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pharma_visualize2.R
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# Pharma visualize two: by Year Range, Age Group, and Biological Sex -----------------
#### Data Source
## Table 39. Prescription drug use in the past 30 days,
## by sex, race and Hispanic origin, and age:
## United States, selected years
## 1988–1994 through 2015–2018
### https://www.cdc.gov/nchs/data/hus/2019/039-508.pdf
### https://www.cdc.gov/nchs/hus/contents2019.htm#Table-039
####
library(tidyverse)
library(here)
library(glue)
# Data and touch up -------------------------------------------------------
load(here::here("data", "tidy_data", "Age_Range_Sex_Tidy.rda"))
load(here::here("data", "tidy_data", "age_range_data_both.rda"))
Age_Range_Tidy$drug_use <- factor(Age_Range_Tidy$drug_use ,
levels = c("At least one",
"At least three",
"At least five"))
Age_Range_Sex_Tidy$drug_use <- factor(Age_Range_Sex_Tidy$drug_use ,
levels = c("At least one",
"At least three",
"At least five"))
jump_range <- c("1988–1994", "2015–2018")
jump_range2 <- c("1999–2002", "2015–2018")
# Begin plots -------------------------------------------------------------
names(Age_Range_Sex_Tidy)
unique(Age_Range_Sex_Tidy$Age_Range)
# "Under 18 years" "18–44 years" "45–64 years" "65 years and over"
unique(Age_Range_Sex_Tidy$Year_Range)
# 1988–1994" "1999–2002" "2001–2004" "2003–2006" "2005–2008"
#"2007–2010" "2009–2012" "2011–2014" "2013–2016" "2015–2018"
# Under 18 years ----------------------------------------------------------
# Fill by drug usage ------------------------------------------------------
Age_Range_Tidy %>%
filter(Age_Range == "Under 18 years" ) %>%
ggplot( aes(x = Year_Range, y = Pop_Percent, fill = drug_use)) +
geom_col( position = "dodge") +
facet_wrap(~ drug_use, scales = "free") +
coord_flip() +
scale_y_continuous(labels = scales::label_percent(scale = 1)) +
guides(fill = "none") +
labs(y = " Percentage of USA Population", x = "",
title = "Prescription drug use past 30 days. Age Group: Under 18 years old",
subtitle = 'Source: cdc.gov/nchs/hus/contents2019.htm#Table-039',
caption ="Data Humanist, CC0 (Public Domain)")
# Jump Fill by Usage ------------------------------------------------------
Age_Range_Tidy %>%
filter(Age_Range == "Under 18 years",
Year_Range %in% jump_range2) %>%
ggplot( aes(x = Year_Range, y = Pop_Percent, fill = drug_use)) +
geom_col( position = "dodge") +
facet_wrap(~ drug_use, scales = "free") +
scale_y_continuous(labels = scales::label_percent(scale = 1)) +
guides(fill = "none") +
labs(y = "", x = "Difference after 16 Years",
title = "Prescription drug use past 30 days. Age Group: Under 18 years old",
subtitle = 'Source: cdc.gov/nchs/hus/contents2019.htm#Table-039',
caption ="Data Humanist, CC0 (Public Domain)")
# Jump Fill Gender --------------------------------------------------------
Age_Range_Sex_Tidy %>%
filter(Age_Range == "Under 18 years",
Year_Range %in% jump_range2) %>%
ggplot( aes(x = Year_Range, y = Pop_Percent, fill = drug_use)) +
geom_col( position = "dodge") +
facet_wrap(~ drug_use, scales = "free") +
scale_y_continuous(labels = scales::label_percent(scale = 1)) +
labs(y = NULL, x = NULL,
title = "Prescription drug use past 30 days. Age Group: Under 18 years old",
subtitle = 'Source: cdc.gov/nchs/hus/contents2019.htm#Table-039',
caption ="Data Humanist, CC0 (Public Domain)",
fill = "Prescripts (#):") +
facet_wrap(~Sex) +
theme(legend.position = "bottom" )
grid_jump2 <- Age_Range_Sex_Tidy %>%
filter(Age_Range == "Under 18 years",
Year_Range %in% jump_range2) %>%
ggplot(aes(x = drug_use, y = Pop_Percent, fill = Pop_Percent) ) +
geom_col() +
guides(fill = "none") +
scale_y_continuous(labels = scales::label_percent(scale = 1) )+
coord_flip() +
scale_fill_gradient(low = "blue", high = "red") +
facet_grid(Year_Range ~ Sex) +
labs(x = "Past 30 Days", y = " Percentage of USA Population",
title = "Prescription drug use past 30 days. Age Group: Under 18 years old.",
subtitle = 'Source: cdc.gov/nchs/hus/contents2019.htm#Table-039',
caption ="Data Humanist, CC0 (Public Domain)") +
geom_text( aes(label = paste0({Pop_Percent},"%")), size = 3,
color= "white", hjust = 1.1)
# 18-44 years -------------------------------------------------------------
Age_Range_Tidy %>%
filter(Age_Range == "18–44 years" ) %>%
ggplot( aes(x = Year_Range, y = Pop_Percent, fill = drug_use)) +
geom_col( position = "dodge") +
facet_wrap(~ drug_use, scales = "free") +
coord_flip() +
scale_y_continuous(labels = scales::label_percent(scale = 1)) +
guides(fill = "none") +
labs(y = " Percentage of USA Population", x = "",
title = "Prescription drug use past 30 days. Age Group: 18–44 years.",
subtitle = 'Source: cdc.gov/nchs/hus/contents2019.htm#Table-039',
caption ="Data Humanist, CC0 (Public Domain)")
Age_Range_Tidy %>%
filter(Age_Range == "18–44 years" ,
Year_Range %in% jump_range) %>%
ggplot( aes(x = Year_Range, y = Pop_Percent, fill = drug_use)) +
geom_col( position = "dodge") +
facet_wrap(~ drug_use, scales = "free") +
scale_y_continuous(labels = scales::label_percent(scale = 1)) +
guides(fill = "none") +
labs(y = "Percentage of US population",
x = "Difference after 21 Years",
title = "Prescription drug use past 30 days. Age Group: 18–44 years",
subtitle = 'Source: cdc.gov/nchs/hus/contents2019.htm#Table-039',
caption ="Data Humanist, CC0 (Public Domain)")
Age_Range_Sex_Tidy %>%
filter(Age_Range == "18–44 years", Sex == "Female" ) %>%
ggplot( aes(x = Year_Range, y = Pop_Percent, fill = drug_use)) +
geom_col( position = "dodge") +
facet_wrap(~ drug_use, scales = "free") +
coord_flip() +
scale_y_continuous(labels = scales::label_percent(scale = 1)) +
labs( title = "Prescription drug use past 30 days. Sex: Female. Age Group: 18–44 years",
x = "",
caption ="Data Humanist, CC0 (Public Domain)",
y = "Source: cdc.gov/nchs/hus/contents2019.htm#Table-039",
fill = "Scripts:")
Age_Range_Sex_Tidy %>%
filter(Age_Range == "18–44 years" ,
Year_Range %in% jump_range) %>%
ggplot( aes(x = Year_Range, y = Pop_Percent, fill = drug_use)) +
geom_col( position = "dodge") +
facet_wrap(~ drug_use, scales = "free") +
scale_y_continuous(labels = scales::label_percent(scale = 1)) +
labs(y = NULL, x = NULL,
title = "Prescription drug use past 30 days. Age Group: 18–44 years",
caption ="Data Humanist, CC0 (Public Domain)",
subtitle = "Source: cdc.gov/nchs/hus/contents2019.htm#Table-039",
fill = "Prescripts (#):") +
facet_wrap(~Sex) +
theme(legend.position = "bottom" )
# Age 45–64 years ---------------------------------------------------------
Age_Range_Sex_Tidy %>%
filter(Age_Range == "45–64 years") %>%
ggplot( aes(x = Year_Range, y = Pop_Percent, fill = drug_use)) +
geom_col( position = "dodge") +
facet_wrap(~ drug_use, scales = "free") +
coord_flip() +
scale_y_continuous(labels = scales::label_percent(scale = 1)) +
guides(fill = "none") +
labs(y = " Percentage of USA Population", x = "",
title = "Prescription drug use past 30 days. Age Group: 45–64 years",
subtitle = 'Source: cdc.gov/nchs/hus/contents2019.htm#Table-039',
caption ="Data Humanist, CC0 (Public Domain)")
Age_Range_Sex_Tidy %>%
filter(Age_Range == "45–64 years",
Year_Range %in% jump_range) %>%
ggplot( aes(x = Year_Range, y = Pop_Percent, fill = drug_use)) +
geom_col( position = "dodge") +
facet_wrap(~ drug_use, scales = "free") +
scale_y_continuous(labels = scales::label_percent(scale = 1)) +
guides(fill = "none") +
labs(y = "", x = "Difference after 21 Years",
title = "Prescription drug use past 30 days. Age Group: 45–64 years",
subtitle = 'Source: cdc.gov/nchs/hus/contents2019.htm#Table-039',
caption ="Data Humanist, CC0 (Public Domain)")
Age_Range_Sex_Tidy %>%
filter(Age_Range == "45–64 years",
Year_Range %in% jump_range ) %>%
ggplot( aes(x = Year_Range, y = Pop_Percent, fill = drug_use)) +
geom_col( position = "dodge") +
facet_wrap(~ drug_use, scales = "free") +
scale_y_continuous(labels = scales::label_percent(scale = 1)) +
labs(y = NULL, x = NULL,
title = "Prescription drug use past 30 days. Age Group: 45–64 years",
subtitle = 'Source: cdc.gov/nchs/hus/contents2019.htm#Table-039',
caption ="Data Humanist, CC0 (Public Domain)",
fill = "Prescripts (#):") +
facet_wrap(~Sex) +
theme(legend.position = "bottom" )
# 65 years and over -------------------------------------------------------
Age_Range_Sex_Tidy %>%
filter(Age_Range == "65 years and over") %>%
ggplot( aes(x = Year_Range, y = Pop_Percent, fill = drug_use)) +
geom_col( position = "dodge") +
facet_wrap(~ drug_use, scales = "free") +
coord_flip() +
scale_y_continuous(labels = scales::label_percent(scale = 1)) +
guides(fill = "none") +
labs(y = " Percentage of USA Population", x = "",
title = "Prescription drug use past 30 days. Age Group: 65 years and over",
subtitle = 'Source: cdc.gov/nchs/hus/contents2019.htm#Table-039',
caption ="Data Humanist, CC0 (Public Domain)")
Age_Range_Sex_Tidy %>%
filter(Age_Range == "65 years and over",
Year_Range %in% jump_range) %>%
ggplot( aes(x = Year_Range, y = Pop_Percent, fill = drug_use)) +
geom_col( position = "dodge") +
facet_wrap(~ drug_use, scales = "free") +
scale_y_continuous(labels = scales::label_percent(scale = 1)) +
guides(fill = "none") +
labs(y = "", x = "Difference after 21 Years",
title = "Prescription drug use past 30 days. Age Group: 65 years and over",
subtitle = 'Source: cdc.gov/nchs/hus/contents2019.htm#Table-039',
caption ="Data Humanist, CC0 (Public Domain)")
Age_Range_Sex_Tidy %>%
filter(Age_Range == "65 years and over",
Year_Range %in% jump_range ) %>%
ggplot( aes(x = Year_Range, y = Pop_Percent, fill = drug_use)) +
geom_col( position = "dodge") +
facet_wrap(~ drug_use, scales = "free") +
scale_y_continuous(labels = scales::label_percent(scale = 1)) +
labs(y = NULL, x = NULL,
title = "Prescription drug use past 30 days. Age Group: 65 years and over",
subtitle = 'Source: cdc.gov/nchs/hus/contents2019.htm#Table-039',
caption ="Data Humanist, CC0 (Public Domain)",
fill = "Prescripts (#):") +
facet_wrap(~Sex) +
theme(legend.position = "bottom" )
Age_Range_Sex_Tidy %>%
filter(Age_Range == "65 years and over",
Year_Range %in% jump_range ) %>%
group_by(Sex) %>%
select(everything() )