\"text\":\"pop_density: 21.225799\nper100k: 29809.5\nstate: Idaho\npopulation: 1754208\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(152,168,0,1)\",\"opacity\":1,\"size\":7.0626045807563775,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(152,168,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Idaho\",\"legendgroup\":\"Idaho\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[229.51115612999999],\"y\":[32241.599999999999],\"text\":\"pop_density: 229.511156\nper100k: 32241.6\nstate: Illinois\npopulation: 12741080\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(137,172,0,1)\",\"opacity\":1,\"size\":14.335954229389923,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(137,172,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Illinois\",\"legendgroup\":\"Illinois\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[186.78751987000001],\"y\":[30762.099999999999],\"text\":\"pop_density: 186.787520\nper100k: 30762.1\nstate: Indiana\npopulation: 6691878\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(119,175,0,1)\",\"opacity\":1,\"size\":11.263948122019215,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(119,175,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Indiana\",\"legendgroup\":\"Indiana\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[56.507023549000003],\"y\":[28750.900000000001],\"text\":\"pop_density: 56.507024\nper100k: 28750.9\nstate: Iowa\npopulation: 3156145\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(98,178,0,1)\",\"opacity\":1,\"size\":8.6398633891624854,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(98,178,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Iowa\",\"legendgroup\":\"Iowa\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[35.610732968000001],\"y\":[32318.099999999999],\"text\":\"pop_density: 35.610733\nper100k: 32318.1\nstate: Kansas\npopulation: 2911505\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(69,181,0,1)\",\"opacity\":1,\"size\":8.4035432449360172,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(69,181,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Kansas\",\"legendgroup\":\"Kansas\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[113.15179107],\"y\":[38918.099999999999],\"text\":\"pop_density: 113.151791\nper100k: 38918.1\nstate: Kentucky\npopulation: 4468402\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(0,183,9,1)\",\"opacity\":1,\"size\":9.7499131078189603,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(0,183,9,1)\"}},\"hoveron\":\"points\",\"name\":\"Kentucky\",\"legendgroup\":\"Kentucky\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[107.86011234],\"y\":[33939.800000000003],\"text\":\"pop_density: 107.860112\nper100k: 33939.8\nstate: Louisiana\npopulation: 4659978\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(0,186,56,1)\",\"opacity\":1,\"size\":9.8951375074608201,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(0,186,56,1)\"}},\"hoveron\":\"points\",\"name\":\"Louisiana\",\"legendgroup\":\"Louisiana\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[43.391688418999998],\"y\":[23909.400000000001],\"text\":\"pop_density: 43.391688\nper100k: 23909.4\nstate: Maine\npopulation: 1338404\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(0,188,81,1)\",\"opacity\":1,\"size\":6.4194292013435774,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(0,188,81,1)\"}},\"hoveron\":\"points\",\"name\":\"Maine\",\"legendgroup\":\"Maine\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[622.26172744999997],\"y\":[22671.200000000001],\"text\":\"pop_density: 622.261727\nper100k: 22671.2\nstate: Maryland\npopulation: 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Multiple line chart  Create a line chart of the naive_CFR for all states over time using plot_ly()  Use hoverinfo to identify states that had a “first peak” Use the zoom and pan tools to inspect the naive_CFR for the states that had a “first peak” in September. How have they changed over time?   Create one more line chart, for Texas only, which shows new_cases and new_deaths together in one plot. Hint: use add_lines()  Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?    # Line chart for naive_CFR for all states over time using `plot_ly()` plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = \u0026quot;scatter\u0026quot;, mode = \u0026quot;lines\u0026quot;)   {\"x\":{\"visdat\":{\"110d4369e977d\":[\"function () 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# Line chart for Texas showing new_cases and new_deaths together ### FINISH THE CODE HERE ### cv_states %\u0026gt;% filter(state==\u0026quot;Texas\u0026quot;) %\u0026gt;% plot_ly(x = ~date, y = ~new_cases, type = \u0026quot;scatter\u0026quot;, mode = \u0026quot;lines\u0026quot;) %\u0026gt;% add_lines(x = ~date, y = ~new_deaths, type = \u0026quot;scatter\u0026quot;, mode = \u0026quot;lines\u0026quot;)   {\"x\":{\"visdat\":{\"110d465ebab8e\":[\"function () 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Heatmaps Create a heatmap to visualize new_cases for each state on each date greater than April 1st, 2020\n  Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes\n  Use plot_ly() to create a heatmap out of this matrix\n  Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks\n  # Map state, date, and new_cases to a matrix library(tidyr) cv_states_mat \u0026lt;- cv_states %\u0026gt;% select(state, date, new_cases) %\u0026gt;% filter(date\u0026gt;as.Date(\u0026quot;2020-04-01\u0026quot;)) cv_states_mat2 \u0026lt;- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases)) rownames(cv_states_mat2) \u0026lt;- cv_states_mat2$date cv_states_mat2$date \u0026lt;- NULL cv_states_mat2 \u0026lt;- as.matrix(cv_states_mat2) # Create a heatmap using plot_ly() 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# Create a second heatmap after filtering to only include dates every other week filter_dates \u0026lt;- seq(as.Date(\u0026quot;2020-04-01\u0026quot;), as.Date(\u0026quot;2020-10-01\u0026quot;), by=\u0026quot;2 weeks\u0026quot;) ### FINISH THE CODE HERE ### cv_states_mat \u0026lt;- cv_states %\u0026gt;% select(state, date, new_cases) %\u0026gt;% filter( date %in% filter_dates ) cv_states_mat2 \u0026lt;- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases)) rownames(cv_states_mat2) \u0026lt;- cv_states_mat2$date cv_states_mat2$date \u0026lt;- NULL cv_states_mat2 \u0026lt;- as.matrix(cv_states_mat2) # Create a heatmap using plot_ly() plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=\u0026quot;heatmap\u0026quot;, showscale=T)   {\"x\":{\"visdat\":{\"110d46b8cd499\":[\"function () 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Virginia\",\"Wisconsin\",\"Wyoming\"],\"y\":[\"2020-04-01\",\"2020-04-15\",\"2020-04-29\",\"2020-05-13\",\"2020-05-27\",\"2020-06-10\",\"2020-06-24\",\"2020-07-08\",\"2020-07-22\",\"2020-08-05\",\"2020-08-19\",\"2020-09-02\",\"2020-09-16\",\"2020-09-30\"],\"z\":[[109,10,115,60,1274,356,429,49,91,1027,631,34,143,986,401,51,54,84,1187,41,323,1118,1663,60,136,230,21,50,166,48,3559,48,8153,64,21,349,153,47,808,47,78,210,22,640,814,125,28,262,296,29,201,17],[288,8,156,101,1349,339,766,88,139,906,670,13,49,1346,432,96,63,85,433,36,624,1755,1049,114,273,209,5,64,75,48,2206,77,11755,131,24,511,79,30,1213,51,278,103,181,210,803,129,7,329,95,28,166,19],[175,4,254,80,2334,441,455,80,112,347,693,3,32,2253,626,467,210,163,374,16,742,1963,974,463,227,120,0,263,122,44,2408,238,4718,379,42,534,63,61,868,65,321,146,61,281,971,156,0,623,325,14,232,8],[236,0,440,72,2068,339,522,211,99,479,555,3,31,1706,368,377,200,99,612,38,753,1165,361,423,182,245,1,340,171,60,817,152,2123,448,76,470,121,57,787,30,221,103,69,484,1474,170,2,946,110,26,290,13],[382,1,479,97,1949,222,0,30,72,379,647,1,32,1126,386,447,101,138,442,28,752,527,504,505,313,187,2,357,91,55,864,122,1132,599,20,433,92,73,773,73,143,207,57,400,1532,88,4,907,287,45,645,10],[567,22,1585,288,2894,151,168,36,63,1371,696,3,36,568,312,462,145,138,418,31,571,267,187,366,374,266,7,137,170,46,550,145,664,912,40,413,116,72,446,144,65,531,81,514,2500,343,11,439,144,14,2353,10],[967,21,1810,697,4850,262,14,42,34,5511,1713,14,238,743,293,427,451,199,882,23,332,172,338,297,526,330,23,129,402,27,158,152,575,1437,44,632,481,145,492,135,73,1284,66,804,6584,461,20,520,622,36,465,28],[1177,49,3551,734,8538,468,75,48,73,9989,2831,23,422,945,450,563,650,384,1888,20,469,264,649,457,674,777,44,232,530,20,252,290,691,1473,72,1277,673,218,831,31,50,1557,79,2113,9952,718,2,635,598,202,692,29],[1455,94,1929,591,12162,595,127,46,102,9785,3255,17,496,1608,782,487,770,531,2773,0,627,287,707,504,1547,1206,99,296,1154,33,335,311,706,2091,160,1527,975,179,807,98,76,1705,58,2146,10967,588,0,1022,696,262,884,50],[952,58,1693,912,5518,651,115,159,45,5409,3933,172,497,1876,741,605,788,614,1486,17,584,440,752,618,1245,1061,121,311,663,26,335,226,639,1218,124,1199,1103,279,727,327,91,1282,89,1653,9362,460,5,798,561,108,984,39],[1117,70,637,729,6243,269,59,50,29,4115,2574,260,343,2082,509,340,701,688,796,21,435,352,708,557,1348,1043,109,215,375,19,319,170,626,1350,186,958,597,202,664,221,103,739,123,2014,5403,362,3,737,562,70,687,70],[623,72,523,615,5407,274,102,14,28,2402,1938,338,356,2167,865,617,1038,942,1151,19,455,0,634,733,781,1560,189,421,219,12,308,152,710,1174,267,1157,719,127,986,582,76,657,254,1559,4310,445,2,927,493,135,573,45],[927,52,696,722,3401,493,135,97,56,2355,2007,101,288,1902,580,958,823,737,612,23,642,306,804,462,711,1310,206,449,248,32,390,118,653,1314,269,1033,970,192,808,112,108,652,297,1708,4525,753,0,845,492,220,1499,128],[1147,105,326,942,3186,632,221,82,26,1948,1466,119,695,2262,968,1014,1006,909,610,54,412,532,1175,688,552,1249,354,520,393,33,708,278,1005,1620,445,1080,980,257,916,288,192,308,392,1498,3484,917,3,755,549,156,2428,127]],\"type\":\"heatmap\",\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null}],\"highlight\":{\"on\":\"plotly_click\",\"persistent\":false,\"dynamic\":false,\"selectize\":false,\"opacityDim\":0.20000000000000001,\"selected\":{\"opacity\":1},\"debounce\":0},\"shinyEvents\":[\"plotly_hover\",\"plotly_click\",\"plotly_selected\",\"plotly_relayout\",\"plotly_brushed\",\"plotly_brushing\",\"plotly_clickannotation\",\"plotly_doubleclick\",\"plotly_deselect\",\"plotly_afterplot\",\"plotly_sunburstclick\"],\"base_url\":\"https://plot.ly\"},\"evals\":[],\"jsHooks\":[]} 10. Map  Create a map to visualize the naive_CFR by state on May 1st, 2020 Compare with a map visualizing the naive_CFR by state on most recent date Plot the two maps side by side using subplot(). Make sure the shading is for the same range of values (google is your friend for this) Describe the difference in the pattern of the CFR.  ### For May 1 2020 # Extract the data for each state by its abbreviation cv_CFR \u0026lt;- cv_states %\u0026gt;% filter(date==\u0026quot;2020-05-01\u0026quot;) %\u0026gt;% select(state, abb, naive_CFR, cases, deaths) # select data cv_CFR$state_name \u0026lt;- cv_CFR$state cv_CFR$state \u0026lt;- cv_CFR$abb cv_CFR$abb \u0026lt;- NULL # Create hover text cv_CFR$hover \u0026lt;- with(cv_CFR, paste(state_name, '\u0026lt;br\u0026gt;', \u0026quot;CFR: \u0026quot;, naive_CFR, '\u0026lt;br\u0026gt;', \u0026quot;Cases: \u0026quot;, cases, '\u0026lt;br\u0026gt;', \u0026quot;Deaths: \u0026quot;, deaths)) # Set up mapping details set_map_details \u0026lt;- list( scope = 'usa', projection = list(type = 'albers usa'), showlakes = TRUE, lakecolor = toRGB('white') ) # Make sure both maps are on the same color scale shadeLimit \u0026lt;- 9 # Create the map fig \u0026lt;- plot_geo(cv_CFR, locationmode = 'USA-states') %\u0026gt;% add_trace( z = ~naive_CFR, text = ~hover, locations = ~state, color = ~naive_CFR, colors = 'Purples' ) fig \u0026lt;- fig %\u0026gt;% colorbar(title = \u0026quot;CFR May 1 2020\u0026quot;, limits = c(0,shadeLimit)) fig \u0026lt;- fig %\u0026gt;% layout( title = paste('CFR by State as of', Sys.Date(), '\u0026lt;br\u0026gt;(Hover for value)'), geo = set_map_details ) fig_May1 \u0026lt;- fig ############# ### For Today # Extract the data for each state by its abbreviation cv_CFR \u0026lt;- cv_states_today %\u0026gt;% select(state, abb, naive_CFR, cases, deaths) # select data cv_CFR$state_name \u0026lt;- cv_CFR$state cv_CFR$state \u0026lt;- cv_CFR$abb cv_CFR$abb \u0026lt;- NULL # Create hover text cv_CFR$hover \u0026lt;- with(cv_CFR, paste(state_name, '\u0026lt;br\u0026gt;', \u0026quot;CFR: \u0026quot;, naive_CFR, '\u0026lt;br\u0026gt;', \u0026quot;Cases: \u0026quot;, cases, '\u0026lt;br\u0026gt;', \u0026quot;Deaths: \u0026quot;, deaths)) # Set up mapping details set_map_details \u0026lt;- list( scope = 'usa', projection = list(type = 'albers usa'), showlakes = TRUE, lakecolor = toRGB('white') ) # Create the map fig \u0026lt;- plot_geo(cv_CFR, locationmode = 'USA-states') %\u0026gt;% add_trace( z = ~naive_CFR, text = ~hover, locations = ~state, color = ~naive_CFR, colors = 'Purples' ) fig \u0026lt;- fig %\u0026gt;% colorbar(title = \u0026quot;CFR May 1 2020\u0026quot;, limits = c(0,shadeLimit)) fig \u0026lt;- fig %\u0026gt;% layout( title = paste('CFR by State as of', Sys.Date(), '\u0026lt;br\u0026gt;(Hover for value)'), geo = set_map_details ) fig_Today \u0026lt;- fig ### Plot side by side ### FINISH THE CODE HERE ### subplot( fig_May1, fig_Today )   {\"x\":{\"data\":[{\"colorbar\":{\"title\":\"CFR May 1 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CFR: 4.07 Cases: 7294 Deaths: 297\",\"Alaska CFR: 1.93 Cases: 362 Deaths: 7\",\"Arizona CFR: 4.14 Cases: 7962 Deaths: 330\",\"Arkansas CFR: 1.93 Cases: 3310 Deaths: 64\",\"California CFR: 4.1 Cases: 52318 Deaths: 2147\",\"Colorado CFR: 5.24 Cases: 15661 Deaths: 820\",\"Connecticut CFR: 8.13 Cases: 28764 Deaths: 2339\",\"Delaware CFR: 3.23 Cases: 4918 Deaths: 159\",\"District of Columbia CFR: 4.96 Cases: 4658 Deaths: 231\",\"Florida CFR: 3.78 Cases: 34720 Deaths: 1313\",\"Georgia CFR: 4.34 Cases: 26594 Deaths: 1153\",\"Hawaii CFR: 2.62 Cases: 610 Deaths: 16\",\"Idaho CFR: 3.1 Cases: 2035 Deaths: 63\",\"Illinois CFR: 4.4 Cases: 56055 Deaths: 2464\",\"Indiana CFR: 6.2 Cases: 18941 Deaths: 1175\",\"Iowa CFR: 2.16 Cases: 7883 Deaths: 170\",\"Kansas CFR: 3.02 Cases: 4510 Deaths: 136\",\"Kentucky CFR: 5.08 Cases: 4879 Deaths: 248\",\"Louisiana CFR: 6.85 Cases: 28754 Deaths: 1970\",\"Maine CFR: 4.9 Cases: 1123 Deaths: 55\",\"Maryland CFR: 5.06 Cases: 23566 Deaths: 1192\",\"Massachusetts CFR: 5.78 Cases: 64311 Deaths: 3716\",\"Michigan CFR: 9.13 Cases: 42348 Deaths: 3866\",\"Minnesota CFR: 6.47 Cases: 5730 Deaths: 371\",\"Mississippi CFR: 3.9 Cases: 7212 Deaths: 281\",\"Missouri CFR: 4.53 Cases: 7840 Deaths: 355\",\"Montana CFR: 3.54 Cases: 452 Deaths: 16\",\"Nebraska CFR: 1.49 Cases: 4838 Deaths: 72\",\"Nevada CFR: 4.84 Cases: 5249 Deaths: 254\",\"New Hampshire CFR: 3.51 Cases: 2310 Deaths: 81\",\"New Jersey CFR: 6.22 Cases: 121190 Deaths: 7538\",\"New Mexico CFR: 3.73 Cases: 3513 Deaths: 131\",\"New York CFR: 7.6 Cases: 313575 Deaths: 23841\",\"North Carolina CFR: 3.71 Cases: 10922 Deaths: 405\",\"North Dakota CFR: 2.08 Cases: 1107 Deaths: 23\",\"Ohio CFR: 5.35 Cases: 18743 Deaths: 1002\",\"Oklahoma CFR: 6.14 Cases: 3748 Deaths: 230\",\"Oregon CFR: 4.03 Cases: 2579 Deaths: 104\",\"Pennsylvania CFR: 5.37 Cases: 49764 Deaths: 2671\",\"Puerto Rico CFR: 5.92 Cases: 1910 Deaths: 113\",\"Rhode Island CFR: 3.11 Cases: 8962 Deaths: 279\",\"South Carolina CFR: 4.09 Cases: 6258 Deaths: 256\",\"South Dakota CFR: 0.83 Cases: 2525 Deaths: 21\",\"Tennessee CFR: 1.74 Cases: 11781 Deaths: 205\",\"Texas CFR: 2.81 Cases: 30342 Deaths: 853\",\"Utah CFR: 0.95 Cases: 4829 Deaths: 46\",\"Vermont CFR: 5.57 Cases: 879 Deaths: 49\",\"Virginia CFR: 3.54 Cases: 16903 Deaths: 599\",\"Washington CFR: 5.48 Cases: 15134 Deaths: 829\",\"West Virginia CFR: 4.08 Cases: 1152 Deaths: 47\",\"Wisconsin CFR: 4.4 Cases: 7439 Deaths: 327\",\"Wyoming CFR: 1.24 Cases: 566 Deaths: 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","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"fdfbc884b4423346da6cfdcce4844a0b","permalink":"https://uscbiostats.github.io/PM566/assignment/11-lab-2020/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/11-lab-2020/","section":"assignment","summary":"Learning Goals  Read in and process the COVID dataset from the New York Times GitHub repository Create interactive graphs of different types using plot_ly() and ggplotly() functions Customize the hoverinfo and other plot features Create a Choropleth map using plot_geo() Create an interactive table using DataTable  Lab Description We will work with the COVID data presented in lecture.","tags":null,"title":"Lab 11 - Interactive Visualization","type":"assignment"},{"authors":null,"categories":null,"content":"                                                                                    Learning Goals  Read in and process the COVID dataset from the New York Times GitHub repository Create interactive graphs of different types using plot_ly() and ggplotly() functions Customize the hoverinfo and other plot features Create a Choropleth map using plot_geo()  Lab Description We will work with COVID data downloaded from the New York Times. The dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic.\nThe objective of this lab is to explore relationships between cases, deaths, and population sizes of US states, and plot data to demonstrate this\nSteps I. Reading and processing the New York Times (NYT) state-level COVID-19 data 1. Read in the data  Read in the COVID data with data.table:fread() from the NYT GitHub repository: “https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv” Read in the state population data with data.table:fread() from the repository: “https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv”” Merge datasets  ## data extracted from New York Times state-level data from NYT Github repository # https://github.com/nytimes/covid-19-data ## state-level population information from us_census_data available on GitHub repository: # https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data # load COVID state-level data from NYT cv_states \u0026lt;- as.data.frame(data.table::fread(\u0026quot;https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv\u0026quot;)) # load state population data state_pops \u0026lt;- as.data.frame(data.table::fread(\u0026quot;https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv\u0026quot;)) state_pops$abb \u0026lt;- state_pops$state state_pops$state \u0026lt;- state_pops$state_name state_pops$state_name \u0026lt;- NULL cv_states \u0026lt;- merge(cv_states, state_pops, by=\u0026quot;state\u0026quot;)  2. Look at the data  Inspect the dimensions, head, and tail of the data Inspect the structure of each variables. Are they in the correct format?  dim(cv_states)  ## [1] 58094 9  head(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 1 Alabama 2023-01-04 1 1587224 21263 1 4887871 96.50939 AL ## 2 Alabama 2020-04-25 1 6213 213 1 4887871 96.50939 AL ## 3 Alabama 2023-02-26 1 1638348 21400 1 4887871 96.50939 AL ## 4 Alabama 2022-12-03 1 1549285 21129 1 4887871 96.50939 AL ## 5 Alabama 2020-05-06 1 8691 343 1 4887871 96.50939 AL ## 6 Alabama 2021-04-21 1 524367 10807 1 4887871 96.50939 AL  tail(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 58089 Wyoming 2022-09-11 56 175290 1884 56 577737 5.950611 WY ## 58090 Wyoming 2022-08-21 56 173487 1871 56 577737 5.950611 WY ## 58091 Wyoming 2021-01-26 56 51152 596 56 577737 5.950611 WY ## 58092 Wyoming 2021-02-21 56 53795 662 56 577737 5.950611 WY ## 58093 Wyoming 2021-08-22 56 70671 809 56 577737 5.950611 WY ## 58094 Wyoming 2021-03-20 56 55581 693 56 577737 5.950611 WY  str(cv_states)  ## 'data.frame':\t58094 obs. of 9 variables: ## $ state : chr \u0026quot;Alabama\u0026quot; \u0026quot;Alabama\u0026quot; \u0026quot;Alabama\u0026quot; \u0026quot;Alabama\u0026quot; ... ## $ date : IDate, format: \u0026quot;2023-01-04\u0026quot; \u0026quot;2020-04-25\u0026quot; ... ## $ fips : int 1 1 1 1 1 1 1 1 1 1 ... ## $ cases : int 1587224 6213 1638348 1549285 8691 524367 1321892 1088370 1153149 814025 ... ## $ deaths : int 21263 213 21400 21129 343 10807 19676 16756 16826 15179 ... ## $ geo_id : int 1 1 1 1 1 1 1 1 1 1 ... ## $ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ... ## $ pop_density: num 96.5 96.5 96.5 96.5 96.5 ... ## $ abb : chr \u0026quot;AL\u0026quot; \u0026quot;AL\u0026quot; \u0026quot;AL\u0026quot; \u0026quot;AL\u0026quot; ...  3. Format the data  Make date into a date variable Make state into a factor variable Order the data first by state, second by date Confirm the variables are now correctly formatted Inspect the range values for each variable. What is the date range? The range of cases and deaths?  # format the date cv_states$date \u0026lt;- as.Date(cv_states$date, format=\u0026quot;%Y-%m-%d\u0026quot;) # format the state and state abbreviation (abb) variables state_list \u0026lt;- unique(cv_states$state) cv_states$state \u0026lt;- factor(cv_states$state, levels = state_list) abb_list \u0026lt;- unique(cv_states$abb) cv_states$abb \u0026lt;- factor(cv_states$abb, levels = abb_list) # order the data first by state, second by date cv_states = cv_states[order(cv_states$state, cv_states$date),] # Confirm the variables are now correctly formatted str(cv_states)  ## 'data.frame':\t58094 obs. of 9 variables: ## $ state : Factor w/ 52 levels \u0026quot;Alabama\u0026quot;,\u0026quot;Alaska\u0026quot;,..: 1 1 1 1 1 1 1 1 1 1 ... ## $ date : Date, format: \u0026quot;2020-03-13\u0026quot; \u0026quot;2020-03-14\u0026quot; ... ## $ fips : int 1 1 1 1 1 1 1 1 1 1 ... ## $ cases : int 6 12 23 29 39 51 78 106 131 157 ... ## $ deaths : int 0 0 0 0 0 0 0 0 0 0 ... ## $ geo_id : int 1 1 1 1 1 1 1 1 1 1 ... ## $ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ... ## $ pop_density: num 96.5 96.5 96.5 96.5 96.5 ... ## $ abb : Factor w/ 52 levels \u0026quot;AL\u0026quot;,\u0026quot;AK\u0026quot;,\u0026quot;AZ\u0026quot;,..: 1 1 1 1 1 1 1 1 1 1 ...  head(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL ## 597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL ## 282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL ## 12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL ## 266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL ## 78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL  tail(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 57902 Wyoming 2023-03-18 56 185640 2009 56 577737 5.950611 WY ## 57916 Wyoming 2023-03-19 56 185640 2009 56 577737 5.950611 WY ## 57647 Wyoming 2023-03-20 56 185640 2009 56 577737 5.950611 WY ## 57867 Wyoming 2023-03-21 56 185800 2014 56 577737 5.950611 WY ## 58057 Wyoming 2023-03-22 56 185800 2014 56 577737 5.950611 WY ## 57812 Wyoming 2023-03-23 56 185800 2014 56 577737 5.950611 WY  # Inspect the range values for each variable. What is the date range? The range of cases and deaths? head(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL ## 597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL ## 282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL ## 12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL ## 266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL ## 78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL  summary(cv_states)  ## state date fips cases ## Washington : 1158 Min. :2020-01-21 Min. : 1.00 Min. : 1 ## Illinois : 1155 1st Qu.:2020-12-06 1st Qu.:16.00 1st Qu.: 112125 ## California : 1154 Median :2021-09-11 Median :29.00 Median : 418120 ## Arizona : 1153 Mean :2021-09-10 Mean :29.78 Mean : 947941 ## Massachusetts: 1147 3rd Qu.:2022-06-17 3rd Qu.:44.00 3rd Qu.: 1134318 ## Wisconsin : 1143 Max. :2023-03-23 Max. :72.00 Max. :12169158 ## (Other) :51184 ## deaths geo_id population pop_density ## Min. : 0 Min. : 1.00 Min. : 577737 Min. : 1.292 ## 1st Qu.: 1598 1st Qu.:16.00 1st Qu.: 1805832 1st Qu.: 43.659 ## Median : 5901 Median :29.00 Median : 4468402 Median : 107.860 ## Mean : 12553 Mean :29.78 Mean : 6397965 Mean : 423.031 ## 3rd Qu.: 15952 3rd Qu.:44.00 3rd Qu.: 7535591 3rd Qu.: 229.511 ## Max. :104277 Max. :72.00 Max. :39557045 Max. :11490.120 ## NA's :1106 ## abb ## WA : 1158 ## IL : 1155 ## CA : 1154 ## AZ : 1153 ## MA : 1147 ## WI : 1143 ## (Other):51184  min(cv_states$date)  ## [1] \u0026quot;2020-01-21\u0026quot;  max(cv_states$date)  ## [1] \u0026quot;2023-03-23\u0026quot;  4. Add new_cases and new_deaths and correct outliers   Add variables for new cases, new_cases, and new deaths, new_deaths:\n Hint: You can set new_cases equal to the difference between cases on date i and date i-1, starting on date i=2    Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?\n  Correct outliers: Set negative values for new_cases or new_deaths to 0\n  Recalculate cases and deaths as cumulative sum of updated new_cases and new_deaths\n  Get the rolling average of new cases\n  # Add variables for new_cases and new_deaths: for (i in 1:length(state_list)) { cv_subset = subset(cv_states, state == state_list[i]) cv_subset = cv_subset[order(cv_subset$date),] # add starting level for new cases and deaths cv_subset$new_cases = cv_subset$cases[1] cv_subset$new_deaths = cv_subset$deaths[1] for (j in 2:nrow(cv_subset)) { cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1] cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1] } # include in main dataset cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths } # Focus on recent dates cv_states \u0026lt;- cv_states %\u0026gt;% dplyr::filter(date \u0026gt;= \u0026quot;2021-06-01\u0026quot;) # Inspect outliers using plotly p1\u0026lt;-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5) ggplotly(p1)   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(y) \",\"x\"],\"11194306916f8\":[\"function (y) \",\"x\"]},\"highlight\":{\"on\":\"plotly_click\",\"persistent\":false,\"dynamic\":false,\"selectize\":false,\"opacityDim\":0.20000000000000001,\"selected\":{\"opacity\":1},\"debounce\":0},\"shinyEvents\":[\"plotly_hover\",\"plotly_click\",\"plotly_selected\",\"plotly_relayout\",\"plotly_brushed\",\"plotly_brushing\",\"plotly_clickannotation\",\"plotly_doubleclick\",\"plotly_deselect\",\"plotly_afterplot\",\"plotly_sunburstclick\"],\"base_url\":\"https://plot.ly\"},\"evals\":[],\"jsHooks\":[]} p2\u0026lt;-NULL # to clear from workspace # set negative new case or death counts to 0 cv_states$new_cases[cv_states$new_cases\u0026lt;0] = 0 cv_states$new_deaths[cv_states$new_deaths\u0026lt;0] = 0 # Recalculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths` for (i in 1:length(state_list)) { cv_subset = subset(cv_states, state == state_list[i]) # add starting level for new cases and deaths cv_subset$cases = cv_subset$cases[1] cv_subset$deaths = cv_subset$deaths[1] for (j in 2:nrow(cv_subset)) { cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1] cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1] } # include in main dataset cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths } # Smooth new counts cv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %\u0026gt;% round(digits = 0) cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %\u0026gt;% round(digits = 0) # Inspect data again p2\u0026lt;-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5) ggplotly(p2)   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(y) \",\"x\"],\"11194140b93c6\":[\"function (y) \",\"x\"]},\"highlight\":{\"on\":\"plotly_click\",\"persistent\":false,\"dynamic\":false,\"selectize\":false,\"opacityDim\":0.20000000000000001,\"selected\":{\"opacity\":1},\"debounce\":0},\"shinyEvents\":[\"plotly_hover\",\"plotly_click\",\"plotly_selected\",\"plotly_relayout\",\"plotly_brushed\",\"plotly_brushing\",\"plotly_clickannotation\",\"plotly_doubleclick\",\"plotly_deselect\",\"plotly_afterplot\",\"plotly_sunburstclick\"],\"base_url\":\"https://plot.ly\"},\"evals\":[],\"jsHooks\":[]} #p2=NULL  5. Add additional variables   Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:\n per100k = cases per 100,000 population newper100k= new cases per 100,000 deathsper100k = deaths per 100,000 newdeathsper100k = new deaths per 100,000    Add a “naive CFR” variable representing deaths / cases on each date for each state\n  Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture\n  # add population normalized (by 100,000) counts for each variable cv_states$per100k = as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1)) cv_states$newper100k = as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1)) cv_states$deathsper100k = as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1)) cv_states$newdeathsper100k = as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1)) # add a naive_CFR variable = deaths / cases cv_states = cv_states %\u0026gt;% mutate(naive_CFR = round((deaths*100/cases),2)) # create a `cv_states_today` variable cv_states_today = subset(cv_states, date==max(cv_states$date))  II. Scatterplots 6. Explore scatterplots using plot_ly()  Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)  Size the points by state population Use hover to identify any outliers. Remove those outliers and replot.   Choose one plot. 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# Adding hoverinfo cv_states_today_scatter %\u0026gt;% plot_ly(x = ~pop_density, y = ~deathsper100k, type = 'scatter', mode = 'markers', color = ~state, size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5), hoverinfo = 'text', text = ~paste( paste(state, \u0026quot;:\u0026quot;, sep=\u0026quot;\u0026quot;), paste(\u0026quot; Cases per 100k: \u0026quot;, per100k, sep=\u0026quot;\u0026quot;) , paste(\u0026quot; Deaths per 100k: \u0026quot;, deathsper100k, sep=\u0026quot;\u0026quot;), sep = \u0026quot;\u0026lt;br\u0026gt;\u0026quot;)) %\u0026gt;% layout(title = \u0026quot;Population-normalized COVID-19 deaths (per 100k) vs. population density for US states\u0026quot;, yaxis = list(title = \u0026quot;Deaths per 100k\u0026quot;), xaxis = list(title = \u0026quot;Population Density\u0026quot;), hovermode = \u0026quot;compare\u0026quot;)   {\"x\":{\"visdat\":{\"1119427e9a9d1\":[\"function () \",\"plotlyVisDat\"]},\"cur_data\":\"1119427e9a9d1\",\"attrs\":{\"1119427e9a9d1\":{\"x\":{},\"y\":{},\"mode\":\"markers\",\"marker\":{\"sizemode\":\"diameter\",\"opacity\":0.5},\"hoverinfo\":\"text\",\"text\":{},\"color\":{},\"size\":{},\"alpha_stroke\":1,\"sizes\":[5,70],\"spans\":[1,20],\"type\":\"scatter\"}},\"layout\":{\"margin\":{\"b\":40,\"l\":60,\"t\":25,\"r\":10},\"title\":\"Population-normalized COVID-19 deaths (per 100k) vs. population density for US states\",\"yaxis\":{\"domain\":[0,1],\"automargin\":true,\"title\":\"Deaths per 100k\"},\"xaxis\":{\"domain\":[0,1],\"automargin\":true,\"title\":\"Population Density\"},\"hovermode\":\"compare\",\"showlegend\":true},\"source\":\"A\",\"config\":{\"modeBarButtonsToAdd\":[\"hoverclosest\",\"hovercompare\"],\"showSendToCloud\":false},\"data\":[{\"x\":[96.509388650000005],\"y\":[442.5],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(102,194,165,1)\",\"size\":[12.187370027194941],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(102,194,165,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Alabama:\nCases per 100k: 33724\nDeaths per 100k: 442.5\",\"type\":\"scatter\",\"name\":\"Alabama\",\"textfont\":{\"color\":\"rgba(102,194,165,1)\",\"size\":12.187370027194941},\"error_y\":{\"color\":\"rgba(102,194,165,1)\",\"width\":12.187370027194941},\"error_x\":{\"color\":\"rgba(102,194,165,1)\",\"width\":12.187370027194941},\"line\":{\"color\":\"rgba(102,194,165,1)\",\"width\":12.187370027194941},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[1.2915230608999999],\"y\":[195.09999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(135,188,156,1)\",\"size\":[5.2663096276619381],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(135,188,156,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Alaska:\nCases per 100k: 41887.3\nDeaths per 100k: 195.1\",\"type\":\"scatter\",\"name\":\"Alaska\",\"textfont\":{\"color\":\"rgba(135,188,156,1)\",\"size\":5.2663096276619381},\"error_y\":{\"color\":\"rgba(135,188,156,1)\",\"width\":5.2663096276619381},\"error_x\":{\"color\":\"rgba(135,188,156,1)\",\"width\":5.2663096276619381},\"line\":{\"color\":\"rgba(135,188,156,1)\",\"width\":5.2663096276619381},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[63.135855048000003],\"y\":[463.19999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(160,182,146,1)\",\"size\":[15.99568224761712],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(160,182,146,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Arizona:\nCases per 100k: 34177.1\nDeaths per 100k: 463.2\",\"type\":\"scatter\",\"name\":\"Arizona\",\"textfont\":{\"color\":\"rgba(160,182,146,1)\",\"size\":15.99568224761712},\"error_y\":{\"color\":\"rgba(160,182,146,1)\",\"width\":15.99568224761712},\"error_x\":{\"color\":\"rgba(160,182,146,1)\",\"width\":15.99568224761712},\"line\":{\"color\":\"rgba(160,182,146,1)\",\"width\":15.99568224761712},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[57.919684465000003],\"y\":[433.69999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(181,176,137,1)\",\"size\":[9.0623019782701117],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(181,176,137,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Arkansas:\nCases per 100k: 33455.9\nDeaths per 100k: 433.7\",\"type\":\"scatter\",\"name\":\"Arkansas\",\"textfont\":{\"color\":\"rgba(181,176,137,1)\",\"size\":9.0623019782701117},\"error_y\":{\"color\":\"rgba(181,176,137,1)\",\"width\":9.0623019782701117},\"error_x\":{\"color\":\"rgba(181,176,137,1)\",\"width\":9.0623019782701117},\"line\":{\"color\":\"rgba(181,176,137,1)\",\"width\":9.0623019782701117},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[253.9065023],\"y\":[266.10000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(200,169,128,1)\",\"size\":[70],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(200,169,128,1)\"}},\"hoverinfo\":\"text\",\"text\":\"California:\nCases per 100k: 30767.6\nDeaths per 100k: 266.1\",\"type\":\"scatter\",\"name\":\"California\",\"textfont\":{\"color\":\"rgba(200,169,128,1)\",\"size\":70},\"error_y\":{\"color\":\"rgba(200,169,128,1)\",\"width\":70},\"error_x\":{\"color\":\"rgba(200,169,128,1)\",\"width\":70},\"line\":{\"color\":\"rgba(200,169,128,1)\",\"width\":70},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[54.955977558999997],\"y\":[256.10000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(217,161,119,1)\",\"size\":[13.534239627855888],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(217,161,119,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Colorado:\nCases per 100k: 31270.9\nDeaths per 100k: 256.1\",\"type\":\"scatter\",\"name\":\"Colorado\",\"textfont\":{\"color\":\"rgba(217,161,119,1)\",\"size\":13.534239627855888},\"error_y\":{\"color\":\"rgba(217,161,119,1)\",\"width\":13.534239627855888},\"error_x\":{\"color\":\"rgba(217,161,119,1)\",\"width\":13.534239627855888},\"line\":{\"color\":\"rgba(217,161,119,1)\",\"width\":13.534239627855888},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[737.74459965000005],\"y\":[344.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(233,153,110,1)\",\"size\":[9.9941964080019083],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(233,153,110,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Connecticut:\nCases per 100k: 27387.3\nDeaths per 100k: 344.6\",\"type\":\"scatter\",\"name\":\"Connecticut\",\"textfont\":{\"color\":\"rgba(233,153,110,1)\",\"size\":9.9941964080019083},\"error_y\":{\"color\":\"rgba(233,153,110,1)\",\"width\":9.9941964080019083},\"error_x\":{\"color\":\"rgba(233,153,110,1)\",\"width\":9.9941964080019083},\"line\":{\"color\":\"rgba(233,153,110,1)\",\"width\":9.9941964080019083},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[496.43246049999999],\"y\":[346.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(248,144,101,1)\",\"size\":[5.6494012156398465],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(248,144,101,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Delaware:\nCases per 100k: 34548.9\nDeaths per 100k: 346.6\",\"type\":\"scatter\",\"name\":\"Delaware\",\"textfont\":{\"color\":\"rgba(248,144,101,1)\",\"size\":5.6494012156398465},\"error_y\":{\"color\":\"rgba(248,144,101,1)\",\"width\":5.6494012156398465},\"error_x\":{\"color\":\"rgba(248,144,101,1)\",\"width\":5.6494012156398465},\"line\":{\"color\":\"rgba(248,144,101,1)\",\"width\":5.6494012156398465},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[397.01575444999997],\"y\":[409.19999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(232,146,123,1)\",\"size\":[39.554313278214174],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(232,146,123,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Florida:\nCases per 100k: 35603.9\nDeaths per 100k: 409.2\",\"type\":\"scatter\",\"name\":\"Florida\",\"textfont\":{\"color\":\"rgba(232,146,123,1)\",\"size\":39.554313278214174},\"error_y\":{\"color\":\"rgba(232,146,123,1)\",\"width\":39.554313278214174},\"error_x\":{\"color\":\"rgba(232,146,123,1)\",\"width\":39.554313278214174},\"line\":{\"color\":\"rgba(232,146,123,1)\",\"width\":39.554313278214174},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[182.26478911000001],\"y\":[390.30000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(220,149,138,1)\",\"size\":[21.578359215612551],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(220,149,138,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Georgia:\nCases per 100k: 28377.8\nDeaths per 100k: 390.3\",\"type\":\"scatter\",\"name\":\"Georgia\",\"textfont\":{\"color\":\"rgba(220,149,138,1)\",\"size\":21.578359215612551},\"error_y\":{\"color\":\"rgba(220,149,138,1)\",\"width\":21.578359215612551},\"error_x\":{\"color\":\"rgba(220,149,138,1)\",\"width\":21.578359215612551},\"line\":{\"color\":\"rgba(220,149,138,1)\",\"width\":21.578359215612551},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[221.17691551999999],\"y\":[130.30000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(206,152,152,1)\",\"size\":[6.4053356206323624],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(206,152,152,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Hawaii:\nCases per 100k: 26216.9\nDeaths per 100k: 130.3\",\"type\":\"scatter\",\"name\":\"Hawaii\",\"textfont\":{\"color\":\"rgba(206,152,152,1)\",\"size\":6.4053356206323624},\"error_y\":{\"color\":\"rgba(206,152,152,1)\",\"width\":6.4053356206323624},\"error_x\":{\"color\":\"rgba(206,152,152,1)\",\"width\":6.4053356206323624},\"line\":{\"color\":\"rgba(206,152,152,1)\",\"width\":6.4053356206323624},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[21.225798535999999],\"y\":[311.30000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(191,154,166,1)\",\"size\":[6.9618258743844299],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(191,154,166,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Idaho:\nCases per 100k: 29809.4\nDeaths per 100k: 311.3\",\"type\":\"scatter\",\"name\":\"Idaho\",\"textfont\":{\"color\":\"rgba(191,154,166,1)\",\"size\":6.9618258743844299},\"error_y\":{\"color\":\"rgba(191,154,166,1)\",\"width\":6.9618258743844299},\"error_x\":{\"color\":\"rgba(191,154,166,1)\",\"width\":6.9618258743844299},\"line\":{\"color\":\"rgba(191,154,166,1)\",\"width\":6.9618258743844299},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[229.51115612999999],\"y\":[326.69999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(174,157,181,1)\",\"size\":[25.282999764900907],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(174,157,181,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Illinois:\nCases per 100k: 32241.6\nDeaths per 100k: 326.7\",\"type\":\"scatter\",\"name\":\"Illinois\",\"textfont\":{\"color\":\"rgba(174,157,181,1)\",\"size\":25.282999764900907},\"error_y\":{\"color\":\"rgba(174,157,181,1)\",\"width\":25.282999764900907},\"error_x\":{\"color\":\"rgba(174,157,181,1)\",\"width\":25.282999764900907},\"line\":{\"color\":\"rgba(174,157,181,1)\",\"width\":25.282999764900907},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[186.78751987000001],\"y\":[391.19999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(154,159,195,1)\",\"size\":[15.195644442943934],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(154,159,195,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Indiana:\nCases per 100k: 30762.1\nDeaths per 100k: 391.2\",\"type\":\"scatter\",\"name\":\"Indiana\",\"textfont\":{\"color\":\"rgba(154,159,195,1)\",\"size\":15.195644442943934},\"error_y\":{\"color\":\"rgba(154,159,195,1)\",\"width\":15.195644442943934},\"error_x\":{\"color\":\"rgba(154,159,195,1)\",\"width\":15.195644442943934},\"line\":{\"color\":\"rgba(154,159,195,1)\",\"width\":15.195644442943934},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[56.507023549000003],\"y\":[341.39999999999998],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(147,159,203,1)\",\"size\":[9.2996278948820752],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(147,159,203,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Iowa:\nCases per 100k: 28750.9\nDeaths per 100k: 341.4\",\"type\":\"scatter\",\"name\":\"Iowa\",\"textfont\":{\"color\":\"rgba(147,159,203,1)\",\"size\":9.2996278948820752},\"error_y\":{\"color\":\"rgba(147,159,203,1)\",\"width\":9.2996278948820752},\"error_x\":{\"color\":\"rgba(147,159,203,1)\",\"width\":9.2996278948820752},\"line\":{\"color\":\"rgba(147,159,203,1)\",\"width\":9.2996278948820752},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[35.610732968000001],\"y\":[354.30000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(162,157,201,1)\",\"size\":[8.8916781180415008],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(162,157,201,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Kansas:\nCases per 100k: 32316.9\nDeaths per 100k: 354.3\",\"type\":\"scatter\",\"name\":\"Kansas\",\"textfont\":{\"color\":\"rgba(162,157,201,1)\",\"size\":8.8916781180415008},\"error_y\":{\"color\":\"rgba(162,157,201,1)\",\"width\":8.8916781180415008},\"error_x\":{\"color\":\"rgba(162,157,201,1)\",\"width\":8.8916781180415008},\"line\":{\"color\":\"rgba(162,157,201,1)\",\"width\":8.8916781180415008},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[113.15179107],\"y\":[410.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(175,154,200,1)\",\"size\":[11.487883904968246],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(175,154,200,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Kentucky:\nCases per 100k: 38918.1\nDeaths per 100k: 410.6\",\"type\":\"scatter\",\"name\":\"Kentucky\",\"textfont\":{\"color\":\"rgba(175,154,200,1)\",\"size\":11.487883904968246},\"error_y\":{\"color\":\"rgba(175,154,200,1)\",\"width\":11.487883904968246},\"error_x\":{\"color\":\"rgba(175,154,200,1)\",\"width\":11.487883904968246},\"line\":{\"color\":\"rgba(175,154,200,1)\",\"width\":11.487883904968246},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[107.86011234],\"y\":[404.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(187,151,199,1)\",\"size\":[11.807346733810668],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(187,151,199,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Louisiana:\nCases per 100k: 33921\nDeaths per 100k: 404.6\",\"type\":\"scatter\",\"name\":\"Louisiana\",\"textfont\":{\"color\":\"rgba(187,151,199,1)\",\"size\":11.807346733810668},\"error_y\":{\"color\":\"rgba(187,151,199,1)\",\"width\":11.807346733810668},\"error_x\":{\"color\":\"rgba(187,151,199,1)\",\"width\":11.807346733810668},\"line\":{\"color\":\"rgba(187,151,199,1)\",\"width\":11.807346733810668},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[43.391688418999998],\"y\":[223],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(199,148,198,1)\",\"size\":[6.2684513280738585],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(199,148,198,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Maine:\nCases per 100k: 23909.4\nDeaths per 100k: 223\",\"type\":\"scatter\",\"name\":\"Maine\",\"textfont\":{\"color\":\"rgba(199,148,198,1)\",\"size\":6.2684513280738585},\"error_y\":{\"color\":\"rgba(199,148,198,1)\",\"width\":6.2684513280738585},\"error_x\":{\"color\":\"rgba(199,148,198,1)\",\"width\":6.2684513280738585},\"line\":{\"color\":\"rgba(199,148,198,1)\",\"width\":6.2684513280738585},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[622.26172744999997],\"y\":[276.19999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(211,145,197,1)\",\"size\":[14.113136769898531],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(211,145,197,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Maryland:\nCases per 100k: 22671.2\nDeaths per 100k: 276.2\",\"type\":\"scatter\",\"name\":\"Maryland\",\"textfont\":{\"color\":\"rgba(211,145,197,1)\",\"size\":14.113136769898531},\"error_y\":{\"color\":\"rgba(211,145,197,1)\",\"width\":14.113136769898531},\"error_x\":{\"color\":\"rgba(211,145,197,1)\",\"width\":14.113136769898531},\"line\":{\"color\":\"rgba(211,145,197,1)\",\"width\":14.113136769898531},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[884.74992171999997],\"y\":[408.69999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(222,141,196,1)\",\"size\":[15.546282145388522],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(222,141,196,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Massachusetts:\nCases per 100k: 32311\nDeaths per 100k: 408.7\",\"type\":\"scatter\",\"name\":\"Massachusetts\",\"textfont\":{\"color\":\"rgba(222,141,196,1)\",\"size\":15.546282145388522},\"error_y\":{\"color\":\"rgba(222,141,196,1)\",\"width\":15.546282145388522},\"error_x\":{\"color\":\"rgba(222,141,196,1)\",\"width\":15.546282145388522},\"line\":{\"color\":\"rgba(222,141,196,1)\",\"width\":15.546282145388522},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[176.59707778999999],\"y\":[423.30000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(230,140,193,1)\",\"size\":[20.705295999610868],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(230,140,193,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Michigan:\nCases per 100k: 30694.5\nDeaths per 100k: 423.3\",\"type\":\"scatter\",\"name\":\"Michigan\",\"textfont\":{\"color\":\"rgba(230,140,193,1)\",\"size\":20.705295999610868},\"error_y\":{\"color\":\"rgba(230,140,193,1)\",\"width\":20.705295999610868},\"error_x\":{\"color\":\"rgba(230,140,193,1)\",\"width\":20.705295999610868},\"line\":{\"color\":\"rgba(230,140,193,1)\",\"width\":20.705295999610868},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[70.469674695999998],\"y\":[266.69999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(224,152,179,1)\",\"size\":[13.39352330215816],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(224,152,179,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Minnesota:\nCases per 100k: 31798.4\nDeaths per 100k: 266.7\",\"type\":\"scatter\",\"name\":\"Minnesota\",\"textfont\":{\"color\":\"rgba(224,152,179,1)\",\"size\":13.39352330215816},\"error_y\":{\"color\":\"rgba(224,152,179,1)\",\"width\":13.39352330215816},\"error_x\":{\"color\":\"rgba(224,152,179,1)\",\"width\":13.39352330215816},\"line\":{\"color\":\"rgba(224,152,179,1)\",\"width\":13.39352330215816},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[63.645625459000001],\"y\":[450.5],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(217,164,165,1)\",\"size\":[9.0167861625455217],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(217,164,165,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Mississippi:\nCases per 100k: 33250.5\nDeaths per 100k: 450.5\",\"type\":\"scatter\",\"name\":\"Mississippi\",\"textfont\":{\"color\":\"rgba(217,164,165,1)\",\"size\":9.0167861625455217},\"error_y\":{\"color\":\"rgba(217,164,165,1)\",\"width\":9.0167861625455217},\"error_x\":{\"color\":\"rgba(217,164,165,1)\",\"width\":9.0167861625455217},\"line\":{\"color\":\"rgba(217,164,165,1)\",\"width\":9.0167861625455217},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[89.117470788999995],\"y\":[393],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(209,175,151,1)\",\"size\":[14.25276751962862],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(209,175,151,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Missouri:\nCases per 100k: 29286.5\nDeaths per 100k: 393\",\"type\":\"scatter\",\"name\":\"Missouri\",\"textfont\":{\"color\":\"rgba(209,175,151,1)\",\"size\":14.25276751962862},\"error_y\":{\"color\":\"rgba(209,175,151,1)\",\"width\":14.25276751962862},\"error_x\":{\"color\":\"rgba(209,175,151,1)\",\"width\":14.25276751962862},\"line\":{\"color\":\"rgba(209,175,151,1)\",\"width\":14.25276751962862},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[7.2987510950000001],\"y\":[348.69999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(201,186,137,1)\",\"size\":[5.8080420514391893],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(201,186,137,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Montana:\nCases per 100k: 31152.1\nDeaths per 100k: 348.7\",\"type\":\"scatter\",\"name\":\"Montana\",\"textfont\":{\"color\":\"rgba(201,186,137,1)\",\"size\":5.8080420514391893},\"error_y\":{\"color\":\"rgba(201,186,137,1)\",\"width\":5.8080420514391893},\"error_x\":{\"color\":\"rgba(201,186,137,1)\",\"width\":5.8080420514391893},\"line\":{\"color\":\"rgba(201,186,137,1)\",\"width\":5.8080420514391893},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[25.114922059000001],\"y\":[268.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(191,196,121,1)\",\"size\":[7.253747424146165],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(191,196,121,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Nebraska:\nCases per 100k: 29874.8\nDeaths per 100k: 268.6\",\"type\":\"scatter\",\"name\":\"Nebraska\",\"textfont\":{\"color\":\"rgba(191,196,121,1)\",\"size\":7.253747424146165},\"error_y\":{\"color\":\"rgba(191,196,121,1)\",\"width\":7.253747424146165},\"error_x\":{\"color\":\"rgba(191,196,121,1)\",\"width\":7.253747424146165},\"line\":{\"color\":\"rgba(191,196,121,1)\",\"width\":7.253747424146165},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[27.6406025],\"y\":[399],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(180,205,105,1)\",\"size\":[9.0965985081110219],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(180,205,105,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Nevada:\nCases per 100k: 29431.5\nDeaths per 100k: 399\",\"type\":\"scatter\",\"name\":\"Nevada\",\"textfont\":{\"color\":\"rgba(180,205,105,1)\",\"size\":9.0965985081110219},\"error_y\":{\"color\":\"rgba(180,205,105,1)\",\"width\":9.0965985081110219},\"error_x\":{\"color\":\"rgba(180,205,105,1)\",\"width\":9.0965985081110219},\"line\":{\"color\":\"rgba(180,205,105,1)\",\"width\":9.0965985081110219},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[151.50059716000001],\"y\":[222.5],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(168,215,87,1)\",\"size\":[6.298557301222484],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(168,215,87,1)\"}},\"hoverinfo\":\"text\",\"text\":\"New Hampshire:\nCases per 100k: 27991.2\nDeaths per 100k: 222.5\",\"type\":\"scatter\",\"name\":\"New 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359.8\",\"type\":\"scatter\",\"name\":\"Ohio\",\"textfont\":{\"color\":\"rgba(239,217,56,1)\",\"size\":23.529339335629047},\"error_y\":{\"color\":\"rgba(239,217,56,1)\",\"width\":23.529339335629047},\"error_x\":{\"color\":\"rgba(239,217,56,1)\",\"width\":23.529339335629047},\"line\":{\"color\":\"rgba(239,217,56,1)\",\"width\":23.529339335629047},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[57.482602245000002],\"y\":[419.80000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(250,217,50,1)\",\"size\":[10.611880795831471],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(250,217,50,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Oklahoma:\nCases per 100k: 32863.5\nDeaths per 100k: 419.8\",\"type\":\"scatter\",\"name\":\"Oklahoma\",\"textfont\":{\"color\":\"rgba(250,217,50,1)\",\"size\":10.611880795831471},\"error_y\":{\"color\":\"rgba(250,217,50,1)\",\"width\":10.611880795831471},\"error_x\":{\"color\":\"rgba(250,217,50,1)\",\"width\":10.611880795831471},\"line\":{\"color\":\"rgba(250,217,50,1)\",\"width\":10.611880795831471},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[43.658853835000002],\"y\":[225.5],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(253,215,59,1)\",\"size\":[11.024823221592339],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(253,215,59,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Oregon:\nCases per 100k: 23078.6\nDeaths per 100k: 225.5\",\"type\":\"scatter\",\"name\":\"Oregon\",\"textfont\":{\"color\":\"rgba(253,215,59,1)\",\"size\":11.024823221592339},\"error_y\":{\"color\":\"rgba(253,215,59,1)\",\"width\":11.024823221592339},\"error_x\":{\"color\":\"rgba(253,215,59,1)\",\"width\":11.024823221592339},\"line\":{\"color\":\"rgba(253,215,59,1)\",\"width\":11.024823221592339},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[286.23455445000002],\"y\":[396.19999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(250,212,76,1)\",\"size\":[25.393024806905242],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(250,212,76,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Pennsylvania:\nCases per 100k: 27668.6\nDeaths per 100k: 396.2\",\"type\":\"scatter\",\"name\":\"Pennsylvania\",\"textfont\":{\"color\":\"rgba(250,212,76,1)\",\"size\":25.393024806905242},\"error_y\":{\"color\":\"rgba(250,212,76,1)\",\"width\":25.393024806905242},\"error_x\":{\"color\":\"rgba(250,212,76,1)\",\"width\":25.393024806905242},\"line\":{\"color\":\"rgba(250,212,76,1)\",\"width\":25.393024806905242},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[1022.6505771],\"y\":[371],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(244,207,104,1)\",\"size\":[5.7997209699053665],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(244,207,104,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Rhode Island:\nCases per 100k: 43677.3\nDeaths per 100k: 371\",\"type\":\"scatter\",\"name\":\"Rhode 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329.6\",\"type\":\"scatter\",\"name\":\"Texas\",\"textfont\":{\"color\":\"rgba(227,195,149,1)\",\"size\":51.898395938686235},\"error_y\":{\"color\":\"rgba(227,195,149,1)\",\"width\":51.898395938686235},\"error_x\":{\"color\":\"rgba(227,195,149,1)\",\"width\":51.898395938686235},\"line\":{\"color\":\"rgba(227,195,149,1)\",\"width\":51.898395938686235},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[38.458214685000002],\"y\":[168.19999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(221,193,154,1)\",\"size\":[9.3078989498736107],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(221,193,154,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Utah:\nCases per 100k: 34579\nDeaths per 100k: 168.2\",\"type\":\"scatter\",\"name\":\"Utah\",\"textfont\":{\"color\":\"rgba(221,193,154,1)\",\"size\":9.3078989498736107},\"error_y\":{\"color\":\"rgba(221,193,154,1)\",\"width\":9.3078989498736107},\"error_x\":{\"color\":\"rgba(221,193,154,1)\",\"width\":9.3078989498736107},\"line\":{\"color\":\"rgba(221,193,154,1)\",\"width\":9.3078989498736107},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[67.943995143999999],\"y\":[149.90000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(214,191,158,1)\",\"size\":[5.0809796315522071],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(214,191,158,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Vermont:\nCases per 100k: 24460.8\nDeaths per 100k: 149.9\",\"type\":\"scatter\",\"name\":\"Vermont\",\"textfont\":{\"color\":\"rgba(214,191,158,1)\",\"size\":5.0809796315522071},\"error_y\":{\"color\":\"rgba(214,191,158,1)\",\"width\":5.0809796315522071},\"error_x\":{\"color\":\"rgba(214,191,158,1)\",\"width\":5.0809796315522071},\"line\":{\"color\":\"rgba(214,191,158,1)\",\"width\":5.0809796315522071},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[215.73630392999999],\"y\":[279.39999999999998],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(208,188,162,1)\",\"size\":[18.24027147942185],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(208,188,162,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Virginia:\nCases per 100k: 26992\nDeaths per 100k: 279.4\",\"type\":\"scatter\",\"name\":\"Virginia\",\"textfont\":{\"color\":\"rgba(208,188,162,1)\",\"size\":18.24027147942185},\"error_y\":{\"color\":\"rgba(208,188,162,1)\",\"width\":18.24027147942185},\"error_x\":{\"color\":\"rgba(208,188,162,1)\",\"width\":18.24027147942185},\"line\":{\"color\":\"rgba(208,188,162,1)\",\"width\":18.24027147942185},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[113.39722716],\"y\":[213],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(201,186,167,1)\",\"size\":[16.602579245378088],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(201,186,167,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Washington:\nCases per 100k: 25808.4\nDeaths per 100k: 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283.8\",\"type\":\"scatter\",\"name\":\"Wisconsin\",\"textfont\":{\"color\":\"rgba(186,181,175,1)\",\"size\":13.731017364392411},\"error_y\":{\"color\":\"rgba(186,181,175,1)\",\"width\":13.731017364392411},\"error_x\":{\"color\":\"rgba(186,181,175,1)\",\"width\":13.731017364392411},\"line\":{\"color\":\"rgba(186,181,175,1)\",\"width\":13.731017364392411},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[5.9506106574000004],\"y\":[348.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(179,179,179,1)\",\"size\":[5],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(179,179,179,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Wyoming:\nCases per 100k: 32268\nDeaths per 100k: 348.6\",\"type\":\"scatter\",\"name\":\"Wyoming\",\"textfont\":{\"color\":\"rgba(179,179,179,1)\",\"size\":5},\"error_y\":{\"color\":\"rgba(179,179,179,1)\",\"width\":5},\"error_x\":{\"color\":\"rgba(179,179,179,1)\",\"width\":5},\"line\":{\"color\":\"rgba(179,179,179,1)\",\"width\":5},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null}],\"highlight\":{\"on\":\"plotly_click\",\"persistent\":false,\"dynamic\":false,\"selectize\":false,\"opacityDim\":0.20000000000000001,\"selected\":{\"opacity\":1},\"debounce\":0},\"shinyEvents\":[\"plotly_hover\",\"plotly_click\",\"plotly_selected\",\"plotly_relayout\",\"plotly_brushed\",\"plotly_brushing\",\"plotly_clickannotation\",\"plotly_doubleclick\",\"plotly_deselect\",\"plotly_afterplot\",\"plotly_sunburstclick\"],\"base_url\":\"https://plot.ly\"},\"evals\":[],\"jsHooks\":[]} 7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()  For pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly() Explore the pattern between \\(x\\) and \\(y\\) using geom_smooth()  Explain what you see. Do you think pop_density is a correlate of newdeathsper100k?    p \u0026lt;- ggplot(cv_states_today_scatter, aes(x=pop_density, y=deathsper100k, size=population)) + geom_point() + geom_smooth() ggplotly(p)   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Multiple line chart  Create a line chart of the naive_CFR for all states over time using plot_ly()  Use the zoom and pan tools to inspect the naive_CFR for the states that had an increase in September. How have they changed over time?   Create one more line chart, for Florida only, which shows new_cases and new_deaths together in one plot. Hint: use add_layer()  Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. 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# Line chart for Florida showing new_cases and new_deaths together cv_states %\u0026gt;% filter(state==\u0026quot;Florida\u0026quot;) %\u0026gt;% plot_ly(x = ~date, y = ~new_cases, type = \u0026quot;scatter\u0026quot;, mode = \u0026quot;lines\u0026quot;) %\u0026gt;% add_lines(x = ~date, y = ~new_deaths, type = \u0026quot;scatter\u0026quot;, mode = \u0026quot;lines\u0026quot;)   {\"x\":{\"visdat\":{\"111943e21b876\":[\"function () 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9. Heatmaps Create a heatmap to visualize new_cases for each state on each date greater than June 1st, 2021\n Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes Use plot_ly() to create a heatmap out of this matrix. Which states stand out? Repeat with newper100k variable. Now which states stand out? Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks  # Map state, date, and new_cases to a matrix library(tidyr) cv_states_mat \u0026lt;- cv_states %\u0026gt;% select(state, date, new_cases) %\u0026gt;% dplyr::filter(date\u0026gt;as.Date(\u0026quot;2021-06-15\u0026quot;)) cv_states_mat2 \u0026lt;- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases)) rownames(cv_states_mat2) \u0026lt;- cv_states_mat2$date cv_states_mat2$date \u0026lt;- NULL cv_states_mat2 \u0026lt;- as.matrix(cv_states_mat2) # Create a heatmap using plot_ly() plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=\u0026quot;heatmap\u0026quot;, showscale=T)   {\"x\":{\"visdat\":{\"111943c599721\":[\"function () 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# Repeat with newper100k cv_states_mat \u0026lt;- cv_states %\u0026gt;% select(state, date, newper100k) %\u0026gt;% dplyr::filter(date\u0026gt;as.Date(\u0026quot;2021-06-15\u0026quot;)) cv_states_mat2 \u0026lt;- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k)) rownames(cv_states_mat2) \u0026lt;- cv_states_mat2$date cv_states_mat2$date \u0026lt;- NULL cv_states_mat2 \u0026lt;- as.matrix(cv_states_mat2) plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=\u0026quot;heatmap\u0026quot;, showscale=T)   {\"x\":{\"visdat\":{\"111941dd1167b\":[\"function () \",\"plotlyVisDat\"]},\"cur_data\":\"111941dd1167b\",\"attrs\":{\"111941dd1167b\":{\"x\":[\"Alabama\",\"Alaska\",\"Arizona\",\"Arkansas\",\"California\",\"Colorado\",\"Connecticut\",\"Delaware\",\"District of Columbia\",\"Florida\",\"Georgia\",\"Hawaii\",\"Idaho\",\"Illinois\",\"Indiana\",\"Iowa\",\"Kansas\",\"Kentucky\",\"Louisiana\",\"Maine\",\"Maryland\",\"Massachusetts\",\"Michigan\",\"Minnesota\",\"Mississippi\",\"Missouri\",\"Montana\",\"Nebraska\",\"Nevada\",\"New Hampshire\",\"New Jersey\",\"New Mexico\",\"New York\",\"North Carolina\",\"North Dakota\",\"Ohio\",\"Oklahoma\",\"Oregon\",\"Pennsylvania\",\"Puerto Rico\",\"Rhode Island\",\"South Carolina\",\"South Dakota\",\"Tennessee\",\"Texas\",\"Utah\",\"Vermont\",\"Virginia\",\"Washington\",\"West 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# Create a second heatmap after filtering to only include dates every other week filter_dates \u0026lt;- seq(as.Date(\u0026quot;2021-06-15\u0026quot;), as.Date(\u0026quot;2021-11-01\u0026quot;), by=\u0026quot;2 weeks\u0026quot;) cv_states_mat \u0026lt;- cv_states %\u0026gt;% select(state, date, newper100k) %\u0026gt;% filter(date %in% filter_dates) cv_states_mat2 \u0026lt;- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k)) rownames(cv_states_mat2) \u0026lt;- cv_states_mat2$date cv_states_mat2$date \u0026lt;- NULL cv_states_mat2 \u0026lt;- as.matrix(cv_states_mat2) # Create a heatmap using plot_ly() plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=\u0026quot;heatmap\u0026quot;, showscale=T)   {\"x\":{\"visdat\":{\"11194a1af488\":[\"function () \",\"plotlyVisDat\"]},\"cur_data\":\"11194a1af488\",\"attrs\":{\"11194a1af488\":{\"x\":[\"Alabama\",\"Alaska\",\"Arizona\",\"Arkansas\",\"California\",\"Colorado\",\"Connecticut\",\"Delaware\",\"District of Columbia\",\"Florida\",\"Georgia\",\"Hawaii\",\"Idaho\",\"Illinois\",\"Indiana\",\"Iowa\",\"Kansas\",\"Kentucky\",\"Louisiana\",\"Maine\",\"Maryland\",\"Massachusetts\",\"Michigan\",\"Minnesota\",\"Mississippi\",\"Missouri\",\"Montana\",\"Nebraska\",\"Nevada\",\"New Hampshire\",\"New Jersey\",\"New Mexico\",\"New York\",\"North Carolina\",\"North Dakota\",\"Ohio\",\"Oklahoma\",\"Oregon\",\"Pennsylvania\",\"Puerto Rico\",\"Rhode Island\",\"South Carolina\",\"South Dakota\",\"Tennessee\",\"Texas\",\"Utah\",\"Vermont\",\"Virginia\",\"Washington\",\"West Virginia\",\"Wisconsin\",\"Wyoming\"],\"y\":[\"2021-06-15\",\"2021-06-29\",\"2021-07-13\",\"2021-07-27\",\"2021-08-10\",\"2021-08-24\",\"2021-09-07\",\"2021-09-21\",\"2021-10-05\",\"2021-10-19\"],\"z\":{},\"showscale\":true,\"alpha_stroke\":1,\"sizes\":[10,100],\"spans\":[1,20],\"type\":\"heatmap\"}},\"layout\":{\"margin\":{\"b\":40,\"l\":60,\"t\":25,\"r\":10},\"xaxis\":{\"domain\":[0,1],\"automargin\":true,\"title\":[]},\"yaxis\":{\"domain\":[0,1],\"automargin\":true,\"title\":[]},\"scene\":{\"zaxis\":{\"title\":\"cv_states_mat2\"}},\"hovermode\":\"closest\",\"showlegend\":false,\"legend\":{\"yanchor\":\"top\",\"y\":0.5}},\"source\":\"A\",\"config\":{\"modeBarButtonsToAdd\":[\"hoverclosest\",\"hovercompare\"],\"showSendToCloud\":false},\"data\":[{\"colorbar\":{\"title\":\"cv_states_mat2\",\"ticklen\":2,\"len\":0.5,\"lenmode\":\"fraction\",\"y\":1,\"yanchor\":\"top\"},\"colorscale\":[[\"0\",\"rgba(68,1,84,1)\"],[\"0.0416666666666667\",\"rgba(70,19,97,1)\"],[\"0.0833333333333333\",\"rgba(72,32,111,1)\"],[\"0.125\",\"rgba(71,45,122,1)\"],[\"0.166666666666667\",\"rgba(68,58,128,1)\"],[\"0.208333333333333\",\"rgba(64,70,135,1)\"],[\"0.25\",\"rgba(60,82,138,1)\"],[\"0.291666666666667\",\"rgba(56,93,140,1)\"],[\"0.333333333333333\",\"rgba(49,104,142,1)\"],[\"0.375\",\"rgba(46,114,142,1)\"],[\"0.416666666666667\",\"rgba(42,123,142,1)\"],[\"0.458333333333333\",\"rgba(38,133,141,1)\"],[\"0.5\",\"rgba(37,144,140,1)\"],[\"0.541666666666667\",\"rgba(33,154,138,1)\"],[\"0.583333333333333\",\"rgba(39,164,133,1)\"],[\"0.625\",\"rgba(47,174,127,1)\"],[\"0.666666666666667\",\"rgba(53,183,121,1)\"],[\"0.708333333333333\",\"rgba(79,191,110,1)\"],[\"0.75\",\"rgba(98,199,98,1)\"],[\"0.791666666666667\",\"rgba(119,207,85,1)\"],[\"0.833333333333333\",\"rgba(147,214,70,1)\"],[\"0.875\",\"rgba(172,220,52,1)\"],[\"0.916666666666667\",\"rgba(199,225,42,1)\"],[\"0.958333333333333\",\"rgba(226,228,40,1)\"],[\"1\",\"rgba(253,231,37,1)\"]],\"showscale\":true,\"x\":[\"Alabama\",\"Alaska\",\"Arizona\",\"Arkansas\",\"California\",\"Colorado\",\"Connecticut\",\"Delaware\",\"District 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Virginia\",\"Wisconsin\",\"Wyoming\"],\"y\":[\"2021-06-15\",\"2021-06-29\",\"2021-07-13\",\"2021-07-27\",\"2021-08-10\",\"2021-08-24\",\"2021-09-07\",\"2021-09-21\",\"2021-10-05\",\"2021-10-19\"],\"z\":[[4.7000000000000002,3.7999999999999998,5.9000000000000004,7.7999999999999998,2.2999999999999998,9.6999999999999993,1.5,3.3999999999999999,1.8999999999999999,7.2000000000000002,3.3999999999999999,3.7999999999999998,5.5,2.2999999999999998,5.0999999999999996,2.7000000000000002,3.7999999999999998,4.5,7.9000000000000004,3.2000000000000002,1.6000000000000001,1.3999999999999999,2.8999999999999999,2.3999999999999999,4.0999999999999996,9.0999999999999996,5.7999999999999998,1.5,6.9000000000000004,2.3999999999999999,2.7999999999999998,4.4000000000000004,2.2999999999999998,4,4.0999999999999996,2.7000000000000002,3.5,6.0999999999999996,3,1.3999999999999999,2.7000000000000002,2.6000000000000001,1.2,5.7999999999999998,6.5,8.5999999999999996,1,1.6000000000000001,7,4.7000000000000002,1.8999999999999999,11.4],[4.2000000000000002,4.0999999999999996,7.0999999999999996,11.9,2.6000000000000001,7,2,2,1.3999999999999999,7.2999999999999998,2.5,2.7999999999999998,4.2999999999999998,2,3.6000000000000001,2.2999999999999998,4.4000000000000004,3,7.4000000000000004,1.6000000000000001,0.90000000000000002,0.80000000000000004,1.7,1.6000000000000001,5.9000000000000004,13.800000000000001,4.7000000000000002,2.1000000000000001,15.300000000000001,1.3999999999999999,2.6000000000000001,2.8999999999999999,1.7,3.2999999999999998,1.6000000000000001,2.1000000000000001,5.2000000000000002,4.7999999999999998,1.3,1.1000000000000001,2,2.5,1.2,1.2,4.4000000000000004,11.5,0.80000000000000004,1.8999999999999999,5.7999999999999998,2.8999999999999999,1.5,12.1],[11.5,10,8,32.5,16.100000000000001,9.4000000000000004,2.3999999999999999,3.1000000000000001,2.3999999999999999,26.199999999999999,7.0999999999999996,4.5999999999999996,7.2000000000000002,4.2999999999999998,5.9000000000000004,3,12,6.7999999999999998,19.399999999999999,1.8,1.7,2.1000000000000001,2.2000000000000002,2.7000000000000002,10.699999999999999,24.899999999999999,6.2000000000000002,3.7000000000000002,21.199999999999999,1.8,3.5,5.0999999999999996,3.3999999999999999,5.7999999999999998,2.7999999999999998,2.6000000000000001,9.1999999999999993,5.2999999999999998,1.8,2.3999999999999999,2.2000000000000002,6.2000000000000002,0.90000000000000002,6.5999999999999996,9,15.5,1.3999999999999999,3.1000000000000001,6.9000000000000004,3.2000000000000002,2.7999999999999998,14.699999999999999],[36.299999999999997,28.600000000000001,19.199999999999999,52.200000000000003,19.399999999999999,11.4,7.2000000000000002,7.0999999999999996,6.7999999999999998,58.700000000000003,21.699999999999999,13.699999999999999,13.9,11.5,11.300000000000001,7.5999999999999996,21.699999999999999,20,79,4.7999999999999998,4.7999999999999998,7.9000000000000004,5.4000000000000004,6.2000000000000002,40.100000000000001,40,10.4,9.3000000000000007,30.100000000000001,3.8999999999999999,8.1999999999999993,11.300000000000001,8.4000000000000004,17,5,6.7000000000000002,30.399999999999999,12.1,4.7999999999999998,10,8.5999999999999996,21.399999999999999,3.2000000000000002,20.300000000000001,22.800000000000001,20.699999999999999,3.7999999999999998,8.0999999999999996,14.300000000000001,8.1999999999999993,8.3000000000000007,19.399999999999999],[68.599999999999994,41.600000000000001,34.799999999999997,77.099999999999994,30,18,13.9,19.600000000000001,18.800000000000001,94.099999999999994,48.700000000000003,36.700000000000003,30.600000000000001,22.300000000000001,27.300000000000001,20.100000000000001,37.100000000000001,47.799999999999997,119.5,10.199999999999999,12.5,15.300000000000001,10.9,14.300000000000001,88.400000000000006,46.700000000000003,23.199999999999999,18.300000000000001,35.600000000000001,11.1,14.800000000000001,26,15.800000000000001,43,13.6,16,51.399999999999999,30.899999999999999,12.6,22.399999999999999,21,55.700000000000003,6.0999999999999996,49.299999999999997,49.399999999999999,25.699999999999999,13.4,19.699999999999999,29.199999999999999,21.399999999999999,21.399999999999999,40.200000000000003],[70.900000000000006,68.200000000000003,41.399999999999999,73.900000000000006,39.299999999999997,26.5,16.699999999999999,34.399999999999999,24.199999999999999,99.799999999999997,76.799999999999997,50.100000000000001,38.5,27.100000000000001,48.899999999999999,29.600000000000001,41.899999999999999,82.5,100.5,12,17,20.100000000000001,17.699999999999999,24.699999999999999,117.09999999999999,41.700000000000003,36.700000000000003,29.800000000000001,38.200000000000003,19.5,22,39.399999999999999,23.100000000000001,56.700000000000003,30.800000000000001,28.199999999999999,57,50,20.399999999999999,25.899999999999999,26.399999999999999,83.099999999999994,33.899999999999999,90,57.600000000000001,35,19.199999999999999,30.5,43.700000000000003,47.299999999999997,27.5,67.200000000000003],[80.799999999999997,81.599999999999994,45.600000000000001,58.899999999999999,31.6000000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10. Map  Create a map to visualize the naive_CFR by state on October 15, 2021 Compare with a map visualizing the naive_CFR by state on most recent date Plot the two maps together using subplot(). Make sure the shading is for the same range of values (google is your friend for this) Describe the difference in the pattern of the CFR.  ### For specified date pick.date = \u0026quot;2021-10-15\u0026quot; # Extract the data for each state by its abbreviation cv_per100 \u0026lt;- cv_states %\u0026gt;% filter(date==pick.date) %\u0026gt;% select(state, abb, newper100k, cases, deaths) # select data cv_per100$state_name \u0026lt;- cv_per100$state cv_per100$state \u0026lt;- cv_per100$abb cv_per100$abb \u0026lt;- NULL # Create hover text cv_per100$hover \u0026lt;- with(cv_per100, paste(state_name, '\u0026lt;br\u0026gt;', \u0026quot;Cases per 100k: \u0026quot;, newper100k, '\u0026lt;br\u0026gt;', \u0026quot;Cases: \u0026quot;, cases, '\u0026lt;br\u0026gt;', \u0026quot;Deaths: \u0026quot;, deaths)) # Set up mapping details set_map_details \u0026lt;- list( scope = 'usa', projection = list(type = 'albers usa'), showlakes = TRUE, lakecolor = toRGB('white') ) # Make sure both maps are on the same color scale shadeLimit \u0026lt;- 125 # Create the map fig \u0026lt;- plot_geo(cv_per100, locationmode = 'USA-states') %\u0026gt;% add_trace( z = ~newper100k, text = ~hover, locations = ~state, color = ~newper100k, colors = 'Purples' ) fig \u0026lt;- fig %\u0026gt;% colorbar(title = paste0(\u0026quot;Cases per 100k: \u0026quot;,pick.date), limits = c(0,shadeLimit)) fig \u0026lt;- fig %\u0026gt;% layout( title = paste('Cases per 100k by State as of ', pick.date, '\u0026lt;br\u0026gt;(Hover for value)'), geo = set_map_details ) fig_pick.date \u0026lt;- fig ############# ### For Today # Extract the data for each state by its abbreviation cv_per100 \u0026lt;- cv_states_today %\u0026gt;% select(state, abb, newper100k, cases, deaths) # select data cv_per100$state_name \u0026lt;- cv_per100$state cv_per100$state \u0026lt;- cv_per100$abb cv_per100$abb \u0026lt;- NULL # Create hover text cv_per100$hover \u0026lt;- with(cv_per100, paste(state_name, '\u0026lt;br\u0026gt;', \u0026quot;Cases per 100k: \u0026quot;, newper100k, '\u0026lt;br\u0026gt;', \u0026quot;Cases: \u0026quot;, cases, '\u0026lt;br\u0026gt;', \u0026quot;Deaths: \u0026quot;, deaths)) # Set up mapping details set_map_details \u0026lt;- list( scope = 'usa', projection = list(type = 'albers usa'), showlakes = TRUE, lakecolor = toRGB('white') ) # Create the map fig \u0026lt;- plot_geo(cv_per100, locationmode = 'USA-states') %\u0026gt;% add_trace( z = ~newper100k, text = ~hover, locations = ~state, color = ~newper100k, colors = 'Purples' ) fig \u0026lt;- fig %\u0026gt;% colorbar(title = paste0(\u0026quot;Cases per 100k: \u0026quot;, Sys.Date()), limits = c(0,shadeLimit)) fig \u0026lt;- fig %\u0026gt;% layout( title = paste('Cases per 100k by State as of', Sys.Date(), '\u0026lt;br\u0026gt;(Hover for value)'), geo = set_map_details ) fig_Today \u0026lt;- fig ### Plot together subplot(fig_pick.date, fig_Today, nrows = 2, margin = .05)   {\"x\":{\"data\":[{\"colorbar\":{\"title\":\"Cases per 100k: 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Cases per 100k: 17.9 Cases: 812694 Deaths: 15142\",\"Alaska Cases per 100k: 123.9 Cases: 126893 Deaths: 599\",\"Arizona Cases per 100k: 30.7 Cases: 1129494 Deaths: 20505\",\"Arkansas Cases per 100k: 21.3 Cases: 506011 Deaths: 8194\",\"California Cases per 100k: 13.9 Cases: 4829925 Deaths: 71672\",\"Colorado Cases per 100k: 41.9 Cases: 708803 Deaths: 8048\",\"Connecticut Cases per 100k: 12.5 Cases: 397123 Deaths: 8730\",\"Delaware Cases per 100k: 39.8 Cases: 139383 Deaths: 2021\",\"District of Columbia Cases per 100k: 14.4 Cases: 62988 Deaths: 1183\",\"Florida Cases per 100k: 12.6 Cases: 3661089 Deaths: 57859\",\"Georgia Cases per 100k: 16.4 Cases: 1579662 Deaths: 26811\",\"Hawaii Cases per 100k: 9.7 Cases: 79562 Deaths: 859\",\"Idaho Cases per 100k: 68.6 Cases: 276139 Deaths: 3256\",\"Illinois Cases per 100k: 17.8 Cases: 1671079 Deaths: 28200\",\"Indiana Cases per 100k: 29.7 Cases: 999870 Deaths: 16246\",\"Iowa Cases per 100k: 36.9 Cases: 469875 Deaths: 6749\",\"Kansas Cases per 100k: 26.6 Cases: 424090 Deaths: 6204\",\"Kentucky Cases per 100k: 43.2 Cases: 729974 Deaths: 9376\",\"Louisiana Cases per 100k: 13.9 Cases: 751874 Deaths: 14321\",\"Maine Cases per 100k: 30.3 Cases: 97185 Deaths: 1093\",\"Maryland Cases per 100k: 16.8 Cases: 548975 Deaths: 10687\",\"Massachusetts Cases per 100k: 20.3 Cases: 833311 Deaths: 18828\",\"Michigan Cases per 100k: 43.7 Cases: 1217412 Deaths: 22951\",\"Minnesota Cases per 100k: 50.3 Cases: 755525 Deaths: 8531\",\"Mississippi Cases per 100k: 16.8 Cases: 497790 Deaths: 9917\",\"Missouri Cases per 100k: 18.9 Cases: 856850 Deaths: 12560\",\"Montana Cases per 100k: 80.6 Cases: 164846 Deaths: 2133\",\"Nebraska Cases per 100k: 30.7 Cases: 275694 Deaths: 2901\",\"Nevada Cases per 100k: 22.5 Cases: 441021 Deaths: 7425\",\"New Hampshire Cases per 100k: 40.4 Cases: 128047 Deaths: 1520\",\"New Jersey Cases per 100k: 17 Cases: 1178936 Deaths: 27693\",\"New Mexico Cases per 100k: 32.8 Cases: 263755 Deaths: 4932\",\"New York Cases per 100k: 23.5 Cases: 2497110 Deaths: 55469\",\"North Carolina Cases per 100k: 29.9 Cases: 1449002 Deaths: 17475\",\"North Dakota Cases per 100k: 68 Cases: 140780 Deaths: 1704\",\"Ohio Cases per 100k: 38.7 Cases: 1490451 Deaths: 23327\",\"Oklahoma Cases per 100k: 32.9 Cases: 631872 Deaths: 9403\",\"Oregon Cases per 100k: 26.1 Cases: 348767 Deaths: 4161\",\"Pennsylvania Cases per 100k: 36 Cases: 1502124 Deaths: 30459\",\"Puerto Rico Cases per 100k: 4 Cases: 216782 Deaths: 3205\",\"Rhode Island Cases per 100k: 24.8 Cases: 176424 Deaths: 2860\",\"South Carolina Cases per 100k: 28 Cases: 883982 Deaths: 13249\",\"South Dakota Cases per 100k: 35.3 Cases: 149996 Deaths: 2189\",\"Tennessee Cases per 100k: 28.1 Cases: 1233924 Deaths: 15696\",\"Texas Cases per 100k: 21.6 Cases: 4167271 Deaths: 69071\",\"Utah Cases per 100k: 40.5 Cases: 529281 Deaths: 3067\",\"Vermont Cases per 100k: 34.8 Cases: 36730 Deaths: 343\",\"Virginia Cases per 100k: 24.8 Cases: 902938 Deaths: 13391\",\"Washington Cases per 100k: 31.5 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Cases per 100k: 5.7 Cases: 1648385 Deaths: 21631\",\"Alaska Cases per 100k: 9.6 Cases: 308893 Deaths: 1439\",\"Arizona Cases per 100k: 5.3 Cases: 2451062 Deaths: 33221\",\"Arkansas Cases per 100k: 1.7 Cases: 1008303 Deaths: 13070\",\"California Cases per 100k: 6.2 Cases: 12170765 Deaths: 105245\",\"Colorado Cases per 100k: 5.8 Cases: 1781053 Deaths: 14588\",\"Connecticut Cases per 100k: 3.5 Cases: 978456 Deaths: 12310\",\"Delaware Cases per 100k: 5 Cases: 334147 Deaths: 3352\",\"District of Columbia Cases per 100k: 2.4 Cases: 178238 Deaths: 1518\",\"Florida Cases per 100k: 7.9 Cases: 7583396 Deaths: 87155\",\"Georgia Cases per 100k: 3.8 Cases: 2985191 Deaths: 41055\",\"Hawaii Cases per 100k: 5.1 Cases: 372408 Deaths: 1851\",\"Idaho Cases per 100k: 5.8 Cases: 522919 Deaths: 5461\",\"Illinois Cases per 100k: 9.6 Cases: 4107931 Deaths: 41621\",\"Indiana Cases per 100k: 0 Cases: 2058560 Deaths: 26180\",\"Iowa Cases per 100k: 7.6 Cases: 907421 Deaths: 10776\",\"Kansas Cases per 100k: 6.3 Cases: 940907 Deaths: 10315\",\"Kentucky Cases per 100k: 6.8 Cases: 1739015 Deaths: 18348\",\"Louisiana Cases per 100k: 12.9 Cases: 1580709 Deaths: 18856\",\"Maine Cases per 100k: 9.6 Cases: 320004 Deaths: 2984\",\"Maryland Cases per 100k: 4.9 Cases: 1369957 Deaths: 16692\",\"Massachusetts Cases per 100k: 5.3 Cases: 2230150 Deaths: 28211\",\"Michigan Cases per 100k: 8.5 Cases: 3068195 Deaths: 42314\",\"Minnesota Cases per 100k: 6.6 Cases: 1784263 Deaths: 14964\",\"Mississippi Cases per 100k: 5.1 Cases: 993035 Deaths: 13455\",\"Missouri Cases per 100k: 6.4 Cases: 1794222 Deaths: 24078\",\"Montana Cases per 100k: 8.8 Cases: 330930 Deaths: 3704\",\"Nebraska Cases per 100k: 11.2 Cases: 576365 Deaths: 5182\",\"Nevada Cases per 100k: 5.2 Cases: 893067 Deaths: 12106\",\"New Hampshire Cases per 100k: 6.4 Cases: 379689 Deaths: 3018\",\"New Jersey Cases per 100k: 6 Cases: 3057442 Deaths: 36100\",\"New Mexico Cases per 100k: 9.2 Cases: 673541 Deaths: 9126\",\"New York Cases per 100k: 5.3 Cases: 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","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"8a027c6e0f5901c832e1e370812adf61","permalink":"https://uscbiostats.github.io/PM566/assignment/11-lab-answers/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/11-lab-answers/","section":"assignment","summary":"Learning Goals  Read in and process the COVID dataset from the New York Times GitHub repository Create interactive graphs of different types using plot_ly() and ggplotly() functions Customize the hoverinfo and other plot features Create a Choropleth map using plot_geo()  Lab Description We will work with COVID data downloaded from the New York Times.","tags":null,"title":"Lab 11 - Interactive Visualization","type":"assignment"},{"authors":null,"categories":null,"content":"Grab lab file using command line:\n# Step 1 cd ~/Documents mkdir lab11 cd lab11 # Step 2 wget https://raw.githubusercontent.com/USCbiostats/PM566/master/website/content/assignment/11-lab.Rmd  And remember to set eval=TRUE\nLearning Goals  Read in and process the COVID dataset from the New York Times GitHub repository Create interactive graphs of different types using plot_ly() and ggplotly() functions Customize the hoverinfo and other plot features Create a Choropleth map using plot_geo()  Lab Description We will work with COVID data downloaded from the New York Times. The dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic.\nThe objective of this lab is to explore relationships between cases, deaths, and population sizes of US states, and plot data to demonstrate this\nSteps I. Reading and processing the New York Times (NYT) state-level COVID-19 data 1. Read in the data  Read in the COVID data with data.table:fread() from the NYT GitHub repository: \u0026ldquo; https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv\" Read in the state population data with data.table:fread() from the repository: \u0026ldquo; https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv\"\" Merge datasets  ## data extracted from New York Times state-level data from NYT Github repository # https://github.com/nytimes/covid-19-data ## state-level population information from us_census_data available on GitHub repository: # https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data ### FINISH THE CODE HERE ### # load COVID state-level data from NYT cv_states \u0026lt;- as.data.frame(______________(\u0026quot;https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv\u0026quot;)) ### FINISH THE CODE HERE ### # load state population data state_pops \u0026lt;- as.data.frame(_____________(\u0026quot;https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv\u0026quot;)) state_pops$abb \u0026lt;- state_pops$state state_pops$state \u0026lt;- state_pops$state_name state_pops$state_name \u0026lt;- NULL ### FINISH THE CODE HERE cv_states \u0026lt;- merge(cv_states, state_pops, by=________)  2. Look at the data  Inspect the dimensions, head, and tail of the data Inspect the structure of each variables. Are they in the correct format?  dim(cv_states) head(cv_states) tail(cv_states) str(cv_states)  3. Format the data  Make date into a date variable Make state into a factor variable Order the data first by state, second by date Confirm the variables are now correctly formatted Inspect the range values for each variable. What is the date range? The range of cases and deaths?  # format the date cv_states$date \u0026lt;- as.Date(cv_states$date, format=\u0026quot;%Y-%m-%d\u0026quot;) # format the state and state abbreviation (abb) variables state_list \u0026lt;- unique(cv_states$state) cv_states$state \u0026lt;- factor(cv_states$state, levels = state_list) abb_list \u0026lt;- unique(cv_states$abb) cv_states$abb \u0026lt;- factor(cv_states$abb, levels = abb_list) ### FINISH THE CODE HERE # order the data first by state, second by date cv_states = cv_states[order(_________, _________),] # Confirm the variables are now correctly formatted str(cv_states) head(cv_states) tail(cv_states) # Inspect the range values for each variable. What is the date range? The range of cases and deaths? head(cv_states) summary(cv_states) min(cv_states$date) max(cv_states$date)  4. Add new_cases and new_deaths and correct outliers   Add variables for new cases, new_cases, and new deaths, new_deaths:\n Hint: You can set new_cases equal to the difference between cases on date i and date i-1, starting on date i=2    Filter to dates after June 1, 2021\n  Use plotly for EDA: See if there are outliers or values that don\u0026rsquo;t make sense for new_cases and new_deaths. Which states and which dates have strange values?\n  Correct outliers: Set negative values for new_cases or new_deaths to 0\n  Recalculate cases and deaths as cumulative sum of updated new_cases and new_deaths\n  Get the rolling average of new cases and new deaths to smooth over time\n  Inspect data again interactively\n  # Add variables for new_cases and new_deaths: for (i in 1:length(state_list)) { cv_subset = subset(cv_states, state == state_list[i]) cv_subset = cv_subset[order(cv_subset$date),] # add starting level for new cases and deaths cv_subset$new_cases = cv_subset$cases[1] cv_subset$new_deaths = cv_subset$deaths[1] ### FINISH THE CODE HERE for (j in 2:nrow(cv_subset)) { cv_subset$new_cases[j] = __________ - __________ cv_subset$new_deaths[j] = __________ - __________ } # include in main dataset cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths } # Focus on recent dates cv_states \u0026lt;- cv_states %\u0026gt;% dplyr::filter(date \u0026gt;= \u0026quot;2021-06-01\u0026quot;) ### FINISH THE CODE HERE # Inspect outliers in new_cases using plotly p1\u0026lt;-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + ________ + geom_point(size = .5, alpha = 0.5) ggplotly(p1) p1\u0026lt;-NULL # to clear from workspace p2\u0026lt;-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + ________ + geom_point(size = .5, alpha = 0.5) ggplotly(p2) p2\u0026lt;-NULL # to clear from workspace # set negative new case or death counts to 0 cv_states$new_cases[cv_states$new_cases\u0026lt;0] = 0 cv_states$new_deaths[cv_states$new_deaths\u0026lt;0] = 0 # Recalculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths` for (i in 1:length(state_list)) { cv_subset = subset(cv_states, state == state_list[i]) # add starting level for new cases and deaths cv_subset$cases = cv_subset$cases[1] cv_subset$deaths = cv_subset$deaths[1] ### FINISH CODE HERE for (j in 2:nrow(cv_subset)) { cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$_________ cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$__________ } # include in main dataset cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths } # Smooth new counts cv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %\u0026gt;% round(digits = 0) cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %\u0026gt;% round(digits = 0) # Inspect data again interactively p2\u0026lt;-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5) ggplotly(p2) #p2=NULL  5. Add additional variables   Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:\n per100k = cases per 100,000 population newper100k= new cases per 100,000 deathsper100k = deaths per 100,000 newdeathsper100k = new deaths per 100,000    Add a \u0026ldquo;naive CFR\u0026rdquo; variable representing deaths / cases on each date for each state\n  Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture\n  ### FINISH CODE HERE # add population normalized (by 100,000) counts for each variable cv_states$per100k = as.numeric(format(round(cv_states$cases/(cv_states$________/100000),1),nsmall=1)) cv_states$newper100k = as.numeric(format(round(cv_states$new_cases/(cv_states$________/100000),1),nsmall=1)) cv_states$deathsper100k = as.numeric(format(round(cv_states$deaths/(cv_states$________/100000),1),nsmall=1)) cv_states$newdeathsper100k = as.numeric(format(round(cv_states$new_deaths/(cv_states$________/100000),1),nsmall=1)) # add a naive_CFR variable = deaths / cases cv_states = cv_states %\u0026gt;% mutate(naive_CFR = round((deaths*100/cases),2)) # create a `cv_states_today` variable cv_states_today = subset(cv_states, date==max(cv_states$date))  II. Scatterplots 6. Explore scatterplots using plot_ly()  Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)  Color points by state and size points by state population Use hover to identify any outliers. Remove those outliers and replot.   Choose one plot. For this plot:  Add hoverinfo specifying the state name, cases per 100k, and deaths per 100k, similarly to how we did this in the lecture notes Add layout information to title the chart and the axes Enable hovermode = \u0026quot;compare\u0026quot;    ### FINISH CODE HERE # pop_density vs. cases cv_states_today %\u0026gt;% plot_ly(x = ~pop_density, y = ~cases, type = _______, mode = 'markers', color = ~______, size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5)) # filter out \u0026quot;District of Columbia\u0026quot; cv_states_today_filter \u0026lt;- cv_states_today %\u0026gt;% filter(state!=\u0026quot;District of Columbia\u0026quot;) # pop_density vs. cases after filtering cv_states_today_filter %\u0026gt;% plot_ly(x = ~pop_density, y = ~cases, type = _______, mode = 'markers', color = ~_______, size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5)) # pop_density vs. deathsper100k cv_states_today_filter %\u0026gt;% plot_ly(x = ~pop_density, y = ~deathsper100k, type = _______, mode = 'markers', color = ~_______, size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5)) # Adding hoverinfo cv_states_today_filter %\u0026gt;% plot_ly(x = ~pop_density, y = ~deathsper100k, type = _______, mode = 'markers', color = ~_______, size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5), hoverinfo = 'text', text = ~paste( paste(state, \u0026quot;:\u0026quot;, sep=\u0026quot;\u0026quot;), paste(\u0026quot; Cases per 100k: \u0026quot;, per100k, sep=\u0026quot;\u0026quot;) , paste(\u0026quot; Deaths per 100k: \u0026quot;, _______, sep=\u0026quot;\u0026quot;), sep = \u0026quot;\u0026lt;br\u0026gt;\u0026quot;)) %\u0026gt;% layout(title = \u0026quot;Population-normalized COVID-19 deaths (per 100k) vs. population density for US states\u0026quot;, yaxis = list(title = \u0026quot;Deaths per 100k\u0026quot;), xaxis = list(title = \u0026quot;Population Density\u0026quot;), hovermode = \u0026quot;compare\u0026quot;)  7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()  For pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly() Explore the pattern between \\(x\\) and \\(y\\) using geom_smooth()  Explain what you see. Do you think pop_density is a correlate of newdeathsper100k?    ### FINISH CODE HERE p \u0026lt;- ggplot(cv_states_today_scatter, aes(x=pop_density, y=deathsper100k, size=population)) + geom_point() + _______ ggplotly(p)  8. Multiple line chart  Create a line chart of the naive_CFR for all states over time using plot_ly()  Use the zoom and pan tools to inspect the naive_CFR for the states that had an increase in September. How have they changed over time?   Create one more line chart, for Florida only, which shows new_cases and new_deaths together in one plot. Hint: use add_layer()  Use hoverinfo to \u0026ldquo;eyeball\u0026rdquo; the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?    ### FINISH CODE HERE # Line chart for naive_CFR for all states over time using `plot_ly()` plot_ly(cv_states, x = ~date, y = ~_______, color = ~state, type = \u0026quot;scatter\u0026quot;, mode = \u0026quot;lines\u0026quot;) ### FINISH CODE HERE # Line chart for Florida showing new_cases and new_deaths together cv_states %\u0026gt;% filter(state==\u0026quot;Florida\u0026quot;) %\u0026gt;% plot_ly(x = ~date, y = ~new_cases, type = \u0026quot;scatter\u0026quot;, mode = \u0026quot;lines\u0026quot;) %\u0026gt;% _______(x = ~date, y = ~new_deaths, type = \u0026quot;scatter\u0026quot;, mode = \u0026quot;lines\u0026quot;)  9. Heatmaps Create a heatmap to visualize new_cases for each state on each date greater than June 1st, 2021\n Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes Use plot_ly() to create a heatmap out of this matrix. Which states stand out? Repeat with newper100k variable. Now which states stand out? Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks  ### FINISH CODE HERE # Map state, date, and new_cases to a matrix library(tidyr) cv_states_mat \u0026lt;- cv_states %\u0026gt;% select(state, date, new_cases) %\u0026gt;% dplyr::filter(date\u0026gt;as.Date(\u0026quot;2021-06-15\u0026quot;)) cv_states_mat2 \u0026lt;- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = ________)) rownames(cv_states_mat2) \u0026lt;- cv_states_mat2$date cv_states_mat2$date \u0026lt;- NULL cv_states_mat2 \u0026lt;- as.matrix(cv_states_mat2) # Create a heatmap using plot_ly() plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=\u0026quot;heatmap\u0026quot;, showscale=T) # Repeat with newper100k cv_states_mat \u0026lt;- cv_states %\u0026gt;% select(state, date, newper100k) %\u0026gt;% dplyr::filter(date\u0026gt;as.Date(\u0026quot;2021-06-15\u0026quot;)) cv_states_mat2 \u0026lt;- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = ________)) rownames(cv_states_mat2) \u0026lt;- cv_states_mat2$date cv_states_mat2$date \u0026lt;- NULL cv_states_mat2 \u0026lt;- as.matrix(cv_states_mat2) plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=\u0026quot;heatmap\u0026quot;, showscale=T) # Create a second heatmap after filtering to only include dates every other week filter_dates \u0026lt;- seq(as.Date(\u0026quot;2021-06-15\u0026quot;), as.Date(\u0026quot;2021-11-01\u0026quot;), by=________) cv_states_mat \u0026lt;- cv_states %\u0026gt;% select(state, date, newper100k) %\u0026gt;% filter(date %in% filter_dates) cv_states_mat2 \u0026lt;- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = ________)) rownames(cv_states_mat2) \u0026lt;- cv_states_mat2$date cv_states_mat2$date \u0026lt;- NULL cv_states_mat2 \u0026lt;- as.matrix(cv_states_mat2) # Create a heatmap using plot_ly() plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=\u0026quot;heatmap\u0026quot;, showscale=T)  10. Map  Create a map to visualize the naive_CFR by state on October 15, 2021 Compare with a map visualizing the naive_CFR by state on most recent date Plot the two maps together using subplot(). Make sure the shading is for the same range of values (google is your friend for this) Describe the difference in the pattern of the CFR.  ### For specified date pick.date = \u0026quot;2021-10-15\u0026quot; # Extract the data for each state by its abbreviation cv_per100 \u0026lt;- cv_states %\u0026gt;% filter(date==pick.date) %\u0026gt;% select(state, abb, newper100k, cases, deaths) # select data cv_per100$state_name \u0026lt;- cv_per100$state cv_per100$state \u0026lt;- cv_per100$abb cv_per100$abb \u0026lt;- NULL # Create hover text cv_per100$hover \u0026lt;- with(cv_per100, paste(state_name, '\u0026lt;br\u0026gt;', \u0026quot;Cases per 100k: \u0026quot;, newper100k, '\u0026lt;br\u0026gt;', \u0026quot;Cases: \u0026quot;, cases, '\u0026lt;br\u0026gt;', \u0026quot;Deaths: \u0026quot;, deaths)) # Set up mapping details set_map_details \u0026lt;- list( scope = 'usa', projection = list(type = 'albers usa'), showlakes = TRUE, lakecolor = toRGB('white') ) # Make sure both maps are on the same color scale shadeLimit \u0026lt;- 125 # Create the map fig \u0026lt;- plot_geo(cv_per100, locationmode = 'USA-states') %\u0026gt;% add_trace( z = ~newper100k, text = ~hover, locations = ~state, color = ~newper100k, colors = 'Purples' ) fig \u0026lt;- fig %\u0026gt;% colorbar(title = paste0(\u0026quot;Cases per 100k: \u0026quot;, pick.date), limits = c(0,shadeLimit)) fig \u0026lt;- fig %\u0026gt;% layout( title = paste('Cases per 100k by State as of ', pick.date, '\u0026lt;br\u0026gt;(Hover for value)'), geo = set_map_details ) fig_pick.date \u0026lt;- fig ############# ### Map for today's date # Extract the data for each state by its abbreviation cv_per100 \u0026lt;- cv_states_today %\u0026gt;% select(state, abb, newper100k, cases, deaths) # select data cv_per100$state_name \u0026lt;- cv_per100$state cv_per100$state \u0026lt;- cv_per100$abb cv_per100$abb \u0026lt;- NULL # Create hover text cv_per100$hover \u0026lt;- with(cv_per100, paste(state_name, '\u0026lt;br\u0026gt;', \u0026quot;Cases per 100k: \u0026quot;, newper100k, '\u0026lt;br\u0026gt;', \u0026quot;Cases: \u0026quot;, cases, '\u0026lt;br\u0026gt;', \u0026quot;Deaths: \u0026quot;, deaths)) # Set up mapping details set_map_details \u0026lt;- list( scope = 'usa', projection = list(type = 'albers usa'), showlakes = TRUE, lakecolor = toRGB('white') ) # Create the map fig \u0026lt;- plot_geo(cv_per100, locationmode = 'USA-states') %\u0026gt;% add_trace( z = ~newper100k, text = ~hover, locations = ~state, color = ~newper100k, colors = 'Purples' ) fig \u0026lt;- fig %\u0026gt;% colorbar(title = paste0(\u0026quot;Cases per 100k: \u0026quot;, Sys.Date()), limits = c(0,shadeLimit)) fig \u0026lt;- fig %\u0026gt;% layout( title = paste('Cases per 100k by State as of', Sys.Date(), '\u0026lt;br\u0026gt;(Hover for value)'), geo = set_map_details ) fig_Today \u0026lt;- fig ### Plot together ________(fig_pick.date, fig_Today, nrows = 2, margin = .05)  ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"d1a7f60577e327e069e44dff1b818953","permalink":"https://uscbiostats.github.io/PM566/assignment/11-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/11-lab/","section":"assignment","summary":"Grab lab file using command line:\n# Step 1 cd ~/Documents mkdir lab11 cd lab11 # Step 2 wget https://raw.githubusercontent.com/USCbiostats/PM566/master/website/content/assignment/11-lab.Rmd  And remember to set eval=TRUE\nLearning Goals  Read in and process the COVID dataset from the New York Times GitHub repository Create interactive graphs of different types using plot_ly() and ggplotly() functions Customize the hoverinfo and other plot features Create a Choropleth map using plot_geo()  Lab Description We will work with COVID data downloaded from the New York Times.","tags":null,"title":"Lab 11 - Interactive Visualization","type":"assignment"},{"authors":null,"categories":null,"content":"Learning goals In this lab, you are expected to learn/put in practice the following skills:\n Evaluate whether a problem can be parallelized or not. Practice with the parallel package.  Problem 1: Vectorization The following functions can be written to be more efficient without using parallel. Write a faster version of each function and show that (1) the outputs are the same as the slow version, and (2) your version is faster.\n This function generates an n x k dataset with all its entries drawn from a Poission distribution with mean lambda.  fun1 \u0026lt;- function(n = 100, k = 4, lambda = 4) { x \u0026lt;- NULL for (i in 1:n){ x \u0026lt;- rbind(x, rpois(k, lambda)) } return(x) } fun1alt \u0026lt;- function(n = 100, k = 4, lambda = 4) { # YOUR CODE HERE }  Show that fun1alt generates a matrix with the same dimensions as fun1 and that the values inside the two matrices follow similar distributions. Then check the speed of the two functions with the following code:\n# Benchmarking microbenchmark::microbenchmark( fun1(), fun1alt() )  This function finds the maximum value of each column of a matrix (hint: check out the max.col() function).  # Data Generating Process (10 x 10,000 matrix) set.seed(1234) x \u0026lt;- matrix(rnorm(1e4), nrow=10) # Find each column's max value fun2 \u0026lt;- function(x) { apply(x, 2, max) } fun2alt \u0026lt;- function(x) { # YOUR CODE HERE }  Show that both functions return the same output for a given input matrix, x. Then check the speed of the two functions.\nProblem 3: Parallelization We will now turn our attention to the statistical concept of bootstrapping. Among its many uses, non-parametric bootstrapping allows us to obtain confidence intervals for parameter estimates without relying on parametric assumptions. Don\u0026rsquo;t worry if these concepts are unfamiliar, we only care about the computation methods in this lab, not the statistics.\nThe main assumption is that we can approximate the results of many repeated experiments by resampling observations from our original dataset, which reflects the population.\n This function implements a serial version of the bootstrap. Edit this function to parallelize the lapply loop, using whichever method you prefer. Rather than specifying the number of cores to use, use the number given by the ncpus argument, so that we can test it with different numbers of cores later.  my_boot \u0026lt;- function(dat, stat, R, ncpus = 1L) { # Getting the random indices n \u0026lt;- nrow(dat) idx \u0026lt;- matrix(sample.int(n, n*R, TRUE), nrow=n, ncol=R) # THIS FUNCTION NEEDS TO BE PARALELLIZED # EDIT THIS CODE: ans \u0026lt;- lapply(seq_len(R), function(i) { stat(dat[idx[,i], , drop=FALSE]) }) # Converting the list into a matrix ans \u0026lt;- do.call(rbind, ans) return(ans) }  Once you have a version of the my_boot() function that runs on multiple cores, check that it provides accurate results by comparing it to a parametric model:  # Bootstrap of an OLS my_stat \u0026lt;- function(d) coef(lm(y ~ x, data=d)) # DATA SIM set.seed(1) n \u0026lt;- 500; R \u0026lt;- 1e4 x \u0026lt;- cbind(rnorm(n)); y \u0026lt;- x*5 + rnorm(n) # Checking if we get something similar as lm ans0 \u0026lt;- confint(lm(y~x)) ans1 \u0026lt;- my_boot(dat = data.frame(x, y), my_stat, R = R, ncpus = 2L) # You should get something like this t(apply(ans1, 2, quantile, c(.025,.975))) ## 2.5% 97.5% ## (Intercept) -0.1372435 0.05074397 ## x 4.8680977 5.04539763 ans0 ## 2.5 % 97.5 % ## (Intercept) -0.1379033 0.04797344 ## x 4.8650100 5.04883353  Check whether your version actually goes faster when it\u0026rsquo;s run on multiple cores (since this might take a little while to run, we\u0026rsquo;ll use system.time and just run each version once, rather than microbenchmark, which would run each version 100 times, by default):  system.time(my_boot(dat = data.frame(x, y), my_stat, R = 4000, ncpus = 1L)) system.time(my_boot(dat = data.frame(x, y), my_stat, R = 4000, ncpus = 2L))  ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"2910dc657da75a7f30622356ed93be1b","permalink":"https://uscbiostats.github.io/PM566/assignment/09-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/09-lab/","section":"assignment","summary":"Learning goals In this lab, you are expected to learn/put in practice the following skills:\n Evaluate whether a problem can be parallelized or not. Practice with the parallel package.  Problem 1: Vectorization The following functions can be written to be more efficient without using parallel. Write a faster version of each function and show that (1) the outputs are the same as the slow version, and (2) your version is faster.","tags":null,"title":"Lab 9 - HPC","type":"assignment"},{"authors":null,"categories":null,"content":"Here’s your roadmap for the semester!\n  Optional reading material to supplement lecture material  Class materials (slides, in-class activities, etc.) will be added on the day of class  Assignments are due by 11:59 PM on the day they are due     Week 1 (8/30) Introduction to Data Science tools: R, Python, Quarto, Git, command line tools       Week 2 (9/6) GitHub, Version Control, \u0026amp; Reproducible Research       Week 3 (9/13) Exploratory Data Analysis      Week 4 (9/20) Data Visualization       Week 5 (9/27) Data Cleaning and Wrangling      Week 6 (10/4) Text Mining \u0026amp; Sentiment Analysis       Week 7 (10/11) NO CLASS - Fall Recess. Midterm Project        Week 8 (10/18) Scraping, APIs, and Regular Expressions       Week 9 (10/25) (Midterm Project due) High Performance Computing, Cloud Computing      Week 10 (11/1) Managing Big Data, SQL and Non-SQL Languages, Google BigQuery       Week 11 (11/8) Interactive Visualization \u0026amp; Effective Data Communication I      Week 12 (11/15) Interactive Visualization and Effective Data Communication II       Week 13 (11/22) Final project workshop      Week 14 (11/29) NO CLASS - Thanksgiving Holiday         Week 15 (12/6) Final Project Due        \u0026lt;/tbody\u0026gt;   ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"bb4a91b4752025cb43953747d43053c7","permalink":"https://uscbiostats.github.io/PM566/schedule/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/schedule/","section":"","summary":"Here’s your roadmap for the semester!\n  Optional reading material to supplement lecture material  Class materials (slides, in-class activities, etc.) will be added on the day of class  Assignments are due by 11:59 PM on the day they are due     Week 1 (8/30) Introduction to Data Science tools: R, Python, Quarto, Git, command line tools       Week 2 (9/6) GitHub, Version Control, \u0026amp; Reproducible Research       Week 3 (9/13) Exploratory Data Analysis      Week 4 (9/20) Data Visualization       Week 5 (9/27) Data Cleaning and Wrangling      Week 6 (10/4) Text Mining \u0026amp; Sentiment Analysis       Week 7 (10/11) NO CLASS - Fall Recess.","tags":null,"title":"Schedule","type":"page"},{"authors":null,"categories":null,"content":"   PM 566: Introduction to Health Data Science Course Overview Learning Objectives Course Notes Technological Proficiency and Hardware/Software Required Readings and Supplementary Materials Supplementary References Description and Assessment of Assignments Grading Breakdown Assignment Submission Policy Schedule Academic Integrity  Statement on the use of Artificial Intelligence  Students and Disability Accommodations: Support Systems:   PM 566: Introduction to Health Data Science Term: Fall 2023\nTime: Friday 9am - 12:55pm\nLocation: SSB 114\nUnits: 4\n Course Overview This course serves as an introduction to data science with a focus on the acquisition and analysis of real-life data. Students will learn the toolsets needed to 1) create workable and reproducible datasets by accessing, scraping, sampling and cleaning data; 2) conduct exploratory data analysis and data visualization; 3) apply statistical tools to learn from data; and 4) build functions and basic apps. Coding languages R and Python will be used.\n Learning Objectives Through this course, students will become familiar with the techniques used in data science, applied to health-related datasets. Students will learn:\n Programming in R, and associated tools Markdown, Git Data visualization – summarizing data through interpretable summaries Data collection – data scraping, wrangling, cleaning, and sampling Exploratory data analysis – generating hypotheses and building intuition Basic statistical algorithms Building software packages and apps  Prerequisite(s): None\nRecommended Preparation: Undergraduate course in statistics and programming\n Course Notes Lecture notes presented in class will be posted on GitHub.\n Technological Proficiency and Hardware/Software Required Computation using R (downloaded from http://cran.r-project.org), and development tools including Git (https://github.com/) and Markdown will be used throughout the semester.\n Readings and Supplementary Materials R Programming for Data Science, 2019. Roger Peng. https://bookdown.org/rdpeng/rprogdatascience/   Supplementary References R for Data Science, 2017. Garrett Grolemund and Hadley Wickham. http://r4ds.had.co.nz/ Exploratory Data Analysis with R, 2020. Roger Peng. https://bookdown.org/rdpeng/exdata/ Mastering Software Development in R, 2017. Roger Peng, Sean Kross, Brooke Anderson. https://bookdown.org/rdpeng/RProgDA/ R Packages, 2023. Hadley Wickham and Jennifer Bryan. https://r-pkgs.org/ Modern Data Science with R, 2023. Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton. https://mdsr-book.github.io/mdsr3e/   Description and Assessment of Assignments Assignments: There will be 5 assignments given throughout the semester, approximately 1 every 2 weeks. Students may discuss the problems with one another, however, individual solutions must be submitted and copying will not be tolerated. All assignments must be completed in Quarto or R Markdown, and submitted through the Github classes portal of the course. Late assignments will be penalized by 20% for each day past the due date.\nFinal Project: The final project will be to write a report for an analysis applied to a real-world dataset and to create a website that includes interactive visualizations to display data/results. The source code, website files, and PDF report will be uploaded to GitHub.\nLabs: Lab attendance is mandatory and participation in the lab is required and counts as part of the overall lab grade.\n Grading Breakdown   Assignment % of Grade    Labs 20%  Homework (5) 30%  Midterm Exam 20%  Final Project 30%  TOTAL 100%     Assignment Submission Policy Assignments shall be submitted on the Github classroom portal of the course. Late homework assignments will not be accepted without penalty, except when verifiable extenuating circumstances can be demonstrated.\n Schedule   Week 1 (8/30) Introduction to Data Science tools: R, Python, Quarto, Git, command line tools   Week 2 (9/6) GitHub, Version Control, \u0026amp; Reproducible Research   Week 3 (9/13) Exploratory Data Analysis   Week 4 (9/20) Data Visualization   Week 5 (9/27) Data Cleaning and Wrangling   Week 6 (10/4) Text Mining \u0026amp; Sentiment Analysis   Week 7 (10/11) NO CLASS - Fall Recess. Midterm Project   Week 8 (10/18) Scraping, APIs, and Regular Expressions   Week 9 (10/25) (Midterm Project due) High Performance Computing, Cloud Computing   Week 10 (11/1) Managing Big Data, SQL and Non-SQL Languages, Google BigQuery   Week 11 (11/8) Interactive Visualization \u0026amp; Effective Data Communication I   Week 12 (11/15) Interactive Visualization and Effective Data Communication II   Week 13 (11/22) Final project workshop   Week 14 (11/29) NO CLASS - Thanksgiving Holiday   Week 15 (12/6) Final Project Due    As the weeks go by, consult the Schedule Page for more information on weekly topics, problem sets, readings, and other materials. The schedule is likely to change as we go. Links to readings, assignments, and other materials from class will be posted on that page.\n Academic Integrity The University of Southern California is foremost a learning community committed to fostering successful scholars and researchers dedicated to the pursuit of knowledge and the transmission of ideas. Academic misconduct is in contrast to the university’s mission to educate students through a broad array of first-rank academic, professional, and extracurricular programs and includes any act of dishonesty in the submission of academic work (either in draft or final form).\nThis course will follow the expectations for academic integrity as stated in the USC Student Handbook. All students are expected to submit assignments that are original work and prepared specifically for the course/section in this academic term. You may not submit work written by others or “recycle” work prepared for other courses without obtaining written permission from the instructor(s). Students suspected of engaging in academic misconduct will be reported to the Office of Academic Integrity.\nOther violations of academic misconduct include, but are not limited to, cheating, plagiarism, fabrication (e.g., falsifying data), knowingly assisting others in acts of academic dishonesty, and any act that gains or is intended to gain an unfair academic advantage.\nThe impact of academic dishonesty is far-reaching and is considered a serious offense against the university and could result in outcomes such as failure on the assignment, failure in the course, suspension, or even expulsion from the university.\nFor more information about academic integrity see the student handbook or the Office of Academic Integrity’s website, and university policies on Research and Scholarship Misconduct.\nStatement on the use of Artificial Intelligence Generative artificial intelligence (AI) may be used under the direction and rules specified by the course instructor in specific circumstances as outlined in the syllabus. The student is responsible for the quality and content of all written assignments. Unless otherwise indicated by the course instructor, generative AI may be used to create an initial literature review, document outline and/or to organize material toward a first draft of a class paper, proofreading, or grammatical accuracy; however the final content of the written document and critical thinking of the ideas presented in the document must represent the student’s individual work and ideas learned through course content and/or research conducted from sources outside of the generative AI system. The student must include an annotation on all materials submitted that explicitly documents how AI was used to generate the document and properly reference both the sources and the AI tools such as ChatGPT (OpenAI, 2023). The student must review the information in the document and edit for accuracy, completeness, proper grammar, and demonstrate that the wording accurately reflects the student’s understanding and purpose in writing the text. Students should be aware that text generated solely from AI generators may include factual errors, bias, and may contain incomplete or inaccurate reference information, in addition to furthering appropriating knowledge produced by historically marginalized scholars without proper crediting. If you have any questions on whether a specific AI tool is allowed for any aspect of your work in this class, please ask your instructor for guidance. Failure to ensure agreement with your instructor on use of AI, prior to doing so, may result in a zero score. (NOTE: instructors have sophisticated tools to determine AI plagiarism.)\n  Students and Disability Accommodations: USC welcomes students with disabilities into all of the University’s educational programs. The Office of Student Accessibility Services (OSAS) is responsible for the determination of appropriate accommodations for students who encounter disability-related barriers. Once a student has completed the OSAS process (registration, initial appointment, and submitted documentation) and accommodations are determined to be reasonable and appropriate, a Letter of Accommodation (LOA) will be available to generate for each course. The LOA must be given to each course instructor by the student and followed up with a discussion. This should be done as early in the semester as possible as accommodations are not retroactive. More information can be found at http://osas.usc.edu. You may contact OSAS at (213) 740-0776 or via email at osasfrontdesk@usc.edu.\n Support Systems: Counseling and Mental Health - (213) 740-9355 – 24/7 on call\nhttps://studenthealth.usc.edu/counseling/\nFree and confidential mental health treatment for students, including short-term psychotherapy, group counseling, stress fitness workshops, and crisis intervention.\nNational Suicide Prevention Lifeline - dial 988 – 24/7 on call\nhttp://www.suicidepreventionlifeline.org\nProvides free and confidential emotional support to people in suicidal crisis or emotional distress 24 hours a day, 7 days a week.\nRelationship and Sexual Violence Prevention Services (RSVP) - (213) 740-9355(WELL), press “0” after hours – 24/7 on call\nhttps://studenthealth.usc.edu/sexual-assault\nFree and confidential therapy services, workshops, and training for situations related to gender-based harm.\nOffice for Equity, Equal Opportunity, and Title IX (EEO-TIX) - (213) 740-5086\nhttp://eeotix.usc.edu\nInformation about how to get help or help someone affected by harassment or discrimination, rights of protected classes, reporting options, and additional resources for students, faculty, staff, visitors, and applicants.\nReporting Incidents of Bias or Harassment - (213) 740-5086 or (213) 821-8298\nhttp://usc-advocate.symplicity.com/care_report\nAvenue to report incidents of bias, hate crimes, and microaggressions to the Office for Equity, Equal Opportunity, and Title for appropriate investigation, supportive measures, and response.\nThe Office of Student Accessibility Services (OSAS) - (213) 740-0776\nhttp://osas.usc.edu\nOSAS ensures equal access for students with disabilities through providing academic accommodations and auxiliary aids in accordance with federal laws and university policy.\nUSC Campus Support and Intervention - (213) 821-4710\nhttp://campussupport.usc.edu\nAssists students and families in resolving complex personal, financial, and academic issues adversely affecting their success as a student.\nDiversity, Equity and Inclusion - (213) 740-2101\nhttp://diversity.usc.edu\nInformation on events, programs and training, the Provost’s Diversity and Inclusion Council, Diversity Liaisons for each academic school, chronology, participation, and various resources for students.\nUSC Emergency - UPC: (213) 740-4321, HSC: (323) 442-1000 – 24/7 on call\nhttp://dps.usc.edu, http://emergency.usc.edu\nEmergency assistance and avenue to report a crime. Latest updates regarding safety, including ways in which instruction will be continued if an officially declared emergency makes travel to campus infeasible.\nUSC Department of Public Safety - UPC: (213) 740-6000, HSC: (323) 442-1200 – 24/7 on call\nhttp://dps.usc.edu\nNon-emergency assistance or information.\nOffice of the Ombuds - (213) 821-9556 (UPC) / (323-442-0382 (HSC)\nhttp://ombuds.usc.edu\nA safe and confidential place to share your USC-related issues with a University Ombuds who will work with you to explore options or paths to manage your concern.\nOccupational Therapy Faculty Practice - (323) 442-3340 or\notfp@med.usc.edu, http://chan.usc.edu/otfp\nConfidential Lifestyle Redesign services for USC students to support health promoting habits and routines that enhance quality of life and academic performance.\n ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"e4d5a4a79239f08c6ad0d7cbf1be756c","permalink":"https://uscbiostats.github.io/PM566/syllabus/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/syllabus/","section":"","summary":"PM 566: Introduction to Health Data Science Course Overview Learning Objectives Course Notes Technological Proficiency and Hardware/Software Required Readings and Supplementary Materials Supplementary References Description and Assessment of Assignments Grading Breakdown Assignment Submission Policy Schedule Academic Integrity  Statement on the use of Artificial Intelligence  Students and Disability Accommodations: Support Systems:   PM 566: Introduction to Health Data Science Term: Fall 2023\nTime: Friday 9am - 12:55pm","tags":null,"title":"Syllabus","type":"page"},{"authors":null,"categories":null,"content":"Required reading You are expected to have read this when you show up for the session.\n   Chapter 1 Why Git? Why GitHub? of the “Happy Git and GitHub for the useR” by Jenny Bryan and Jim Hester.\n  Roger Peng’s Mastering Software Development Book Section 3.8 Version control and Github (link)\n  You are also expected to have git installed in your system and having a GitHub account. For this see here, here and here.\nOptional reading  Chapter one of the Pro git book will give you a more technical overview of what’s git and what happens under-the-hood.  ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"eff19a3a562a8cf333f635177dcf87a8","permalink":"https://uscbiostats.github.io/PM566/reading/02-reading/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/reading/02-reading/","section":"reading","summary":"Required reading You are expected to have read this when you show up for the session.\n   Chapter 1 Why Git? Why GitHub? of the “Happy Git and GitHub for the useR” by Jenny Bryan and Jim Hester.\n  Roger Peng’s Mastering Software Development Book Section 3.8 Version control and Github (link)\n  You are also expected to have git installed in your system and having a GitHub account.","tags":null,"title":"Version control and reproducible research","type":"reading"}]
\ No newline at end of file
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them.","tags":null,"title":"Reading","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise To be announced\nSlides To be announced\nLinks To be announced\n","date":1701993600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1701993600,"objectID":"b19988fbc4a596745287cbd320c34ce7","permalink":"https://uscbiostats.github.io/PM566/class/14-class/","publishdate":"2023-12-08T00:00:00Z","relpermalink":"/PM566/class/14-class/","section":"class","summary":"Lab Exercise To be announced\nSlides To be announced\nLinks To be announced","tags":null,"title":"Final Project","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise To be announced\nSlides To be announced\nLinks To be 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https://www.w3schools.com/sql/\n","date":1698364800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1698364800,"objectID":"1a77c377ec266054be4f99d55ca51959","permalink":"https://uscbiostats.github.io/PM566/class/10-class/","publishdate":"2023-10-27T00:00:00Z","relpermalink":"/PM566/class/10-class/","section":"class","summary":"Lab Exercise  link\nSlides  link\nLinks  https://www.w3schools.com/sql/","tags":null,"title":"SQL","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise  link\nSlides  link\nLinks  CRAN Task View on High-Performance Computing (link) USC’s Center for Advance Research Computing (CARC) (link)  ","date":1697760000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1697760000,"objectID":"ac13c8c9f9cbf2a9e59eb2af5882f72b","permalink":"https://uscbiostats.github.io/PM566/class/09-class/","publishdate":"2023-10-20T00:00:00Z","relpermalink":"/PM566/class/09-class/","section":"class","summary":"Lab Exercise  link\nSlides  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announced\n","date":1694131200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1694131200,"objectID":"0ac6b606b4526ab8d78934908811a979","permalink":"https://uscbiostats.github.io/PM566/class/03-class/","publishdate":"2023-09-08T00:00:00Z","relpermalink":"/PM566/class/03-class/","section":"class","summary":"Lab Exercise  link\nSlides  link\nLinks To be announced","tags":null,"title":"Exploratory data analysis","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise  link\nSlides  link\nLinks   Git  GitHub  ","date":1693526400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1693526400,"objectID":"d8f16c438fb963107e4972dd50006da1","permalink":"https://uscbiostats.github.io/PM566/class/02-class/","publishdate":"2023-09-01T00:00:00Z","relpermalink":"/PM566/class/02-class/","section":"class","summary":"Lab Exercise  link\nSlides  link\nLinks   Git  GitHub  ","tags":null,"title":"Version control and reproducible research","type":"docs"},{"authors":null,"categories":null,"content":"Lab Exercise  link\n Download Template (click the “download” icon on the right, near the “Raw” button)\nSlides  link\nLinks   The R Project for Statistical Computing  R Studio  ","date":1692921600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1692921600,"objectID":"cbd2b035124ee81c843cf99929c787d5","permalink":"https://uscbiostats.github.io/PM566/class/01-class/","publishdate":"2023-08-25T00:00:00Z","relpermalink":"/PM566/class/01-class/","section":"class","summary":"Lab Exercise  link\n Download Template (click the “download” icon on the right, near the “Raw” button)\nSlides  link\nLinks   The R Project for Statistical Computing  R Studio  ","tags":null,"title":"Look at Data / Up and Running in R and ggplot","type":"docs"},{"authors":null,"categories":null,"content":"Due date: Friday December 6th, 2024, by 11:59pm Pacific Time.\nLearning Objective: To apply the skills learned in PM 566 by analyzing and interpreting a dataset of your choice.\nNarrative: Through this project you will launch a portfolio of data science projects that will become seminal for your job hunt.\nUsing the dataset from your midterm, make sure you have formulated a clear and concise question to answer. You will apply the skills learned throughout the semester to answer this question.\nDeliverables  Written report (single spaced, 6-8 pages where 3-4 pages are tables and figures) Website (summarize your analysis, include interactive figures, provide a link to download the full written report)  Written report The actual analysis should be included in the PDF report. The PDF report can refer to interactive visualizations included in the website, but otherwise all figures and tables should be included in the PDF.\nThe report should have the following sections (elaborate from what was written in the midterm): Introduction (provide background on your dataset and formulated question), Methods (include how and where the data were acquired, how you cleaned and wrangled the data, what tools you used for data exploration), Results (provide final, publication ready tables and figures from your analysis, make separate subsections as needed), and a brief Conclusion and Summary where you describe your findings.\nIn your report, please do not include any code (so make sure echo = FALSE), unformatted output, or dataset summaries (e.g. output from head(), str(), etc.)\nWebsite Checklist See the checklist below for additional details about the website requirements:\n  Create a website (HTML document and all the required files, including figures). It should feature:\n  A brief description of the project,\n  Interactive visualizations, also with a description so that people can understand what they are looking at, and\n  A link to the PDF version of the actual report (i.e. a link to “Download the report.”).\n  Your home page should feature no more than five interactive tables and figures. If you want to include more to showcase your skills, we request you do so by adding extra pages to the website. We will only evaluate the homepage of the website.\n    Upload everything, source code, website files, and PDF report, to the GitHub repository.\n  Make sure that the website, which is to be hosted in GitHub pages, actually works, i.e., figures and interactive visualizations are properly rendered when visiting the website.\n  Have a README.md file in the upper level of the repository. This file provides general information about the project, like title, brief description, etc.\n  The README.md file should provide a link to the website, e.g. https://USERNAME.github.io/pm566-project.\n  Have a data folder with either the dataset or instructions about how to acquire it. You should provide the instructions in a README.md file within that folder.\n  ","date":1732233600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1732233600,"objectID":"915965f8a000fc393c5e18173dbf000b","permalink":"https://uscbiostats.github.io/PM566/assignment/14-final/","publishdate":"2024-11-22T00:00:00Z","relpermalink":"/PM566/assignment/14-final/","section":"assignment","summary":"Due date: Friday December 6th, 2024, by 11:59pm Pacific Time.\nLearning Objective: To apply the skills learned in PM 566 by analyzing and interpreting a dataset of your choice.\nNarrative: Through this project you will launch a portfolio of data science projects that will become seminal for your job hunt.\nUsing the dataset from your midterm, make sure you have formulated a clear and concise question to answer. You will apply the skills learned throughout the semester to answer this question.","tags":null,"title":"Final Project","type":"assignment"},{"authors":null,"categories":null,"content":"Due Date This assignment is due by 11:59pm Pacific Time, November 22nd, 2024.\nThe learning objectives are to write faster code for computational task requiring a loop and to implement some queries and basic data wrangling in SQL.\nHPC Make things run faster Rewrite the following R functions to make them faster. It is OK (and recommended) to take a look at StackOverflow and Google\n# Total row sums fun1 \u0026lt;- function(mat) { n \u0026lt;- nrow(mat) ans \u0026lt;- double(n) for (i in 1:n) { ans[i] \u0026lt;- sum(mat[i, ]) } ans } fun1alt \u0026lt;- function(mat) { # YOUR CODE HERE } # Cumulative sum by row fun2 \u0026lt;- function(mat) { n \u0026lt;- nrow(mat) k \u0026lt;- ncol(mat) ans \u0026lt;- mat for (i in 1:n) { for (j in 2:k) { ans[i,j] \u0026lt;- mat[i, j] + ans[i, j - 1] } } ans } fun2alt \u0026lt;- function(mat) { # YOUR CODE HERE }  Question 1 Using the dataset generated below (dat), check that the output of both of your new functions matches the output of the original functions. Then use microbenchmark to check that your version is actually faster.\n# Use the data with this code set.seed(2315) dat \u0026lt;- matrix(rnorm(200 * 100), nrow = 200) # Test for the first microbenchmark::microbenchmark( fun1(dat), fun1alt(dat), unit = \u0026quot;relative\u0026quot; ) # Test for the second microbenchmark::microbenchmark( fun2(dat), fun2alt(dat), unit = \u0026quot;relative\u0026quot; )  Make things run faster with parallel computing The following function allows simulating pi:\nsim_pi \u0026lt;- function(n = 1000, i = NULL) { p \u0026lt;- matrix(runif(n*2), ncol = 2) mean(rowSums(p^2) \u0026lt; 1) * 4 } # Here is an example of the run set.seed(156) sim_pi(1000) # 3.132  In order to get accurate estimates, we can run this function multiple times, with the following code:\n# This runs the simulation a 4,000 times, each with 10,000 points set.seed(1231) system.time({ ans \u0026lt;- unlist(lapply(1:4000, sim_pi, n = 10000)) print(mean(ans)) })  Question 2 Rewrite the previous code using parLapply() (or your parallelization method of choice) to parallelize it. Run the code once, using system.time(), to show that your version is faster.\n# YOUR CODE HERE system.time({ # YOUR CODE HERE ans \u0026lt;- # YOUR CODE HERE print(mean(ans)) # YOUR CODE HERE })  SQL Setup a temporary database by running the following chunk\n# install.packages(c(\u0026quot;RSQLite\u0026quot;, \u0026quot;DBI\u0026quot;)) library(RSQLite) library(DBI) # Initialize a temporary in memory database con \u0026lt;- dbConnect(SQLite(), \u0026quot;:memory:\u0026quot;) # Download tables film \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/film.csv\u0026quot;) film_category \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/film_category.csv\u0026quot;) category \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/category.csv\u0026quot;) # Copy data.frames to database dbWriteTable(con, \u0026quot;film\u0026quot;, film) dbWriteTable(con, \u0026quot;film_category\u0026quot;, film_category) dbWriteTable(con, \u0026quot;category\u0026quot;, category)  When you write a new chunk, remember to replace the r with sql, connection=con. Some of these questions will require you to use an inner join. Read more about them here https://www.w3schools.com/sql/sql_join_inner.asp\nQuestion 3 How many many movies are available in each rating category?\nQuestion 4 What is the average replacement cost and rental rate for each rating category?\nQuestion 5 Use table film_category together with film to find how many films there are with each category ID.\nQuestion 6 Incorporate the category table into the answer to the previous question to find the name of the most popular category.\n","date":1731024000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1731024000,"objectID":"bb9dff9b6c32caa35a8d0693f3d633ce","permalink":"https://uscbiostats.github.io/PM566/assignment/11-hw4/","publishdate":"2024-11-08T00:00:00Z","relpermalink":"/PM566/assignment/11-hw4/","section":"assignment","summary":"Due Date This assignment is due by 11:59pm Pacific Time, November 22nd, 2024.\nThe learning objectives are to write faster code for computational task requiring a loop and to implement some queries and basic data wrangling in SQL.\nHPC Make things run faster Rewrite the following R functions to make them faster. It is OK (and recommended) to take a look at StackOverflow and Google\n# Total row sums fun1 \u0026lt;- function(mat) { n \u0026lt;- nrow(mat) ans \u0026lt;- double(n) for (i in 1:n) { ans[i] \u0026lt;- sum(mat[i, ]) } ans } fun1alt \u0026lt;- function(mat) { # YOUR CODE HERE } # Cumulative sum by row fun2 \u0026lt;- function(mat) { n \u0026lt;- nrow(mat) k \u0026lt;- ncol(mat) ans \u0026lt;- mat for (i in 1:n) { for (j in 2:k) { ans[i,j] \u0026lt;- mat[i, j] + ans[i, j - 1] } } ans } fun2alt \u0026lt;- function(mat) { # YOUR CODE HERE }  Question 1 Using the dataset generated below (dat), check that the output of both of your new functions matches the output of the original functions.","tags":null,"title":"Assignment 04 - HPC and SQL","type":"assignment"},{"authors":null,"categories":null,"content":"Due Date This assignment is due by 11:59pm Pacific Time, November 8th, 2024.\nText Mining A new dataset has been added to the data science data repository https://github.com/USCbiostats/data-science-data/tree/master/03_pubmed. The dataset contains 3,241 abstracts from articles collected via 5 PubMed searches. The search terms are listed in the second column, term and these will serve as the \u0026ldquo;documents.\u0026rdquo; Your job is to analyse these abstracts to find interesting insights.\n Tokenize the abstracts and count the number of each token. Do you see anything interesting? Does removing stop words change what tokens appear as the most frequent? What are the 5 most common tokens for each search term after removing stopwords? Tokenize the abstracts into bigrams. Find the 10 most common bigrams and visualize them with ggplot2. Calculate the TF-IDF value for each word-search term combination (here you want the search term to be the \u0026ldquo;document\u0026rdquo;). What are the 5 tokens from each search term with the highest TF-IDF value? How are the results different from the answers you got in question 1?  ","date":1729814400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1729814400,"objectID":"c5b38245189a89adfe25370d23380195","permalink":"https://uscbiostats.github.io/PM566/assignment/09-hw3/","publishdate":"2024-10-25T00:00:00Z","relpermalink":"/PM566/assignment/09-hw3/","section":"assignment","summary":"Due Date This assignment is due by 11:59pm Pacific Time, November 8th, 2024.\nText Mining A new dataset has been added to the data science data repository https://github.com/USCbiostats/data-science-data/tree/master/03_pubmed. The dataset contains 3,241 abstracts from articles collected via 5 PubMed searches. The search terms are listed in the second column, term and these will serve as the \u0026ldquo;documents.\u0026rdquo; Your job is to analyse these abstracts to find interesting insights.\n Tokenize the abstracts and count the number of each token.","tags":null,"title":"Assignment 03 - Text Mining","type":"assignment"},{"authors":null,"categories":null,"content":"Due Sunday, October 27th, 2024 by 11:59pm Pacific Time.\nLearning Objective: To apply the skills learned in PM 566 (through Week 6) by analyzing and interpreting a dataset of your choice.\nNarrative: Through this project you will launch a portfolio of data science projects that will become seminal for your job hunt. This midterm is a stepping stone for the final project. The first step in any data analysis is to have a dataset for which you have formulated an interesting question. If you do not have a dataset to work with, you may choose one from our list of suggestions. With your dataset, formulate a clear and concise question to answer and conduct data wrangling, exploratory data analysis, and data visualization to explore/answer this question.\nDeliverable: A knitted Quarto/R markdown written report (HTML or PDF) with embedded tables and figures that is submitted to a project-specific github repository that you create. The report should have the following sections: Introduction (provide background on your dataset and formulated question), Methods (include how and where the data were acquired, how you cleaned and wrangled the data, what tools you used for data exploration), Preliminary Results (provide summary statistics in tabular form and publication-quality figures, take a look at the kable function from knitr to write nice tables in Rmarkdown), and a brief Conclusion about what you found in terms of the formulated question.\nIn your report, please do not include unformatted output or dataset summaries (e.g. output from head(), str(), etc.). You should summarize these aspects of your data within the text.\n","date":1728604800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1728604800,"objectID":"5606a4bc71acb3181d0674120a22b374","permalink":"https://uscbiostats.github.io/PM566/assignment/08-midterm/","publishdate":"2024-10-11T00:00:00Z","relpermalink":"/PM566/assignment/08-midterm/","section":"assignment","summary":"Due Sunday, October 27th, 2024 by 11:59pm Pacific Time.\nLearning Objective: To apply the skills learned in PM 566 (through Week 6) by analyzing and interpreting a dataset of your choice.\nNarrative: Through this project you will launch a portfolio of data science projects that will become seminal for your job hunt. This midterm is a stepping stone for the final project. The first step in any data analysis is to have a dataset for which you have formulated an interesting question.","tags":null,"title":"Midterm Exam/Project","type":"assignment"},{"authors":null,"categories":null,"content":"Due Date This assignment is due by 11:59pm Pacific Time, October 11th, 2024.\nFor this assignment, we will be analyzing data from USC’s Children’s Health Study. The learning objectives are to conduct data wrangling and visualize the data with key questions in mind.\nData Wrangling You will need to download two datasets from https://github.com/USCbiostats/data-science-data. The individual and regional CHS datasets in 01_chs. The individual data includes personal and health characteristics of children in 12 communities across Southern California. The regional data include air quality measurements at the community level. Once downloaded, you can merge these datasets using the location variable. Once combined, you will need to do the following:\n  After merging the data, make sure you don’t have any duplicates by counting the number of rows. Make sure it matches.\nIn the case of missing values, impute data using the average amongst individuals with the same values for the \u0026ldquo;male\u0026rdquo; and \u0026ldquo;hispanic\u0026rdquo; variables. For categorical variables, take the mode. If you are interested (and feel adventurous) in the theme of Data Imputation, take a look at this paper on \u0026ldquo;Multiple Imputation\u0026rdquo; using the Amelia R package here.\n  Create a new categorical variable named “obesity_level” using the BMI measurement (underweight BMI\u0026lt;14; normal BMI 14-22; overweight BMI 22-24; obese BMI\u0026gt;24). To make sure the variable is rightly coded, create a summary table that contains the minimum BMI, maximum BMI, and the total number of observations per category.\n  Create another categorical variable named \u0026ldquo;smoke_gas_exposure\u0026rdquo; that summarizes \u0026ldquo;Second Hand Smoke\u0026rdquo; and \u0026ldquo;Gas Stove.\u0026rdquo; The variable should have four categories in total.\n  Create four summary tables showing the average (or proportion, if binary) and sd of “Forced expiratory volume in 1 second (ml)” (an asthma indicator) by town, sex, obesity level, and \u0026ldquo;smoke_gas_exposure.\u0026rdquo;\n  Looking at the Data (EDA) The primary questions of interest are:\n What is the association between BMI and FEV (forced expiratory volume)? What is the association between smoke and gas exposure and FEV? What is the association between PM2.5 exposure and FEV?  Follow the EDA checklist from week 3 and the previous assignment. Be sure to focus on the key variables.\nVisualization Create the following figures and interpret them. Be sure to include easily understandable axes, titles, and legends.\n Facet plot showing scatterplots with regression lines of BMI vs FEV by “townname”. Stacked histograms of FEV by BMI category and FEV by smoke/gas exposure. Use different color schemes than the ggplot default. Barchart of BMI by smoke/gas exposure. Statistical summary graphs of FEV by BMI and FEV by smoke/gas exposure category. A leaflet map showing the concentrations of PM2.5 mass in each of the CHS communities. Choose a visualization to examine whether PM2.5 mass is associated with FEV.  ","date":1727395200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1727395200,"objectID":"9499827750596518aa6839e0f7343f77","permalink":"https://uscbiostats.github.io/PM566/assignment/05-hw2/","publishdate":"2024-09-27T00:00:00Z","relpermalink":"/PM566/assignment/05-hw2/","section":"assignment","summary":"Due Date This assignment is due by 11:59pm Pacific Time, October 11th, 2024.\nFor this assignment, we will be analyzing data from USC’s Children’s Health Study. The learning objectives are to conduct data wrangling and visualize the data with key questions in mind.\nData Wrangling You will need to download two datasets from https://github.com/USCbiostats/data-science-data. The individual and regional CHS datasets in 01_chs. The individual data includes personal and health characteristics of children in 12 communities across Southern California.","tags":null,"title":"Assignment 02 - Data Viz and Wrangling","type":"assignment"},{"authors":null,"categories":null,"content":"Due Date This assignment is due by 11:59pm Pacific Time, September 27th, 2024.\nLearning Goals  Download, read, and get familiar with an external dataset. Step through the EDA \u0026ldquo;checklist\u0026rdquo; presented in class Practice making exploratory plots  Assignment Description We will work with air pollution data from the U.S. Environmental Protection Agency (EPA). The EPA has a national monitoring network of air pollution sites that The primary question you will answer is whether daily concentrations of PM$_{2.5}$ (particulate matter air pollution with aerodynamic diameter less than 2.5 \\(\\mu\\)m) have decreased in California over the last 20 years (from 2002 to 2022).\nA primer on particulate matter air pollution can be found here.\nYour assignment should be completed in Quarto or R Markdown.\nSteps   Given the formulated question from the assignment description, you will now conduct EDA Checklist items 2-4. First, download 2002 and 2022 data for all sites in California from the EPA Air Quality Data website. Read in the data using data.table(). For each of the two datasets, check the dimensions, headers, footers, variable names and variable types. Check for any data issues, particularly in the key variable we are analyzing. Make sure you write up a summary of all of your findings.\n  Combine the two years of data into one data frame. Use the Date variable to create a new column for year, which will serve as an identifier. Change the names of the key variables so that they are easier to refer to in your code.\n  Create a basic map in leaflet() that shows the locations of the sites (make sure to use different colors for each year). Summarize the spatial distribution of the monitoring sites.\n  Check for any missing or implausible values of PM$_{2.5}$ in the combined dataset. Explore the proportions of each and provide a summary of any temporal patterns you see in these observations.\n  Explore the main question of interest at three different spatial levels. Create exploratory plots (e.g. boxplots, histograms, line plots) and summary statistics that best suit each level of data. Be sure to write up explanations of what you observe in these data.\n state county sites in Los Angeles     This homework has been adapted from the case study in Roger Peng\u0026rsquo;s Exploratory Data Analysis with R\n","date":1726185600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1726185600,"objectID":"590007c4654c7f5d368d9a80c65beeea","permalink":"https://uscbiostats.github.io/PM566/assignment/03-hw1/","publishdate":"2024-09-13T00:00:00Z","relpermalink":"/PM566/assignment/03-hw1/","section":"assignment","summary":"Due Date This assignment is due by 11:59pm Pacific Time, September 27th, 2024.\nLearning Goals  Download, read, and get familiar with an external dataset. Step through the EDA \u0026ldquo;checklist\u0026rdquo; presented in class Practice making exploratory plots  Assignment Description We will work with air pollution data from the U.S. Environmental Protection Agency (EPA). The EPA has a national monitoring network of air pollution sites that The primary question you will answer is whether daily concentrations of PM$_{2.","tags":null,"title":"Assignment 01 - Exploratory Data Analysis","type":"assignment"},{"authors":null,"categories":null,"content":"Due Date This assignment is due by midnight Pacific Time, December 1st, 2023.\nInteractive Visualization I and II   Using your midterm/final project data, create 3 different interactive visualizations using the tools learned in week 11 class/lab.\n  Include the visualizations created in (1) on your final project website hosted on GitHub Pages.\n  Rename your final project website by modifying the relevant text in the .yml configuration file and the YAML header in the index.Rmd file. You may also modify the name of your GitHub repo so that the website url also provides a descriptive name.\n  Extra Credit: Create content across 2 pages on your website. You can use the example content from Lab 12 as an example.\n","date":1700784000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1700784000,"objectID":"d52385349ae5b2e8c3f9a4325925001c","permalink":"https://uscbiostats.github.io/PM566/assignment/13-hw5/","publishdate":"2023-11-24T00:00:00Z","relpermalink":"/PM566/assignment/13-hw5/","section":"assignment","summary":"Due Date This assignment is due by midnight Pacific Time, December 1st, 2023.\nInteractive Visualization I and II   Using your midterm/final project data, create 3 different interactive visualizations using the tools learned in week 11 class/lab.\n  Include the visualizations created in (1) on your final project website hosted on GitHub Pages.\n  Rename your final project website by modifying the relevant text in the .yml configuration file and the YAML header in the index.","tags":null,"title":"Assignment 05 - Interactive Visualization I and II","type":"assignment"},{"authors":null,"categories":null,"content":"Required reading To be announced\nOptional reading To be announced\n","date":1669766400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1669766400,"objectID":"b99ea743a9cce93a77ae79561642096a","permalink":"https://uscbiostats.github.io/PM566/reading/13-reading/","publishdate":"2022-11-30T00:00:00Z","relpermalink":"/PM566/reading/13-reading/","section":"reading","summary":"Required reading To be announced\nOptional reading To be announced","tags":null,"title":"Final project workshop: review project progress, preliminary presentations","type":"reading"},{"authors":null,"categories":null,"content":"Required reading We will be developing a website using rmarkdown’s site generator. Please read:\n   R Markdown: The Definitive Guide, Yihui Xie, J. J. Allaire, Garrett Grolemund\n   Chapter 10.5: Websites in rmarkdown’s site generator\n  Optional reading   For a helpful tutorial on creating a website using rmarkdown, see: Creating websites in R, Emily C. Zabor\n  For more advanced static website creation using blogdown and the website generators Hugo and Jeckyll see\n   Creating websites with R Markdown, by Yihui Xie, Amber Thomas, Alison Presmanes Hill. Appendix B presents basic information about websites, such as HTML, CSS, and JavaScript.\n   Hugo – can explore themes and operate with them using blogdown.\n  ","date":1668556800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1668556800,"objectID":"995bf726bde285fc49dd3fa3422061de","permalink":"https://uscbiostats.github.io/PM566/reading/12-reading/","publishdate":"2022-11-16T00:00:00Z","relpermalink":"/PM566/reading/12-reading/","section":"reading","summary":"Required reading We will be developing a website using rmarkdown’s site generator. Please read:\n   R Markdown: The Definitive Guide, Yihui Xie, J. J. Allaire, Garrett Grolemund\n   Chapter 10.5: Websites in rmarkdown’s site generator\n  Optional reading   For a helpful tutorial on creating a website using rmarkdown, see: Creating websites in R, Emily C. Zabor\n  For more advanced static website creation using blogdown and the website generators Hugo and Jeckyll see","tags":null,"title":"Interactive visualization and effective data communication II","type":"reading"},{"authors":null,"categories":null,"content":"Required reading You are expected to have read these items before class. Most of these linked sections/chapters are quite short.\nPlotly Please read the following sections of Interactive web-based data visualization with R, plotly, and shiny, by Carson Sievert:\n Creating Views (Plotly features): Sections 1-3, 5-7 Publishing Views: Sections 9-12 Arranging and Animating Views: Sections 13-14 Linking Multiple Data Views: Sections 15 Server-side linking with Shiny: Section 17  Shiny   Introduction to Interactive Documents: provides a quick guide to getting started with Shiny.  Dashboards   Chapter 5: Dashboards (Layout, Components, Shiny) from R Markdown: The Definitive Guide, Yihui Xie, J. J. Allaire, Garrett Grolemund, provides a guide for using Shiny with flexdashboard to create interactive documents from R Markdown  Optional reading Interactive visualization  The official Shiny website includes extensive articles, tutorials, and examples.  Coursera course on developing data products, sessions on Shiny, GoogleVis, and Plotly  Data visualization best practices   Data Visualization: A Practical Introduction, Kieran Healy  Fundamentals of Data Visualization, Claus O. Wilke  ","date":1667952000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1667952000,"objectID":"8e0343f063c60970b2ee0db51d983550","permalink":"https://uscbiostats.github.io/PM566/reading/11-reading/","publishdate":"2022-11-09T00:00:00Z","relpermalink":"/PM566/reading/11-reading/","section":"reading","summary":"Required reading You are expected to have read these items before class. Most of these linked sections/chapters are quite short.\nPlotly Please read the following sections of Interactive web-based data visualization with R, plotly, and shiny, by Carson Sievert:\n Creating Views (Plotly features): Sections 1-3, 5-7 Publishing Views: Sections 9-12 Arranging and Animating Views: Sections 13-14 Linking Multiple Data Views: Sections 15 Server-side linking with Shiny: Section 17  Shiny   Introduction to Interactive Documents: provides a quick guide to getting started with Shiny.","tags":null,"title":"Interactive visualization and effective data communication I","type":"reading"},{"authors":null,"categories":null,"content":"Required reading You are expected to have read this when you show up for the session:\n  Take a look at the High Performance Computing CRAN task View ( link)\n  Chapter 22 of RD Peng’s R Programming for Data Science ( link)\n  Optional reading TBD\n","date":1666742400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1666742400,"objectID":"cb6e89c2506f4879aa8a94abb8ddd1c5","permalink":"https://uscbiostats.github.io/PM566/reading/09-reading/","publishdate":"2022-10-26T00:00:00Z","relpermalink":"/PM566/reading/09-reading/","section":"reading","summary":"Required reading You are expected to have read this when you show up for the session:\n  Take a look at the High Performance Computing CRAN task View ( link)\n  Chapter 22 of RD Peng’s R Programming for Data Science ( link)\n  Optional reading TBD","tags":null,"title":"High performance computing, cloud computing","type":"reading"},{"authors":null,"categories":null,"content":"Required reading   Chapter 17, Regular Expressions, from “R Programming for Data Science” ( link)\n  The vignette from the R package rvest: “Harvesting the web with rvest” ( link)\n  This post “HTTP: The Protocol Every Web Developer Must Know - Part 1”, excluding the section “Tools to View HTTP Traffic” link.\n  Optional reading  A Full course on APIs by Zapier ( link) R for Data Science chapter on Strings (includes a discussion of the stringr package, which we will be using) ( link)  ","date":1664928000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1664928000,"objectID":"5338e944486d33d9f0da8560c0f2991d","permalink":"https://uscbiostats.github.io/PM566/reading/07-reading/","publishdate":"2022-10-05T00:00:00Z","relpermalink":"/PM566/reading/07-reading/","section":"reading","summary":"Required reading   Chapter 17, Regular Expressions, from “R Programming for Data Science” ( link)\n  The vignette from the R package rvest: “Harvesting the web with rvest” ( link)\n  This post “HTTP: The Protocol Every Web Developer Must Know - Part 1”, excluding the section “Tools to View HTTP Traffic” link.\n  Optional reading  A Full course on APIs by Zapier ( link) R for Data Science chapter on Strings (includes a discussion of the stringr package, which we will be using) ( link)  ","tags":null,"title":"Scraping, APIs, and Regular Expressions","type":"reading"},{"authors":null,"categories":null,"content":"Required reading   Chapter 14 or R for Data Science by Garrett Grolemund and Hadley Wickham.  Chapter 1 of Text Mining with R by Julia Silge and David Robinson.  Optional reading If you want more indept information about what is happening with regards to using text as data, tokenization, stopwords etc. Then you can look at the first 4 chapters of Supervised Machine Learning for Text Analysis in R by Emil Hvitfeldt and Julia Silge.\n","date":1664323200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1664323200,"objectID":"df2711c67a6e0014f1ff7731e4b038b6","permalink":"https://uscbiostats.github.io/PM566/reading/06-reading/","publishdate":"2022-09-28T00:00:00Z","relpermalink":"/PM566/reading/06-reading/","section":"reading","summary":"Required reading   Chapter 14 or R for Data Science by Garrett Grolemund and Hadley Wickham.  Chapter 1 of Text Mining with R by Julia Silge and David Robinson.  Optional reading If you want more indept information about what is happening with regards to using text as data, tokenization, stopwords etc. Then you can look at the first 4 chapters of Supervised Machine Learning for Text Analysis in R by Emil Hvitfeldt and Julia Silge.","tags":null,"title":"Text Mining","type":"reading"},{"authors":null,"categories":null,"content":"Required reading You are expected to have read this when you show up for the session.\n  The Introduction to data.table vignette included in the data.table R package\n   “Chapter 5: Data Transformation” of the book R for Data Science\n  Optional reading   We recommend you going through the instructions to download Python here and installing jupyter here.\n  While we will not be using Pandas, here is a neat short intro to it.\n  ","date":1663718400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1663718400,"objectID":"25a92a1f3c48ccf25100c7abb1371dd0","permalink":"https://uscbiostats.github.io/PM566/reading/05-reading/","publishdate":"2022-09-21T00:00:00Z","relpermalink":"/PM566/reading/05-reading/","section":"reading","summary":"Required reading You are expected to have read this when you show up for the session.\n  The Introduction to data.table vignette included in the data.table R package\n   “Chapter 5: Data Transformation” of the book R for Data Science\n  Optional reading   We recommend you going through the instructions to download Python here and installing jupyter here.\n  While we will not be using Pandas, here is a neat short intro to it.","tags":null,"title":"Data cleaning and wrangling","type":"reading"},{"authors":null,"categories":null,"content":"Required reading Chapter 3 of R for Data Science\nOptional reading Chapter 15 of Exploratory Data Analysis with R\n","date":1663113600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1663113600,"objectID":"db2e5ee70039459f918f68ed6f964cf0","permalink":"https://uscbiostats.github.io/PM566/reading/04-reading/","publishdate":"2022-09-14T00:00:00Z","relpermalink":"/PM566/reading/04-reading/","section":"reading","summary":"Required reading Chapter 3 of R for Data Science\nOptional reading Chapter 15 of Exploratory Data Analysis with R","tags":null,"title":"Data visualization","type":"reading"},{"authors":null,"categories":null,"content":"Required Reading Exploratory Data Analysis  The main text we will use for EDA is another book by Roger Peng, Exploratory Data Analysis with R Please focus on Chapter 4, Chapter 5 and Chapter 6  Optional reading The following are recommended (but not expected) for you to read\n  Introduction to data.table  Chapter 7 of R for Data Science  ","date":1662508800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1662508800,"objectID":"68871e83f1fea259615904a0d5972d28","permalink":"https://uscbiostats.github.io/PM566/reading/03-reading/","publishdate":"2022-09-07T00:00:00Z","relpermalink":"/PM566/reading/03-reading/","section":"reading","summary":"Required Reading Exploratory Data Analysis  The main text we will use for EDA is another book by Roger Peng, Exploratory Data Analysis with R Please focus on Chapter 4, Chapter 5 and Chapter 6  Optional reading The following are recommended (but not expected) for you to read\n  Introduction to data.table  Chapter 7 of R for Data Science  ","tags":null,"title":"Exploratory data analysis","type":"reading"},{"authors":null,"categories":null,"content":"Required reading You are expected to have read this when you show up for the first day of class. The following items are all rather short and shouldn’t take too long. The “How to read an R help page” section stops once you reach “A.1.2 The basics of accessing and selecting things”.\n The Syllabus rmarkdown lessons 1, 2, 3 and 8  How to read an R help page  Additionally depending on your previous knowledge I would recommend you take a look at the programming basics primer.\nOptional reading The following articles are recommended (but not expected) for you to read if you want to get a glimpse into the world of data science and R as a language.\n  How R changed me as an analyst  Data scientists mostly just do arithmetic and that’s a good thing  How rOpenSci uses Code Review to Promote Reproducible Science  50 Years of Data Science  ","date":1661299200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1661299200,"objectID":"779a2db7f9aaaf0fb69f23ae1b1d9d0c","permalink":"https://uscbiostats.github.io/PM566/reading/01-reading/","publishdate":"2022-08-24T00:00:00Z","relpermalink":"/PM566/reading/01-reading/","section":"reading","summary":"Required reading You are expected to have read this when you show up for the first day of class. The following items are all rather short and shouldn’t take too long. The “How to read an R help page” section stops once you reach “A.1.2 The basics of accessing and selecting things”.\n The Syllabus rmarkdown lessons 1, 2, 3 and 8  How to read an R help page  Additionally depending on your previous knowledge I would recommend you take a look at the programming basics primer.","tags":null,"title":"Introduction to Data Science tools: R, R Markdown","type":"reading"},{"authors":null,"categories":null,"content":"Learning goals  Use the merge() function to join two datasets. Deal with missings and impute data. Identify relevant observations using quantile(). Practice your GitHub skills.  Lab description For this lab we will be, again, dealing with the meteorological dataset downloaded from the NOAA, the met. In this case, we will use data.table to answer some questions regarding the met dataset, while at the same time practice your Git+GitHub skills for this project.\nThis markdown document should be rendered using github_document document.\nPart 1: Setup the Git project and the GitHub repository   Go to your documents (or wherever you are planning to store the data) in your computer, and create a folder for this project, for example, \u0026ldquo;PM566-labs\u0026rdquo;\n  In that folder, save this template as \u0026ldquo;README.Rmd\u0026rdquo;. This will be the markdown file where all the magic will happen.\n  Go to your GitHub account and create a new repository, hopefully of the same name that this folder has, i.e., \u0026ldquo;PM566-labs\u0026rdquo;.\n  Initialize the Git project, add the \u0026ldquo;README.Rmd\u0026rdquo; file, and make your first commit.\n  Add the repo you just created on GitHub.com to the list of remotes, and push your commit to origin while setting the upstream.\n  Most of the steps can be done using command line:\n# Step 1 cd ~/Documents mkdir PM566-labs cd PM566-labs # Step 2 wget https://raw.githubusercontent.com/USCbiostats/PM566/master/website/content/assignment/05-lab.Rmd mv 05-lab.Rmd README.md # Step 3 # Happens on github # Step 4 git init git add README.Rmd git commit -m \u0026quot;First commit\u0026quot; # Step 5 git remote add origin git@github.com:[username]/PM566-labs git push -u origin master  You can also complete the steps in R (replace with your paths/username when needed)\n# Step 1 setwd(\u0026quot;~/Documents\u0026quot;) dir.create(\u0026quot;PM566-labs\u0026quot;) setwd(\u0026quot;PM566-labs\u0026quot;) # Step 2 download.file( \u0026quot;https://raw.githubusercontent.com/USCbiostats/PM566/master/website/content/assignment/05-lab.Rmd\u0026quot;, destfile = \u0026quot;README.Rmd\u0026quot; ) # Step 3: Happens on Github # Step 4 system(\u0026quot;git init \u0026amp;\u0026amp; git add README.Rmd\u0026quot;) system('git commit -m \u0026quot;First commit\u0026quot;') # Step 5 system(\u0026quot;git remote add origin git@github.com:[username]/PM566-labs\u0026quot;) system(\u0026quot;git push -u origin master\u0026quot;)  Once you are done setting up the project, you can now start working with the MET data.\nSetup in R   Load the data.table (and the dtplyr and dplyr packages if you plan to work with those).\n  Load the met data from https://raw.githubusercontent.com/USCbiostats/data-science-data/master/02_met/met_all.gz, and also the station data. For the later, you can use the code we used during lecture to pre-process the stations data:\n  # Download the data stations \u0026lt;- fread(\u0026quot;https://noaa-isd-pds.s3.amazonaws.com/isd-history.csv\u0026quot;) stations[, USAF := as.integer(USAF)] # Dealing with NAs and 999999 stations[, USAF := fifelse(USAF == 999999, NA_integer_, USAF)] stations[, CTRY := fifelse(CTRY == \u0026quot;\u0026quot;, NA_character_, CTRY)] stations[, STATE := fifelse(STATE == \u0026quot;\u0026quot;, NA_character_, STATE)] # Selecting the three relevant columns, and keeping unique records stations \u0026lt;- unique(stations[, list(USAF, CTRY, STATE)]) # Dropping NAs stations \u0026lt;- stations[!is.na(USAF)] # Removing duplicates stations[, n := 1:.N, by = .(USAF)] stations \u0026lt;- stations[n == 1,][, n := NULL]  Merge the data as we did during the lecture.  Question 1: Representative station for the US What is the median station in terms of temperature, wind speed, and atmospheric pressure? Look for the three weather stations that best represent continental US using the quantile() function. Do these three coincide?\nKnit the document, commit your changes, and push it to GitHub. Don\u0026rsquo;t forget to add README.md to the tree, the first time you render it.\nQuestion 2: Representative station per state Just like the previous question, you are asked to identify what is the most representative, the median, station per state. This time, instead of looking at one variable at a time, look at the euclidean distance. If multiple stations show in the median, select the one located at the lowest latitude.\nKnit the doc and save it on GitHub.\nQuestion 3: In the middle? For each state, identify what is the station that is closest to the mid-point of the state. Combining these with the stations you identified in the previous question, use leaflet() to visualize all ~100 points in the same figure, applying different colors for those identified in this question.\nKnit the doc and save it on GitHub.\nQuestion 4: Means of means Using the quantile() function, generate a summary table that shows the number of states included, average temperature, wind-speed, and atmospheric pressure by the variable \u0026ldquo;average temperature level,\u0026rdquo; which you\u0026rsquo;ll need to create.\nStart by computing the states' average temperature. Use that measurement to classify them according to the following criteria:\n low: temp \u0026lt; 20 Mid: temp \u0026gt;= 20 and temp \u0026lt; 25 High: temp \u0026gt;= 25  Once you are done with that, you can compute the following:\n Number of entries (records), Number of NA entries, Number of stations, Number of states included, and Mean temperature, wind-speed, and atmospheric pressure.  All by the levels described before.\nKnit the document, commit your changes, and push them to GitHub. Once you\u0026rsquo;re done, you can email me the link to your repository or, preferably, link to the Lab 5 Issue in your commit message, as follows:\ngit commit -a -m \u0026quot;Finalizing lab 5 https://github.com/USCbiostats/PM566/issues/68\u0026quot;  This will let me know which version of your repository to look at for grading purposes.\n","date":1632355200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1632355200,"objectID":"5f15b828c0bd14a8fe4dc38804fd31a4","permalink":"https://uscbiostats.github.io/PM566/assignment/05-lab/","publishdate":"2021-09-23T00:00:00Z","relpermalink":"/PM566/assignment/05-lab/","section":"assignment","summary":"Learning goals  Use the merge() function to join two datasets. Deal with missings and impute data. Identify relevant observations using quantile(). Practice your GitHub skills.  Lab description For this lab we will be, again, dealing with the meteorological dataset downloaded from the NOAA, the met. In this case, we will use data.table to answer some questions regarding the met dataset, while at the same time practice your Git+GitHub skills for this project.","tags":null,"title":"Lab 05 - Data Wrangling","type":"assignment"},{"authors":null,"categories":null,"content":"Required reading To be announced\nOptional reading To be announced\n","date":1605657600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1605657600,"objectID":"50371a905277d0453e50747202316409","permalink":"https://uscbiostats.github.io/PM566/reading/14-reading/","publishdate":"2020-11-18T00:00:00Z","relpermalink":"/PM566/reading/14-reading/","section":"reading","summary":"Required reading To be announced\nOptional reading To be announced","tags":null,"title":"Final Project","type":"reading"},{"authors":null,"categories":null,"content":"Required reading No Reading for todays class\nOptional reading ","date":1603238400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1603238400,"objectID":"9bb8bcf808ed7241aa731039ffa9b4ed","permalink":"https://uscbiostats.github.io/PM566/reading/10-reading/","publishdate":"2020-10-21T00:00:00Z","relpermalink":"/PM566/reading/10-reading/","section":"reading","summary":"Required reading No Reading for todays class\nOptional reading ","tags":null,"title":"SQL","type":"reading"},{"authors":null,"categories":null,"content":"Required reading To be announced\nOptional reading To be announced\n","date":1602028800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1602028800,"objectID":"89e63c8106614b5d2cd6c1f496355320","permalink":"https://uscbiostats.github.io/PM566/reading/08-reading/","publishdate":"2020-10-07T00:00:00Z","relpermalink":"/PM566/reading/08-reading/","section":"reading","summary":"Required reading To be announced\nOptional reading To be announced","tags":null,"title":"Midterm Exam","type":"reading"},{"authors":null,"categories":null,"content":"Learning goals  Get acquainted with R and RStudio, which we will be using throughout the course to analyze data. Appreciate the value of visualization in exploring the relationship between variables. Start using R for building plots and calculating summary statistics.  Terminology We\u0026rsquo;ve already thrown around a few new terms, so let\u0026rsquo;s define them before we proceed.\n R: Name of the programming language we will be using throughout the course. RStudio: An integrated development environment for R. In other words, a convenient interface for writing and running R code.  I like to think of R as the engine of the car, and RStudio is the dashboard.\nStarting slow As the labs progress, you are encouraged to explore beyond what the labs dictate; a willingness to experiment will make you a much better programmer. Before we get to that stage, however, you need to build some basic fluency in R. Today we begin with the fundamental building blocks of R and RStudio: the interface, reading in data, and basic commands.\nGetting started 1 Download R If you don\u0026rsquo;t have R installed. Go to the CRAN and download R, make sure you get the version that matches your operating system.\nIf you have R installed If you have R installed run the following code\nR.version  ## _ ## platform x86_64-apple-darwin20 ## arch x86_64 ## os darwin20 ## system x86_64, darwin20 ## status ## major 4 ## minor 3.3 ## year 2024 ## month 02 ## day 29 ## svn rev 86002 ## language R ## version.string R version 4.3.3 (2024-02-29) ## nickname Angel Food Cake  This should tell you what version of R you are currently using. If your R version is lower then 4.3.0, I would strongly recommend updating. In general, it is a good idea to keep your R version up to date, unless you have a project right now that depends on a specific version of R.\n1.2 Download RStudio We recommend using RStudio as your IDE if you don\u0026rsquo;t already have it installed. You can go to the RStudio website to download and install the software. Once it is installed, open RStudio and use it to complete the rest of this lab.\nHello RStudio! RStudio is comprised of four panes.\n  On the bottom left is the Console, this is where you can write code that will be evaluated. Try typing 2 + 2 here and hit enter, what do you get?\n  On the bottom right is the Files pane, as well as other panes that will come handy as we start our analysis.\n  If you click on a file, it will open in the editor, on the top left pane.\n  Finally, the top right pane shows your Environment. If you define a variable it would show up there. Try typing x \u0026lt;- 2 in the Console and hit enter, what do you get in the Environment pane?\n  Start a new Project RStudio Projects are a great way to stay organized and keep all of your work on a particular topic in one place. Project files keep track of things like the R objects you are using and which files you have open, so that you can quickly jump in and out of different work environments.\nWe\u0026rsquo;re going to start a new Project called PM566labs. If you want to keep all of your materials for this class in one place, you may want to open Finder (MacOS) or File Explorer (Windows) and create a new folder (directory) for this class.\nIn the top right of the RStudio window, you should see a drop-down menu that says \u0026ldquo;Project: (None)\u0026rdquo;. Click on this and then \u0026ldquo;New Project\u0026hellip;\u0026rdquo;, which will open up a dialogue box. If you had already created a PM566labs directory, you could choose the \u0026ldquo;Existing Directory\u0026rdquo; option to associate this Project with that directory. Since we haven\u0026rsquo;t done that, we\u0026rsquo;ll use the \u0026ldquo;New Directory\u0026rdquo; option, then select the generic Project type, \u0026ldquo;New Project\u0026rdquo;. Give your new directory the name PM566labs and use the \u0026ldquo;Browse\u0026rdquo; button to choose where you want to save it on your computer, then click \u0026ldquo;Create Project\u0026rdquo;. Now in the top right, you should see \u0026ldquo;PM566labs\u0026rdquo; next to the R Project logo.\nCreate a Quarto document We will use Quarto documents a lot in this course because they are fully reproducible and allow us to seamlessly integrate code and text. We expect you to use Quarto for all homework assignments and (almost) all labs.\nIn the top left, you will see a \u0026ldquo;New File\u0026rdquo; icon (a white \u0026ldquo;plus\u0026rdquo; sign in a green circle over a blank document) that leads to a drop-down menu. Click on this and then select \u0026ldquo;Quarto document\u0026hellip;\u0026rdquo;, which will open up a dialogue box. You can leave most of the settings on their defaults for now, just give your document a title (like \u0026ldquo;Lab 1\u0026rdquo;) and an author (yourself) and click \u0026ldquo;Create\u0026rdquo;. RStudio may ask you if you would like to install a package that is required, and if so, click \u0026ldquo;Install\u0026rdquo;.\nThis will open the default Quarto document, which already contains some example content. Read through it, then remove this content. Please do not hand in assignments that contain the default content, as I have seen it plenty of times! Set up new sections in your document titled \u0026ldquo;Question 1\u0026rdquo; through \u0026ldquo;Question 6\u0026rdquo;. This document will serve as the template for your responses to this lab. Under each section title, add an R code chunk via \u0026ldquo;Insert\u0026hellip;\u0026rdquo;, \u0026ldquo;Executable Cell\u0026rdquo;, \u0026ldquo;R\u0026rdquo;. Alternatively, you can switch to the \u0026ldquo;Source\u0026rdquo; editor (in the top left of the Editor pane) and add an R chunk by typing the following:\n```{r}\n```\nThis will create an R code chunk and any code you add inside of it will be executed when you Render the document.\nSave your Quarto markdown (qmd) file as lab-01.qmd and see what happens when you click \u0026ldquo;Render\u0026rdquo;.\nPackages R is an open-source language with a vibrant community of users and developers. Developers can contribute new functionality to R via packages. In this lab we will work with the datasauRus package, which contains a fun example dataset.\nYou can load this package with the following command:\nlibrary(datasauRus)  If you haven\u0026rsquo;t installed it yet and R complains, then you can install the package by running the following command (note that R package names are case-sensitive)\u0026quot;\ninstall.packages(\u0026quot;datasauRus\u0026quot;)  We encourage you to work interactively on this lab (and future assignments) by running code directly in your Console pane. Try breaking down complex or unfamiliar lines of code into smaller, executable pieces to get a sense for how the code works. You may also want to open a new tab in the Editor windown that is just an R Script, where you can play around with pieces of code before adding them to the final product that is your Quarto document.\nHowever, you should note that Quarto documents are Rendered in their own unique environment, separate from your interactive session. So while you should run library(datasauRus) in your Console, you will also need to include it inside an R code chunk if you want your Quarto document to use this package. Similarly, if you create a variable called var while running code in the Console, Quarto won\u0026rsquo;t have acess to it, so you will need to re-create that variable inside a code chunk.\nWarm up Before we introduce the data, let\u0026rsquo;s warm up with some simple exercises.\nYAML The top portion of your Quarto file (between the three dashed lines) is called YAML. It stands for \u0026ldquo;YAML Ain\u0026rsquo;t Markup Language\u0026rdquo;. It is a human-friendly data serialization standard for all programming languages. All you need to know is that this area is called the YAML (we will refer to it as such) and that it contains meta information about your document.\nOpen the Quarto (qmd) file in your project, make sure the author name is your name. You can add additional lines to the YAML section to control more aspects of your document. For example, you can set the output to be a PDF or HTML document by adding either format: pdf or format: html.\nRegardless of what output format you choose, add another line to the YAML header that reads embed-resources: true. This tells Quarto that you want to produce a \u0026ldquo;stand-alone\u0026rdquo; document that contains all images directly in the document. If you don\u0026rsquo;t do this, Quarto will often create a separate directory (called \u0026lt;document\u0026gt;_files) that contains these images and your output will depend on this outside directory.\nWhen you\u0026rsquo;re done, click \u0026ldquo;Render\u0026rdquo; to compile the document.\nData The data frame we will be working with today is called datasaurus_dozen and it\u0026rsquo;s in the datasauRus package. Actually, this single data frame contains 13 datasets, designed to show us why data visualization is important and how summary statistics alone can be misleading. The different datasets are marked by the dataset variable. The data is contained in an object called a data.frame. Type datasaurus_dozen into the R Console and hit Return to see a printout of the dataset. Alternatively, you can use View(datasaurus_dozen) to open a new tab with the full dataset (but be careful! This command will not work in a Quarto document, because they are not interactive).\nTo find out more about the dataset, type the following in your Console: ?datasaurus_dozen. A question mark before the name of an object will always bring up its help file. Similar to View, this command must be run in the Console.\nQuestion 1  Based on the help file, how many rows and how many columns does the datasaurus_dozen file have? What are the variables included in the data frame? Add your responses to your lab report, with relevant code in the associated R code chunk, and free-form text outside of the code chunk.  Let\u0026rsquo;s take a look at what these datasets are. To start, we can make a frequency table of the dataset variable:\ntable(datasaurus_dozen$dataset)  Here, we used the $ operator to access a specific column (variable) of the dataset. Then we used the table function to summarize that variable. table is great for quickly summarizing categorical variables, but it\u0026rsquo;s not very useful for summarizing continuous variables, where most unique values are only present once. For continuous variables, try the summary function.\nThe original Datasaurus (dino) was created by Alberto Cairo in this great blog post. The other Dozen were generated using simulated annealing and the process is described in the paper Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing by Justin Matejka and George Fitzmaurice. In the paper, the authors simulate a variety of datasets with the same summary statistics as the Datasaurus, but with very different distributions.\nData visualization and summary Question 2 Plot y vs. x for the dino dataset. Then, calculate the correlation coefficient between x and y for just this dataset.  Below is the code you will need to complete this exercise. Basically, the answer is already given, but you need to include relevant bits in your Quarto document and successfully Render it and view the results.\nWe\u0026rsquo;re going to start by subsetting our data down to just the dino dataset.\nSubsetting dino_data \u0026lt;- datasaurus_dozen[datasaurus_dozen$dataset == 'dino', ]  There is a lot going on here, so let\u0026rsquo;s slow down and unpack it a bit.\nThe first thing to note is the assignment operator: \u0026lt;-. This special symbol is used to create a new object or assign a new value to an existing one. In this case, we\u0026rsquo;re creating a new object called dino_data.\nThe second important feature is the set of square brackets: []. When these come immediately after the name of an object, they are used for subsetting that object, or taking a smaller piece of it. In this case, we want to subset the datasaurus_dozen object. Since this is a two-dimensional data.frame, we can subset it by either rows or columns, or both. The comma (,) separates our row subset from our column subset. In this case, we only want to subset by row and keep all the columns, so there is nothing after the comma.\nIn R (as in Python and many other programming languages), we can check whether or not two values are equal by using the == operator. Here, we again use the $ to access the dataset variable and we check if it is equal to the value 'dino'. If it is, this comparison will return TRUE and we will keep that row. If it isn\u0026rsquo;t, this comparison will return FALSE and we will not keep that row.\nPlotting Next, we need to visualize these data. We will use the plot function for this, which is R\u0026rsquo;s most basic plotting function. If you provide the plot function with two numeric vectors, it will plot them in a scatter plot. Let\u0026rsquo;s see what happens when we plot the x and y variables from our dino_data object:\nplot(dino_data$x, dino_data$y) # ggplot(data = dino_data, mapping = aes(x = x, y = y)) + # geom_point()  (I\u0026rsquo;ve also included code for producing similar plots using the ggplot function from the ggplot2 package. This is another widely-used plotting function that includes some more aesthetically pleasing defaults, but requires more complicated syntax)\nWe will talk a lot more about the philosophy of data visualization, how to choose the right plot type, and constructing visualizations in layers in the coming weeks. But for now, you can follow along with the code that is provided.\nFor the second part of these exercises, we need to calculate a summary statistic: the correlation coefficient. The correlation coefficient, often referred to as \\(r\\) in statistics, measures the linear association between two variables. You will see that some of the pairs of variables we plot do not have a linear relationship between them. This is exactly why we want to visualize first: visualize to assess the form of the relationship, and calculate \\(r\\) only if relevant. In this case, calculating a correlation coefficient really doesn\u0026rsquo;t make sense since the relationship between x and y is definitely not linear \u0026ndash; it\u0026rsquo;s dinosaurial!\nBut, for illustrative purposes, let\u0026rsquo;s calculate correlation coefficient between x and y. Like plot, the cor function takes two numeric variables and calculates their correlation coefficient:\ncor(dino_data$x, dino_data$y) # dino_data |\u0026gt; # summarize(r = cor(x, y))  (I\u0026rsquo;ve also included the \u0026ldquo;tidyverse\u0026rdquo; way of calculating this value. The \u0026ldquo;tidyverse\u0026rdquo; is a collection of packages that includes ggplot2 and is based on the concept of \u0026ldquo;tidy\u0026rdquo;, rectangular datasets. We will tend to focus on the \u0026ldquo;Base R\u0026rdquo; way of doing things, as learning the \u0026ldquo;tidyverse\u0026rdquo; coding style is almost like learning another language in addition to R)\nQuestion 3 Now try it on your own! Plot y vs. x for the star dataset, another one of the datasaurus_dozen. You can (and should) re-use code we introduced above, just replace the dataset name with the desired dataset. Then, calculate the correlation coefficient between x and y for this dataset. How does this value compare to the r of dino?  Question 4 Plot y vs. x for the circle dataset. You can (and should) reuse code we introduced above, just replace the dataset name with the desired dataset. Then, calculate the correlation coefficient between x and y for this dataset. How does this value compare to the r of dino?  Question 5 Now let\u0026rsquo;s plot all 13 datasets at once. In order to do this we will make use of the layout function. This function allows you to put multiple plots in the same plotting window. We will create a 4x4 matrix containing the values 1 through 16 and pass this to layout, which lets it know where we want each plot to go (we only have 13 datasets, so there will be a few empty spots at the end). This may be too many plots for a small viewing window, so if you get the error message Error in plot.new() : figure margins too large, try making your plotting window larger.\nThen we use a for loop to perform a set of actions over every unique value of the dataset variable. This creates a new object, called name, that takes on each of those unique values, but it only exists within the context (interior) of the loop. Then we subset and plot the data, as we have before.\nlayout(matrix(1:16, nrow=4, ncol=4)) for(name in unique(datasaurus_dozen$dataset)){ subset \u0026lt;- datasaurus_dozen[datasaurus_dozen$dataset == name, ] plot(subset$x, subset$y, main = name) } layout(1)  # ggplot(datasaurus_dozen, aes(x = x, y = y, color = dataset))+ # geom_point()+ # facet_wrap(~ dataset, ncol = 3) + # theme(legend.position = \u0026quot;none\u0026quot;)  The second call to layout will reset the plotting window, so that the next plot will take up the entire window, rather than 1/16th of it.\nOptional:If you really want to maximize the plotting area of this figure, you can also adjust the plotting margins via the par function (short for \u0026ldquo;graphical parameters\u0026rdquo;). The setting for margins is called mar and it takes a vector of length 4, specifying the bottom, left, top, and right margins, in order (the units are lines of text). You can set each margin to a value of 2 with par(mar = c(2,2,2,2)) and then reset to the default values with par(mar = c(5,4,4,2) + 0.1).\nQuestion 6 Finally, we want to calculate the correlation between the x and y variables for all 13 datasets. Like before, we will use a loop, but this time, since we want to return a specific value every time through the loop, we will use the sapply function. sapply is useful way to apply a function to every element of a vector. In this case, we provide the vector of unique dataset names (like before) and then our own custom function. This function subsets the data as before, and then returns the correlation coefficient as the output of the function.\nsapply(unique(datasaurus_dozen$dataset), function(name){ subset \u0026lt;- datasaurus_dozen[datasaurus_dozen$dataset == name, ] return(cor(subset$x, subset$y)) })  # datasaurus_dozen |\u0026gt; # group_by(dataset) |\u0026gt; # summarize(r = cor(x, y))  You\u0026rsquo;re now done with the data analysis exercises, but we\u0026rsquo;d like you to do two more things:\nResize your figures:\nAdd the fields fig-width and fig-height to the YAML header of your document. These will allow you to specify the size (in inches) of any figures generated by the code chunks in your report.\nYou can also use different figure sizes for different figures. If you are in the Visual editor mode, switch to Source mode. Notice that each R chunk starts and ends with three backticks. Click on the gear icon in the top right of a code chunk and select \u0026ldquo;Use custom figure size\u0026rdquo; in the pop-up menu. Set the height and width of the figures and hit Apply when done. Then, render your document and see how you like the new sizes. Try making the plot for Question 5 larger, until you are happy with its size. Note that changing the figure sizes added new options to these chunks: fig.width and fig.height. You can change them by defining different values directly in your Quarto document as well.\nChange the look of your report:\nIf you have time, you can explore the different ways you can add styling to your document. Try adding a theme field to the YAML header and see if you can find valid names of different themes.\nHere is a Quarto cheatsheet and a general markdown cheatsheet that shows some of the many cool features you can make use of in a Quarto document.\nSubmitting your lab Since we have not covered GitHub yet, for this week (and ONLY this week) you may email me a fully rendered PDF or HTML version of your completed lab at kelly(dot)street(at)usc(dot)edu.\n This set of lab exersices have been adopted from Mine Çetinkaya-Rundel\u0026rsquo;s class Introduction to Data Science.\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"ec913329e57aa90c7d5ae82a1e52ac76","permalink":"https://uscbiostats.github.io/PM566/assignment/01-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/01-lab/","section":"assignment","summary":"Learning goals  Get acquainted with R and RStudio, which we will be using throughout the course to analyze data. Appreciate the value of visualization in exploring the relationship between variables. Start using R for building plots and calculating summary statistics.  Terminology We\u0026rsquo;ve already thrown around a few new terms, so let\u0026rsquo;s define them before we proceed.\n R: Name of the programming language we will be using throughout the course.","tags":null,"title":"Lab 01 - Hello R!","type":"assignment"},{"authors":null,"categories":null,"content":"Learning goals In this lab, you are expected to learn/put in practice the following skills:\n Forking a repository on GitHub Git workflow clone/commit/push Using pull requests (PR)  Updating a single repo One of the most common tasks that people use git for is for collaborating. While in general team members organize such that there is no overlapping editing of the files, git is (usually) smart enough to avoid clashes when multiple edits are done in the same document1. To show this, we will do a collaborative edit of a file!\nWe will be working with the repository https://github.com/USCbiostats/PM566-whoami\nStep 1: Fork the project to create your own repo Not a term/command actually available in Git, forking is a feature available in Github (as in other services) that allows users to create copies of other people\u0026rsquo;s projects to propose changes (i.e. make pull requests, i.e. \u0026ldquo;I have this great update for your project! Would you like to add it by pulling it into your repo?\u0026quot;).\nTo start, you just need to use the Fork button available on the main page of the repository you would like to contribute to2:\nOnce you \u0026ldquo;Fork\u0026rdquo; a project, GitHub will:\n  Create a copy (using git clone) of that project in your account.\n  Set up a pipeline to generate pull requests for the original repository.\n  Once you have a copy of the project in your account, you can proceede by \u0026ldquo;downloading it\u0026rdquo; to your computer using the git clone command. For example, if your github user name is statsnerd and the original repository is PM566-whoami, you could use the following in your command line\ncd where/you/want/to/download/the/thing git clone https://github.com/statsnerd/PM566-whoami.git  And if you have your ssh credentials set up, you can do instead\ncd where/you/want/to/download/the/thing git clone git@github.com:statsnerd/PM566-whoami.git  This way you will get a copy of the repository in your local machine. Now, let\u0026rsquo;s see how can we update the project!\nStep 2: Modifying the corresponding line If you got the correct copy, you should find a very simple repository with only two files: CODE_OF_CONDUCT.md and README.md. The first file is a general code of conduct for the project, which we do not need to edit. The second file is the one that we will be playing with. The README file, which happens to be a Markdown file, contains, or at least will contain, your and your team members' biographies. Here is what you need to do:\n  Find the line with your name.\n  In that single line (i.e. not spanning multiple lines), write something about yourself, e.g. \u0026ldquo;I am from XYZ, I love doing ABC, \u0026hellip;\u0026rdquo;.\n  (optional) if you feel like it, add at the end of the line a picture of yourself (or avatar) using either html or markdown. This will require you to include the figure in the repo (if you are not linking a web fig).\n  Commit the changes and push the changes to your repo using git commit and git push, e.g.\n  git commit -a -m \u0026quot;[A short but meaningful message]\u0026quot; # git add [your-avatar.png] ... if you need to add a picture git push  You are now one step closer to make your first \u0026ldquo;pull request\u0026rdquo;. We will see how that happens in the next part.\nStep 3: Do the pull request This is the final step. Overall, pull requests (PR) are as complex as the proposed changes are. The PR that you are about to make should go smoothly, yet, any time that you make a new PR, the changes should be able to be merged in the original repository without conflicts. Conflicts may only appear if the proposed changes are out-dated with respect to the main repository, meaning that the main repository was modified after your fork and your proposed changes cannot be merged without generating conflicts3. For now, let\u0026rsquo;s just look at the simple case.\nTo create the PR, you just need to go to your online copy of the project and click on \u0026ldquo;Contribute\u0026rdquo; then \u0026ldquo;Open Pull Request\u0026rdquo;:\n You can submit pull requests to the original repo from your copy of it via the \"Contribute\" button.  This will create a PR in the original repository. GitHub will automatically analyze the PR and check whether merging the PR to the master branch will result in a conflict or not. If all is OK, then the owner/admin of the repository can merge the PR. Otherwise, if there\u0026rsquo;s a conflict, you can go back to your local repo, make the needed changes, commit the changes, and push the changes to your copy on GitHub. In this stage, the PR will automatically update to reflect the new changes you made in your copy of the project.\nFor more information, check out Creating a pull request from a fork  on GitHub.\n  Team-members could be working on the same file but editing different lines of code. If this is the case, after pull/push, git will integrate the changes without conflicts.\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n For more details, take a look at the Forking Projects article in GitHub guides.\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n More info about how to deal with conflicts in this very neat post on stackoverflow.com How to resolve merge conflicts in Git. GitHub also has a way to solve conflicts in PRs, but this is only available to the admins of target repo. More info here,\u0026#160;\u0026#x21a9;\u0026#xfe0e;\n   ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"859a20e34ce01209178958c65cb80780","permalink":"https://uscbiostats.github.io/PM566/assignment/02-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/02-lab/","section":"assignment","summary":"Learning goals In this lab, you are expected to learn/put in practice the following skills:\n Forking a repository on GitHub Git workflow clone/commit/push Using pull requests (PR)  Updating a single repo One of the most common tasks that people use git for is for collaborating. While in general team members organize such that there is no overlapping editing of the files, git is (usually) smart enough to avoid clashes when multiple edits are done in the same document1.","tags":null,"title":"Lab 02 - GitHub","type":"assignment"},{"authors":null,"categories":null,"content":"        Learning Goals  Read in and get familiar with the meteorology dataset Step through the EDA “checklist” presented in the class slides Practice making exploratory graphs  As you do this, think about what questions you would like to ask regarding this data. What would you ask a collaborator who was more familiar with it?\nLab Description We will work with the meteorological data presented in lecture. Recall the dataset consists of weather station readings in the continental US.\nThe objectives of the lab are to find the weather station with the highest elevation and look at patterns in the time series of its wind speed and temperature. 1. Read in the data First download and then read in with data.table::fread(). This is slightly faster than some of the more common functions, such as read.table, but it produces a different type of object, which is why we need to convert it into a data.frame after reading it in.\ndownload.file( \u0026quot;https://raw.githubusercontent.com/USCbiostats/data-science-data/master/02_met/met_all.gz\u0026quot;, destfile = file.path(\u0026quot;~\u0026quot;, \u0026quot;Downloads\u0026quot;, \u0026quot;met_all.gz\u0026quot;), method = \u0026quot;libcurl\u0026quot;, timeout = 60 ) met \u0026lt;- data.table::fread(file.path(\u0026quot;~\u0026quot;, \u0026quot;Downloads\u0026quot;, \u0026quot;met_all.gz\u0026quot;)) met \u0026lt;- as.data.frame(met)  2. Check the dimensions, headers, footers. How many columns, rows are there? Some useful functions for this are dim, head, and tail. 3. Take a look at the variables. Show the type (class) of each variable (hint: try the str function). What are the names of the key variables related to our question of interest? 4. Take a closer look at the key variables. table(met$year)  ## ## 2019 ## 2377343  table(met$day)  ## ## 1 2 3 4 5 6 7 8 9 10 11 12 13 ## 75975 75923 76915 76594 76332 76734 77677 77766 75366 75450 76187 75052 76906 ## 14 15 16 17 18 19 20 21 22 23 24 25 26 ## 77852 76217 78015 78219 79191 76709 75527 75786 78312 77413 76965 76806 79114 ## 27 28 29 30 31 ## 79789 77059 71712 74931 74849  table(met$hour)  ## ## 0 1 2 3 4 5 6 7 8 9 10 ## 99434 93482 93770 96703 110504 112128 106235 101985 100310 102915 101880 ## 11 12 13 14 15 16 17 18 19 20 21 ## 100470 103605 97004 96507 97635 94942 94184 100179 94604 94928 96070 ## 22 23 ## 94046 93823  summary(met$temp)  ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## -40.00 19.60 23.50 23.59 27.80 56.00 60089  summary(met$elev)  ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## -13.0 101.0 252.0 415.8 400.0 9999.0  summary(met$wind.sp)  ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 0.00 0.00 2.10 2.46 3.60 36.00 79693  It looks like the elevation variable has observations with 9999.0, which is probably an indicator for missing. We should take a deeper look at the data dictionary to confirm. The wind speed variable is OK but there is a lot of missing data.\nAfter checking the data we should make the appropriate modifications. Replace elevations with 9999 as NA.\nmet[met$elev==9999.0, ] \u0026lt;- NA summary(met$elev)  ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## -13 101 252 413 400 4113 710  At what elevation is the highest weather station? We also have the issue of the minimum temperature being -40C, which seems implausible, so we should remove those observations.\nmet \u0026lt;- met[met$temp \u0026gt; -40, ] head(met[order(met$temp), ])  ## USAFID WBAN year month day hour min lat lon elev wind.dir ## 1203053 722817 3068 2019 8 1 0 56 38.767 -104.3 1838 190 ## 1203055 722817 3068 2019 8 1 1 56 38.767 -104.3 1838 180 ## 1203128 722817 3068 2019 8 3 11 56 38.767 -104.3 1838 NA ## 1203129 722817 3068 2019 8 3 12 56 38.767 -104.3 1838 NA ## 1203222 722817 3068 2019 8 6 21 56 38.767 -104.3 1838 280 ## 1203225 722817 3068 2019 8 6 22 56 38.767 -104.3 1838 240 ## wind.dir.qc wind.type.code wind.sp wind.sp.qc ceiling.ht ceiling.ht.qc ## 1203053 5 N 7.2 5 NA 9 ## 1203055 5 N 7.7 5 NA 9 ## 1203128 9 C 0.0 5 NA 9 ## 1203129 9 C 0.0 5 NA 9 ## 1203222 5 N 2.6 5 NA 9 ## 1203225 5 N 7.7 5 NA 9 ## ceiling.ht.method sky.cond vis.dist vis.dist.qc vis.var vis.var.qc ## 1203053 9 N NA 9 N 5 ## 1203055 9 N NA 9 N 5 ## 1203128 9 N NA 9 N 5 ## 1203129 9 N NA 9 N 5 ## 1203222 9 N NA 9 N 5 ## 1203225 9 N NA 9 N 5 ## temp temp.qc dew.point dew.point.qc atm.press atm.press.qc rh ## 1203053 -17.2 5 NA 9 NA 9 NA ## 1203055 -17.2 5 NA 9 NA 9 NA ## 1203128 -17.2 5 NA 9 NA 9 NA ## 1203129 -17.2 5 NA 9 NA 9 NA ## 1203222 -17.2 5 NA 9 NA 9 NA ## 1203225 -17.2 5 NA 9 NA 9 NA  There are still some suspiciously low values for temperature (-17.2C), but we will deal with those later.\nWe should also check the wind speed variable for any abnormalities.\nHow many missing values are there in the wind.sp variable? 5. Check the data against an external data source. We should check the suspicious temperature value (where is it located?) and validate that the range of elevations make sense (-13m to 4113m).\nGoogle is your friend here.\nFix any problems that arise in your checks.\nWhere was the location for the coldest temperature readings (-17.2C)? Do these seem reasonable in context? Does the range of values for elevation make sense? Why or why not? 6. Calculate summary statistics Remember to keep the initial question in mind. We want to pick out the weather station with maximum elevation and examine its wind speed and temperature.\nSome ideas: select the weather station with maximum elevation; look at the correlation between temperature and wind speed; look at the correlation between temperature and wind speed with hour and day of the month.\nelev \u0026lt;- met[which(met$elev == max(met$elev, na.rm = TRUE)), ] summary(elev)  ## USAFID WBAN year month day ## Min. :720385 Min. :419 Min. :2019 Min. :8 Min. : 1.0 ## 1st Qu.:720385 1st Qu.:419 1st Qu.:2019 1st Qu.:8 1st Qu.: 8.0 ## Median :720385 Median :419 Median :2019 Median :8 Median :16.0 ## Mean :720385 Mean :419 Mean :2019 Mean :8 Mean :16.1 ## 3rd Qu.:720385 3rd Qu.:419 3rd Qu.:2019 3rd Qu.:8 3rd Qu.:24.0 ## Max. :720385 Max. :419 Max. :2019 Max. :8 Max. :31.0 ## ## hour min lat lon elev ## Min. : 0.00 Min. : 6.00 Min. :39.8 Min. :-105.8 Min. :4113 ## 1st Qu.: 6.00 1st Qu.:13.00 1st Qu.:39.8 1st Qu.:-105.8 1st Qu.:4113 ## Median :12.00 Median :36.00 Median :39.8 Median :-105.8 Median :4113 ## Mean :11.66 Mean :34.38 Mean :39.8 Mean :-105.8 Mean :4113 ## 3rd Qu.:18.00 3rd Qu.:53.00 3rd Qu.:39.8 3rd Qu.:-105.8 3rd Qu.:4113 ## Max. :23.00 Max. :59.00 Max. :39.8 Max. :-105.8 Max. :4113 ## ## wind.dir wind.dir.qc wind.type.code wind.sp ## Min. : 10.0 Length:2117 Length:2117 Min. : 0.000 ## 1st Qu.:250.0 Class :character Class :character 1st Qu.: 4.100 ## Median :300.0 Mode :character Mode :character Median : 6.700 ## Mean :261.5 Mean : 7.245 ## 3rd Qu.:310.0 3rd Qu.: 9.800 ## Max. :360.0 Max. :21.100 ## NA's :237 NA's :168 ## wind.sp.qc ceiling.ht ceiling.ht.qc ceiling.ht.method ## Length:2117 Min. : 30 Min. :5.000 Length:2117 ## Class :character 1st Qu.: 2591 1st Qu.:5.000 Class :character ## Mode :character Median :22000 Median :5.000 Mode :character ## Mean :15145 Mean :5.008 ## 3rd Qu.:22000 3rd Qu.:5.000 ## Max. :22000 Max. :9.000 ## NA's :4 ## sky.cond vis.dist vis.dist.qc vis.var ## Length:2117 Min. : 0 Length:2117 Length:2117 ## Class :character 1st Qu.:16093 Class :character Class :character ## Mode :character Median :16093 Mode :character Mode :character ## Mean :15913 ## 3rd Qu.:16093 ## Max. :16093 ## NA's :683 ## vis.var.qc temp temp.qc dew.point ## Length:2117 Min. : 1.00 Length:2117 Min. :-6.0000 ## Class :character 1st Qu.: 6.00 Class :character 1st Qu.: 0.0000 ## Mode :character Median : 8.00 Mode :character Median : 0.0000 ## Mean : 8.13 Mean : 0.8729 ## 3rd Qu.:10.00 3rd Qu.: 2.0000 ## Max. :15.00 Max. : 7.0000 ## ## dew.point.qc atm.press atm.press.qc rh ## Length:2117 Min. : NA Min. :9 Min. :53.63 ## Class :character 1st Qu.: NA 1st Qu.:9 1st Qu.:58.10 ## Mode :character Median : NA Median :9 Median :61.39 ## Mean :NaN Mean :9 Mean :60.62 ## 3rd Qu.: NA 3rd Qu.:9 3rd Qu.:61.85 ## Max. : NA Max. :9 Max. :70.01 ## NA's :2117  Note that to find the maximum elevation, we had to add na.rm = TRUE, because the elevation variable contains missing values. This is an example of how missing values can quickly propagate throughout an analysis (as the “maximum” of 1, 2, and NA is NA, because it cannot be defined).\nAlso note that we used the which function to tell us which elements of the logical comparison are TRUE. We did this because some of them were NA, which can lead to issues when subsetting by a logical variable.\ncor(elev$temp, elev$wind.sp, use=\u0026quot;complete\u0026quot;)  ## [1] -0.09373843  cor(elev$temp, elev$hour, use=\u0026quot;complete\u0026quot;)  ## [1] 0.4397261  cor(elev$wind.sp, elev$day, use=\u0026quot;complete\u0026quot;)  ## [1] 0.3643079  cor(elev$wind.sp, elev$hour, use=\u0026quot;complete\u0026quot;)  ## [1] 0.08807315  cor(elev$temp, elev$day, use=\u0026quot;complete\u0026quot;)  ## [1] -0.003857766  The use=\u0026quot;complete\u0026quot; argument is another thing we added to avoid compounding NAs.\n7. Exploratory graphs We should look at the distributions of all of the key variables to make sure there are no remaining issues with the data.\nUse the hist function to make histograms of the elevation, temperature, and wind speed variables for the whole dataset One thing we should consider for later analyses is to log transform wind speed and elevation as they are very skewed.\nLook at where the weather station with highest elevation is located.\nleaflet(elev) %\u0026gt;% addProviderTiles('OpenStreetMap') %\u0026gt;% addCircles(lat=~lat,lng=~lon, opacity=1, fillOpacity=1, radius=100)   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Look at the time series of temperature and wind speed at this location. For this we will need to create a date-time variable for the x-axis.\nlibrary(lubridate) elev$date \u0026lt;- with(elev, ymd_h(paste(year, month, day, hour, sep= ' '))) summary(elev$date)  ## Min. 1st Qu. ## \u0026quot;2019-08-01 00:00:00.0000\u0026quot; \u0026quot;2019-08-08 11:00:00.0000\u0026quot; ## Median Mean ## \u0026quot;2019-08-16 22:00:00.0000\u0026quot; \u0026quot;2019-08-16 14:09:56.8823\u0026quot; ## 3rd Qu. Max. ## \u0026quot;2019-08-24 11:00:00.0000\u0026quot; \u0026quot;2019-08-31 22:00:00.0000\u0026quot;  elev \u0026lt;- elev[order(elev$date), ] head(elev)  ## USAFID WBAN year month day hour min lat lon elev wind.dir ## 221697 720385 419 2019 8 1 0 36 39.8 -105.766 4113 170 ## 221698 720385 419 2019 8 1 0 54 39.8 -105.766 4113 100 ## 221699 720385 419 2019 8 1 1 12 39.8 -105.766 4113 90 ## 221700 720385 419 2019 8 1 1 35 39.8 -105.766 4113 110 ## 221701 720385 419 2019 8 1 1 53 39.8 -105.766 4113 120 ## 221702 720385 419 2019 8 1 2 12 39.8 -105.766 4113 120 ## wind.dir.qc wind.type.code wind.sp wind.sp.qc ceiling.ht ceiling.ht.qc ## 221697 5 N 8.8 5 1372 5 ## 221698 5 N 2.6 5 1372 5 ## 221699 5 N 3.1 5 1981 5 ## 221700 5 N 4.1 5 2134 5 ## 221701 5 N 4.6 5 2134 5 ## 221702 5 N 6.2 5 22000 5 ## ceiling.ht.method sky.cond vis.dist vis.dist.qc vis.var vis.var.qc temp ## 221697 M N NA 9 N 5 9 ## 221698 M N NA 9 N 5 9 ## 221699 M N NA 9 N 5 9 ## 221700 M N NA 9 N 5 9 ## 221701 M N NA 9 N 5 9 ## 221702 9 N NA 9 N 5 9 ## temp.qc dew.point dew.point.qc atm.press atm.press.qc rh ## 221697 5 1 5 NA 9 57.61039 ## 221698 5 1 5 NA 9 57.61039 ## 221699 5 2 5 NA 9 61.85243 ## 221700 5 2 5 NA 9 61.85243 ## 221701 5 2 5 NA 9 61.85243 ## 221702 5 2 5 NA 9 61.85243 ## date ## 221697 2019-08-01 00:00:00 ## 221698 2019-08-01 00:00:00 ## 221699 2019-08-01 01:00:00 ## 221700 2019-08-01 01:00:00 ## 221701 2019-08-01 01:00:00 ## 221702 2019-08-01 02:00:00  With the date-time variable we can plot the time series of temperature and wind speed.\nUse the plot function to make line graphs of temperature vs. date and wind speed vs. date Summarize any trends that you see in these time series plots. 8. Ask questions By now, you might have some specific questions about how the data was gathered and what some of the different variables and values mean. Alternatively, maybe you have an idea for how some of the variable should be related and you want to explore that relationship. In a real-world analysis, these questions could potentially be answered by a collaborator, who may have been part of the team that collected the data.\nWhat questions do you have about the data? If you haven’t already, now would be a good time to look at the accompanying data dictionary for this dataset and see if it can answer any of your questions. If you have questions about the nature of the dataset and how it was gathered, this might be able to help.\nFor questions about variables in the dataset or relationships between them, try making some more exploratory plots. Do you see the patterns you would expect? There are many different types of summaries and visualization strategies that we have not discussed, but which could provide interesting perspectives on the data.\nSome other useful plotting functions include:\n pairs for making all pairwise scatter plots in a dataset with \u0026gt;2 dimensions. heatmap and/or corrplot (from the corrplot package) for visualizing matrices in general or correlation matrices in particular. image a low-level matrix visualization function barplot, especially with table, for visualizing frequencies of categorical variables.  ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"408129ada1d90a82b52f15f5d96d2d85","permalink":"https://uscbiostats.github.io/PM566/assignment/03-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/03-lab/","section":"assignment","summary":"Learning Goals  Read in and get familiar with the meteorology dataset Step through the EDA “checklist” presented in the class slides Practice making exploratory graphs  As you do this, think about what questions you would like to ask regarding this data. What would you ask a collaborator who was more familiar with it?\nLab Description We will work with the meteorological data presented in lecture.","tags":null,"title":"Lab 03 - Exploratory Data Analysis","type":"assignment"},{"authors":null,"categories":null,"content":"Learning Goals  Read in and prepare the meteorological dataset Create several graphs with different geoms() in ggplot2 Create a facet graph Conduct some customizations to the graphs Create a more detailed map using leaflet()  Lab Description We will again work with the meteorological data presented in lecture.\nThe objective of the lab is to examine the association between weekly average dew point and wind speed in four regions of the US and by elevation.\nPer Wikipedia: \u0026ldquo;The dew point of a given body of air is the temperature to which it must be cooled to become saturated with water vapor. This temperature depends on the pressure and water content of the air.\u0026rdquo;\nAgain, feel free to supplement your knowledge of this dataset by checking out the data dictionary.\nSteps 1. Read in the data First download and then read in with data.table::fread()\nif (!file.exists(\u0026quot;met_all.gz\u0026quot;)) download.file( url = \u0026quot;https://raw.githubusercontent.com/USCbiostats/data-science-data/master/02_met/met_all.gz\u0026quot;, destfile = \u0026quot;met_all.gz\u0026quot;, method = \u0026quot;libcurl\u0026quot;, timeout = 60 ) met \u0026lt;- data.table::fread(\u0026quot;met_all.gz\u0026quot;)  2. Prepare the data  Remove temperatures less than -17C Make sure there are no missing data in the key variables coded as 9999, 999, etc Generate a date variable using the functions as.Date() (hint: You will need the following to create a date paste(year, month, day, sep = \u0026quot;-\u0026quot;)). Using the data.table::week function, keep the observations of the first week of the month. Compute the mean by station of the variables temp, rh, wind.sp, vis.dist, dew.point, lat, lon, and elev. Create a region variable for NW, SW, NE, SE based on lon = -98.00 and lat = 39.71 degrees Create a categorical variable for elevation as in the lecture slides  3. Use geom_violin to examine the wind speed and dew point by region You saw how to use geom_boxplot in class. Try using geom_violin instead (take a look at the help). (hint: You will need to set the x aesthetic to 1)\n Use facets Make sure to deal with NAs Describe what you observe in the graph  4. Use geom_jitter with stat_smooth to examine the association between dew point and wind speed by region  Color points by region Make sure to deal with NAs Fit a linear regression line by region Describe what you observe in the graph  5. Use geom_bar to create barplots of the weather stations by elevation category colored by region  Bars by elevation category using position=\u0026quot;dodge\u0026quot; Change colors from the default. Color by region using scale_fill_brewer see this Create nice labels on the axes and add a title Describe what you observe in the graph Make sure to deal with NA values  6. Use stat_summary to examine mean dew point and wind speed by region with standard deviation error bars   Make sure to remove NAs\n  Use fun.data=\u0026quot;mean_sdl\u0026quot; in stat_summary\n  Add another layer of stats_summary but change the geom to \u0026quot;errorbar\u0026quot; (see the help).\n  Describe the graph and what you observe\n  Dew point is\u0026hellip;\n  Wind speed is\u0026hellip;\n  7. Make a map showing the spatial trend in relative humidity in the US   Make sure to remove NAs\n  Use leaflet()\n  Make a color palette with custom colors\n  Use addMarkers to include the top 10 places in relative humidity (hint: this will be useful rank(-rh) \u0026lt;= 10)\n  Add a legend\n  Describe the trend in RH across the US\n  8. Use a ggplot extension  Pick an extension (except cowplot) from here and make a plot of your choice using the met data (or met_avg) Might want to try examples that come with the extension first (e.g. ggtech, gganimate, ggforce)  ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"1ba8ca6f733939b9bce0616b9af32e16","permalink":"https://uscbiostats.github.io/PM566/assignment/04-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/04-lab/","section":"assignment","summary":"Learning Goals  Read in and prepare the meteorological dataset Create several graphs with different geoms() in ggplot2 Create a facet graph Conduct some customizations to the graphs Create a more detailed map using leaflet()  Lab Description We will again work with the meteorological data presented in lecture.\nThe objective of the lab is to examine the association between weekly average dew point and wind speed in four regions of the US and by elevation.","tags":null,"title":"Lab 04 - Data Visualization","type":"assignment"},{"authors":null,"categories":null,"content":"knitr::opts_chunk$set(eval = FALSE, include = TRUE)  Learning goals  Use unnest_tokens() and unnest_ngrams() to extract tokens and ngrams from text. Use dplyr and ggplot2 to analyze text data  Lab description For this lab we will be working with a new dataset. The dataset contains transcription samples from https://www.mtsamples.com/. And is loaded and \u0026ldquo;fairly\u0026rdquo; cleaned at https://raw.githubusercontent.com/USCbiostats/data-science-data/master/00_mtsamples/mtsamples.csv.\nSetup packages You should load in dplyr, (or data.table if you want to work that way), ggplot2 and tidytext. If you don\u0026rsquo;t already have tidytext then you can install with\ninstall.packages(\u0026quot;tidytext\u0026quot;)  read in Medical Transcriptions Loading in reference transcription samples from https://www.mtsamples.com/\nlibrary(readr) library(dplyr) mt_samples \u0026lt;- read_csv(\u0026quot;https://raw.githubusercontent.com/USCbiostats/data-science-data/master/00_mtsamples/mtsamples.csv\u0026quot;) mt_samples \u0026lt;- mt_samples |\u0026gt; select(description, medical_specialty, transcription) head(mt_samples)   Question 1: What specialties do we have? We can use count() from dplyr to figure out how many different catagories do we have? Are these catagories related? overlapping? evenly distributed?\nmt_samples %\u0026gt;% count(___, sort = TRUE)   Question 2  Tokenize the the words in the transcription column Count the number of times each token appears Visualize the top 20 most frequent words  Explain what we see from this result. Does it makes sense? What insights (if any) do we get?\n Question 3  Redo visualization but remove stopwords before Bonus points if you remove numbers as well  What do we see know that we have removed stop words? Does it give us a better idea of what the text is about?\n Question 4 repeat question 2, but this time tokenize into bi-grams. how does the result change if you look at tri-grams?\n Question 5 Using the results you got from questions 4. Pick a word and count the words that appears after and before it.\n Question 6 Which words are most used in each of the specialties. you can use group_by() and top_n() from dplyr to have the calculations be done within each specialty. Remember to remove stopwords. How about the most 5 used words?\nQuestion 7 - extra Find your own insight in the data:\nIdeas:\n Interesting ngrams See if certain words are used more in some specialties then others  ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"f00132d85ef0ce8610dfed06d117afb4","permalink":"https://uscbiostats.github.io/PM566/assignment/06-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/06-lab/","section":"assignment","summary":"knitr::opts_chunk$set(eval = FALSE, include = TRUE)  Learning goals  Use unnest_tokens() and unnest_ngrams() to extract tokens and ngrams from text. Use dplyr and ggplot2 to analyze text data  Lab description For this lab we will be working with a new dataset. The dataset contains transcription samples from https://www.mtsamples.com/. And is loaded and \u0026ldquo;fairly\u0026rdquo; cleaned at https://raw.githubusercontent.com/USCbiostats/data-science-data/master/00_mtsamples/mtsamples.csv.\nSetup packages You should load in dplyr, (or data.table if you want to work that way), ggplot2 and tidytext.","tags":null,"title":"Lab 06 - Text Mining","type":"assignment"},{"authors":null,"categories":null,"content":"Learning goals  Use a real world API to make queries and process the data. Use regular expressions to parse the information. Practice your GitHub skills.  Lab description In this lab, we will be working with the NCBI API to make queries and extract information using XML and regular expressions. For this lab, we will be using the httr, xml2, and stringr R packages.\nQuestion 1: How many sars-cov-2 papers? Build an automatic counter of sars-cov-2 papers using PubMed. You will need to apply XPath as we did during the lecture to extract the number of results returned by PubMed in the following web address:\nhttps://pubmed.ncbi.nlm.nih.gov/?term=sars-cov-2  Complete the lines of code:\n# Downloading the website website \u0026lt;- xml2::read_html(\u0026quot;[URL]\u0026quot;) # Finding the counts counts \u0026lt;- xml2::xml_find_first(website, \u0026quot;[XPath]\u0026quot;) # Turning it into text counts \u0026lt;- as.character(counts) # Extracting the data using regex stringr::str_extract(counts, \u0026quot;[REGEX FOR NUMBERS WITH COMMAS/DOTS]\u0026quot;)  Question 2: Academic publications on COVID19 and Hawaii You need to query the following. The parameters passed to the query are documented here.\nUse the function httr::GET() to make the following query:\n  Baseline URL: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi\n  Query parameters:\n db: pubmed term: covid19 hawaii retmax: 1000    library(httr) query_ids \u0026lt;- GET( url = \u0026quot;BASELINE URL\u0026quot;, query = list(\u0026quot;QUERY PARAMETERS\u0026quot;) ) # Extracting the content of the response of GET ids \u0026lt;- httr::content(query_ids)  The query will return an XML object, we can turn it into a character list to analyze the text directly with as.character(). Another way of processing the data could be using lists with the function xml2::as_list(). We will skip the latter for now.\nTake a look at the data, and continue with the next question.\nQuestion 3: Get details about the articles The IDs are wrapped around text in the following way: \u0026lt;Id\u0026gt;... id number ...\u0026lt;/Id\u0026gt;. We can use a regular expression that extracts that information. Fill out the following lines of code:\n# Turn the result into a character vector ids \u0026lt;- as.character(ids) # Find all the ids ids \u0026lt;- stringr::str_extract_all(ids, \u0026quot;PATTERN\u0026quot;)[[1]] # Remove all the leading and trailing \u0026lt;Id\u0026gt; \u0026lt;/Id\u0026gt;. Make use of \u0026quot;|\u0026quot; ids \u0026lt;- stringr::str_remove_all(ids, \u0026quot;PATTERN\u0026quot;)  With the IDs in hand, we can now try to get the abstracts of the papers. As before, we will need to coerce the contents (results) to a list using:\n  Baseline url: https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi\n  Query parameters:\n db: pubmed id: A character with all the ids separated by comma, e.g., \u0026ldquo;1232131,546464,13131\u0026rdquo; retmax: 1000 rettype: abstract    Pro-tip: If you want GET() to take some element literal, wrap it around I() (as you would do in a formula in R). For example, the text \u0026quot;123,456\u0026quot; is replaced with \u0026quot;123%2C456\u0026quot;. If you don\u0026rsquo;t want that behavior, you would need to do the following I(\u0026quot;123,456\u0026quot;).\npublications \u0026lt;- GET( url = \u0026quot;BASELINE URL HERE\u0026quot;, query = list( \u0026quot;PARAMETERS OF THE QUERY\u0026quot; ) ) # Turning the output into character vector publications \u0026lt;- httr::content(publications) publications_txt \u0026lt;- as.character(publications)  With this in hand, we can now analyze the data. This is also a good time for committing and pushing your work!\nQuestion 4: Distribution of universities, schools, and departments Using the function stringr::str_extract_all() applied on publications_txt, capture all the terms of the form:\n University of \u0026hellip; \u0026hellip; Institute of \u0026hellip;  Write a regular expression that captures all such instances\ninstitution \u0026lt;- str_extract_all( publications_txt, \u0026quot;[YOUR REGULAR EXPRESSION HERE]\u0026quot; ) institution \u0026lt;- unlist(institution) table(institution)  Repeat the exercise and this time focus on schools and departments in the form of\n School of \u0026hellip; Department of \u0026hellip;  And tabulate the results\nschools_and_deps \u0026lt;- str_extract_all( abstracts_txt, \u0026quot;[YOUR REGULAR EXPRESSION HERE]\u0026quot; ) table(schools_and_deps)  Question 5: Form a database We want to build a dataset which includes the title and the abstract of the paper. The title of all records is enclosed by the HTML tag ArticleTitle, and the abstract by Abstract.\nBefore applying the functions to extract text directly, it will help to process the XML a bit. We will use the xml2::xml_children() function to keep one element per ID. This way, if a paper is missing the abstract, or something else, we will be able to properly match PUBMED IDS with their corresponding records.\npub_char_list \u0026lt;- xml2::xml_children(publications) pub_char_list \u0026lt;- sapply(pub_char_list, as.character)  Now, extract the abstract and article title for each one of the elements of pub_char_list. You can either use sapply() as we just did, or simply take advantage of vectorization of stringr::str_extract\nabstracts \u0026lt;- str_extract(pub_char_list, \u0026quot;[YOUR REGULAR EXPRESSION]\u0026quot;) abstracts \u0026lt;- str_remove_all(abstracts, \u0026quot;[CLEAN ALL THE HTML TAGS]\u0026quot;) abstracts \u0026lt;- str_remove_all(abstracts, \u0026quot;[CLEAN ALL EXTRA WHITE SPACE AND NEW LINES]\u0026quot;)  How many of these don\u0026rsquo;t have an abstract? Now, the title\ntitles \u0026lt;- str_extract(pub_char_list, \u0026quot;[YOUR REGULAR EXPRESSION]\u0026quot;) titles \u0026lt;- str_remove_all(titles, \u0026quot;[CLEAN ALL THE HTML TAGS]\u0026quot;)  Finally, put everything together into a single data.frame and use knitr::kable to print the results\ndatabase \u0026lt;- data.frame( \u0026quot;[DATA TO CONCATENATE]\u0026quot; ) knitr::kable(database)  Done! Knit the document, commit, and push.\n","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"30eb5ee3cc44d8e2fba241e8f4996c51","permalink":"https://uscbiostats.github.io/PM566/assignment/07-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/07-lab/","section":"assignment","summary":"Learning goals  Use a real world API to make queries and process the data. Use regular expressions to parse the information. Practice your GitHub skills.  Lab description In this lab, we will be working with the NCBI API to make queries and extract information using XML and regular expressions. For this lab, we will be using the httr, xml2, and stringr R packages.\nQuestion 1: How many sars-cov-2 papers?","tags":null,"title":"Lab 07 - Web scraping and Regular Expressions","type":"assignment"},{"authors":null,"categories":null,"content":"Setup # install.packages(c(\u0026quot;RSQLite\u0026quot;, \u0026quot;DBI\u0026quot;)) library(RSQLite) library(DBI) # Initialize a temporary in memory database con \u0026lt;- dbConnect(SQLite(), \u0026quot;:memory:\u0026quot;) # Download tables actor \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/actor.csv\u0026quot;) rental \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/rental.csv\u0026quot;) customer \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/customer.csv\u0026quot;) payment \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/payment_p2007_01.csv\u0026quot;) # Copy data.frames to database dbWriteTable(con, \u0026quot;actor\u0026quot;, actor) dbWriteTable(con, \u0026quot;rental\u0026quot;, rental) dbWriteTable(con, \u0026quot;customer\u0026quot;, customer) dbWriteTable(con, \u0026quot;payment\u0026quot;, payment)  dbListTables(con)  TIP: You can use the following QUERY to see the structure of a table\nPRAGMA table_info(actor)  SQL references:\n https://www.w3schools.com/sql/\nExercise 1 Edit the code below to retrieve the actor ID, first name and last name for all actors using the actor table. Sort by last name and then by first name (note that the code chunk below is set up to run SQL code rather than R code).\nSELECT FROM ORDER by  Exercise 2 Retrieve the actor ID, first name, and last name for actors whose last name equals \u0026lsquo;WILLIAMS\u0026rsquo; or \u0026lsquo;DAVIS\u0026rsquo;.\nSELECT FROM WHERE ___ IN ('WILLIAMS', 'DAVIS')  Exercise 3 Write a query against the rental table that returns the IDs of the customers who rented a film on July 5, 2005 (use the rental.rental_date column, and you can use the date() function to ignore the time component). Include a single row for each distinct customer ID.\nSELECT DISTINCT FROM WHERE date(___) = '2005-07-05'  Exercise 4 Exercise 4.1 Construct a query that retrieves all rows from the payment table where the amount is either 1.99, 7.99, 9.99.\nSELECT * FROM ___ WHERE ___ IN (1.99, 7.99, 9.99)  Exercise 4.2 Construct a query that retrieves all rows from the payment table where the amount is greater then 5.\nSELECT * FROM WHERE  Exercise 4.2 Construct a query that retrieves all rows from the payment table where the amount is greater then 5 and less then 8.\nSELECT * FROM ___ WHERE ___ AND ___  Exercise 5 Retrieve all the payment IDs and their amounts from the customers whose last name is \u0026lsquo;DAVIS\u0026rsquo;.\nSELECT FROM INNER JOIN WHERE AND  Exercise 6 Exercise 6.1 Use COUNT(*) to count the number of rows in rental.\n Exercise 6.2 Use COUNT(*) and GROUP BY to count the number of rentals for each customer_id.\n Exercise 6.3 Repeat the previous query and sort by the count in descending order.\n Exercise 6.4 Repeat the previous query but use HAVING to only keep the groups with 40 or more.\n Exercise 7 Write a query that calculates a number of summary statistics for the payment table using MAX, MIN, AVG and SUM\n Exercise 7.1 Modify the above query to do those calculations for each customer_id.\n Exercise 7.2 Modify the above query to only keep the customer_ids that have more then 5 payments.\n Cleanup Run the following chunk to disconnect from the connection.\n# clean up dbDisconnect(con)  ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"3ef79e3e3bbf9328d3c80aafd06adf32","permalink":"https://uscbiostats.github.io/PM566/assignment/10-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/10-lab/","section":"assignment","summary":"Setup # install.packages(c(\u0026quot;RSQLite\u0026quot;, \u0026quot;DBI\u0026quot;)) library(RSQLite) library(DBI) # Initialize a temporary in memory database con \u0026lt;- dbConnect(SQLite(), \u0026quot;:memory:\u0026quot;) # Download tables actor \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/actor.csv\u0026quot;) rental \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/rental.csv\u0026quot;) customer \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/customer.csv\u0026quot;) payment \u0026lt;- read.csv(\u0026quot;https://raw.githubusercontent.com/ivanceras/sakila/master/csv-sakila-db/payment_p2007_01.csv\u0026quot;) # Copy data.frames to database dbWriteTable(con, \u0026quot;actor\u0026quot;, actor) dbWriteTable(con, \u0026quot;rental\u0026quot;, rental) dbWriteTable(con, \u0026quot;customer\u0026quot;, customer) dbWriteTable(con, \u0026quot;payment\u0026quot;, payment)  dbListTables(con)  TIP: You can use the following QUERY to see the structure of a table\nPRAGMA table_info(actor)  SQL references:","tags":null,"title":"Lab 10 - SQL","type":"assignment"},{"authors":null,"categories":null,"content":"                                                                        Learning Goals  Read in and process the COVID dataset from the New York Times GitHub repository Create interactive graphs of different types using plot_ly() and ggplotly() functions Customize the hoverinfo and other plot features Create a Choropleth map using plot_geo() Create an interactive table using DataTable  Lab Description We will work with the COVID data presented in lecture. Recall the dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic. We will explore cases, deaths, and their population normalized values over time to identify trends.\nSteps I. Reading and processing the New York Times (NYT) state-level COVID-19 data 1. Read in the data  Read in the COVID data with data.table:fread() from the NYT GitHub repository: “https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv” Read in the state population data with data.table:fread() from the repository: “https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv” Merge datasets  ## data extracted from New York Times state-level data from NYT Github repository # https://github.com/nytimes/covid-19-data ## state-level population information from us_census_data available on GitHub repository: # https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data # load COVID state-level data from NYT ### FINISH THE CODE HERE ### cv_states_readin \u0026lt;- as.data.frame(data.table::fread(\u0026quot;https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv\u0026quot;) ) # load state population data ### FINISH THE CODE HERE ### state_pops_readin \u0026lt;- as.data.frame(data.table::fread(\u0026quot;https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv\u0026quot;)) state_pops \u0026lt;- state_pops_readin state_pops$abb \u0026lt;- state_pops$state state_pops$state \u0026lt;- state_pops$state_name state_pops$state_name \u0026lt;- NULL ### FINISH THE CODE HERE ### cv_states \u0026lt;- merge( cv_states_readin, state_pops, by=\u0026quot;state\u0026quot;)  2. Look at the data  Inspect the dimensions, head, and tail of the data Inspect the structure of each variables. Are they in the correct format?  dim(cv_states)  ## [1] 58094 9  head(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 1 Alabama 2023-01-04 1 1587224 21263 1 4887871 96.50939 AL ## 2 Alabama 2020-04-25 1 6213 213 1 4887871 96.50939 AL ## 3 Alabama 2023-02-26 1 1638348 21400 1 4887871 96.50939 AL ## 4 Alabama 2022-12-03 1 1549285 21129 1 4887871 96.50939 AL ## 5 Alabama 2020-05-06 1 8691 343 1 4887871 96.50939 AL ## 6 Alabama 2021-04-21 1 524367 10807 1 4887871 96.50939 AL  tail(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 58089 Wyoming 2022-09-11 56 175290 1884 56 577737 5.950611 WY ## 58090 Wyoming 2022-08-21 56 173487 1871 56 577737 5.950611 WY ## 58091 Wyoming 2021-01-26 56 51152 596 56 577737 5.950611 WY ## 58092 Wyoming 2021-02-21 56 53795 662 56 577737 5.950611 WY ## 58093 Wyoming 2021-08-22 56 70671 809 56 577737 5.950611 WY ## 58094 Wyoming 2021-03-20 56 55581 693 56 577737 5.950611 WY  str(cv_states)  ## 'data.frame':\t58094 obs. of 9 variables: ## $ state : chr \u0026quot;Alabama\u0026quot; \u0026quot;Alabama\u0026quot; \u0026quot;Alabama\u0026quot; \u0026quot;Alabama\u0026quot; ... ## $ date : IDate, format: \u0026quot;2023-01-04\u0026quot; \u0026quot;2020-04-25\u0026quot; ... ## $ fips : int 1 1 1 1 1 1 1 1 1 1 ... ## $ cases : int 1587224 6213 1638348 1549285 8691 524367 1321892 1088370 1153149 814025 ... ## $ deaths : int 21263 213 21400 21129 343 10807 19676 16756 16826 15179 ... ## $ geo_id : int 1 1 1 1 1 1 1 1 1 1 ... ## $ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ... ## $ pop_density: num 96.5 96.5 96.5 96.5 96.5 ... ## $ abb : chr \u0026quot;AL\u0026quot; \u0026quot;AL\u0026quot; \u0026quot;AL\u0026quot; \u0026quot;AL\u0026quot; ...  3. Format the data  Make date into a date variable Make state and abb into a factor variable Order the data first by state, second by date Confirm the variables are now correctly formatted Inspect the range values for each variable. What is the date range? The range of cases and deaths?  # format the date cv_states$date \u0026lt;- as.Date(cv_states$date, format=\u0026quot;%Y-%m-%d\u0026quot;) # format the state variable state_list \u0026lt;- unique(cv_states$state) cv_states$state \u0026lt;- factor(cv_states$state, levels = state_list) # format the state abbreviation (abb) variable ### FINISH THE CODE HERE ### abb_list = unique(cv_states$abb) cv_states$abb = factor(cv_states$abb, levels = abb_list) # order the data first by state, second by date cv_states = cv_states[order(cv_states$state, cv_states$date),] # Confirm the variables are now correctly formatted str(cv_states)  ## 'data.frame':\t58094 obs. of 9 variables: ## $ state : Factor w/ 52 levels \u0026quot;Alabama\u0026quot;,\u0026quot;Alaska\u0026quot;,..: 1 1 1 1 1 1 1 1 1 1 ... ## $ date : Date, format: \u0026quot;2020-03-13\u0026quot; \u0026quot;2020-03-14\u0026quot; ... ## $ fips : int 1 1 1 1 1 1 1 1 1 1 ... ## $ cases : int 6 12 23 29 39 51 78 106 131 157 ... ## $ deaths : int 0 0 0 0 0 0 0 0 0 0 ... ## $ geo_id : int 1 1 1 1 1 1 1 1 1 1 ... ## $ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ... ## $ pop_density: num 96.5 96.5 96.5 96.5 96.5 ... ## $ abb : Factor w/ 52 levels \u0026quot;AL\u0026quot;,\u0026quot;AK\u0026quot;,\u0026quot;AZ\u0026quot;,..: 1 1 1 1 1 1 1 1 1 1 ...  head(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL ## 597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL ## 282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL ## 12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL ## 266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL ## 78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL  tail(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 57902 Wyoming 2023-03-18 56 185640 2009 56 577737 5.950611 WY ## 57916 Wyoming 2023-03-19 56 185640 2009 56 577737 5.950611 WY ## 57647 Wyoming 2023-03-20 56 185640 2009 56 577737 5.950611 WY ## 57867 Wyoming 2023-03-21 56 185800 2014 56 577737 5.950611 WY ## 58057 Wyoming 2023-03-22 56 185800 2014 56 577737 5.950611 WY ## 57812 Wyoming 2023-03-23 56 185800 2014 56 577737 5.950611 WY  # Inspect the range values for each variable. What is the date range? The range of cases and deaths? head(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL ## 597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL ## 282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL ## 12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL ## 266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL ## 78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL  summary(cv_states)  ## state date fips cases ## Washington : 1158 Min. :2020-01-21 Min. : 1.00 Min. : 1 ## Illinois : 1155 1st Qu.:2020-12-06 1st Qu.:16.00 1st Qu.: 112125 ## California : 1154 Median :2021-09-11 Median :29.00 Median : 418120 ## Arizona : 1153 Mean :2021-09-10 Mean :29.78 Mean : 947941 ## Massachusetts: 1147 3rd Qu.:2022-06-17 3rd Qu.:44.00 3rd Qu.: 1134318 ## Wisconsin : 1143 Max. :2023-03-23 Max. :72.00 Max. :12169158 ## (Other) :51184 ## deaths geo_id population pop_density ## Min. : 0 Min. : 1.00 Min. : 577737 Min. : 1.292 ## 1st Qu.: 1598 1st Qu.:16.00 1st Qu.: 1805832 1st Qu.: 43.659 ## Median : 5901 Median :29.00 Median : 4468402 Median : 107.860 ## Mean : 12553 Mean :29.78 Mean : 6397965 Mean : 423.031 ## 3rd Qu.: 15952 3rd Qu.:44.00 3rd Qu.: 7535591 3rd Qu.: 229.511 ## Max. :104277 Max. :72.00 Max. :39557045 Max. :11490.120 ## NA's :1106 ## abb ## WA : 1158 ## IL : 1155 ## CA : 1154 ## AZ : 1153 ## MA : 1147 ## WI : 1143 ## (Other):51184  min(cv_states$date)  ## [1] \u0026quot;2020-01-21\u0026quot;  max(cv_states$date)  ## [1] \u0026quot;2023-03-23\u0026quot;  4. Add new_cases and new_deaths and correct outliers   Add variables for new cases, new_cases, and new deaths, new_deaths:\n Hint: new_cases is equal to the difference between cases on date i and date i-1, starting on date i=2    Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?\n  Correct outliers: Set negative values for new_cases or new_deaths to 0\n  Recalculate cases and deaths as cumulative sum of updates new_cases and new_deaths\n  # Add variables for new_cases and new_deaths: for (i in 1:length(state_list)) { cv_subset = subset(cv_states, state == state_list[i]) cv_subset = cv_subset[order(cv_subset$date),] # add starting level for new cases and deaths cv_subset$new_cases = cv_subset$cases[1] cv_subset$new_deaths = cv_subset$deaths[1] #### FINISH THE CODE HERE ### for (j in 2:nrow(cv_subset)) { cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1] cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1] } # include in main dataset cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths } # Inspect outliers in new_cases and new_deaths using plotly ### FINISH THE CODE HERE ### p1\u0026lt;-ggplot(cv_states, aes( x=date, y=new_cases, color=state ) ) + geom_line() + geom_point(size = .5, alpha = 0.5) ggplotly(p1)   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(y) \",\"x\"],\"110d419b04a8b\":[\"function (y) \",\"x\"]},\"highlight\":{\"on\":\"plotly_click\",\"persistent\":false,\"dynamic\":false,\"selectize\":false,\"opacityDim\":0.20000000000000001,\"selected\":{\"opacity\":1},\"debounce\":0},\"shinyEvents\":[\"plotly_hover\",\"plotly_click\",\"plotly_selected\",\"plotly_relayout\",\"plotly_brushed\",\"plotly_brushing\",\"plotly_clickannotation\",\"plotly_doubleclick\",\"plotly_deselect\",\"plotly_afterplot\",\"plotly_sunburstclick\"],\"base_url\":\"https://plot.ly\"},\"evals\":[],\"jsHooks\":[]} p1\u0026lt;-NULL # to clear from workspace ### FINISH THE CODE HERE ### p2\u0026lt;-ggplot(cv_states, aes(x=date, y=new_deaths, color=state ) ) + geom_line() + geom_point(size = .5, alpha = 0.5) ggplotly(p2)   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2020-03-13\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-14\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-15\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-16\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-17\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-18\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-19\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-20\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-21\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-22\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-23\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-24\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-25\nnew_deaths: 1\nstate: Alabama\",\"date: 2020-03-26\nnew_deaths: 2\nstate: Alabama\",\"date: 2020-03-27\nnew_deaths: 1\nstate: Alabama\",\"date: 2020-03-28\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-03-29\nnew_deaths: 1\nstate: Alabama\",\"date: 2020-03-30\nnew_deaths: 6\nstate: Alabama\",\"date: 2020-03-31\nnew_deaths: 3\nstate: Alabama\",\"date: 2020-04-01\nnew_deaths: 14\nstate: Alabama\",\"date: 2020-04-02\nnew_deaths: 4\nstate: Alabama\",\"date: 2020-04-03\nnew_deaths: 6\nstate: Alabama\",\"date: 2020-04-04\nnew_deaths: 6\nstate: Alabama\",\"date: 2020-04-05\nnew_deaths: 1\nstate: Alabama\",\"date: 2020-04-06\nnew_deaths: 8\nstate: Alabama\",\"date: 2020-04-07\nnew_deaths: 11\nstate: Alabama\",\"date: 2020-04-08\nnew_deaths: 3\nstate: Alabama\",\"date: 2020-04-09\nnew_deaths: 11\nstate: Alabama\",\"date: 2020-04-10\nnew_deaths: 2\nstate: Alabama\",\"date: 2020-04-11\nnew_deaths: 13\nstate: Alabama\",\"date: 2020-04-12\nnew_deaths: 0\nstate: Alabama\",\"date: 2020-04-13\nnew_deaths: 11\nstate: Alabama\",\"date: 2020-04-14\nnew_deaths: 10\nstate: Alabama\",\"date: 2020-04-15\nnew_deaths: 9\nstate: Alabama\",\"date: 2020-04-16\nnew_deaths: 14\nstate: Alabama\",\"date: 2020-04-17\nnew_deaths: 14\nstate: Alabama\",\"date: 2020-04-18\nnew_deaths: -4\nstate: Alabama\",\"date: 2020-04-19\nnew_deaths: 13\nstate: Alabama\",\"date: 2020-04-20\nnew_deaths: 4\nstate: Alabama\",\"date: 2020-04-21\nnew_deaths: 22\nstate: Alabama\",\"date: 2020-04-22\nnew_deaths: 15\nstate: Alabama\",\"date: 2020-04-23\nnew_deaths: -4\nstate: Alabama\",\"date: 2020-04-24\nnew_deaths: 12\nstate: Alabama\",\"date: 2020-04-25\nnew_deaths: 4\nstate: Alabama\",\"date: 2020-04-26\nnew_deaths: 6\nstate: Alabama\",\"date: 2020-04-27\nnew_deaths: 9\nstate: Alabama\",\"date: 2020-04-28\nnew_deaths: 14\nstate: Alabama\",\"date: 2020-04-29\nnew_deaths: 20\nstate: Alabama\",\"date: 2020-04-30\nnew_deaths: 10\nstate: Alabama\",\"date: 2020-05-01\nnew_deaths: 17\nstate: Alabama\",\"date: 2020-05-02\nnew_deaths: -1\nstate: Alabama\",\"date: 2020-05-03\nnew_deaths: 2\nstate: Alabama\",\"date: 2020-05-04\nnew_deaths: 8\nstate: Alabama\",\"date: 2020-05-05\nnew_deaths: 17\nstate: Alabama\",\"date: 2020-05-06\nnew_deaths: 28\nstate: Alabama\",\"date: 2020-05-07\nnew_deaths: 26\nstate: Alabama\",\"date: 2020-05-08\nnew_deaths: 14\nstate: Alabama\",\"date: 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(y) \",\"x\"],\"110d44f19d980\":[\"function (y) \",\"x\"]},\"highlight\":{\"on\":\"plotly_click\",\"persistent\":false,\"dynamic\":false,\"selectize\":false,\"opacityDim\":0.20000000000000001,\"selected\":{\"opacity\":1},\"debounce\":0},\"shinyEvents\":[\"plotly_hover\",\"plotly_click\",\"plotly_selected\",\"plotly_relayout\",\"plotly_brushed\",\"plotly_brushing\",\"plotly_clickannotation\",\"plotly_doubleclick\",\"plotly_deselect\",\"plotly_afterplot\",\"plotly_sunburstclick\"],\"base_url\":\"https://plot.ly\"},\"evals\":[],\"jsHooks\":[]} p2\u0026lt;-NULL # to clear from workspace # set negative new case or death counts to 0 cv_states$new_cases[cv_states$new_cases\u0026lt;0] = 0 cv_states$new_deaths[cv_states$new_deaths\u0026lt;0] = 0 # Recalculate `cases` and `deaths` as cumulative sum of updates `new_cases` and `new_deaths` for (i in 1:length(state_list)) { cv_subset = subset(cv_states, state == state_list[i]) # add starting level for new cases and deaths cv_subset$cases = cv_subset$cases[1] cv_subset$deaths = cv_subset$deaths[1] for (j in 2:nrow(cv_subset)) { cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1] cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1] } # include in main dataset cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths }  5. Add additional variables   Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:\n per100k = cases per 100,000 population newper100k= new cases per 100,000 deathsper100k = deaths per 100,000 newdeathsper100k = new deaths per 100,000    Add a “naive CFR” variable representing deaths / cases on each date for each state\n  Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture\n  # add population normalized (by 100,000) counts for each variable cv_states$per100k = as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1)) cv_states$newper100k = as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1)) cv_states$deathsper100k = as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1)) cv_states$newdeathsper100k = as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1)) # add a naive_CFR variable = deaths / cases cv_states = cv_states %\u0026gt;% mutate(naive_CFR = round((deaths*100/cases),2)) # create a `cv_states_today` variable ### FINISH THE CODE HERE ### max_date \u0026lt;- max(cv_states$date) cv_states_today = cv_states %\u0026gt;% filter(date==as.Date(max_date))  II. Interactive plots 6. Explore scatterplots using plot_ly()  Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)  Use hover to identify any outliers. Remove those outliers and replot.   Choose one plot. 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195.4\",\"type\":\"scatter\",\"name\":\"Alaska\",\"textfont\":{\"color\":\"rgba(135,188,156,1)\",\"size\":5.2663096276619381},\"error_y\":{\"color\":\"rgba(135,188,156,1)\",\"width\":5.2663096276619381},\"error_x\":{\"color\":\"rgba(135,188,156,1)\",\"width\":5.2663096276619381},\"line\":{\"color\":\"rgba(135,188,156,1)\",\"width\":5.2663096276619381},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[63.135855048000003],\"y\":[463.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(160,182,146,1)\",\"size\":[15.99568224761712],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(160,182,146,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Arizona:\nCases per 100k: 34178.7\nDeaths per 100k: 463.6\",\"type\":\"scatter\",\"name\":\"Arizona\",\"textfont\":{\"color\":\"rgba(160,182,146,1)\",\"size\":15.99568224761712},\"error_y\":{\"color\":\"rgba(160,182,146,1)\",\"width\":15.99568224761712},\"error_x\":{\"color\":\"rgba(160,182,146,1)\",\"width\":15.99568224761712},\"line\":{\"color\":\"rgba(160,182,146,1)\",\"width\":15.99568224761712},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[57.919684465000003],\"y\":[439.5],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(181,176,137,1)\",\"size\":[9.0623019782701117],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(181,176,137,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Arkansas:\nCases per 100k: 33469.2\nDeaths per 100k: 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266.1\",\"type\":\"scatter\",\"name\":\"California\",\"textfont\":{\"color\":\"rgba(200,169,128,1)\",\"size\":70},\"error_y\":{\"color\":\"rgba(200,169,128,1)\",\"width\":70},\"error_x\":{\"color\":\"rgba(200,169,128,1)\",\"width\":70},\"line\":{\"color\":\"rgba(200,169,128,1)\",\"width\":70},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[54.955977558999997],\"y\":[256.30000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(217,161,119,1)\",\"size\":[13.534239627855888],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(217,161,119,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Colorado:\nCases per 100k: 31270.9\nDeaths per 100k: 256.3\",\"type\":\"scatter\",\"name\":\"Colorado\",\"textfont\":{\"color\":\"rgba(217,161,119,1)\",\"size\":13.534239627855888},\"error_y\":{\"color\":\"rgba(217,161,119,1)\",\"width\":13.534239627855888},\"error_x\":{\"color\":\"rgba(217,161,119,1)\",\"width\":13.534239627855888},\"line\":{\"color\":\"rgba(217,161,119,1)\",\"width\":13.534239627855888},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[737.74459965000005],\"y\":[344.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(233,153,110,1)\",\"size\":[9.9941964080019083],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(233,153,110,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Connecticut:\nCases per 100k: 27388\nDeaths per 100k: 344.6\",\"type\":\"scatter\",\"name\":\"Connecticut\",\"textfont\":{\"color\":\"rgba(233,153,110,1)\",\"size\":9.9941964080019083},\"error_y\":{\"color\":\"rgba(233,153,110,1)\",\"width\":9.9941964080019083},\"error_x\":{\"color\":\"rgba(233,153,110,1)\",\"width\":9.9941964080019083},\"line\":{\"color\":\"rgba(233,153,110,1)\",\"width\":9.9941964080019083},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[496.43246049999999],\"y\":[346.69999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(248,144,101,1)\",\"size\":[5.6494012156398465],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(248,144,101,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Delaware:\nCases per 100k: 34553.4\nDeaths per 100k: 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266.7\",\"type\":\"scatter\",\"name\":\"Minnesota\",\"textfont\":{\"color\":\"rgba(224,152,179,1)\",\"size\":13.39352330215816},\"error_y\":{\"color\":\"rgba(224,152,179,1)\",\"width\":13.39352330215816},\"error_x\":{\"color\":\"rgba(224,152,179,1)\",\"width\":13.39352330215816},\"line\":{\"color\":\"rgba(224,152,179,1)\",\"width\":13.39352330215816},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[63.645625459000001],\"y\":[450.5],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(217,164,165,1)\",\"size\":[9.0167861625455217],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(217,164,165,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Mississippi:\nCases per 100k: 33250.5\nDeaths per 100k: 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348.9\",\"type\":\"scatter\",\"name\":\"Montana\",\"textfont\":{\"color\":\"rgba(201,186,137,1)\",\"size\":5.8080420514391893},\"error_y\":{\"color\":\"rgba(201,186,137,1)\",\"width\":5.8080420514391893},\"error_x\":{\"color\":\"rgba(201,186,137,1)\",\"width\":5.8080420514391893},\"line\":{\"color\":\"rgba(201,186,137,1)\",\"width\":5.8080420514391893},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[25.114922059000001],\"y\":[275.19999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(191,196,121,1)\",\"size\":[7.253747424146165],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(191,196,121,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Nebraska:\nCases per 100k: 29875.6\nDeaths per 100k: 275.2\",\"type\":\"scatter\",\"name\":\"Nebraska\",\"textfont\":{\"color\":\"rgba(191,196,121,1)\",\"size\":7.253747424146165},\"error_y\":{\"color\":\"rgba(191,196,121,1)\",\"width\":7.253747424146165},\"error_x\":{\"color\":\"rgba(191,196,121,1)\",\"width\":7.253747424146165},\"line\":{\"color\":\"rgba(191,196,121,1)\",\"width\":7.253747424146165},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[27.6406025],\"y\":[399],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(180,205,105,1)\",\"size\":[9.0965985081110219],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(180,205,105,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Nevada:\nCases per 100k: 29431.5\nDeaths per 100k: 399\",\"type\":\"scatter\",\"name\":\"Nevada\",\"textfont\":{\"color\":\"rgba(180,205,105,1)\",\"size\":9.0965985081110219},\"error_y\":{\"color\":\"rgba(180,205,105,1)\",\"width\":9.0965985081110219},\"error_x\":{\"color\":\"rgba(180,205,105,1)\",\"width\":9.0965985081110219},\"line\":{\"color\":\"rgba(180,205,105,1)\",\"width\":9.0965985081110219},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[151.50059716000001],\"y\":[222.5],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(168,215,87,1)\",\"size\":[6.298557301222484],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(168,215,87,1)\"}},\"hoverinfo\":\"text\",\"text\":\"New Hampshire:\nCases per 100k: 27991.2\nDeaths per 100k: 222.5\",\"type\":\"scatter\",\"name\":\"New 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364.9\",\"type\":\"scatter\",\"name\":\"Ohio\",\"textfont\":{\"color\":\"rgba(239,217,56,1)\",\"size\":23.529339335629047},\"error_y\":{\"color\":\"rgba(239,217,56,1)\",\"width\":23.529339335629047},\"error_x\":{\"color\":\"rgba(239,217,56,1)\",\"width\":23.529339335629047},\"line\":{\"color\":\"rgba(239,217,56,1)\",\"width\":23.529339335629047},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[57.482602245000002],\"y\":[419.80000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(250,217,50,1)\",\"size\":[10.611880795831471],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(250,217,50,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Oklahoma:\nCases per 100k: 32863.5\nDeaths per 100k: 419.8\",\"type\":\"scatter\",\"name\":\"Oklahoma\",\"textfont\":{\"color\":\"rgba(250,217,50,1)\",\"size\":10.611880795831471},\"error_y\":{\"color\":\"rgba(250,217,50,1)\",\"width\":10.611880795831471},\"error_x\":{\"color\":\"rgba(250,217,50,1)\",\"width\":10.611880795831471},\"line\":{\"color\":\"rgba(250,217,50,1)\",\"width\":10.611880795831471},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[43.658853835000002],\"y\":[225.59999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(253,215,59,1)\",\"size\":[11.024823221592339],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(253,215,59,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Oregon:\nCases per 100k: 23078.6\nDeaths per 100k: 225.6\",\"type\":\"scatter\",\"name\":\"Oregon\",\"textfont\":{\"color\":\"rgba(253,215,59,1)\",\"size\":11.024823221592339},\"error_y\":{\"color\":\"rgba(253,215,59,1)\",\"width\":11.024823221592339},\"error_x\":{\"color\":\"rgba(253,215,59,1)\",\"width\":11.024823221592339},\"line\":{\"color\":\"rgba(253,215,59,1)\",\"width\":11.024823221592339},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[286.23455445000002],\"y\":[396.30000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(250,212,76,1)\",\"size\":[25.393024806905242],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(250,212,76,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Pennsylvania:\nCases per 100k: 27668.6\nDeaths per 100k: 396.3\",\"type\":\"scatter\",\"name\":\"Pennsylvania\",\"textfont\":{\"color\":\"rgba(250,212,76,1)\",\"size\":25.393024806905242},\"error_y\":{\"color\":\"rgba(250,212,76,1)\",\"width\":25.393024806905242},\"error_x\":{\"color\":\"rgba(250,212,76,1)\",\"width\":25.393024806905242},\"line\":{\"color\":\"rgba(250,212,76,1)\",\"width\":25.393024806905242},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[1022.6505771],\"y\":[371.10000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(244,207,104,1)\",\"size\":[5.7997209699053665],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(244,207,104,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Rhode Island:\nCases per 100k: 43691.6\nDeaths per 100k: 371.1\",\"type\":\"scatter\",\"name\":\"Rhode 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329.6\",\"type\":\"scatter\",\"name\":\"Texas\",\"textfont\":{\"color\":\"rgba(227,195,149,1)\",\"size\":51.898395938686235},\"error_y\":{\"color\":\"rgba(227,195,149,1)\",\"width\":51.898395938686235},\"error_x\":{\"color\":\"rgba(227,195,149,1)\",\"width\":51.898395938686235},\"line\":{\"color\":\"rgba(227,195,149,1)\",\"width\":51.898395938686235},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[38.458214685000002],\"y\":[168.19999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(221,193,154,1)\",\"size\":[9.3078989498736107],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(221,193,154,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Utah:\nCases per 100k: 34579\nDeaths per 100k: 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149.9\",\"type\":\"scatter\",\"name\":\"Vermont\",\"textfont\":{\"color\":\"rgba(214,191,158,1)\",\"size\":5.0809796315522071},\"error_y\":{\"color\":\"rgba(214,191,158,1)\",\"width\":5.0809796315522071},\"error_x\":{\"color\":\"rgba(214,191,158,1)\",\"width\":5.0809796315522071},\"line\":{\"color\":\"rgba(214,191,158,1)\",\"width\":5.0809796315522071},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[215.73630392999999],\"y\":[280.69999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(208,188,162,1)\",\"size\":[18.24027147942185],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(208,188,162,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Virginia:\nCases per 100k: 26992\nDeaths per 100k: 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215\",\"type\":\"scatter\",\"name\":\"Washington\",\"textfont\":{\"color\":\"rgba(201,186,167,1)\",\"size\":16.602579245378088},\"error_y\":{\"color\":\"rgba(201,186,167,1)\",\"width\":16.602579245378088},\"error_x\":{\"color\":\"rgba(201,186,167,1)\",\"width\":16.602579245378088},\"line\":{\"color\":\"rgba(201,186,167,1)\",\"width\":16.602579245378088},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[75.113987850000001],\"y\":[466],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(194,184,171,1)\",\"size\":[7.0479115483527828],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(194,184,171,1)\"}},\"hoverinfo\":\"text\",\"text\":\"West Virginia:\nCases per 100k: 35756.9\nDeaths per 100k: 466\",\"type\":\"scatter\",\"name\":\"West Virginia\",\"textfont\":{\"color\":\"rgba(194,184,171,1)\",\"size\":7.0479115483527828},\"error_y\":{\"color\":\"rgba(194,184,171,1)\",\"width\":7.0479115483527828},\"error_x\":{\"color\":\"rgba(194,184,171,1)\",\"width\":7.0479115483527828},\"line\":{\"color\":\"rgba(194,184,171,1)\",\"width\":7.0479115483527828},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[107.32815663],\"y\":[284],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(186,181,175,1)\",\"size\":[13.731017364392411],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(186,181,175,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Wisconsin:\nCases per 100k: 34652.1\nDeaths per 100k: 284\",\"type\":\"scatter\",\"name\":\"Wisconsin\",\"textfont\":{\"color\":\"rgba(186,181,175,1)\",\"size\":13.731017364392411},\"error_y\":{\"color\":\"rgba(186,181,175,1)\",\"width\":13.731017364392411},\"error_x\":{\"color\":\"rgba(186,181,175,1)\",\"width\":13.731017364392411},\"line\":{\"color\":\"rgba(186,181,175,1)\",\"width\":13.731017364392411},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[5.9506106574000004],\"y\":[348.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(179,179,179,1)\",\"size\":[5],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(179,179,179,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Wyoming:\nCases per 100k: 32269.4\nDeaths per 100k: 348.6\",\"type\":\"scatter\",\"name\":\"Wyoming\",\"textfont\":{\"color\":\"rgba(179,179,179,1)\",\"size\":5},\"error_y\":{\"color\":\"rgba(179,179,179,1)\",\"width\":5},\"error_x\":{\"color\":\"rgba(179,179,179,1)\",\"width\":5},\"line\":{\"color\":\"rgba(179,179,179,1)\",\"width\":5},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null}],\"highlight\":{\"on\":\"plotly_click\",\"persistent\":false,\"dynamic\":false,\"selectize\":false,\"opacityDim\":0.20000000000000001,\"selected\":{\"opacity\":1},\"debounce\":0},\"shinyEvents\":[\"plotly_hover\",\"plotly_click\",\"plotly_selected\",\"plotly_relayout\",\"plotly_brushed\",\"plotly_brushing\",\"plotly_clickannotation\",\"plotly_doubleclick\",\"plotly_deselect\",\"plotly_afterplot\",\"plotly_sunburstclick\"],\"base_url\":\"https://plot.ly\"},\"evals\":[],\"jsHooks\":[]} 7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()  For pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly()  What’s the geom_*() we need here?   Explore the pattern between \\(x\\) and \\(y\\) using geom_smooth()  Explain what you see. Do you think pop_density is a correlate of newdeathsper100k?    ### FINISH THE CODE HERE ### p \u0026lt;- ggplot(cv_states_today_scatter, aes(x=pop_density, y=per100k, color=state, size=population)) + geom_point() + geom_smooth() ggplotly(p)   {\"x\":{\"data\":[{\"x\":[96.509388650000005],\"y\":[33724],\"text\":\"pop_density: 96.509389\nper100k: 33724.0\nstate: Alabama\npopulation: 4887871\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(248,118,109,1)\",\"opacity\":1,\"size\":10.063522757639795,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(248,118,109,1)\"}},\"hoveron\":\"points\",\"name\":\"Alabama\",\"legendgroup\":\"Alabama\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[1.2915230608999999],\"y\":[41888],\"text\":\"pop_density: 1.291523\nper100k: 41888.0\nstate: Alaska\npopulation: 737438\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(243,123,89,1)\",\"opacity\":1,\"size\":4.989134516452693,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(243,123,89,1)\"}},\"hoveron\":\"points\",\"name\":\"Alaska\",\"legendgroup\":\"Alaska\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[63.135855048000003],\"y\":[34178.699999999997],\"text\":\"pop_density: 63.135855\nper100k: 34178.7\nstate: Arizona\npopulation: 7171646\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(238,128,66,1)\",\"opacity\":1,\"size\":11.552049076298584,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(238,128,66,1)\"}},\"hoveron\":\"points\",\"name\":\"Arizona\",\"legendgroup\":\"Arizona\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[57.919684465000003],\"y\":[33469.199999999997],\"text\":\"pop_density: 57.919684\nper100k: 33469.2\nstate: Arkansas\npopulation: 3013825\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(231,133,30,1)\",\"opacity\":1,\"size\":8.5038218636115648,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(231,133,30,1)\"}},\"hoveron\":\"points\",\"name\":\"Arkansas\",\"legendgroup\":\"Arkansas\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[253.9065023],\"y\":[30767.599999999999],\"text\":\"pop_density: 253.906502\nper100k: 30767.6\nstate: California\npopulation: 39557045\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(224,138,0,1)\",\"opacity\":1,\"size\":22.677165354330711,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(224,138,0,1)\"}},\"hoveron\":\"points\",\"name\":\"California\",\"legendgroup\":\"California\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[54.955977558999997],\"y\":[31270.900000000001],\"text\":\"pop_density: 54.955978\nper100k: 31270.9\nstate: Colorado\npopulation: 5695564\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(217,143,0,1)\",\"opacity\":1,\"size\":10.627046763294087,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(217,143,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Colorado\",\"legendgroup\":\"Colorado\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[737.74459965000005],\"y\":[27388],\"text\":\"pop_density: 737.744600\nper100k: 27388.0\nstate: Connecticut\npopulation: 3572665\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(208,148,0,1)\",\"opacity\":1,\"size\":9.017746558042079,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(208,148,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Connecticut\",\"legendgroup\":\"Connecticut\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[496.43246049999999],\"y\":[34553.400000000001],\"text\":\"pop_density: 496.432460\nper100k: 34553.4\nstate: Delaware\npopulation: 967171\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(199,152,0,1)\",\"opacity\":1,\"size\":5.6684207064933991,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(199,152,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Delaware\",\"legendgroup\":\"Delaware\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[397.01575444999997],\"y\":[35603.900000000001],\"text\":\"pop_density: 397.015754\nper100k: 35603.9\nstate: Florida\npopulation: 21299325\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(189,157,0,1)\",\"opacity\":1,\"size\":17.558028671037185,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(189,157,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Florida\",\"legendgroup\":\"Florida\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[182.26478911000001],\"y\":[28379.299999999999],\"text\":\"pop_density: 182.264789\nper100k: 28379.3\nstate: Georgia\npopulation: 10519475\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(178,161,0,1)\",\"opacity\":1,\"size\":13.323333834114047,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(178,161,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Georgia\",\"legendgroup\":\"Georgia\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[221.17691551999999],\"y\":[26217.200000000001],\"text\":\"pop_density: 221.176916\nper100k: 26217.2\nstate: Hawaii\npopulation: 1420491\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(165,165,0,1)\",\"opacity\":1,\"size\":6.5582224950863113,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(165,165,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Hawaii\",\"legendgroup\":\"Hawaii\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[21.225798535999999],\"y\":[29809.5],\"text\":\"pop_density: 21.225799\nper100k: 29809.5\nstate: Idaho\npopulation: 1754208\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(152,168,0,1)\",\"opacity\":1,\"size\":7.0626045807563775,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(152,168,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Idaho\",\"legendgroup\":\"Idaho\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[229.51115612999999],\"y\":[32241.599999999999],\"text\":\"pop_density: 229.511156\nper100k: 32241.6\nstate: Illinois\npopulation: 12741080\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(137,172,0,1)\",\"opacity\":1,\"size\":14.335954229389923,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(137,172,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Illinois\",\"legendgroup\":\"Illinois\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[186.78751987000001],\"y\":[30762.099999999999],\"text\":\"pop_density: 186.787520\nper100k: 30762.1\nstate: Indiana\npopulation: 6691878\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(119,175,0,1)\",\"opacity\":1,\"size\":11.263948122019215,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(119,175,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Indiana\",\"legendgroup\":\"Indiana\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[56.507023549000003],\"y\":[28750.900000000001],\"text\":\"pop_density: 56.507024\nper100k: 28750.9\nstate: Iowa\npopulation: 3156145\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(98,178,0,1)\",\"opacity\":1,\"size\":8.6398633891624854,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(98,178,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Iowa\",\"legendgroup\":\"Iowa\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[35.610732968000001],\"y\":[32318.099999999999],\"text\":\"pop_density: 35.610733\nper100k: 32318.1\nstate: Kansas\npopulation: 2911505\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(69,181,0,1)\",\"opacity\":1,\"size\":8.4035432449360172,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(69,181,0,1)\"}},\"hoveron\":\"points\",\"name\":\"Kansas\",\"legendgroup\":\"Kansas\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[113.15179107],\"y\":[38918.099999999999],\"text\":\"pop_density: 113.151791\nper100k: 38918.1\nstate: Kentucky\npopulation: 4468402\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(0,183,9,1)\",\"opacity\":1,\"size\":9.7499131078189603,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(0,183,9,1)\"}},\"hoveron\":\"points\",\"name\":\"Kentucky\",\"legendgroup\":\"Kentucky\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[107.86011234],\"y\":[33939.800000000003],\"text\":\"pop_density: 107.860112\nper100k: 33939.8\nstate: Louisiana\npopulation: 4659978\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(0,186,56,1)\",\"opacity\":1,\"size\":9.8951375074608201,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(0,186,56,1)\"}},\"hoveron\":\"points\",\"name\":\"Louisiana\",\"legendgroup\":\"Louisiana\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[43.391688418999998],\"y\":[23909.400000000001],\"text\":\"pop_density: 43.391688\nper100k: 23909.4\nstate: Maine\npopulation: 1338404\",\"type\":\"scatter\",\"mode\":\"markers\",\"marker\":{\"autocolorscale\":false,\"color\":\"rgba(0,188,81,1)\",\"opacity\":1,\"size\":6.4194292013435774,\"symbol\":\"circle\",\"line\":{\"width\":1.8897637795275593,\"color\":\"rgba(0,188,81,1)\"}},\"hoveron\":\"points\",\"name\":\"Maine\",\"legendgroup\":\"Maine\",\"showlegend\":true,\"xaxis\":\"x\",\"yaxis\":\"y\",\"hoverinfo\":\"text\",\"frame\":null},{\"x\":[622.26172744999997],\"y\":[22671.200000000001],\"text\":\"pop_density: 622.261727\nper100k: 22671.2\nstate: Maryland\npopulation: 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Multiple line chart  Create a line chart of the naive_CFR for all states over time using plot_ly()  Use hoverinfo to identify states that had a “first peak” Use the zoom and pan tools to inspect the naive_CFR for the states that had a “first peak” in September. How have they changed over time?   Create one more line chart, for Texas only, which shows new_cases and new_deaths together in one plot. Hint: use add_lines()  Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?    # Line chart for naive_CFR for all states over time using `plot_ly()` plot_ly(cv_states, x = ~date, y = ~naive_CFR, color = ~state, type = \u0026quot;scatter\u0026quot;, mode = \u0026quot;lines\u0026quot;)   {\"x\":{\"visdat\":{\"110d4369e977d\":[\"function () 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# Line chart for Texas showing new_cases and new_deaths together ### FINISH THE CODE HERE ### cv_states %\u0026gt;% filter(state==\u0026quot;Texas\u0026quot;) %\u0026gt;% plot_ly(x = ~date, y = ~new_cases, type = \u0026quot;scatter\u0026quot;, mode = \u0026quot;lines\u0026quot;) %\u0026gt;% add_lines(x = ~date, y = ~new_deaths, type = \u0026quot;scatter\u0026quot;, mode = \u0026quot;lines\u0026quot;)   {\"x\":{\"visdat\":{\"110d465ebab8e\":[\"function () 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Heatmaps Create a heatmap to visualize new_cases for each state on each date greater than April 1st, 2020\n  Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes\n  Use plot_ly() to create a heatmap out of this matrix\n  Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks\n  # Map state, date, and new_cases to a matrix library(tidyr) cv_states_mat \u0026lt;- cv_states %\u0026gt;% select(state, date, new_cases) %\u0026gt;% filter(date\u0026gt;as.Date(\u0026quot;2020-04-01\u0026quot;)) cv_states_mat2 \u0026lt;- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases)) rownames(cv_states_mat2) \u0026lt;- cv_states_mat2$date cv_states_mat2$date \u0026lt;- NULL cv_states_mat2 \u0026lt;- as.matrix(cv_states_mat2) # Create a heatmap using plot_ly() 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# Create a second heatmap after filtering to only include dates every other week filter_dates \u0026lt;- seq(as.Date(\u0026quot;2020-04-01\u0026quot;), as.Date(\u0026quot;2020-10-01\u0026quot;), by=\u0026quot;2 weeks\u0026quot;) ### FINISH THE CODE HERE ### cv_states_mat \u0026lt;- cv_states %\u0026gt;% select(state, date, new_cases) %\u0026gt;% filter( date %in% filter_dates ) cv_states_mat2 \u0026lt;- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases)) rownames(cv_states_mat2) \u0026lt;- cv_states_mat2$date cv_states_mat2$date \u0026lt;- NULL cv_states_mat2 \u0026lt;- as.matrix(cv_states_mat2) # Create a heatmap using plot_ly() plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=\u0026quot;heatmap\u0026quot;, showscale=T)   {\"x\":{\"visdat\":{\"110d46b8cd499\":[\"function () 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Virginia\",\"Wisconsin\",\"Wyoming\"],\"y\":[\"2020-04-01\",\"2020-04-15\",\"2020-04-29\",\"2020-05-13\",\"2020-05-27\",\"2020-06-10\",\"2020-06-24\",\"2020-07-08\",\"2020-07-22\",\"2020-08-05\",\"2020-08-19\",\"2020-09-02\",\"2020-09-16\",\"2020-09-30\"],\"z\":[[109,10,115,60,1274,356,429,49,91,1027,631,34,143,986,401,51,54,84,1187,41,323,1118,1663,60,136,230,21,50,166,48,3559,48,8153,64,21,349,153,47,808,47,78,210,22,640,814,125,28,262,296,29,201,17],[288,8,156,101,1349,339,766,88,139,906,670,13,49,1346,432,96,63,85,433,36,624,1755,1049,114,273,209,5,64,75,48,2206,77,11755,131,24,511,79,30,1213,51,278,103,181,210,803,129,7,329,95,28,166,19],[175,4,254,80,2334,441,455,80,112,347,693,3,32,2253,626,467,210,163,374,16,742,1963,974,463,227,120,0,263,122,44,2408,238,4718,379,42,534,63,61,868,65,321,146,61,281,971,156,0,623,325,14,232,8],[236,0,440,72,2068,339,522,211,99,479,555,3,31,1706,368,377,200,99,612,38,753,1165,361,423,182,245,1,340,171,60,817,152,2123,448,76,470,121,57,787,30,221,103,69,484,1474,170,2,946,110,26,290,13],[382,1,479,97,1949,222,0,30,72,379,647,1,32,1126,386,447,101,138,442,28,752,527,504,505,313,187,2,357,91,55,864,122,1132,599,20,433,92,73,773,73,143,207,57,400,1532,88,4,907,287,45,645,10],[567,22,1585,288,2894,151,168,36,63,1371,696,3,36,568,312,462,145,138,418,31,571,267,187,366,374,266,7,137,170,46,550,145,664,912,40,413,116,72,446,144,65,531,81,514,2500,343,11,439,144,14,2353,10],[967,21,1810,697,4850,262,14,42,34,5511,1713,14,238,743,293,427,451,199,882,23,332,172,338,297,526,330,23,129,402,27,158,152,575,1437,44,632,481,145,492,135,73,1284,66,804,6584,461,20,520,622,36,465,28],[1177,49,3551,734,8538,468,75,48,73,9989,2831,23,422,945,450,563,650,384,1888,20,469,264,649,457,674,777,44,232,530,20,252,290,691,1473,72,1277,673,218,831,31,50,1557,79,2113,9952,718,2,635,598,202,692,29],[1455,94,1929,591,12162,595,127,46,102,9785,3255,17,496,1608,782,487,770,531,2773,0,627,287,707,504,1547,1206,99,296,1154,33,335,311,706,2091,160,1527,975,179,807,98,76,1705,58,2146,10967,588,0,1022,696,262,884,50],[952,58,1693,912,5518,651,115,159,45,5409,3933,172,497,1876,741,605,788,614,1486,17,584,440,752,618,1245,1061,121,311,663,26,335,226,639,1218,124,1199,1103,279,727,327,91,1282,89,1653,9362,460,5,798,561,108,984,39],[1117,70,637,729,6243,269,59,50,29,4115,2574,260,343,2082,509,340,701,688,796,21,435,352,708,557,1348,1043,109,215,375,19,319,170,626,1350,186,958,597,202,664,221,103,739,123,2014,5403,362,3,737,562,70,687,70],[623,72,523,615,5407,274,102,14,28,2402,1938,338,356,2167,865,617,1038,942,1151,19,455,0,634,733,781,1560,189,421,219,12,308,152,710,1174,267,1157,719,127,986,582,76,657,254,1559,4310,445,2,927,493,135,573,45],[927,52,696,722,3401,493,135,97,56,2355,2007,101,288,1902,580,958,823,737,612,23,642,306,804,462,711,1310,206,449,248,32,390,118,653,1314,269,1033,970,192,808,112,108,652,297,1708,4525,753,0,845,492,220,1499,128],[1147,105,326,942,3186,632,221,82,26,1948,1466,119,695,2262,968,1014,1006,909,610,54,412,532,1175,688,552,1249,354,520,393,33,708,278,1005,1620,445,1080,980,257,916,288,192,308,392,1498,3484,917,3,755,549,156,2428,127]],\"type\":\"heatmap\",\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null}],\"highlight\":{\"on\":\"plotly_click\",\"persistent\":false,\"dynamic\":false,\"selectize\":false,\"opacityDim\":0.20000000000000001,\"selected\":{\"opacity\":1},\"debounce\":0},\"shinyEvents\":[\"plotly_hover\",\"plotly_click\",\"plotly_selected\",\"plotly_relayout\",\"plotly_brushed\",\"plotly_brushing\",\"plotly_clickannotation\",\"plotly_doubleclick\",\"plotly_deselect\",\"plotly_afterplot\",\"plotly_sunburstclick\"],\"base_url\":\"https://plot.ly\"},\"evals\":[],\"jsHooks\":[]} 10. Map  Create a map to visualize the naive_CFR by state on May 1st, 2020 Compare with a map visualizing the naive_CFR by state on most recent date Plot the two maps side by side using subplot(). Make sure the shading is for the same range of values (google is your friend for this) Describe the difference in the pattern of the CFR.  ### For May 1 2020 # Extract the data for each state by its abbreviation cv_CFR \u0026lt;- cv_states %\u0026gt;% filter(date==\u0026quot;2020-05-01\u0026quot;) %\u0026gt;% select(state, abb, naive_CFR, cases, deaths) # select data cv_CFR$state_name \u0026lt;- cv_CFR$state cv_CFR$state \u0026lt;- cv_CFR$abb cv_CFR$abb \u0026lt;- NULL # Create hover text cv_CFR$hover \u0026lt;- with(cv_CFR, paste(state_name, '\u0026lt;br\u0026gt;', \u0026quot;CFR: \u0026quot;, naive_CFR, '\u0026lt;br\u0026gt;', \u0026quot;Cases: \u0026quot;, cases, '\u0026lt;br\u0026gt;', \u0026quot;Deaths: \u0026quot;, deaths)) # Set up mapping details set_map_details \u0026lt;- list( scope = 'usa', projection = list(type = 'albers usa'), showlakes = TRUE, lakecolor = toRGB('white') ) # Make sure both maps are on the same color scale shadeLimit \u0026lt;- 9 # Create the map fig \u0026lt;- plot_geo(cv_CFR, locationmode = 'USA-states') %\u0026gt;% add_trace( z = ~naive_CFR, text = ~hover, locations = ~state, color = ~naive_CFR, colors = 'Purples' ) fig \u0026lt;- fig %\u0026gt;% colorbar(title = \u0026quot;CFR May 1 2020\u0026quot;, limits = c(0,shadeLimit)) fig \u0026lt;- fig %\u0026gt;% layout( title = paste('CFR by State as of', Sys.Date(), '\u0026lt;br\u0026gt;(Hover for value)'), geo = set_map_details ) fig_May1 \u0026lt;- fig ############# ### For Today # Extract the data for each state by its abbreviation cv_CFR \u0026lt;- cv_states_today %\u0026gt;% select(state, abb, naive_CFR, cases, deaths) # select data cv_CFR$state_name \u0026lt;- cv_CFR$state cv_CFR$state \u0026lt;- cv_CFR$abb cv_CFR$abb \u0026lt;- NULL # Create hover text cv_CFR$hover \u0026lt;- with(cv_CFR, paste(state_name, '\u0026lt;br\u0026gt;', \u0026quot;CFR: \u0026quot;, naive_CFR, '\u0026lt;br\u0026gt;', \u0026quot;Cases: \u0026quot;, cases, '\u0026lt;br\u0026gt;', \u0026quot;Deaths: \u0026quot;, deaths)) # Set up mapping details set_map_details \u0026lt;- list( scope = 'usa', projection = list(type = 'albers usa'), showlakes = TRUE, lakecolor = toRGB('white') ) # Create the map fig \u0026lt;- plot_geo(cv_CFR, locationmode = 'USA-states') %\u0026gt;% add_trace( z = ~naive_CFR, text = ~hover, locations = ~state, color = ~naive_CFR, colors = 'Purples' ) fig \u0026lt;- fig %\u0026gt;% colorbar(title = \u0026quot;CFR May 1 2020\u0026quot;, limits = c(0,shadeLimit)) fig \u0026lt;- fig %\u0026gt;% layout( title = paste('CFR by State as of', Sys.Date(), '\u0026lt;br\u0026gt;(Hover for value)'), geo = set_map_details ) fig_Today \u0026lt;- fig ### Plot side by side ### FINISH THE CODE HERE ### subplot( fig_May1, fig_Today )   {\"x\":{\"data\":[{\"colorbar\":{\"title\":\"CFR May 1 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CFR: 4.07 Cases: 7294 Deaths: 297\",\"Alaska CFR: 1.93 Cases: 362 Deaths: 7\",\"Arizona CFR: 4.14 Cases: 7962 Deaths: 330\",\"Arkansas CFR: 1.93 Cases: 3310 Deaths: 64\",\"California CFR: 4.1 Cases: 52318 Deaths: 2147\",\"Colorado CFR: 5.24 Cases: 15661 Deaths: 820\",\"Connecticut CFR: 8.13 Cases: 28764 Deaths: 2339\",\"Delaware CFR: 3.23 Cases: 4918 Deaths: 159\",\"District of Columbia CFR: 4.96 Cases: 4658 Deaths: 231\",\"Florida CFR: 3.78 Cases: 34720 Deaths: 1313\",\"Georgia CFR: 4.34 Cases: 26594 Deaths: 1153\",\"Hawaii CFR: 2.62 Cases: 610 Deaths: 16\",\"Idaho CFR: 3.1 Cases: 2035 Deaths: 63\",\"Illinois CFR: 4.4 Cases: 56055 Deaths: 2464\",\"Indiana CFR: 6.2 Cases: 18941 Deaths: 1175\",\"Iowa CFR: 2.16 Cases: 7883 Deaths: 170\",\"Kansas CFR: 3.02 Cases: 4510 Deaths: 136\",\"Kentucky CFR: 5.08 Cases: 4879 Deaths: 248\",\"Louisiana CFR: 6.85 Cases: 28754 Deaths: 1970\",\"Maine CFR: 4.9 Cases: 1123 Deaths: 55\",\"Maryland CFR: 5.06 Cases: 23566 Deaths: 1192\",\"Massachusetts CFR: 5.78 Cases: 64311 Deaths: 3716\",\"Michigan CFR: 9.13 Cases: 42348 Deaths: 3866\",\"Minnesota CFR: 6.47 Cases: 5730 Deaths: 371\",\"Mississippi CFR: 3.9 Cases: 7212 Deaths: 281\",\"Missouri CFR: 4.53 Cases: 7840 Deaths: 355\",\"Montana CFR: 3.54 Cases: 452 Deaths: 16\",\"Nebraska CFR: 1.49 Cases: 4838 Deaths: 72\",\"Nevada CFR: 4.84 Cases: 5249 Deaths: 254\",\"New Hampshire CFR: 3.51 Cases: 2310 Deaths: 81\",\"New Jersey CFR: 6.22 Cases: 121190 Deaths: 7538\",\"New Mexico CFR: 3.73 Cases: 3513 Deaths: 131\",\"New York CFR: 7.6 Cases: 313575 Deaths: 23841\",\"North Carolina CFR: 3.71 Cases: 10922 Deaths: 405\",\"North Dakota CFR: 2.08 Cases: 1107 Deaths: 23\",\"Ohio CFR: 5.35 Cases: 18743 Deaths: 1002\",\"Oklahoma CFR: 6.14 Cases: 3748 Deaths: 230\",\"Oregon CFR: 4.03 Cases: 2579 Deaths: 104\",\"Pennsylvania CFR: 5.37 Cases: 49764 Deaths: 2671\",\"Puerto Rico CFR: 5.92 Cases: 1910 Deaths: 113\",\"Rhode Island CFR: 3.11 Cases: 8962 Deaths: 279\",\"South Carolina CFR: 4.09 Cases: 6258 Deaths: 256\",\"South Dakota CFR: 0.83 Cases: 2525 Deaths: 21\",\"Tennessee CFR: 1.74 Cases: 11781 Deaths: 205\",\"Texas CFR: 2.81 Cases: 30342 Deaths: 853\",\"Utah CFR: 0.95 Cases: 4829 Deaths: 46\",\"Vermont CFR: 5.57 Cases: 879 Deaths: 49\",\"Virginia CFR: 3.54 Cases: 16903 Deaths: 599\",\"Washington CFR: 5.48 Cases: 15134 Deaths: 829\",\"West Virginia CFR: 4.08 Cases: 1152 Deaths: 47\",\"Wisconsin CFR: 4.4 Cases: 7439 Deaths: 327\",\"Wyoming CFR: 1.24 Cases: 566 Deaths: 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","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"fdfbc884b4423346da6cfdcce4844a0b","permalink":"https://uscbiostats.github.io/PM566/assignment/11-lab-2020/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/11-lab-2020/","section":"assignment","summary":"Learning Goals  Read in and process the COVID dataset from the New York Times GitHub repository Create interactive graphs of different types using plot_ly() and ggplotly() functions Customize the hoverinfo and other plot features Create a Choropleth map using plot_geo() Create an interactive table using DataTable  Lab Description We will work with the COVID data presented in lecture.","tags":null,"title":"Lab 11 - Interactive Visualization","type":"assignment"},{"authors":null,"categories":null,"content":"                                                                                    Learning Goals  Read in and process the COVID dataset from the New York Times GitHub repository Create interactive graphs of different types using plot_ly() and ggplotly() functions Customize the hoverinfo and other plot features Create a Choropleth map using plot_geo()  Lab Description We will work with COVID data downloaded from the New York Times. The dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic.\nThe objective of this lab is to explore relationships between cases, deaths, and population sizes of US states, and plot data to demonstrate this\nSteps I. Reading and processing the New York Times (NYT) state-level COVID-19 data 1. Read in the data  Read in the COVID data with data.table:fread() from the NYT GitHub repository: “https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv” Read in the state population data with data.table:fread() from the repository: “https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv”” Merge datasets  ## data extracted from New York Times state-level data from NYT Github repository # https://github.com/nytimes/covid-19-data ## state-level population information from us_census_data available on GitHub repository: # https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data # load COVID state-level data from NYT cv_states \u0026lt;- as.data.frame(data.table::fread(\u0026quot;https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv\u0026quot;)) # load state population data state_pops \u0026lt;- as.data.frame(data.table::fread(\u0026quot;https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv\u0026quot;)) state_pops$abb \u0026lt;- state_pops$state state_pops$state \u0026lt;- state_pops$state_name state_pops$state_name \u0026lt;- NULL cv_states \u0026lt;- merge(cv_states, state_pops, by=\u0026quot;state\u0026quot;)  2. Look at the data  Inspect the dimensions, head, and tail of the data Inspect the structure of each variables. Are they in the correct format?  dim(cv_states)  ## [1] 58094 9  head(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 1 Alabama 2023-01-04 1 1587224 21263 1 4887871 96.50939 AL ## 2 Alabama 2020-04-25 1 6213 213 1 4887871 96.50939 AL ## 3 Alabama 2023-02-26 1 1638348 21400 1 4887871 96.50939 AL ## 4 Alabama 2022-12-03 1 1549285 21129 1 4887871 96.50939 AL ## 5 Alabama 2020-05-06 1 8691 343 1 4887871 96.50939 AL ## 6 Alabama 2021-04-21 1 524367 10807 1 4887871 96.50939 AL  tail(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 58089 Wyoming 2022-09-11 56 175290 1884 56 577737 5.950611 WY ## 58090 Wyoming 2022-08-21 56 173487 1871 56 577737 5.950611 WY ## 58091 Wyoming 2021-01-26 56 51152 596 56 577737 5.950611 WY ## 58092 Wyoming 2021-02-21 56 53795 662 56 577737 5.950611 WY ## 58093 Wyoming 2021-08-22 56 70671 809 56 577737 5.950611 WY ## 58094 Wyoming 2021-03-20 56 55581 693 56 577737 5.950611 WY  str(cv_states)  ## 'data.frame':\t58094 obs. of 9 variables: ## $ state : chr \u0026quot;Alabama\u0026quot; \u0026quot;Alabama\u0026quot; \u0026quot;Alabama\u0026quot; \u0026quot;Alabama\u0026quot; ... ## $ date : IDate, format: \u0026quot;2023-01-04\u0026quot; \u0026quot;2020-04-25\u0026quot; ... ## $ fips : int 1 1 1 1 1 1 1 1 1 1 ... ## $ cases : int 1587224 6213 1638348 1549285 8691 524367 1321892 1088370 1153149 814025 ... ## $ deaths : int 21263 213 21400 21129 343 10807 19676 16756 16826 15179 ... ## $ geo_id : int 1 1 1 1 1 1 1 1 1 1 ... ## $ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ... ## $ pop_density: num 96.5 96.5 96.5 96.5 96.5 ... ## $ abb : chr \u0026quot;AL\u0026quot; \u0026quot;AL\u0026quot; \u0026quot;AL\u0026quot; \u0026quot;AL\u0026quot; ...  3. Format the data  Make date into a date variable Make state into a factor variable Order the data first by state, second by date Confirm the variables are now correctly formatted Inspect the range values for each variable. What is the date range? The range of cases and deaths?  # format the date cv_states$date \u0026lt;- as.Date(cv_states$date, format=\u0026quot;%Y-%m-%d\u0026quot;) # format the state and state abbreviation (abb) variables state_list \u0026lt;- unique(cv_states$state) cv_states$state \u0026lt;- factor(cv_states$state, levels = state_list) abb_list \u0026lt;- unique(cv_states$abb) cv_states$abb \u0026lt;- factor(cv_states$abb, levels = abb_list) # order the data first by state, second by date cv_states = cv_states[order(cv_states$state, cv_states$date),] # Confirm the variables are now correctly formatted str(cv_states)  ## 'data.frame':\t58094 obs. of 9 variables: ## $ state : Factor w/ 52 levels \u0026quot;Alabama\u0026quot;,\u0026quot;Alaska\u0026quot;,..: 1 1 1 1 1 1 1 1 1 1 ... ## $ date : Date, format: \u0026quot;2020-03-13\u0026quot; \u0026quot;2020-03-14\u0026quot; ... ## $ fips : int 1 1 1 1 1 1 1 1 1 1 ... ## $ cases : int 6 12 23 29 39 51 78 106 131 157 ... ## $ deaths : int 0 0 0 0 0 0 0 0 0 0 ... ## $ geo_id : int 1 1 1 1 1 1 1 1 1 1 ... ## $ population : int 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 4887871 ... ## $ pop_density: num 96.5 96.5 96.5 96.5 96.5 ... ## $ abb : Factor w/ 52 levels \u0026quot;AL\u0026quot;,\u0026quot;AK\u0026quot;,\u0026quot;AZ\u0026quot;,..: 1 1 1 1 1 1 1 1 1 1 ...  head(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL ## 597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL ## 282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL ## 12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL ## 266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL ## 78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL  tail(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 57902 Wyoming 2023-03-18 56 185640 2009 56 577737 5.950611 WY ## 57916 Wyoming 2023-03-19 56 185640 2009 56 577737 5.950611 WY ## 57647 Wyoming 2023-03-20 56 185640 2009 56 577737 5.950611 WY ## 57867 Wyoming 2023-03-21 56 185800 2014 56 577737 5.950611 WY ## 58057 Wyoming 2023-03-22 56 185800 2014 56 577737 5.950611 WY ## 57812 Wyoming 2023-03-23 56 185800 2014 56 577737 5.950611 WY  # Inspect the range values for each variable. What is the date range? The range of cases and deaths? head(cv_states)  ## state date fips cases deaths geo_id population pop_density abb ## 1029 Alabama 2020-03-13 1 6 0 1 4887871 96.50939 AL ## 597 Alabama 2020-03-14 1 12 0 1 4887871 96.50939 AL ## 282 Alabama 2020-03-15 1 23 0 1 4887871 96.50939 AL ## 12 Alabama 2020-03-16 1 29 0 1 4887871 96.50939 AL ## 266 Alabama 2020-03-17 1 39 0 1 4887871 96.50939 AL ## 78 Alabama 2020-03-18 1 51 0 1 4887871 96.50939 AL  summary(cv_states)  ## state date fips cases ## Washington : 1158 Min. :2020-01-21 Min. : 1.00 Min. : 1 ## Illinois : 1155 1st Qu.:2020-12-06 1st Qu.:16.00 1st Qu.: 112125 ## California : 1154 Median :2021-09-11 Median :29.00 Median : 418120 ## Arizona : 1153 Mean :2021-09-10 Mean :29.78 Mean : 947941 ## Massachusetts: 1147 3rd Qu.:2022-06-17 3rd Qu.:44.00 3rd Qu.: 1134318 ## Wisconsin : 1143 Max. :2023-03-23 Max. :72.00 Max. :12169158 ## (Other) :51184 ## deaths geo_id population pop_density ## Min. : 0 Min. : 1.00 Min. : 577737 Min. : 1.292 ## 1st Qu.: 1598 1st Qu.:16.00 1st Qu.: 1805832 1st Qu.: 43.659 ## Median : 5901 Median :29.00 Median : 4468402 Median : 107.860 ## Mean : 12553 Mean :29.78 Mean : 6397965 Mean : 423.031 ## 3rd Qu.: 15952 3rd Qu.:44.00 3rd Qu.: 7535591 3rd Qu.: 229.511 ## Max. :104277 Max. :72.00 Max. :39557045 Max. :11490.120 ## NA's :1106 ## abb ## WA : 1158 ## IL : 1155 ## CA : 1154 ## AZ : 1153 ## MA : 1147 ## WI : 1143 ## (Other):51184  min(cv_states$date)  ## [1] \u0026quot;2020-01-21\u0026quot;  max(cv_states$date)  ## [1] \u0026quot;2023-03-23\u0026quot;  4. Add new_cases and new_deaths and correct outliers   Add variables for new cases, new_cases, and new deaths, new_deaths:\n Hint: You can set new_cases equal to the difference between cases on date i and date i-1, starting on date i=2    Use plotly for EDA: See if there are outliers or values that don’t make sense for new_cases and new_deaths. Which states and which dates have strange values?\n  Correct outliers: Set negative values for new_cases or new_deaths to 0\n  Recalculate cases and deaths as cumulative sum of updated new_cases and new_deaths\n  Get the rolling average of new cases\n  # Add variables for new_cases and new_deaths: for (i in 1:length(state_list)) { cv_subset = subset(cv_states, state == state_list[i]) cv_subset = cv_subset[order(cv_subset$date),] # add starting level for new cases and deaths cv_subset$new_cases = cv_subset$cases[1] cv_subset$new_deaths = cv_subset$deaths[1] for (j in 2:nrow(cv_subset)) { cv_subset$new_cases[j] = cv_subset$cases[j] - cv_subset$cases[j-1] cv_subset$new_deaths[j] = cv_subset$deaths[j] - cv_subset$deaths[j-1] } # include in main dataset cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths } # Focus on recent dates cv_states \u0026lt;- cv_states %\u0026gt;% dplyr::filter(date \u0026gt;= \u0026quot;2021-06-01\u0026quot;) # Inspect outliers using plotly p1\u0026lt;-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5) ggplotly(p1)   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(y) \",\"x\"],\"11194306916f8\":[\"function (y) \",\"x\"]},\"highlight\":{\"on\":\"plotly_click\",\"persistent\":false,\"dynamic\":false,\"selectize\":false,\"opacityDim\":0.20000000000000001,\"selected\":{\"opacity\":1},\"debounce\":0},\"shinyEvents\":[\"plotly_hover\",\"plotly_click\",\"plotly_selected\",\"plotly_relayout\",\"plotly_brushed\",\"plotly_brushing\",\"plotly_clickannotation\",\"plotly_doubleclick\",\"plotly_deselect\",\"plotly_afterplot\",\"plotly_sunburstclick\"],\"base_url\":\"https://plot.ly\"},\"evals\":[],\"jsHooks\":[]} p2\u0026lt;-NULL # to clear from workspace # set negative new case or death counts to 0 cv_states$new_cases[cv_states$new_cases\u0026lt;0] = 0 cv_states$new_deaths[cv_states$new_deaths\u0026lt;0] = 0 # Recalculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths` for (i in 1:length(state_list)) { cv_subset = subset(cv_states, state == state_list[i]) # add starting level for new cases and deaths cv_subset$cases = cv_subset$cases[1] cv_subset$deaths = cv_subset$deaths[1] for (j in 2:nrow(cv_subset)) { cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$cases[j-1] cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$deaths[j-1] } # include in main dataset cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths } # Smooth new counts cv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %\u0026gt;% round(digits = 0) cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %\u0026gt;% round(digits = 0) # Inspect data again p2\u0026lt;-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5) ggplotly(p2)   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(y) \",\"x\"],\"11194140b93c6\":[\"function (y) \",\"x\"]},\"highlight\":{\"on\":\"plotly_click\",\"persistent\":false,\"dynamic\":false,\"selectize\":false,\"opacityDim\":0.20000000000000001,\"selected\":{\"opacity\":1},\"debounce\":0},\"shinyEvents\":[\"plotly_hover\",\"plotly_click\",\"plotly_selected\",\"plotly_relayout\",\"plotly_brushed\",\"plotly_brushing\",\"plotly_clickannotation\",\"plotly_doubleclick\",\"plotly_deselect\",\"plotly_afterplot\",\"plotly_sunburstclick\"],\"base_url\":\"https://plot.ly\"},\"evals\":[],\"jsHooks\":[]} #p2=NULL  5. Add additional variables   Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:\n per100k = cases per 100,000 population newper100k= new cases per 100,000 deathsper100k = deaths per 100,000 newdeathsper100k = new deaths per 100,000    Add a “naive CFR” variable representing deaths / cases on each date for each state\n  Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture\n  # add population normalized (by 100,000) counts for each variable cv_states$per100k = as.numeric(format(round(cv_states$cases/(cv_states$population/100000),1),nsmall=1)) cv_states$newper100k = as.numeric(format(round(cv_states$new_cases/(cv_states$population/100000),1),nsmall=1)) cv_states$deathsper100k = as.numeric(format(round(cv_states$deaths/(cv_states$population/100000),1),nsmall=1)) cv_states$newdeathsper100k = as.numeric(format(round(cv_states$new_deaths/(cv_states$population/100000),1),nsmall=1)) # add a naive_CFR variable = deaths / cases cv_states = cv_states %\u0026gt;% mutate(naive_CFR = round((deaths*100/cases),2)) # create a `cv_states_today` variable cv_states_today = subset(cv_states, date==max(cv_states$date))  II. Scatterplots 6. Explore scatterplots using plot_ly()  Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)  Size the points by state population Use hover to identify any outliers. Remove those outliers and replot.   Choose one plot. 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# Adding hoverinfo cv_states_today_scatter %\u0026gt;% plot_ly(x = ~pop_density, y = ~deathsper100k, type = 'scatter', mode = 'markers', color = ~state, size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5), hoverinfo = 'text', text = ~paste( paste(state, \u0026quot;:\u0026quot;, sep=\u0026quot;\u0026quot;), paste(\u0026quot; Cases per 100k: \u0026quot;, per100k, sep=\u0026quot;\u0026quot;) , paste(\u0026quot; Deaths per 100k: \u0026quot;, deathsper100k, sep=\u0026quot;\u0026quot;), sep = \u0026quot;\u0026lt;br\u0026gt;\u0026quot;)) %\u0026gt;% layout(title = \u0026quot;Population-normalized COVID-19 deaths (per 100k) vs. population density for US states\u0026quot;, yaxis = list(title = \u0026quot;Deaths per 100k\u0026quot;), xaxis = list(title = \u0026quot;Population Density\u0026quot;), hovermode = \u0026quot;compare\u0026quot;)   {\"x\":{\"visdat\":{\"1119427e9a9d1\":[\"function () \",\"plotlyVisDat\"]},\"cur_data\":\"1119427e9a9d1\",\"attrs\":{\"1119427e9a9d1\":{\"x\":{},\"y\":{},\"mode\":\"markers\",\"marker\":{\"sizemode\":\"diameter\",\"opacity\":0.5},\"hoverinfo\":\"text\",\"text\":{},\"color\":{},\"size\":{},\"alpha_stroke\":1,\"sizes\":[5,70],\"spans\":[1,20],\"type\":\"scatter\"}},\"layout\":{\"margin\":{\"b\":40,\"l\":60,\"t\":25,\"r\":10},\"title\":\"Population-normalized COVID-19 deaths (per 100k) vs. population density for US states\",\"yaxis\":{\"domain\":[0,1],\"automargin\":true,\"title\":\"Deaths per 100k\"},\"xaxis\":{\"domain\":[0,1],\"automargin\":true,\"title\":\"Population Density\"},\"hovermode\":\"compare\",\"showlegend\":true},\"source\":\"A\",\"config\":{\"modeBarButtonsToAdd\":[\"hoverclosest\",\"hovercompare\"],\"showSendToCloud\":false},\"data\":[{\"x\":[96.509388650000005],\"y\":[442.5],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(102,194,165,1)\",\"size\":[12.187370027194941],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(102,194,165,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Alabama:\nCases per 100k: 33724\nDeaths per 100k: 442.5\",\"type\":\"scatter\",\"name\":\"Alabama\",\"textfont\":{\"color\":\"rgba(102,194,165,1)\",\"size\":12.187370027194941},\"error_y\":{\"color\":\"rgba(102,194,165,1)\",\"width\":12.187370027194941},\"error_x\":{\"color\":\"rgba(102,194,165,1)\",\"width\":12.187370027194941},\"line\":{\"color\":\"rgba(102,194,165,1)\",\"width\":12.187370027194941},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[1.2915230608999999],\"y\":[195.09999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(135,188,156,1)\",\"size\":[5.2663096276619381],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(135,188,156,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Alaska:\nCases per 100k: 41887.3\nDeaths per 100k: 195.1\",\"type\":\"scatter\",\"name\":\"Alaska\",\"textfont\":{\"color\":\"rgba(135,188,156,1)\",\"size\":5.2663096276619381},\"error_y\":{\"color\":\"rgba(135,188,156,1)\",\"width\":5.2663096276619381},\"error_x\":{\"color\":\"rgba(135,188,156,1)\",\"width\":5.2663096276619381},\"line\":{\"color\":\"rgba(135,188,156,1)\",\"width\":5.2663096276619381},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[63.135855048000003],\"y\":[463.19999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(160,182,146,1)\",\"size\":[15.99568224761712],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(160,182,146,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Arizona:\nCases per 100k: 34177.1\nDeaths per 100k: 463.2\",\"type\":\"scatter\",\"name\":\"Arizona\",\"textfont\":{\"color\":\"rgba(160,182,146,1)\",\"size\":15.99568224761712},\"error_y\":{\"color\":\"rgba(160,182,146,1)\",\"width\":15.99568224761712},\"error_x\":{\"color\":\"rgba(160,182,146,1)\",\"width\":15.99568224761712},\"line\":{\"color\":\"rgba(160,182,146,1)\",\"width\":15.99568224761712},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[57.919684465000003],\"y\":[433.69999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(181,176,137,1)\",\"size\":[9.0623019782701117],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(181,176,137,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Arkansas:\nCases per 100k: 33455.9\nDeaths per 100k: 433.7\",\"type\":\"scatter\",\"name\":\"Arkansas\",\"textfont\":{\"color\":\"rgba(181,176,137,1)\",\"size\":9.0623019782701117},\"error_y\":{\"color\":\"rgba(181,176,137,1)\",\"width\":9.0623019782701117},\"error_x\":{\"color\":\"rgba(181,176,137,1)\",\"width\":9.0623019782701117},\"line\":{\"color\":\"rgba(181,176,137,1)\",\"width\":9.0623019782701117},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[253.9065023],\"y\":[266.10000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(200,169,128,1)\",\"size\":[70],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(200,169,128,1)\"}},\"hoverinfo\":\"text\",\"text\":\"California:\nCases per 100k: 30767.6\nDeaths per 100k: 266.1\",\"type\":\"scatter\",\"name\":\"California\",\"textfont\":{\"color\":\"rgba(200,169,128,1)\",\"size\":70},\"error_y\":{\"color\":\"rgba(200,169,128,1)\",\"width\":70},\"error_x\":{\"color\":\"rgba(200,169,128,1)\",\"width\":70},\"line\":{\"color\":\"rgba(200,169,128,1)\",\"width\":70},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[54.955977558999997],\"y\":[256.10000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(217,161,119,1)\",\"size\":[13.534239627855888],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(217,161,119,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Colorado:\nCases per 100k: 31270.9\nDeaths per 100k: 256.1\",\"type\":\"scatter\",\"name\":\"Colorado\",\"textfont\":{\"color\":\"rgba(217,161,119,1)\",\"size\":13.534239627855888},\"error_y\":{\"color\":\"rgba(217,161,119,1)\",\"width\":13.534239627855888},\"error_x\":{\"color\":\"rgba(217,161,119,1)\",\"width\":13.534239627855888},\"line\":{\"color\":\"rgba(217,161,119,1)\",\"width\":13.534239627855888},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[737.74459965000005],\"y\":[344.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(233,153,110,1)\",\"size\":[9.9941964080019083],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(233,153,110,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Connecticut:\nCases per 100k: 27387.3\nDeaths per 100k: 344.6\",\"type\":\"scatter\",\"name\":\"Connecticut\",\"textfont\":{\"color\":\"rgba(233,153,110,1)\",\"size\":9.9941964080019083},\"error_y\":{\"color\":\"rgba(233,153,110,1)\",\"width\":9.9941964080019083},\"error_x\":{\"color\":\"rgba(233,153,110,1)\",\"width\":9.9941964080019083},\"line\":{\"color\":\"rgba(233,153,110,1)\",\"width\":9.9941964080019083},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[496.43246049999999],\"y\":[346.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(248,144,101,1)\",\"size\":[5.6494012156398465],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(248,144,101,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Delaware:\nCases per 100k: 34548.9\nDeaths per 100k: 346.6\",\"type\":\"scatter\",\"name\":\"Delaware\",\"textfont\":{\"color\":\"rgba(248,144,101,1)\",\"size\":5.6494012156398465},\"error_y\":{\"color\":\"rgba(248,144,101,1)\",\"width\":5.6494012156398465},\"error_x\":{\"color\":\"rgba(248,144,101,1)\",\"width\":5.6494012156398465},\"line\":{\"color\":\"rgba(248,144,101,1)\",\"width\":5.6494012156398465},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[397.01575444999997],\"y\":[409.19999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(232,146,123,1)\",\"size\":[39.554313278214174],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(232,146,123,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Florida:\nCases per 100k: 35603.9\nDeaths per 100k: 409.2\",\"type\":\"scatter\",\"name\":\"Florida\",\"textfont\":{\"color\":\"rgba(232,146,123,1)\",\"size\":39.554313278214174},\"error_y\":{\"color\":\"rgba(232,146,123,1)\",\"width\":39.554313278214174},\"error_x\":{\"color\":\"rgba(232,146,123,1)\",\"width\":39.554313278214174},\"line\":{\"color\":\"rgba(232,146,123,1)\",\"width\":39.554313278214174},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[182.26478911000001],\"y\":[390.30000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(220,149,138,1)\",\"size\":[21.578359215612551],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(220,149,138,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Georgia:\nCases per 100k: 28377.8\nDeaths per 100k: 390.3\",\"type\":\"scatter\",\"name\":\"Georgia\",\"textfont\":{\"color\":\"rgba(220,149,138,1)\",\"size\":21.578359215612551},\"error_y\":{\"color\":\"rgba(220,149,138,1)\",\"width\":21.578359215612551},\"error_x\":{\"color\":\"rgba(220,149,138,1)\",\"width\":21.578359215612551},\"line\":{\"color\":\"rgba(220,149,138,1)\",\"width\":21.578359215612551},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[221.17691551999999],\"y\":[130.30000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(206,152,152,1)\",\"size\":[6.4053356206323624],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(206,152,152,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Hawaii:\nCases per 100k: 26216.9\nDeaths per 100k: 130.3\",\"type\":\"scatter\",\"name\":\"Hawaii\",\"textfont\":{\"color\":\"rgba(206,152,152,1)\",\"size\":6.4053356206323624},\"error_y\":{\"color\":\"rgba(206,152,152,1)\",\"width\":6.4053356206323624},\"error_x\":{\"color\":\"rgba(206,152,152,1)\",\"width\":6.4053356206323624},\"line\":{\"color\":\"rgba(206,152,152,1)\",\"width\":6.4053356206323624},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[21.225798535999999],\"y\":[311.30000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(191,154,166,1)\",\"size\":[6.9618258743844299],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(191,154,166,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Idaho:\nCases per 100k: 29809.4\nDeaths per 100k: 311.3\",\"type\":\"scatter\",\"name\":\"Idaho\",\"textfont\":{\"color\":\"rgba(191,154,166,1)\",\"size\":6.9618258743844299},\"error_y\":{\"color\":\"rgba(191,154,166,1)\",\"width\":6.9618258743844299},\"error_x\":{\"color\":\"rgba(191,154,166,1)\",\"width\":6.9618258743844299},\"line\":{\"color\":\"rgba(191,154,166,1)\",\"width\":6.9618258743844299},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[229.51115612999999],\"y\":[326.69999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(174,157,181,1)\",\"size\":[25.282999764900907],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(174,157,181,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Illinois:\nCases per 100k: 32241.6\nDeaths per 100k: 326.7\",\"type\":\"scatter\",\"name\":\"Illinois\",\"textfont\":{\"color\":\"rgba(174,157,181,1)\",\"size\":25.282999764900907},\"error_y\":{\"color\":\"rgba(174,157,181,1)\",\"width\":25.282999764900907},\"error_x\":{\"color\":\"rgba(174,157,181,1)\",\"width\":25.282999764900907},\"line\":{\"color\":\"rgba(174,157,181,1)\",\"width\":25.282999764900907},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[186.78751987000001],\"y\":[391.19999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(154,159,195,1)\",\"size\":[15.195644442943934],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(154,159,195,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Indiana:\nCases per 100k: 30762.1\nDeaths per 100k: 391.2\",\"type\":\"scatter\",\"name\":\"Indiana\",\"textfont\":{\"color\":\"rgba(154,159,195,1)\",\"size\":15.195644442943934},\"error_y\":{\"color\":\"rgba(154,159,195,1)\",\"width\":15.195644442943934},\"error_x\":{\"color\":\"rgba(154,159,195,1)\",\"width\":15.195644442943934},\"line\":{\"color\":\"rgba(154,159,195,1)\",\"width\":15.195644442943934},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[56.507023549000003],\"y\":[341.39999999999998],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(147,159,203,1)\",\"size\":[9.2996278948820752],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(147,159,203,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Iowa:\nCases per 100k: 28750.9\nDeaths per 100k: 341.4\",\"type\":\"scatter\",\"name\":\"Iowa\",\"textfont\":{\"color\":\"rgba(147,159,203,1)\",\"size\":9.2996278948820752},\"error_y\":{\"color\":\"rgba(147,159,203,1)\",\"width\":9.2996278948820752},\"error_x\":{\"color\":\"rgba(147,159,203,1)\",\"width\":9.2996278948820752},\"line\":{\"color\":\"rgba(147,159,203,1)\",\"width\":9.2996278948820752},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[35.610732968000001],\"y\":[354.30000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(162,157,201,1)\",\"size\":[8.8916781180415008],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(162,157,201,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Kansas:\nCases per 100k: 32316.9\nDeaths per 100k: 354.3\",\"type\":\"scatter\",\"name\":\"Kansas\",\"textfont\":{\"color\":\"rgba(162,157,201,1)\",\"size\":8.8916781180415008},\"error_y\":{\"color\":\"rgba(162,157,201,1)\",\"width\":8.8916781180415008},\"error_x\":{\"color\":\"rgba(162,157,201,1)\",\"width\":8.8916781180415008},\"line\":{\"color\":\"rgba(162,157,201,1)\",\"width\":8.8916781180415008},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[113.15179107],\"y\":[410.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(175,154,200,1)\",\"size\":[11.487883904968246],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(175,154,200,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Kentucky:\nCases per 100k: 38918.1\nDeaths per 100k: 410.6\",\"type\":\"scatter\",\"name\":\"Kentucky\",\"textfont\":{\"color\":\"rgba(175,154,200,1)\",\"size\":11.487883904968246},\"error_y\":{\"color\":\"rgba(175,154,200,1)\",\"width\":11.487883904968246},\"error_x\":{\"color\":\"rgba(175,154,200,1)\",\"width\":11.487883904968246},\"line\":{\"color\":\"rgba(175,154,200,1)\",\"width\":11.487883904968246},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[107.86011234],\"y\":[404.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(187,151,199,1)\",\"size\":[11.807346733810668],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(187,151,199,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Louisiana:\nCases per 100k: 33921\nDeaths per 100k: 404.6\",\"type\":\"scatter\",\"name\":\"Louisiana\",\"textfont\":{\"color\":\"rgba(187,151,199,1)\",\"size\":11.807346733810668},\"error_y\":{\"color\":\"rgba(187,151,199,1)\",\"width\":11.807346733810668},\"error_x\":{\"color\":\"rgba(187,151,199,1)\",\"width\":11.807346733810668},\"line\":{\"color\":\"rgba(187,151,199,1)\",\"width\":11.807346733810668},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[43.391688418999998],\"y\":[223],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(199,148,198,1)\",\"size\":[6.2684513280738585],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(199,148,198,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Maine:\nCases per 100k: 23909.4\nDeaths per 100k: 223\",\"type\":\"scatter\",\"name\":\"Maine\",\"textfont\":{\"color\":\"rgba(199,148,198,1)\",\"size\":6.2684513280738585},\"error_y\":{\"color\":\"rgba(199,148,198,1)\",\"width\":6.2684513280738585},\"error_x\":{\"color\":\"rgba(199,148,198,1)\",\"width\":6.2684513280738585},\"line\":{\"color\":\"rgba(199,148,198,1)\",\"width\":6.2684513280738585},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[622.26172744999997],\"y\":[276.19999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(211,145,197,1)\",\"size\":[14.113136769898531],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(211,145,197,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Maryland:\nCases per 100k: 22671.2\nDeaths per 100k: 276.2\",\"type\":\"scatter\",\"name\":\"Maryland\",\"textfont\":{\"color\":\"rgba(211,145,197,1)\",\"size\":14.113136769898531},\"error_y\":{\"color\":\"rgba(211,145,197,1)\",\"width\":14.113136769898531},\"error_x\":{\"color\":\"rgba(211,145,197,1)\",\"width\":14.113136769898531},\"line\":{\"color\":\"rgba(211,145,197,1)\",\"width\":14.113136769898531},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[884.74992171999997],\"y\":[408.69999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(222,141,196,1)\",\"size\":[15.546282145388522],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(222,141,196,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Massachusetts:\nCases per 100k: 32311\nDeaths per 100k: 408.7\",\"type\":\"scatter\",\"name\":\"Massachusetts\",\"textfont\":{\"color\":\"rgba(222,141,196,1)\",\"size\":15.546282145388522},\"error_y\":{\"color\":\"rgba(222,141,196,1)\",\"width\":15.546282145388522},\"error_x\":{\"color\":\"rgba(222,141,196,1)\",\"width\":15.546282145388522},\"line\":{\"color\":\"rgba(222,141,196,1)\",\"width\":15.546282145388522},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[176.59707778999999],\"y\":[423.30000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(230,140,193,1)\",\"size\":[20.705295999610868],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(230,140,193,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Michigan:\nCases per 100k: 30694.5\nDeaths per 100k: 423.3\",\"type\":\"scatter\",\"name\":\"Michigan\",\"textfont\":{\"color\":\"rgba(230,140,193,1)\",\"size\":20.705295999610868},\"error_y\":{\"color\":\"rgba(230,140,193,1)\",\"width\":20.705295999610868},\"error_x\":{\"color\":\"rgba(230,140,193,1)\",\"width\":20.705295999610868},\"line\":{\"color\":\"rgba(230,140,193,1)\",\"width\":20.705295999610868},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[70.469674695999998],\"y\":[266.69999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(224,152,179,1)\",\"size\":[13.39352330215816],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(224,152,179,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Minnesota:\nCases per 100k: 31798.4\nDeaths per 100k: 266.7\",\"type\":\"scatter\",\"name\":\"Minnesota\",\"textfont\":{\"color\":\"rgba(224,152,179,1)\",\"size\":13.39352330215816},\"error_y\":{\"color\":\"rgba(224,152,179,1)\",\"width\":13.39352330215816},\"error_x\":{\"color\":\"rgba(224,152,179,1)\",\"width\":13.39352330215816},\"line\":{\"color\":\"rgba(224,152,179,1)\",\"width\":13.39352330215816},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[63.645625459000001],\"y\":[450.5],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(217,164,165,1)\",\"size\":[9.0167861625455217],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(217,164,165,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Mississippi:\nCases per 100k: 33250.5\nDeaths per 100k: 450.5\",\"type\":\"scatter\",\"name\":\"Mississippi\",\"textfont\":{\"color\":\"rgba(217,164,165,1)\",\"size\":9.0167861625455217},\"error_y\":{\"color\":\"rgba(217,164,165,1)\",\"width\":9.0167861625455217},\"error_x\":{\"color\":\"rgba(217,164,165,1)\",\"width\":9.0167861625455217},\"line\":{\"color\":\"rgba(217,164,165,1)\",\"width\":9.0167861625455217},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[89.117470788999995],\"y\":[393],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(209,175,151,1)\",\"size\":[14.25276751962862],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(209,175,151,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Missouri:\nCases per 100k: 29286.5\nDeaths per 100k: 393\",\"type\":\"scatter\",\"name\":\"Missouri\",\"textfont\":{\"color\":\"rgba(209,175,151,1)\",\"size\":14.25276751962862},\"error_y\":{\"color\":\"rgba(209,175,151,1)\",\"width\":14.25276751962862},\"error_x\":{\"color\":\"rgba(209,175,151,1)\",\"width\":14.25276751962862},\"line\":{\"color\":\"rgba(209,175,151,1)\",\"width\":14.25276751962862},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[7.2987510950000001],\"y\":[348.69999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(201,186,137,1)\",\"size\":[5.8080420514391893],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(201,186,137,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Montana:\nCases per 100k: 31152.1\nDeaths per 100k: 348.7\",\"type\":\"scatter\",\"name\":\"Montana\",\"textfont\":{\"color\":\"rgba(201,186,137,1)\",\"size\":5.8080420514391893},\"error_y\":{\"color\":\"rgba(201,186,137,1)\",\"width\":5.8080420514391893},\"error_x\":{\"color\":\"rgba(201,186,137,1)\",\"width\":5.8080420514391893},\"line\":{\"color\":\"rgba(201,186,137,1)\",\"width\":5.8080420514391893},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[25.114922059000001],\"y\":[268.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(191,196,121,1)\",\"size\":[7.253747424146165],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(191,196,121,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Nebraska:\nCases per 100k: 29874.8\nDeaths per 100k: 268.6\",\"type\":\"scatter\",\"name\":\"Nebraska\",\"textfont\":{\"color\":\"rgba(191,196,121,1)\",\"size\":7.253747424146165},\"error_y\":{\"color\":\"rgba(191,196,121,1)\",\"width\":7.253747424146165},\"error_x\":{\"color\":\"rgba(191,196,121,1)\",\"width\":7.253747424146165},\"line\":{\"color\":\"rgba(191,196,121,1)\",\"width\":7.253747424146165},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[27.6406025],\"y\":[399],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(180,205,105,1)\",\"size\":[9.0965985081110219],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(180,205,105,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Nevada:\nCases per 100k: 29431.5\nDeaths per 100k: 399\",\"type\":\"scatter\",\"name\":\"Nevada\",\"textfont\":{\"color\":\"rgba(180,205,105,1)\",\"size\":9.0965985081110219},\"error_y\":{\"color\":\"rgba(180,205,105,1)\",\"width\":9.0965985081110219},\"error_x\":{\"color\":\"rgba(180,205,105,1)\",\"width\":9.0965985081110219},\"line\":{\"color\":\"rgba(180,205,105,1)\",\"width\":9.0965985081110219},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[151.50059716000001],\"y\":[222.5],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(168,215,87,1)\",\"size\":[6.298557301222484],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(168,215,87,1)\"}},\"hoverinfo\":\"text\",\"text\":\"New Hampshire:\nCases per 100k: 27991.2\nDeaths per 100k: 222.5\",\"type\":\"scatter\",\"name\":\"New 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359.8\",\"type\":\"scatter\",\"name\":\"Ohio\",\"textfont\":{\"color\":\"rgba(239,217,56,1)\",\"size\":23.529339335629047},\"error_y\":{\"color\":\"rgba(239,217,56,1)\",\"width\":23.529339335629047},\"error_x\":{\"color\":\"rgba(239,217,56,1)\",\"width\":23.529339335629047},\"line\":{\"color\":\"rgba(239,217,56,1)\",\"width\":23.529339335629047},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[57.482602245000002],\"y\":[419.80000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(250,217,50,1)\",\"size\":[10.611880795831471],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(250,217,50,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Oklahoma:\nCases per 100k: 32863.5\nDeaths per 100k: 419.8\",\"type\":\"scatter\",\"name\":\"Oklahoma\",\"textfont\":{\"color\":\"rgba(250,217,50,1)\",\"size\":10.611880795831471},\"error_y\":{\"color\":\"rgba(250,217,50,1)\",\"width\":10.611880795831471},\"error_x\":{\"color\":\"rgba(250,217,50,1)\",\"width\":10.611880795831471},\"line\":{\"color\":\"rgba(250,217,50,1)\",\"width\":10.611880795831471},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[43.658853835000002],\"y\":[225.5],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(253,215,59,1)\",\"size\":[11.024823221592339],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(253,215,59,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Oregon:\nCases per 100k: 23078.6\nDeaths per 100k: 225.5\",\"type\":\"scatter\",\"name\":\"Oregon\",\"textfont\":{\"color\":\"rgba(253,215,59,1)\",\"size\":11.024823221592339},\"error_y\":{\"color\":\"rgba(253,215,59,1)\",\"width\":11.024823221592339},\"error_x\":{\"color\":\"rgba(253,215,59,1)\",\"width\":11.024823221592339},\"line\":{\"color\":\"rgba(253,215,59,1)\",\"width\":11.024823221592339},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[286.23455445000002],\"y\":[396.19999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(250,212,76,1)\",\"size\":[25.393024806905242],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(250,212,76,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Pennsylvania:\nCases per 100k: 27668.6\nDeaths per 100k: 396.2\",\"type\":\"scatter\",\"name\":\"Pennsylvania\",\"textfont\":{\"color\":\"rgba(250,212,76,1)\",\"size\":25.393024806905242},\"error_y\":{\"color\":\"rgba(250,212,76,1)\",\"width\":25.393024806905242},\"error_x\":{\"color\":\"rgba(250,212,76,1)\",\"width\":25.393024806905242},\"line\":{\"color\":\"rgba(250,212,76,1)\",\"width\":25.393024806905242},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[1022.6505771],\"y\":[371],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(244,207,104,1)\",\"size\":[5.7997209699053665],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(244,207,104,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Rhode Island:\nCases per 100k: 43677.3\nDeaths per 100k: 371\",\"type\":\"scatter\",\"name\":\"Rhode 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329.6\",\"type\":\"scatter\",\"name\":\"Texas\",\"textfont\":{\"color\":\"rgba(227,195,149,1)\",\"size\":51.898395938686235},\"error_y\":{\"color\":\"rgba(227,195,149,1)\",\"width\":51.898395938686235},\"error_x\":{\"color\":\"rgba(227,195,149,1)\",\"width\":51.898395938686235},\"line\":{\"color\":\"rgba(227,195,149,1)\",\"width\":51.898395938686235},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[38.458214685000002],\"y\":[168.19999999999999],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(221,193,154,1)\",\"size\":[9.3078989498736107],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(221,193,154,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Utah:\nCases per 100k: 34579\nDeaths per 100k: 168.2\",\"type\":\"scatter\",\"name\":\"Utah\",\"textfont\":{\"color\":\"rgba(221,193,154,1)\",\"size\":9.3078989498736107},\"error_y\":{\"color\":\"rgba(221,193,154,1)\",\"width\":9.3078989498736107},\"error_x\":{\"color\":\"rgba(221,193,154,1)\",\"width\":9.3078989498736107},\"line\":{\"color\":\"rgba(221,193,154,1)\",\"width\":9.3078989498736107},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[67.943995143999999],\"y\":[149.90000000000001],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(214,191,158,1)\",\"size\":[5.0809796315522071],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(214,191,158,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Vermont:\nCases per 100k: 24460.8\nDeaths per 100k: 149.9\",\"type\":\"scatter\",\"name\":\"Vermont\",\"textfont\":{\"color\":\"rgba(214,191,158,1)\",\"size\":5.0809796315522071},\"error_y\":{\"color\":\"rgba(214,191,158,1)\",\"width\":5.0809796315522071},\"error_x\":{\"color\":\"rgba(214,191,158,1)\",\"width\":5.0809796315522071},\"line\":{\"color\":\"rgba(214,191,158,1)\",\"width\":5.0809796315522071},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[215.73630392999999],\"y\":[279.39999999999998],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(208,188,162,1)\",\"size\":[18.24027147942185],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(208,188,162,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Virginia:\nCases per 100k: 26992\nDeaths per 100k: 279.4\",\"type\":\"scatter\",\"name\":\"Virginia\",\"textfont\":{\"color\":\"rgba(208,188,162,1)\",\"size\":18.24027147942185},\"error_y\":{\"color\":\"rgba(208,188,162,1)\",\"width\":18.24027147942185},\"error_x\":{\"color\":\"rgba(208,188,162,1)\",\"width\":18.24027147942185},\"line\":{\"color\":\"rgba(208,188,162,1)\",\"width\":18.24027147942185},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[113.39722716],\"y\":[213],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(201,186,167,1)\",\"size\":[16.602579245378088],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(201,186,167,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Washington:\nCases per 100k: 25808.4\nDeaths per 100k: 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283.8\",\"type\":\"scatter\",\"name\":\"Wisconsin\",\"textfont\":{\"color\":\"rgba(186,181,175,1)\",\"size\":13.731017364392411},\"error_y\":{\"color\":\"rgba(186,181,175,1)\",\"width\":13.731017364392411},\"error_x\":{\"color\":\"rgba(186,181,175,1)\",\"width\":13.731017364392411},\"line\":{\"color\":\"rgba(186,181,175,1)\",\"width\":13.731017364392411},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null},{\"x\":[5.9506106574000004],\"y\":[348.60000000000002],\"mode\":\"markers\",\"marker\":{\"color\":\"rgba(179,179,179,1)\",\"size\":[5],\"sizemode\":\"diameter\",\"opacity\":0.5,\"line\":{\"color\":\"rgba(179,179,179,1)\"}},\"hoverinfo\":\"text\",\"text\":\"Wyoming:\nCases per 100k: 32268\nDeaths per 100k: 348.6\",\"type\":\"scatter\",\"name\":\"Wyoming\",\"textfont\":{\"color\":\"rgba(179,179,179,1)\",\"size\":5},\"error_y\":{\"color\":\"rgba(179,179,179,1)\",\"width\":5},\"error_x\":{\"color\":\"rgba(179,179,179,1)\",\"width\":5},\"line\":{\"color\":\"rgba(179,179,179,1)\",\"width\":5},\"xaxis\":\"x\",\"yaxis\":\"y\",\"frame\":null}],\"highlight\":{\"on\":\"plotly_click\",\"persistent\":false,\"dynamic\":false,\"selectize\":false,\"opacityDim\":0.20000000000000001,\"selected\":{\"opacity\":1},\"debounce\":0},\"shinyEvents\":[\"plotly_hover\",\"plotly_click\",\"plotly_selected\",\"plotly_relayout\",\"plotly_brushed\",\"plotly_brushing\",\"plotly_clickannotation\",\"plotly_doubleclick\",\"plotly_deselect\",\"plotly_afterplot\",\"plotly_sunburstclick\"],\"base_url\":\"https://plot.ly\"},\"evals\":[],\"jsHooks\":[]} 7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()  For pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly() Explore the pattern between \\(x\\) and \\(y\\) using geom_smooth()  Explain what you see. Do you think pop_density is a correlate of newdeathsper100k?    p \u0026lt;- ggplot(cv_states_today_scatter, aes(x=pop_density, y=deathsper100k, size=population)) + geom_point() + geom_smooth() ggplotly(p)   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Multiple line chart  Create a line chart of the naive_CFR for all states over time using plot_ly()  Use the zoom and pan tools to inspect the naive_CFR for the states that had an increase in September. How have they changed over time?   Create one more line chart, for Florida only, which shows new_cases and new_deaths together in one plot. Hint: use add_layer()  Use hoverinfo to “eyeball” the approximate peak of deaths and peak of cases. 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# Line chart for Florida showing new_cases and new_deaths together cv_states %\u0026gt;% filter(state==\u0026quot;Florida\u0026quot;) %\u0026gt;% plot_ly(x = ~date, y = ~new_cases, type = \u0026quot;scatter\u0026quot;, mode = \u0026quot;lines\u0026quot;) %\u0026gt;% add_lines(x = ~date, y = ~new_deaths, type = \u0026quot;scatter\u0026quot;, mode = \u0026quot;lines\u0026quot;)   {\"x\":{\"visdat\":{\"111943e21b876\":[\"function () 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9. Heatmaps Create a heatmap to visualize new_cases for each state on each date greater than June 1st, 2021\n Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes Use plot_ly() to create a heatmap out of this matrix. Which states stand out? Repeat with newper100k variable. Now which states stand out? Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks  # Map state, date, and new_cases to a matrix library(tidyr) cv_states_mat \u0026lt;- cv_states %\u0026gt;% select(state, date, new_cases) %\u0026gt;% dplyr::filter(date\u0026gt;as.Date(\u0026quot;2021-06-15\u0026quot;)) cv_states_mat2 \u0026lt;- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = new_cases)) rownames(cv_states_mat2) \u0026lt;- cv_states_mat2$date cv_states_mat2$date \u0026lt;- NULL cv_states_mat2 \u0026lt;- as.matrix(cv_states_mat2) # Create a heatmap using plot_ly() plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=\u0026quot;heatmap\u0026quot;, showscale=T)   {\"x\":{\"visdat\":{\"111943c599721\":[\"function () 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# Repeat with newper100k cv_states_mat \u0026lt;- cv_states %\u0026gt;% select(state, date, newper100k) %\u0026gt;% dplyr::filter(date\u0026gt;as.Date(\u0026quot;2021-06-15\u0026quot;)) cv_states_mat2 \u0026lt;- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k)) rownames(cv_states_mat2) \u0026lt;- cv_states_mat2$date cv_states_mat2$date \u0026lt;- NULL cv_states_mat2 \u0026lt;- as.matrix(cv_states_mat2) plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=\u0026quot;heatmap\u0026quot;, showscale=T)   {\"x\":{\"visdat\":{\"111941dd1167b\":[\"function () \",\"plotlyVisDat\"]},\"cur_data\":\"111941dd1167b\",\"attrs\":{\"111941dd1167b\":{\"x\":[\"Alabama\",\"Alaska\",\"Arizona\",\"Arkansas\",\"California\",\"Colorado\",\"Connecticut\",\"Delaware\",\"District of Columbia\",\"Florida\",\"Georgia\",\"Hawaii\",\"Idaho\",\"Illinois\",\"Indiana\",\"Iowa\",\"Kansas\",\"Kentucky\",\"Louisiana\",\"Maine\",\"Maryland\",\"Massachusetts\",\"Michigan\",\"Minnesota\",\"Mississippi\",\"Missouri\",\"Montana\",\"Nebraska\",\"Nevada\",\"New Hampshire\",\"New Jersey\",\"New Mexico\",\"New York\",\"North Carolina\",\"North Dakota\",\"Ohio\",\"Oklahoma\",\"Oregon\",\"Pennsylvania\",\"Puerto Rico\",\"Rhode Island\",\"South Carolina\",\"South Dakota\",\"Tennessee\",\"Texas\",\"Utah\",\"Vermont\",\"Virginia\",\"Washington\",\"West 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# Create a second heatmap after filtering to only include dates every other week filter_dates \u0026lt;- seq(as.Date(\u0026quot;2021-06-15\u0026quot;), as.Date(\u0026quot;2021-11-01\u0026quot;), by=\u0026quot;2 weeks\u0026quot;) cv_states_mat \u0026lt;- cv_states %\u0026gt;% select(state, date, newper100k) %\u0026gt;% filter(date %in% filter_dates) cv_states_mat2 \u0026lt;- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = newper100k)) rownames(cv_states_mat2) \u0026lt;- cv_states_mat2$date cv_states_mat2$date \u0026lt;- NULL cv_states_mat2 \u0026lt;- as.matrix(cv_states_mat2) # Create a heatmap using plot_ly() plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=\u0026quot;heatmap\u0026quot;, showscale=T)   {\"x\":{\"visdat\":{\"11194a1af488\":[\"function () \",\"plotlyVisDat\"]},\"cur_data\":\"11194a1af488\",\"attrs\":{\"11194a1af488\":{\"x\":[\"Alabama\",\"Alaska\",\"Arizona\",\"Arkansas\",\"California\",\"Colorado\",\"Connecticut\",\"Delaware\",\"District of Columbia\",\"Florida\",\"Georgia\",\"Hawaii\",\"Idaho\",\"Illinois\",\"Indiana\",\"Iowa\",\"Kansas\",\"Kentucky\",\"Louisiana\",\"Maine\",\"Maryland\",\"Massachusetts\",\"Michigan\",\"Minnesota\",\"Mississippi\",\"Missouri\",\"Montana\",\"Nebraska\",\"Nevada\",\"New Hampshire\",\"New Jersey\",\"New Mexico\",\"New York\",\"North Carolina\",\"North Dakota\",\"Ohio\",\"Oklahoma\",\"Oregon\",\"Pennsylvania\",\"Puerto Rico\",\"Rhode Island\",\"South Carolina\",\"South Dakota\",\"Tennessee\",\"Texas\",\"Utah\",\"Vermont\",\"Virginia\",\"Washington\",\"West Virginia\",\"Wisconsin\",\"Wyoming\"],\"y\":[\"2021-06-15\",\"2021-06-29\",\"2021-07-13\",\"2021-07-27\",\"2021-08-10\",\"2021-08-24\",\"2021-09-07\",\"2021-09-21\",\"2021-10-05\",\"2021-10-19\"],\"z\":{},\"showscale\":true,\"alpha_stroke\":1,\"sizes\":[10,100],\"spans\":[1,20],\"type\":\"heatmap\"}},\"layout\":{\"margin\":{\"b\":40,\"l\":60,\"t\":25,\"r\":10},\"xaxis\":{\"domain\":[0,1],\"automargin\":true,\"title\":[]},\"yaxis\":{\"domain\":[0,1],\"automargin\":true,\"title\":[]},\"scene\":{\"zaxis\":{\"title\":\"cv_states_mat2\"}},\"hovermode\":\"closest\",\"showlegend\":false,\"legend\":{\"yanchor\":\"top\",\"y\":0.5}},\"source\":\"A\",\"config\":{\"modeBarButtonsToAdd\":[\"hoverclosest\",\"hovercompare\"],\"showSendToCloud\":false},\"data\":[{\"colorbar\":{\"title\":\"cv_states_mat2\",\"ticklen\":2,\"len\":0.5,\"lenmode\":\"fraction\",\"y\":1,\"yanchor\":\"top\"},\"colorscale\":[[\"0\",\"rgba(68,1,84,1)\"],[\"0.0416666666666667\",\"rgba(70,19,97,1)\"],[\"0.0833333333333333\",\"rgba(72,32,111,1)\"],[\"0.125\",\"rgba(71,45,122,1)\"],[\"0.166666666666667\",\"rgba(68,58,128,1)\"],[\"0.208333333333333\",\"rgba(64,70,135,1)\"],[\"0.25\",\"rgba(60,82,138,1)\"],[\"0.291666666666667\",\"rgba(56,93,140,1)\"],[\"0.333333333333333\",\"rgba(49,104,142,1)\"],[\"0.375\",\"rgba(46,114,142,1)\"],[\"0.416666666666667\",\"rgba(42,123,142,1)\"],[\"0.458333333333333\",\"rgba(38,133,141,1)\"],[\"0.5\",\"rgba(37,144,140,1)\"],[\"0.541666666666667\",\"rgba(33,154,138,1)\"],[\"0.583333333333333\",\"rgba(39,164,133,1)\"],[\"0.625\",\"rgba(47,174,127,1)\"],[\"0.666666666666667\",\"rgba(53,183,121,1)\"],[\"0.708333333333333\",\"rgba(79,191,110,1)\"],[\"0.75\",\"rgba(98,199,98,1)\"],[\"0.791666666666667\",\"rgba(119,207,85,1)\"],[\"0.833333333333333\",\"rgba(147,214,70,1)\"],[\"0.875\",\"rgba(172,220,52,1)\"],[\"0.916666666666667\",\"rgba(199,225,42,1)\"],[\"0.958333333333333\",\"rgba(226,228,40,1)\"],[\"1\",\"rgba(253,231,37,1)\"]],\"showscale\":true,\"x\":[\"Alabama\",\"Alaska\",\"Arizona\",\"Arkansas\",\"California\",\"Colorado\",\"Connecticut\",\"Delaware\",\"District 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Virginia\",\"Wisconsin\",\"Wyoming\"],\"y\":[\"2021-06-15\",\"2021-06-29\",\"2021-07-13\",\"2021-07-27\",\"2021-08-10\",\"2021-08-24\",\"2021-09-07\",\"2021-09-21\",\"2021-10-05\",\"2021-10-19\"],\"z\":[[4.7000000000000002,3.7999999999999998,5.9000000000000004,7.7999999999999998,2.2999999999999998,9.6999999999999993,1.5,3.3999999999999999,1.8999999999999999,7.2000000000000002,3.3999999999999999,3.7999999999999998,5.5,2.2999999999999998,5.0999999999999996,2.7000000000000002,3.7999999999999998,4.5,7.9000000000000004,3.2000000000000002,1.6000000000000001,1.3999999999999999,2.8999999999999999,2.3999999999999999,4.0999999999999996,9.0999999999999996,5.7999999999999998,1.5,6.9000000000000004,2.3999999999999999,2.7999999999999998,4.4000000000000004,2.2999999999999998,4,4.0999999999999996,2.7000000000000002,3.5,6.0999999999999996,3,1.3999999999999999,2.7000000000000002,2.6000000000000001,1.2,5.7999999999999998,6.5,8.5999999999999996,1,1.6000000000000001,7,4.7000000000000002,1.8999999999999999,11.4],[4.2000000000000002,4.0999999999999996,7.0999999999999996,11.9,2.6000000000000001,7,2,2,1.3999999999999999,7.2999999999999998,2.5,2.7999999999999998,4.2999999999999998,2,3.6000000000000001,2.2999999999999998,4.4000000000000004,3,7.4000000000000004,1.6000000000000001,0.90000000000000002,0.80000000000000004,1.7,1.6000000000000001,5.9000000000000004,13.800000000000001,4.7000000000000002,2.1000000000000001,15.300000000000001,1.3999999999999999,2.6000000000000001,2.8999999999999999,1.7,3.2999999999999998,1.6000000000000001,2.1000000000000001,5.2000000000000002,4.7999999999999998,1.3,1.1000000000000001,2,2.5,1.2,1.2,4.4000000000000004,11.5,0.80000000000000004,1.8999999999999999,5.7999999999999998,2.8999999999999999,1.5,12.1],[11.5,10,8,32.5,16.100000000000001,9.4000000000000004,2.3999999999999999,3.1000000000000001,2.3999999999999999,26.199999999999999,7.0999999999999996,4.5999999999999996,7.2000000000000002,4.2999999999999998,5.9000000000000004,3,12,6.7999999999999998,19.399999999999999,1.8,1.7,2.1000000000000001,2.2000000000000002,2.7000000000000002,10.699999999999999,24.899999999999999,6.2000000000000002,3.7000000000000002,21.199999999999999,1.8,3.5,5.0999999999999996,3.3999999999999999,5.7999999999999998,2.7999999999999998,2.6000000000000001,9.1999999999999993,5.2999999999999998,1.8,2.3999999999999999,2.2000000000000002,6.2000000000000002,0.90000000000000002,6.5999999999999996,9,15.5,1.3999999999999999,3.1000000000000001,6.9000000000000004,3.2000000000000002,2.7999999999999998,14.699999999999999],[36.299999999999997,28.600000000000001,19.199999999999999,52.200000000000003,19.399999999999999,11.4,7.2000000000000002,7.0999999999999996,6.7999999999999998,58.700000000000003,21.699999999999999,13.699999999999999,13.9,11.5,11.300000000000001,7.5999999999999996,21.699999999999999,20,79,4.7999999999999998,4.7999999999999998,7.9000000000000004,5.4000000000000004,6.2000000000000002,40.100000000000001,40,10.4,9.3000000000000007,30.100000000000001,3.8999999999999999,8.1999999999999993,11.300000000000001,8.4000000000000004,17,5,6.7000000000000002,30.399999999999999,12.1,4.7999999999999998,10,8.5999999999999996,21.399999999999999,3.2000000000000002,20.300000000000001,22.800000000000001,20.699999999999999,3.7999999999999998,8.0999999999999996,14.300000000000001,8.1999999999999993,8.3000000000000007,19.399999999999999],[68.599999999999994,41.600000000000001,34.799999999999997,77.099999999999994,30,18,13.9,19.600000000000001,18.800000000000001,94.099999999999994,48.700000000000003,36.700000000000003,30.600000000000001,22.300000000000001,27.300000000000001,20.100000000000001,37.100000000000001,47.799999999999997,119.5,10.199999999999999,12.5,15.300000000000001,10.9,14.300000000000001,88.400000000000006,46.700000000000003,23.199999999999999,18.300000000000001,35.600000000000001,11.1,14.800000000000001,26,15.800000000000001,43,13.6,16,51.399999999999999,30.899999999999999,12.6,22.399999999999999,21,55.700000000000003,6.0999999999999996,49.299999999999997,49.399999999999999,25.699999999999999,13.4,19.699999999999999,29.199999999999999,21.399999999999999,21.399999999999999,40.200000000000003],[70.900000000000006,68.200000000000003,41.399999999999999,73.900000000000006,39.299999999999997,26.5,16.699999999999999,34.399999999999999,24.199999999999999,99.799999999999997,76.799999999999997,50.100000000000001,38.5,27.100000000000001,48.899999999999999,29.600000000000001,41.899999999999999,82.5,100.5,12,17,20.100000000000001,17.699999999999999,24.699999999999999,117.09999999999999,41.700000000000003,36.700000000000003,29.800000000000001,38.200000000000003,19.5,22,39.399999999999999,23.100000000000001,56.700000000000003,30.800000000000001,28.199999999999999,57,50,20.399999999999999,25.899999999999999,26.399999999999999,83.099999999999994,33.899999999999999,90,57.600000000000001,35,19.199999999999999,30.5,43.700000000000003,47.299999999999997,27.5,67.200000000000003],[80.799999999999997,81.599999999999994,45.600000000000001,58.899999999999999,31.6000000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10. Map  Create a map to visualize the naive_CFR by state on October 15, 2021 Compare with a map visualizing the naive_CFR by state on most recent date Plot the two maps together using subplot(). Make sure the shading is for the same range of values (google is your friend for this) Describe the difference in the pattern of the CFR.  ### For specified date pick.date = \u0026quot;2021-10-15\u0026quot; # Extract the data for each state by its abbreviation cv_per100 \u0026lt;- cv_states %\u0026gt;% filter(date==pick.date) %\u0026gt;% select(state, abb, newper100k, cases, deaths) # select data cv_per100$state_name \u0026lt;- cv_per100$state cv_per100$state \u0026lt;- cv_per100$abb cv_per100$abb \u0026lt;- NULL # Create hover text cv_per100$hover \u0026lt;- with(cv_per100, paste(state_name, '\u0026lt;br\u0026gt;', \u0026quot;Cases per 100k: \u0026quot;, newper100k, '\u0026lt;br\u0026gt;', \u0026quot;Cases: \u0026quot;, cases, '\u0026lt;br\u0026gt;', \u0026quot;Deaths: \u0026quot;, deaths)) # Set up mapping details set_map_details \u0026lt;- list( scope = 'usa', projection = list(type = 'albers usa'), showlakes = TRUE, lakecolor = toRGB('white') ) # Make sure both maps are on the same color scale shadeLimit \u0026lt;- 125 # Create the map fig \u0026lt;- plot_geo(cv_per100, locationmode = 'USA-states') %\u0026gt;% add_trace( z = ~newper100k, text = ~hover, locations = ~state, color = ~newper100k, colors = 'Purples' ) fig \u0026lt;- fig %\u0026gt;% colorbar(title = paste0(\u0026quot;Cases per 100k: \u0026quot;,pick.date), limits = c(0,shadeLimit)) fig \u0026lt;- fig %\u0026gt;% layout( title = paste('Cases per 100k by State as of ', pick.date, '\u0026lt;br\u0026gt;(Hover for value)'), geo = set_map_details ) fig_pick.date \u0026lt;- fig ############# ### For Today # Extract the data for each state by its abbreviation cv_per100 \u0026lt;- cv_states_today %\u0026gt;% select(state, abb, newper100k, cases, deaths) # select data cv_per100$state_name \u0026lt;- cv_per100$state cv_per100$state \u0026lt;- cv_per100$abb cv_per100$abb \u0026lt;- NULL # Create hover text cv_per100$hover \u0026lt;- with(cv_per100, paste(state_name, '\u0026lt;br\u0026gt;', \u0026quot;Cases per 100k: \u0026quot;, newper100k, '\u0026lt;br\u0026gt;', \u0026quot;Cases: \u0026quot;, cases, '\u0026lt;br\u0026gt;', \u0026quot;Deaths: \u0026quot;, deaths)) # Set up mapping details set_map_details \u0026lt;- list( scope = 'usa', projection = list(type = 'albers usa'), showlakes = TRUE, lakecolor = toRGB('white') ) # Create the map fig \u0026lt;- plot_geo(cv_per100, locationmode = 'USA-states') %\u0026gt;% add_trace( z = ~newper100k, text = ~hover, locations = ~state, color = ~newper100k, colors = 'Purples' ) fig \u0026lt;- fig %\u0026gt;% colorbar(title = paste0(\u0026quot;Cases per 100k: \u0026quot;, Sys.Date()), limits = c(0,shadeLimit)) fig \u0026lt;- fig %\u0026gt;% layout( title = paste('Cases per 100k by State as of', Sys.Date(), '\u0026lt;br\u0026gt;(Hover for value)'), geo = set_map_details ) fig_Today \u0026lt;- fig ### Plot together subplot(fig_pick.date, fig_Today, nrows = 2, margin = .05)   {\"x\":{\"data\":[{\"colorbar\":{\"title\":\"Cases per 100k: 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Cases per 100k: 17.9 Cases: 812694 Deaths: 15142\",\"Alaska Cases per 100k: 123.9 Cases: 126893 Deaths: 599\",\"Arizona Cases per 100k: 30.7 Cases: 1129494 Deaths: 20505\",\"Arkansas Cases per 100k: 21.3 Cases: 506011 Deaths: 8194\",\"California Cases per 100k: 13.9 Cases: 4829925 Deaths: 71672\",\"Colorado Cases per 100k: 41.9 Cases: 708803 Deaths: 8048\",\"Connecticut Cases per 100k: 12.5 Cases: 397123 Deaths: 8730\",\"Delaware Cases per 100k: 39.8 Cases: 139383 Deaths: 2021\",\"District of Columbia Cases per 100k: 14.4 Cases: 62988 Deaths: 1183\",\"Florida Cases per 100k: 12.6 Cases: 3661089 Deaths: 57859\",\"Georgia Cases per 100k: 16.4 Cases: 1579662 Deaths: 26811\",\"Hawaii Cases per 100k: 9.7 Cases: 79562 Deaths: 859\",\"Idaho Cases per 100k: 68.6 Cases: 276139 Deaths: 3256\",\"Illinois Cases per 100k: 17.8 Cases: 1671079 Deaths: 28200\",\"Indiana Cases per 100k: 29.7 Cases: 999870 Deaths: 16246\",\"Iowa Cases per 100k: 36.9 Cases: 469875 Deaths: 6749\",\"Kansas Cases per 100k: 26.6 Cases: 424090 Deaths: 6204\",\"Kentucky Cases per 100k: 43.2 Cases: 729974 Deaths: 9376\",\"Louisiana Cases per 100k: 13.9 Cases: 751874 Deaths: 14321\",\"Maine Cases per 100k: 30.3 Cases: 97185 Deaths: 1093\",\"Maryland Cases per 100k: 16.8 Cases: 548975 Deaths: 10687\",\"Massachusetts Cases per 100k: 20.3 Cases: 833311 Deaths: 18828\",\"Michigan Cases per 100k: 43.7 Cases: 1217412 Deaths: 22951\",\"Minnesota Cases per 100k: 50.3 Cases: 755525 Deaths: 8531\",\"Mississippi Cases per 100k: 16.8 Cases: 497790 Deaths: 9917\",\"Missouri Cases per 100k: 18.9 Cases: 856850 Deaths: 12560\",\"Montana Cases per 100k: 80.6 Cases: 164846 Deaths: 2133\",\"Nebraska Cases per 100k: 30.7 Cases: 275694 Deaths: 2901\",\"Nevada Cases per 100k: 22.5 Cases: 441021 Deaths: 7425\",\"New Hampshire Cases per 100k: 40.4 Cases: 128047 Deaths: 1520\",\"New Jersey Cases per 100k: 17 Cases: 1178936 Deaths: 27693\",\"New Mexico Cases per 100k: 32.8 Cases: 263755 Deaths: 4932\",\"New York Cases per 100k: 23.5 Cases: 2497110 Deaths: 55469\",\"North Carolina Cases per 100k: 29.9 Cases: 1449002 Deaths: 17475\",\"North Dakota Cases per 100k: 68 Cases: 140780 Deaths: 1704\",\"Ohio Cases per 100k: 38.7 Cases: 1490451 Deaths: 23327\",\"Oklahoma Cases per 100k: 32.9 Cases: 631872 Deaths: 9403\",\"Oregon Cases per 100k: 26.1 Cases: 348767 Deaths: 4161\",\"Pennsylvania Cases per 100k: 36 Cases: 1502124 Deaths: 30459\",\"Puerto Rico Cases per 100k: 4 Cases: 216782 Deaths: 3205\",\"Rhode Island Cases per 100k: 24.8 Cases: 176424 Deaths: 2860\",\"South Carolina Cases per 100k: 28 Cases: 883982 Deaths: 13249\",\"South Dakota Cases per 100k: 35.3 Cases: 149996 Deaths: 2189\",\"Tennessee Cases per 100k: 28.1 Cases: 1233924 Deaths: 15696\",\"Texas Cases per 100k: 21.6 Cases: 4167271 Deaths: 69071\",\"Utah Cases per 100k: 40.5 Cases: 529281 Deaths: 3067\",\"Vermont Cases per 100k: 34.8 Cases: 36730 Deaths: 343\",\"Virginia Cases per 100k: 24.8 Cases: 902938 Deaths: 13391\",\"Washington Cases per 100k: 31.5 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Cases per 100k: 5.7 Cases: 1648385 Deaths: 21631\",\"Alaska Cases per 100k: 9.6 Cases: 308893 Deaths: 1439\",\"Arizona Cases per 100k: 5.3 Cases: 2451062 Deaths: 33221\",\"Arkansas Cases per 100k: 1.7 Cases: 1008303 Deaths: 13070\",\"California Cases per 100k: 6.2 Cases: 12170765 Deaths: 105245\",\"Colorado Cases per 100k: 5.8 Cases: 1781053 Deaths: 14588\",\"Connecticut Cases per 100k: 3.5 Cases: 978456 Deaths: 12310\",\"Delaware Cases per 100k: 5 Cases: 334147 Deaths: 3352\",\"District of Columbia Cases per 100k: 2.4 Cases: 178238 Deaths: 1518\",\"Florida Cases per 100k: 7.9 Cases: 7583396 Deaths: 87155\",\"Georgia Cases per 100k: 3.8 Cases: 2985191 Deaths: 41055\",\"Hawaii Cases per 100k: 5.1 Cases: 372408 Deaths: 1851\",\"Idaho Cases per 100k: 5.8 Cases: 522919 Deaths: 5461\",\"Illinois Cases per 100k: 9.6 Cases: 4107931 Deaths: 41621\",\"Indiana Cases per 100k: 0 Cases: 2058560 Deaths: 26180\",\"Iowa Cases per 100k: 7.6 Cases: 907421 Deaths: 10776\",\"Kansas Cases per 100k: 6.3 Cases: 940907 Deaths: 10315\",\"Kentucky Cases per 100k: 6.8 Cases: 1739015 Deaths: 18348\",\"Louisiana Cases per 100k: 12.9 Cases: 1580709 Deaths: 18856\",\"Maine Cases per 100k: 9.6 Cases: 320004 Deaths: 2984\",\"Maryland Cases per 100k: 4.9 Cases: 1369957 Deaths: 16692\",\"Massachusetts Cases per 100k: 5.3 Cases: 2230150 Deaths: 28211\",\"Michigan Cases per 100k: 8.5 Cases: 3068195 Deaths: 42314\",\"Minnesota Cases per 100k: 6.6 Cases: 1784263 Deaths: 14964\",\"Mississippi Cases per 100k: 5.1 Cases: 993035 Deaths: 13455\",\"Missouri Cases per 100k: 6.4 Cases: 1794222 Deaths: 24078\",\"Montana Cases per 100k: 8.8 Cases: 330930 Deaths: 3704\",\"Nebraska Cases per 100k: 11.2 Cases: 576365 Deaths: 5182\",\"Nevada Cases per 100k: 5.2 Cases: 893067 Deaths: 12106\",\"New Hampshire Cases per 100k: 6.4 Cases: 379689 Deaths: 3018\",\"New Jersey Cases per 100k: 6 Cases: 3057442 Deaths: 36100\",\"New Mexico Cases per 100k: 9.2 Cases: 673541 Deaths: 9126\",\"New York Cases per 100k: 5.3 Cases: 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","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"8a027c6e0f5901c832e1e370812adf61","permalink":"https://uscbiostats.github.io/PM566/assignment/11-lab-answers/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/11-lab-answers/","section":"assignment","summary":"Learning Goals  Read in and process the COVID dataset from the New York Times GitHub repository Create interactive graphs of different types using plot_ly() and ggplotly() functions Customize the hoverinfo and other plot features Create a Choropleth map using plot_geo()  Lab Description We will work with COVID data downloaded from the New York Times.","tags":null,"title":"Lab 11 - Interactive Visualization","type":"assignment"},{"authors":null,"categories":null,"content":"Grab lab file using command line:\n# Step 1 cd ~/Documents mkdir lab11 cd lab11 # Step 2 wget https://raw.githubusercontent.com/USCbiostats/PM566/master/website/content/assignment/11-lab.Rmd  And remember to set eval=TRUE\nLearning Goals  Read in and process the COVID dataset from the New York Times GitHub repository Create interactive graphs of different types using plot_ly() and ggplotly() functions Customize the hoverinfo and other plot features Create a Choropleth map using plot_geo()  Lab Description We will work with COVID data downloaded from the New York Times. The dataset consists of COVID-19 cases and deaths in each US state during the course of the COVID epidemic.\nThe objective of this lab is to explore relationships between cases, deaths, and population sizes of US states, and plot data to demonstrate this\nSteps I. Reading and processing the New York Times (NYT) state-level COVID-19 data 1. Read in the data  Read in the COVID data with data.table:fread() from the NYT GitHub repository: \u0026ldquo; https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv\" Read in the state population data with data.table:fread() from the repository: \u0026ldquo; https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv\"\" Merge datasets  ## data extracted from New York Times state-level data from NYT Github repository # https://github.com/nytimes/covid-19-data ## state-level population information from us_census_data available on GitHub repository: # https://github.com/COVID19Tracking/associated-data/tree/master/us_census_data ### FINISH THE CODE HERE ### # load COVID state-level data from NYT cv_states \u0026lt;- as.data.frame(______________(\u0026quot;https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv\u0026quot;)) ### FINISH THE CODE HERE ### # load state population data state_pops \u0026lt;- as.data.frame(_____________(\u0026quot;https://raw.githubusercontent.com/COVID19Tracking/associated-data/master/us_census_data/us_census_2018_population_estimates_states.csv\u0026quot;)) state_pops$abb \u0026lt;- state_pops$state state_pops$state \u0026lt;- state_pops$state_name state_pops$state_name \u0026lt;- NULL ### FINISH THE CODE HERE cv_states \u0026lt;- merge(cv_states, state_pops, by=________)  2. Look at the data  Inspect the dimensions, head, and tail of the data Inspect the structure of each variables. Are they in the correct format?  dim(cv_states) head(cv_states) tail(cv_states) str(cv_states)  3. Format the data  Make date into a date variable Make state into a factor variable Order the data first by state, second by date Confirm the variables are now correctly formatted Inspect the range values for each variable. What is the date range? The range of cases and deaths?  # format the date cv_states$date \u0026lt;- as.Date(cv_states$date, format=\u0026quot;%Y-%m-%d\u0026quot;) # format the state and state abbreviation (abb) variables state_list \u0026lt;- unique(cv_states$state) cv_states$state \u0026lt;- factor(cv_states$state, levels = state_list) abb_list \u0026lt;- unique(cv_states$abb) cv_states$abb \u0026lt;- factor(cv_states$abb, levels = abb_list) ### FINISH THE CODE HERE # order the data first by state, second by date cv_states = cv_states[order(_________, _________),] # Confirm the variables are now correctly formatted str(cv_states) head(cv_states) tail(cv_states) # Inspect the range values for each variable. What is the date range? The range of cases and deaths? head(cv_states) summary(cv_states) min(cv_states$date) max(cv_states$date)  4. Add new_cases and new_deaths and correct outliers   Add variables for new cases, new_cases, and new deaths, new_deaths:\n Hint: You can set new_cases equal to the difference between cases on date i and date i-1, starting on date i=2    Filter to dates after June 1, 2021\n  Use plotly for EDA: See if there are outliers or values that don\u0026rsquo;t make sense for new_cases and new_deaths. Which states and which dates have strange values?\n  Correct outliers: Set negative values for new_cases or new_deaths to 0\n  Recalculate cases and deaths as cumulative sum of updated new_cases and new_deaths\n  Get the rolling average of new cases and new deaths to smooth over time\n  Inspect data again interactively\n  # Add variables for new_cases and new_deaths: for (i in 1:length(state_list)) { cv_subset = subset(cv_states, state == state_list[i]) cv_subset = cv_subset[order(cv_subset$date),] # add starting level for new cases and deaths cv_subset$new_cases = cv_subset$cases[1] cv_subset$new_deaths = cv_subset$deaths[1] ### FINISH THE CODE HERE for (j in 2:nrow(cv_subset)) { cv_subset$new_cases[j] = __________ - __________ cv_subset$new_deaths[j] = __________ - __________ } # include in main dataset cv_states$new_cases[cv_states$state==state_list[i]] = cv_subset$new_cases cv_states$new_deaths[cv_states$state==state_list[i]] = cv_subset$new_deaths } # Focus on recent dates cv_states \u0026lt;- cv_states %\u0026gt;% dplyr::filter(date \u0026gt;= \u0026quot;2021-06-01\u0026quot;) ### FINISH THE CODE HERE # Inspect outliers in new_cases using plotly p1\u0026lt;-ggplot(cv_states, aes(x = date, y = new_cases, color = state)) + ________ + geom_point(size = .5, alpha = 0.5) ggplotly(p1) p1\u0026lt;-NULL # to clear from workspace p2\u0026lt;-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + ________ + geom_point(size = .5, alpha = 0.5) ggplotly(p2) p2\u0026lt;-NULL # to clear from workspace # set negative new case or death counts to 0 cv_states$new_cases[cv_states$new_cases\u0026lt;0] = 0 cv_states$new_deaths[cv_states$new_deaths\u0026lt;0] = 0 # Recalculate `cases` and `deaths` as cumulative sum of updated `new_cases` and `new_deaths` for (i in 1:length(state_list)) { cv_subset = subset(cv_states, state == state_list[i]) # add starting level for new cases and deaths cv_subset$cases = cv_subset$cases[1] cv_subset$deaths = cv_subset$deaths[1] ### FINISH CODE HERE for (j in 2:nrow(cv_subset)) { cv_subset$cases[j] = cv_subset$new_cases[j] + cv_subset$_________ cv_subset$deaths[j] = cv_subset$new_deaths[j] + cv_subset$__________ } # include in main dataset cv_states$cases[cv_states$state==state_list[i]] = cv_subset$cases cv_states$deaths[cv_states$state==state_list[i]] = cv_subset$deaths } # Smooth new counts cv_states$new_cases = zoo::rollmean(cv_states$new_cases, k=7, fill=NA, align='right') %\u0026gt;% round(digits = 0) cv_states$new_deaths = zoo::rollmean(cv_states$new_deaths, k=7, fill=NA, align='right') %\u0026gt;% round(digits = 0) # Inspect data again interactively p2\u0026lt;-ggplot(cv_states, aes(x = date, y = new_deaths, color = state)) + geom_line() + geom_point(size = .5, alpha = 0.5) ggplotly(p2) #p2=NULL  5. Add additional variables   Add population-normalized (by 100,000) variables for each variable type (rounded to 1 decimal place). Make sure the variables you calculate are in the correct format (numeric). You can use the following variable names:\n per100k = cases per 100,000 population newper100k= new cases per 100,000 deathsper100k = deaths per 100,000 newdeathsper100k = new deaths per 100,000    Add a \u0026ldquo;naive CFR\u0026rdquo; variable representing deaths / cases on each date for each state\n  Create a dataframe representing values on the most recent date, cv_states_today, as done in lecture\n  ### FINISH CODE HERE # add population normalized (by 100,000) counts for each variable cv_states$per100k = as.numeric(format(round(cv_states$cases/(cv_states$________/100000),1),nsmall=1)) cv_states$newper100k = as.numeric(format(round(cv_states$new_cases/(cv_states$________/100000),1),nsmall=1)) cv_states$deathsper100k = as.numeric(format(round(cv_states$deaths/(cv_states$________/100000),1),nsmall=1)) cv_states$newdeathsper100k = as.numeric(format(round(cv_states$new_deaths/(cv_states$________/100000),1),nsmall=1)) # add a naive_CFR variable = deaths / cases cv_states = cv_states %\u0026gt;% mutate(naive_CFR = round((deaths*100/cases),2)) # create a `cv_states_today` variable cv_states_today = subset(cv_states, date==max(cv_states$date))  II. Scatterplots 6. Explore scatterplots using plot_ly()  Create a scatterplot using plot_ly() representing pop_density vs. various variables (e.g. cases, per100k, deaths, deathsper100k) for each state on most recent date (cv_states_today)  Color points by state and size points by state population Use hover to identify any outliers. Remove those outliers and replot.   Choose one plot. For this plot:  Add hoverinfo specifying the state name, cases per 100k, and deaths per 100k, similarly to how we did this in the lecture notes Add layout information to title the chart and the axes Enable hovermode = \u0026quot;compare\u0026quot;    ### FINISH CODE HERE # pop_density vs. cases cv_states_today %\u0026gt;% plot_ly(x = ~pop_density, y = ~cases, type = _______, mode = 'markers', color = ~______, size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5)) # filter out \u0026quot;District of Columbia\u0026quot; cv_states_today_filter \u0026lt;- cv_states_today %\u0026gt;% filter(state!=\u0026quot;District of Columbia\u0026quot;) # pop_density vs. cases after filtering cv_states_today_filter %\u0026gt;% plot_ly(x = ~pop_density, y = ~cases, type = _______, mode = 'markers', color = ~_______, size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5)) # pop_density vs. deathsper100k cv_states_today_filter %\u0026gt;% plot_ly(x = ~pop_density, y = ~deathsper100k, type = _______, mode = 'markers', color = ~_______, size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5)) # Adding hoverinfo cv_states_today_filter %\u0026gt;% plot_ly(x = ~pop_density, y = ~deathsper100k, type = _______, mode = 'markers', color = ~_______, size = ~population, sizes = c(5, 70), marker = list(sizemode='diameter', opacity=0.5), hoverinfo = 'text', text = ~paste( paste(state, \u0026quot;:\u0026quot;, sep=\u0026quot;\u0026quot;), paste(\u0026quot; Cases per 100k: \u0026quot;, per100k, sep=\u0026quot;\u0026quot;) , paste(\u0026quot; Deaths per 100k: \u0026quot;, _______, sep=\u0026quot;\u0026quot;), sep = \u0026quot;\u0026lt;br\u0026gt;\u0026quot;)) %\u0026gt;% layout(title = \u0026quot;Population-normalized COVID-19 deaths (per 100k) vs. population density for US states\u0026quot;, yaxis = list(title = \u0026quot;Deaths per 100k\u0026quot;), xaxis = list(title = \u0026quot;Population Density\u0026quot;), hovermode = \u0026quot;compare\u0026quot;)  7. Explore scatterplot trend interactively using ggplotly() and geom_smooth()  For pop_density vs. newdeathsper100k create a chart with the same variables using gglot_ly() Explore the pattern between \\(x\\) and \\(y\\) using geom_smooth()  Explain what you see. Do you think pop_density is a correlate of newdeathsper100k?    ### FINISH CODE HERE p \u0026lt;- ggplot(cv_states_today_scatter, aes(x=pop_density, y=deathsper100k, size=population)) + geom_point() + _______ ggplotly(p)  8. Multiple line chart  Create a line chart of the naive_CFR for all states over time using plot_ly()  Use the zoom and pan tools to inspect the naive_CFR for the states that had an increase in September. How have they changed over time?   Create one more line chart, for Florida only, which shows new_cases and new_deaths together in one plot. Hint: use add_layer()  Use hoverinfo to \u0026ldquo;eyeball\u0026rdquo; the approximate peak of deaths and peak of cases. What is the time delay between the peak of cases and the peak of deaths?    ### FINISH CODE HERE # Line chart for naive_CFR for all states over time using `plot_ly()` plot_ly(cv_states, x = ~date, y = ~_______, color = ~state, type = \u0026quot;scatter\u0026quot;, mode = \u0026quot;lines\u0026quot;) ### FINISH CODE HERE # Line chart for Florida showing new_cases and new_deaths together cv_states %\u0026gt;% filter(state==\u0026quot;Florida\u0026quot;) %\u0026gt;% plot_ly(x = ~date, y = ~new_cases, type = \u0026quot;scatter\u0026quot;, mode = \u0026quot;lines\u0026quot;) %\u0026gt;% _______(x = ~date, y = ~new_deaths, type = \u0026quot;scatter\u0026quot;, mode = \u0026quot;lines\u0026quot;)  9. Heatmaps Create a heatmap to visualize new_cases for each state on each date greater than June 1st, 2021\n Start by mapping selected features in the dataframe into a matrix using the tidyr package function pivot_wider(), naming the rows and columns, as done in the lecture notes Use plot_ly() to create a heatmap out of this matrix. Which states stand out? Repeat with newper100k variable. Now which states stand out? Create a second heatmap in which the pattern of new_cases for each state over time becomes more clear by filtering to only look at dates every two weeks  ### FINISH CODE HERE # Map state, date, and new_cases to a matrix library(tidyr) cv_states_mat \u0026lt;- cv_states %\u0026gt;% select(state, date, new_cases) %\u0026gt;% dplyr::filter(date\u0026gt;as.Date(\u0026quot;2021-06-15\u0026quot;)) cv_states_mat2 \u0026lt;- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = ________)) rownames(cv_states_mat2) \u0026lt;- cv_states_mat2$date cv_states_mat2$date \u0026lt;- NULL cv_states_mat2 \u0026lt;- as.matrix(cv_states_mat2) # Create a heatmap using plot_ly() plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=\u0026quot;heatmap\u0026quot;, showscale=T) # Repeat with newper100k cv_states_mat \u0026lt;- cv_states %\u0026gt;% select(state, date, newper100k) %\u0026gt;% dplyr::filter(date\u0026gt;as.Date(\u0026quot;2021-06-15\u0026quot;)) cv_states_mat2 \u0026lt;- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = ________)) rownames(cv_states_mat2) \u0026lt;- cv_states_mat2$date cv_states_mat2$date \u0026lt;- NULL cv_states_mat2 \u0026lt;- as.matrix(cv_states_mat2) plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=\u0026quot;heatmap\u0026quot;, showscale=T) # Create a second heatmap after filtering to only include dates every other week filter_dates \u0026lt;- seq(as.Date(\u0026quot;2021-06-15\u0026quot;), as.Date(\u0026quot;2021-11-01\u0026quot;), by=________) cv_states_mat \u0026lt;- cv_states %\u0026gt;% select(state, date, newper100k) %\u0026gt;% filter(date %in% filter_dates) cv_states_mat2 \u0026lt;- as.data.frame(pivot_wider(cv_states_mat, names_from = state, values_from = ________)) rownames(cv_states_mat2) \u0026lt;- cv_states_mat2$date cv_states_mat2$date \u0026lt;- NULL cv_states_mat2 \u0026lt;- as.matrix(cv_states_mat2) # Create a heatmap using plot_ly() plot_ly(x=colnames(cv_states_mat2), y=rownames(cv_states_mat2), z=~cv_states_mat2, type=\u0026quot;heatmap\u0026quot;, showscale=T)  10. Map  Create a map to visualize the naive_CFR by state on October 15, 2021 Compare with a map visualizing the naive_CFR by state on most recent date Plot the two maps together using subplot(). Make sure the shading is for the same range of values (google is your friend for this) Describe the difference in the pattern of the CFR.  ### For specified date pick.date = \u0026quot;2021-10-15\u0026quot; # Extract the data for each state by its abbreviation cv_per100 \u0026lt;- cv_states %\u0026gt;% filter(date==pick.date) %\u0026gt;% select(state, abb, newper100k, cases, deaths) # select data cv_per100$state_name \u0026lt;- cv_per100$state cv_per100$state \u0026lt;- cv_per100$abb cv_per100$abb \u0026lt;- NULL # Create hover text cv_per100$hover \u0026lt;- with(cv_per100, paste(state_name, '\u0026lt;br\u0026gt;', \u0026quot;Cases per 100k: \u0026quot;, newper100k, '\u0026lt;br\u0026gt;', \u0026quot;Cases: \u0026quot;, cases, '\u0026lt;br\u0026gt;', \u0026quot;Deaths: \u0026quot;, deaths)) # Set up mapping details set_map_details \u0026lt;- list( scope = 'usa', projection = list(type = 'albers usa'), showlakes = TRUE, lakecolor = toRGB('white') ) # Make sure both maps are on the same color scale shadeLimit \u0026lt;- 125 # Create the map fig \u0026lt;- plot_geo(cv_per100, locationmode = 'USA-states') %\u0026gt;% add_trace( z = ~newper100k, text = ~hover, locations = ~state, color = ~newper100k, colors = 'Purples' ) fig \u0026lt;- fig %\u0026gt;% colorbar(title = paste0(\u0026quot;Cases per 100k: \u0026quot;, pick.date), limits = c(0,shadeLimit)) fig \u0026lt;- fig %\u0026gt;% layout( title = paste('Cases per 100k by State as of ', pick.date, '\u0026lt;br\u0026gt;(Hover for value)'), geo = set_map_details ) fig_pick.date \u0026lt;- fig ############# ### Map for today's date # Extract the data for each state by its abbreviation cv_per100 \u0026lt;- cv_states_today %\u0026gt;% select(state, abb, newper100k, cases, deaths) # select data cv_per100$state_name \u0026lt;- cv_per100$state cv_per100$state \u0026lt;- cv_per100$abb cv_per100$abb \u0026lt;- NULL # Create hover text cv_per100$hover \u0026lt;- with(cv_per100, paste(state_name, '\u0026lt;br\u0026gt;', \u0026quot;Cases per 100k: \u0026quot;, newper100k, '\u0026lt;br\u0026gt;', \u0026quot;Cases: \u0026quot;, cases, '\u0026lt;br\u0026gt;', \u0026quot;Deaths: \u0026quot;, deaths)) # Set up mapping details set_map_details \u0026lt;- list( scope = 'usa', projection = list(type = 'albers usa'), showlakes = TRUE, lakecolor = toRGB('white') ) # Create the map fig \u0026lt;- plot_geo(cv_per100, locationmode = 'USA-states') %\u0026gt;% add_trace( z = ~newper100k, text = ~hover, locations = ~state, color = ~newper100k, colors = 'Purples' ) fig \u0026lt;- fig %\u0026gt;% colorbar(title = paste0(\u0026quot;Cases per 100k: \u0026quot;, Sys.Date()), limits = c(0,shadeLimit)) fig \u0026lt;- fig %\u0026gt;% layout( title = paste('Cases per 100k by State as of', Sys.Date(), '\u0026lt;br\u0026gt;(Hover for value)'), geo = set_map_details ) fig_Today \u0026lt;- fig ### Plot together ________(fig_pick.date, fig_Today, nrows = 2, margin = .05)  ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"d1a7f60577e327e069e44dff1b818953","permalink":"https://uscbiostats.github.io/PM566/assignment/11-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/11-lab/","section":"assignment","summary":"Grab lab file using command line:\n# Step 1 cd ~/Documents mkdir lab11 cd lab11 # Step 2 wget https://raw.githubusercontent.com/USCbiostats/PM566/master/website/content/assignment/11-lab.Rmd  And remember to set eval=TRUE\nLearning Goals  Read in and process the COVID dataset from the New York Times GitHub repository Create interactive graphs of different types using plot_ly() and ggplotly() functions Customize the hoverinfo and other plot features Create a Choropleth map using plot_geo()  Lab Description We will work with COVID data downloaded from the New York Times.","tags":null,"title":"Lab 11 - Interactive Visualization","type":"assignment"},{"authors":null,"categories":null,"content":"Learning goals In this lab, you are expected to learn/put in practice the following skills:\n Evaluate whether a problem can be parallelized or not. Practice with the parallel package.  Problem 1: Vectorization The following functions can be written to be more efficient without using parallel. Write a faster version of each function and show that (1) the outputs are the same as the slow version, and (2) your version is faster.\n This function generates an n x k dataset with all its entries drawn from a Poission distribution with mean lambda.  fun1 \u0026lt;- function(n = 100, k = 4, lambda = 4) { x \u0026lt;- NULL for (i in 1:n){ x \u0026lt;- rbind(x, rpois(k, lambda)) } return(x) } fun1alt \u0026lt;- function(n = 100, k = 4, lambda = 4) { # YOUR CODE HERE }  Show that fun1alt generates a matrix with the same dimensions as fun1 and that the values inside the two matrices follow similar distributions. Then check the speed of the two functions with the following code:\n# Benchmarking microbenchmark::microbenchmark( fun1(), fun1alt() )  This function finds the maximum value of each column of a matrix (hint: check out the max.col() function).  # Data Generating Process (10 x 10,000 matrix) set.seed(1234) x \u0026lt;- matrix(rnorm(1e4), nrow=10) # Find each column's max value fun2 \u0026lt;- function(x) { apply(x, 2, max) } fun2alt \u0026lt;- function(x) { # YOUR CODE HERE }  Show that both functions return the same output for a given input matrix, x. Then check the speed of the two functions.\nProblem 3: Parallelization We will now turn our attention to the statistical concept of bootstrapping. Among its many uses, non-parametric bootstrapping allows us to obtain confidence intervals for parameter estimates without relying on parametric assumptions. Don\u0026rsquo;t worry if these concepts are unfamiliar, we only care about the computation methods in this lab, not the statistics.\nThe main assumption is that we can approximate the results of many repeated experiments by resampling observations from our original dataset, which reflects the population.\n This function implements a serial version of the bootstrap. Edit this function to parallelize the lapply loop, using whichever method you prefer. Rather than specifying the number of cores to use, use the number given by the ncpus argument, so that we can test it with different numbers of cores later.  my_boot \u0026lt;- function(dat, stat, R, ncpus = 1L) { # Getting the random indices n \u0026lt;- nrow(dat) idx \u0026lt;- matrix(sample.int(n, n*R, TRUE), nrow=n, ncol=R) # THIS FUNCTION NEEDS TO BE PARALELLIZED # EDIT THIS CODE: ans \u0026lt;- lapply(seq_len(R), function(i) { stat(dat[idx[,i], , drop=FALSE]) }) # Converting the list into a matrix ans \u0026lt;- do.call(rbind, ans) return(ans) }  Once you have a version of the my_boot() function that runs on multiple cores, check that it provides accurate results by comparing it to a parametric model:  # Bootstrap of an OLS my_stat \u0026lt;- function(d) coef(lm(y ~ x, data=d)) # DATA SIM set.seed(1) n \u0026lt;- 500; R \u0026lt;- 1e4 x \u0026lt;- cbind(rnorm(n)); y \u0026lt;- x*5 + rnorm(n) # Checking if we get something similar as lm ans0 \u0026lt;- confint(lm(y~x)) ans1 \u0026lt;- my_boot(dat = data.frame(x, y), my_stat, R = R, ncpus = 2L) # You should get something like this t(apply(ans1, 2, quantile, c(.025,.975))) ## 2.5% 97.5% ## (Intercept) -0.1372435 0.05074397 ## x 4.8680977 5.04539763 ans0 ## 2.5 % 97.5 % ## (Intercept) -0.1379033 0.04797344 ## x 4.8650100 5.04883353  Check whether your version actually goes faster when it\u0026rsquo;s run on multiple cores (since this might take a little while to run, we\u0026rsquo;ll use system.time and just run each version once, rather than microbenchmark, which would run each version 100 times, by default):  system.time(my_boot(dat = data.frame(x, y), my_stat, R = 4000, ncpus = 1L)) system.time(my_boot(dat = data.frame(x, y), my_stat, R = 4000, ncpus = 2L))  ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"2910dc657da75a7f30622356ed93be1b","permalink":"https://uscbiostats.github.io/PM566/assignment/09-lab/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/assignment/09-lab/","section":"assignment","summary":"Learning goals In this lab, you are expected to learn/put in practice the following skills:\n Evaluate whether a problem can be parallelized or not. Practice with the parallel package.  Problem 1: Vectorization The following functions can be written to be more efficient without using parallel. Write a faster version of each function and show that (1) the outputs are the same as the slow version, and (2) your version is faster.","tags":null,"title":"Lab 9 - HPC","type":"assignment"},{"authors":null,"categories":null,"content":"Here’s your roadmap for the semester!\n  Optional reading material to supplement lecture material  Class materials (slides, in-class activities, etc.) will be added on the day of class  Assignments are due by 11:59 PM on the day they are due     Week 1 (8/30) Introduction to Data Science tools: R, Python, Quarto, Git, command line tools       Week 2 (9/6) GitHub, Version Control, \u0026amp; Reproducible Research       Week 3 (9/13) Exploratory Data Analysis      Week 4 (9/20) Data Visualization       Week 5 (9/27) Data Cleaning and Wrangling      Week 6 (10/4) Text Mining \u0026amp; Sentiment Analysis       Week 7 (10/11) NO CLASS - Fall Recess. Midterm Project        Week 8 (10/18) Scraping, APIs, and Regular Expressions       Week 9 (10/25) (Midterm Project due) High Performance Computing, Cloud Computing      Week 10 (11/1) Managing Big Data, SQL and Non-SQL Languages, Google BigQuery       Week 11 (11/8) Interactive Visualization \u0026amp; Effective Data Communication I      Week 12 (11/15) Interactive Visualization and Effective Data Communication II       Week 13 (11/22) Final project workshop      Week 14 (11/29) NO CLASS - Thanksgiving Holiday         Week 15 (12/6) Final Project Due        \u0026lt;/tbody\u0026gt;   ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"bb4a91b4752025cb43953747d43053c7","permalink":"https://uscbiostats.github.io/PM566/schedule/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/schedule/","section":"","summary":"Here’s your roadmap for the semester!\n  Optional reading material to supplement lecture material  Class materials (slides, in-class activities, etc.) will be added on the day of class  Assignments are due by 11:59 PM on the day they are due     Week 1 (8/30) Introduction to Data Science tools: R, Python, Quarto, Git, command line tools       Week 2 (9/6) GitHub, Version Control, \u0026amp; Reproducible Research       Week 3 (9/13) Exploratory Data Analysis      Week 4 (9/20) Data Visualization       Week 5 (9/27) Data Cleaning and Wrangling      Week 6 (10/4) Text Mining \u0026amp; Sentiment Analysis       Week 7 (10/11) NO CLASS - Fall Recess.","tags":null,"title":"Schedule","type":"page"},{"authors":null,"categories":null,"content":"   PM 566: Introduction to Health Data Science Course Overview Learning Objectives Course Notes Technological Proficiency and Hardware/Software Required Readings and Supplementary Materials Supplementary References Description and Assessment of Assignments Grading Breakdown Assignment Submission Policy Schedule Academic Integrity  Statement on the use of Artificial Intelligence  Students and Disability Accommodations: Support Systems:   PM 566: Introduction to Health Data Science Term: Fall 2023\nTime: Friday 9am - 12:55pm\nLocation: SSB 114\nUnits: 4\n Course Overview This course serves as an introduction to data science with a focus on the acquisition and analysis of real-life data. Students will learn the toolsets needed to 1) create workable and reproducible datasets by accessing, scraping, sampling and cleaning data; 2) conduct exploratory data analysis and data visualization; 3) apply statistical tools to learn from data; and 4) build functions and basic apps. Coding languages R and Python will be used.\n Learning Objectives Through this course, students will become familiar with the techniques used in data science, applied to health-related datasets. Students will learn:\n Programming in R, and associated tools Markdown, Git Data visualization – summarizing data through interpretable summaries Data collection – data scraping, wrangling, cleaning, and sampling Exploratory data analysis – generating hypotheses and building intuition Basic statistical algorithms Building software packages and apps  Prerequisite(s): None\nRecommended Preparation: Undergraduate course in statistics and programming\n Course Notes Lecture notes presented in class will be posted on GitHub.\n Technological Proficiency and Hardware/Software Required Computation using R (downloaded from http://cran.r-project.org), and development tools including Git (https://github.com/) and Markdown will be used throughout the semester.\n Readings and Supplementary Materials R Programming for Data Science, 2019. Roger Peng. https://bookdown.org/rdpeng/rprogdatascience/   Supplementary References R for Data Science, 2017. Garrett Grolemund and Hadley Wickham. http://r4ds.had.co.nz/ Exploratory Data Analysis with R, 2020. Roger Peng. https://bookdown.org/rdpeng/exdata/ Mastering Software Development in R, 2017. Roger Peng, Sean Kross, Brooke Anderson. https://bookdown.org/rdpeng/RProgDA/ R Packages, 2023. Hadley Wickham and Jennifer Bryan. https://r-pkgs.org/ Modern Data Science with R, 2023. Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton. https://mdsr-book.github.io/mdsr3e/   Description and Assessment of Assignments Assignments: There will be 5 assignments given throughout the semester, approximately 1 every 2 weeks. Students may discuss the problems with one another, however, individual solutions must be submitted and copying will not be tolerated. All assignments must be completed in Quarto or R Markdown, and submitted through the Github classes portal of the course. Late assignments will be penalized by 20% for each day past the due date.\nFinal Project: The final project will be to write a report for an analysis applied to a real-world dataset and to create a website that includes interactive visualizations to display data/results. The source code, website files, and PDF report will be uploaded to GitHub.\nLabs: Lab attendance is mandatory and participation in the lab is required and counts as part of the overall lab grade.\n Grading Breakdown   Assignment % of Grade    Labs 20%  Homework (5) 30%  Midterm Exam 20%  Final Project 30%  TOTAL 100%     Assignment Submission Policy Assignments shall be submitted on the Github classroom portal of the course. Late homework assignments will not be accepted without penalty, except when verifiable extenuating circumstances can be demonstrated.\n Schedule   Week 1 (8/30) Introduction to Data Science tools: R, Python, Quarto, Git, command line tools   Week 2 (9/6) GitHub, Version Control, \u0026amp; Reproducible Research   Week 3 (9/13) Exploratory Data Analysis   Week 4 (9/20) Data Visualization   Week 5 (9/27) Data Cleaning and Wrangling   Week 6 (10/4) Text Mining \u0026amp; Sentiment Analysis   Week 7 (10/11) NO CLASS - Fall Recess. Midterm Project   Week 8 (10/18) Scraping, APIs, and Regular Expressions   Week 9 (10/25) (Midterm Project due) High Performance Computing, Cloud Computing   Week 10 (11/1) Managing Big Data, SQL and Non-SQL Languages, Google BigQuery   Week 11 (11/8) Interactive Visualization \u0026amp; Effective Data Communication I   Week 12 (11/15) Interactive Visualization and Effective Data Communication II   Week 13 (11/22) Final project workshop   Week 14 (11/29) NO CLASS - Thanksgiving Holiday   Week 15 (12/6) Final Project Due    As the weeks go by, consult the Schedule Page for more information on weekly topics, problem sets, readings, and other materials. The schedule is likely to change as we go. Links to readings, assignments, and other materials from class will be posted on that page.\n Academic Integrity The University of Southern California is foremost a learning community committed to fostering successful scholars and researchers dedicated to the pursuit of knowledge and the transmission of ideas. Academic misconduct is in contrast to the university’s mission to educate students through a broad array of first-rank academic, professional, and extracurricular programs and includes any act of dishonesty in the submission of academic work (either in draft or final form).\nThis course will follow the expectations for academic integrity as stated in the USC Student Handbook. All students are expected to submit assignments that are original work and prepared specifically for the course/section in this academic term. You may not submit work written by others or “recycle” work prepared for other courses without obtaining written permission from the instructor(s). Students suspected of engaging in academic misconduct will be reported to the Office of Academic Integrity.\nOther violations of academic misconduct include, but are not limited to, cheating, plagiarism, fabrication (e.g., falsifying data), knowingly assisting others in acts of academic dishonesty, and any act that gains or is intended to gain an unfair academic advantage.\nThe impact of academic dishonesty is far-reaching and is considered a serious offense against the university and could result in outcomes such as failure on the assignment, failure in the course, suspension, or even expulsion from the university.\nFor more information about academic integrity see the student handbook or the Office of Academic Integrity’s website, and university policies on Research and Scholarship Misconduct.\nStatement on the use of Artificial Intelligence Generative artificial intelligence (AI) may be used under the direction and rules specified by the course instructor in specific circumstances as outlined in the syllabus. The student is responsible for the quality and content of all written assignments. Unless otherwise indicated by the course instructor, generative AI may be used to create an initial literature review, document outline and/or to organize material toward a first draft of a class paper, proofreading, or grammatical accuracy; however the final content of the written document and critical thinking of the ideas presented in the document must represent the student’s individual work and ideas learned through course content and/or research conducted from sources outside of the generative AI system. The student must include an annotation on all materials submitted that explicitly documents how AI was used to generate the document and properly reference both the sources and the AI tools such as ChatGPT (OpenAI, 2023). The student must review the information in the document and edit for accuracy, completeness, proper grammar, and demonstrate that the wording accurately reflects the student’s understanding and purpose in writing the text. Students should be aware that text generated solely from AI generators may include factual errors, bias, and may contain incomplete or inaccurate reference information, in addition to furthering appropriating knowledge produced by historically marginalized scholars without proper crediting. If you have any questions on whether a specific AI tool is allowed for any aspect of your work in this class, please ask your instructor for guidance. Failure to ensure agreement with your instructor on use of AI, prior to doing so, may result in a zero score. (NOTE: instructors have sophisticated tools to determine AI plagiarism.)\n  Students and Disability Accommodations: USC welcomes students with disabilities into all of the University’s educational programs. The Office of Student Accessibility Services (OSAS) is responsible for the determination of appropriate accommodations for students who encounter disability-related barriers. Once a student has completed the OSAS process (registration, initial appointment, and submitted documentation) and accommodations are determined to be reasonable and appropriate, a Letter of Accommodation (LOA) will be available to generate for each course. The LOA must be given to each course instructor by the student and followed up with a discussion. This should be done as early in the semester as possible as accommodations are not retroactive. More information can be found at http://osas.usc.edu. You may contact OSAS at (213) 740-0776 or via email at osasfrontdesk@usc.edu.\n Support Systems: Counseling and Mental Health - (213) 740-9355 – 24/7 on call\nhttps://studenthealth.usc.edu/counseling/\nFree and confidential mental health treatment for students, including short-term psychotherapy, group counseling, stress fitness workshops, and crisis intervention.\nNational Suicide Prevention Lifeline - dial 988 – 24/7 on call\nhttp://www.suicidepreventionlifeline.org\nProvides free and confidential emotional support to people in suicidal crisis or emotional distress 24 hours a day, 7 days a week.\nRelationship and Sexual Violence Prevention Services (RSVP) - (213) 740-9355(WELL), press “0” after hours – 24/7 on call\nhttps://studenthealth.usc.edu/sexual-assault\nFree and confidential therapy services, workshops, and training for situations related to gender-based harm.\nOffice for Equity, Equal Opportunity, and Title IX (EEO-TIX) - (213) 740-5086\nhttp://eeotix.usc.edu\nInformation about how to get help or help someone affected by harassment or discrimination, rights of protected classes, reporting options, and additional resources for students, faculty, staff, visitors, and applicants.\nReporting Incidents of Bias or Harassment - (213) 740-5086 or (213) 821-8298\nhttp://usc-advocate.symplicity.com/care_report\nAvenue to report incidents of bias, hate crimes, and microaggressions to the Office for Equity, Equal Opportunity, and Title for appropriate investigation, supportive measures, and response.\nThe Office of Student Accessibility Services (OSAS) - (213) 740-0776\nhttp://osas.usc.edu\nOSAS ensures equal access for students with disabilities through providing academic accommodations and auxiliary aids in accordance with federal laws and university policy.\nUSC Campus Support and Intervention - (213) 821-4710\nhttp://campussupport.usc.edu\nAssists students and families in resolving complex personal, financial, and academic issues adversely affecting their success as a student.\nDiversity, Equity and Inclusion - (213) 740-2101\nhttp://diversity.usc.edu\nInformation on events, programs and training, the Provost’s Diversity and Inclusion Council, Diversity Liaisons for each academic school, chronology, participation, and various resources for students.\nUSC Emergency - UPC: (213) 740-4321, HSC: (323) 442-1000 – 24/7 on call\nhttp://dps.usc.edu, http://emergency.usc.edu\nEmergency assistance and avenue to report a crime. Latest updates regarding safety, including ways in which instruction will be continued if an officially declared emergency makes travel to campus infeasible.\nUSC Department of Public Safety - UPC: (213) 740-6000, HSC: (323) 442-1200 – 24/7 on call\nhttp://dps.usc.edu\nNon-emergency assistance or information.\nOffice of the Ombuds - (213) 821-9556 (UPC) / (323-442-0382 (HSC)\nhttp://ombuds.usc.edu\nA safe and confidential place to share your USC-related issues with a University Ombuds who will work with you to explore options or paths to manage your concern.\nOccupational Therapy Faculty Practice - (323) 442-3340 or\notfp@med.usc.edu, http://chan.usc.edu/otfp\nConfidential Lifestyle Redesign services for USC students to support health promoting habits and routines that enhance quality of life and academic performance.\n ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"e4d5a4a79239f08c6ad0d7cbf1be756c","permalink":"https://uscbiostats.github.io/PM566/syllabus/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/syllabus/","section":"","summary":"PM 566: Introduction to Health Data Science Course Overview Learning Objectives Course Notes Technological Proficiency and Hardware/Software Required Readings and Supplementary Materials Supplementary References Description and Assessment of Assignments Grading Breakdown Assignment Submission Policy Schedule Academic Integrity  Statement on the use of Artificial Intelligence  Students and Disability Accommodations: Support Systems:   PM 566: Introduction to Health Data Science Term: Fall 2023\nTime: Friday 9am - 12:55pm","tags":null,"title":"Syllabus","type":"page"},{"authors":null,"categories":null,"content":"Required reading You are expected to have read this when you show up for the session.\n   Chapter 1 Why Git? Why GitHub? of the “Happy Git and GitHub for the useR” by Jenny Bryan and Jim Hester.\n  Roger Peng’s Mastering Software Development Book Section 3.8 Version control and Github (link)\n  You are also expected to have git installed in your system and having a GitHub account. For this see here, here and here.\nOptional reading  Chapter one of the Pro git book will give you a more technical overview of what’s git and what happens under-the-hood.  ","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"eff19a3a562a8cf333f635177dcf87a8","permalink":"https://uscbiostats.github.io/PM566/reading/02-reading/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/PM566/reading/02-reading/","section":"reading","summary":"Required reading You are expected to have read this when you show up for the session.\n   Chapter 1 Why Git? Why GitHub? of the “Happy Git and GitHub for the useR” by Jenny Bryan and Jim Hester.\n  Roger Peng’s Mastering Software Development Book Section 3.8 Version control and Github (link)\n  You are also expected to have git installed in your system and having a GitHub account.","tags":null,"title":"Version control and reproducible research","type":"reading"}]
\ No newline at end of file
diff --git a/index.xml b/index.xml
index 975b4f4..de8cb72 100644
--- a/index.xml
+++ b/index.xml
@@ -1901,7 +1901,7 @@ If you don&amp;rsquo;t already have &lt;code&gt;tidytext&lt;/code&gt; then you c
 &lt;pre&gt;&lt;code class=&#34;language-r&#34;&gt;library(readr)
 library(dplyr)
 mt_samples &amp;lt;- read_csv(&amp;quot;https://raw.githubusercontent.com/USCbiostats/data-science-data/master/00_mtsamples/mtsamples.csv&amp;quot;)
-mt_samples &amp;lt;- mt_samples %&amp;gt;%
+mt_samples &amp;lt;- mt_samples |&amp;gt;
   select(description, medical_specialty, transcription)
 
 head(mt_samples)