From 473134e822e1c1b185e2f7aaf9f8357ede0dfeec Mon Sep 17 00:00:00 2001 From: Kelly Street Date: Thu, 26 Sep 2024 09:57:14 -0700 Subject: [PATCH] updated complex variable creation --- .../libs/header-attrs-2.28/header-attrs.js | 12 ++ .../slides/05-data-wrangling/slides.Rmd | 36 +++++- .../slides/05-data-wrangling/slides.html | 121 ++++++++++++++++-- .../figure-html/unnamed-chunk-1-1.png | Bin 108855 -> 110939 bytes 4 files changed, 153 insertions(+), 16 deletions(-) create mode 100644 website/static/slides/05-data-wrangling/libs/header-attrs-2.28/header-attrs.js diff --git a/website/static/slides/05-data-wrangling/libs/header-attrs-2.28/header-attrs.js b/website/static/slides/05-data-wrangling/libs/header-attrs-2.28/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/website/static/slides/05-data-wrangling/libs/header-attrs-2.28/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/website/static/slides/05-data-wrangling/slides.Rmd b/website/static/slides/05-data-wrangling/slides.Rmd index 7e75b97a..0c2e4e73 100755 --- a/website/static/slides/05-data-wrangling/slides.Rmd +++ b/website/static/slides/05-data-wrangling/slides.Rmd @@ -1,7 +1,7 @@ --- title: "Week 5: Data Wrangling" subtitle: "PM 566: Introduction to Health Data Science" -author: "George G. Vega Yon and Kelly Streets" +author: "George G. Vega Yon and Kelly Street" # output: # slidy_presentation: # slide_level: 1 @@ -660,15 +660,16 @@ dat |> Don't forget about loops! `for` loops and `sapply` may be slow on a dataset of this size, but they can be quite handy for creating variables that rely on complicated relationships between variables. Consider this a "brute force" approach. Vectorized methods will *always* be faster, but these can be easier to conceptualize and, in rare cases, may be the only option. -Let's demonstrate this by creating a weird variable: `wind.temp`. This will take on 4 possible values, based on the temperature and wind speed: cool & still, cool & windy, warm & still, or warm & windy. We will split each variable based on their median value. +Consider the problem creating a weird variable: `wind.temp`. This will take on 4 possible values, based on the temperature and wind speed: cool & still, cool & windy, warm & still, or warm & windy. We will split each variable based on their median value. Note that this code is too slow to actually run on this large dataset. --- ## Complex variable creation (cont 1) -Here's how we would do that with the `sapply` function: +Here's how we would do that with the `sapply` function (and a custom, unnamed function): -```{r new-var-sapply} +```{r new-var-sapply, eval=FALSE} +# create the new variable one entry at a time wind.temp <- sapply(1:nrow(dat), function(i){ if(is.na(dat$temp[i]) | is.na(dat$wind.sp[i])){ return(NA) @@ -687,8 +688,8 @@ wind.temp <- sapply(1:nrow(dat), function(i){ } } }) -head(wind.temp) ``` + Check: what would we need to change to add this variable to our dataset? --- @@ -700,7 +701,7 @@ Here's the code for doing that with a `for` loop: ```{r new-var-for-loop, eval=FALSE} # initialize a variable of all missing values wind.temp <- rep(NA, nrow(dat)) -# fill in the values +# fill in the values one at a time for(i in 1:nrow(dat)){ if(is.na(dat$temp[i]) | is.na(dat$wind.sp[i])){ return(NA) @@ -724,6 +725,29 @@ for(i in 1:nrow(dat)){ Check: why do we need to include `na.rm=TRUE` when calculating the medians? +--- + +## Complex variable creation (cont 3) + +Here's a simple vectorized approach that will actually run on a large dataset. This works for our current case, but it's still a brute force approach, because we had to specifically assign every possible value of our new variable. You can imagine that as the number of possible values increases, this code will get increasingly cumbersome. + +```{r new-var-subset} +# initialize a variable of all missing values +wind.temp <- rep(NA, nrow(dat)) +# assign every possible value by subsetting +wind.temp[dat$temp <= median(dat$temp, na.rm=TRUE) & + dat$wind.sp <= median(dat$wind.sp, na.rm=TRUE)] <- 'cool & still' +wind.temp[dat$temp <= median(dat$temp, na.rm=TRUE) & + dat$wind.sp > median(dat$wind.sp, na.rm=TRUE)] <- 'cool & windy' +wind.temp[dat$temp > median(dat$temp, na.rm=TRUE) & + dat$wind.sp <= median(dat$wind.sp, na.rm=TRUE)] <- 'warm & still' +wind.temp[dat$temp > median(dat$temp, na.rm=TRUE) & + dat$wind.sp > median(dat$wind.sp, na.rm=TRUE)] <- 'warm & windy' + +head(wind.temp) +``` + + --- ## Merging data diff --git a/website/static/slides/05-data-wrangling/slides.html b/website/static/slides/05-data-wrangling/slides.html index 2abbe950..55d9811a 100755 --- a/website/static/slides/05-data-wrangling/slides.html +++ b/website/static/slides/05-data-wrangling/slides.html @@ -3,9 +3,8 @@ Week 5: Data Wrangling - - - + + @@ -20,10 +19,7 @@ ## PM 566: Introduction to Health Data Science ] .author[ -### George G. Vega Yon and Kelly Streets -] -.date[ -### 2021-09-23 +### George G. Vega Yon and Kelly Street ] --- @@ -64,7 +60,7 @@ ## Disclaimer -There's a lot of extraneous information in these slides! While the `data.table` package and Python both have a lot of useful functionality, we strongly recommend sticking to the base R and `tidyverse` tools presented here. Slides covering material outside this scope will be marked with an asterisk (`*`). +There's a lot of extraneous information in these slides! While the `data.table` package and Python both have a lot of useful functionality, we strongly recommend sticking to the base R and `tidyverse` tools presented here. Slides covering material outside this scope will be marked with an asterisk (`*`); you should be extremely cautious about using code from those slides! --- @@ -66992,8 +66988,11 @@ ``` r -# Data.table -dat <- dat |> select(USAFID, WBAN, year, month, day, hour, min, lat, lon, elev, wind.sp, temp, atm.press) +# select only the relevant variables +dat <- dat |> + select(USAFID, WBAN, year, month, day, + hour, min, lat, lon, elev, + wind.sp, temp, atm.press) ``` --- @@ -96114,6 +96113,108 @@ ## 4 2.374970 5.495707 249.2077 ``` +--- + +## Complex variable creation + +Don't forget about loops! `for` loops and `sapply` may be slow on a dataset of this size, but they can be quite handy for creating variables that rely on complicated relationships between variables. Consider this a "brute force" approach. Vectorized methods will *always* be faster, but these can be easier to conceptualize and, in rare cases, may be the only option. + +Consider the problem creating a weird variable: `wind.temp`. This will take on 4 possible values, based on the temperature and wind speed: cool & still, cool & windy, warm & still, or warm & windy. We will split each variable based on their median value. Note that this code is too slow to actually run on this large dataset. + +--- + +## Complex variable creation (cont 1) + +Here's how we would do that with the `sapply` function (and a custom, unnamed function): + + +``` r +# create the new variable one entry at a time +wind.temp <- sapply(1:nrow(dat), function(i){ + if(is.na(dat$temp[i]) | is.na(dat$wind.sp[i])){ + return(NA) + } + if(dat$temp[i] <= median(dat$temp, na.rm=TRUE)){ + if(dat$wind.sp[i] <= median(dat$wind.sp, na.rm=TRUE)){ + return('cool & still') + }else{ + return('cool & windy') + } + }else{ + if(dat$wind.sp[i] <= median(dat$wind.sp, na.rm=TRUE)){ + return('warm & still') + }else{ + return('warm & windy') + } + } +}) +``` + +Check: what would we need to change to add this variable to our dataset? + +--- + +## Complex variable creation (cont 2) + +Here's the code for doing that with a `for` loop: + + +``` r +# initialize a variable of all missing values +wind.temp <- rep(NA, nrow(dat)) +# fill in the values one at a time +for(i in 1:nrow(dat)){ + if(is.na(dat$temp[i]) | is.na(dat$wind.sp[i])){ + return(NA) + }else{ + if(dat$temp[i] <= median(dat$temp, na.rm=TRUE)){ + if(dat$wind.sp[i] <= median(dat$wind.sp, na.rm=TRUE)){ + wind.temp[i] <- 'cool & still' + }else{ + wind.temp[i] <- 'cool & windy' + } + }else{ + if(dat$wind.sp[i] <= median(dat$wind.sp, na.rm=TRUE)){ + wind.temp[i] <- 'warm & still' + }else{ + wind.temp[i] <- 'warm & windy' + } + } + } +} +``` + +Check: why do we need to include `na.rm=TRUE` when calculating the medians? + +--- + +## Complex variable creation (cont 3) + +Here's a simple vectorized approach that will actually run on a large dataset. This works for our current case, but it's still a brute force approach, because we had to specifically assign every possible value of our new variable. You can imagine that as the number of possible values increases, this code will get increasingly cumbersome. + + +``` r +# initialize a variable of all missing values +wind.temp <- rep(NA, nrow(dat)) +# assign every possible value by subsetting +wind.temp[dat$temp <= median(dat$temp, na.rm=TRUE) & + dat$wind.sp <= median(dat$wind.sp, na.rm=TRUE)] <- 'cool & still' +wind.temp[dat$temp <= median(dat$temp, na.rm=TRUE) & + dat$wind.sp > median(dat$wind.sp, na.rm=TRUE)] <- 'cool & windy' +wind.temp[dat$temp > median(dat$temp, na.rm=TRUE) & + dat$wind.sp <= median(dat$wind.sp, na.rm=TRUE)] <- 'warm & still' +wind.temp[dat$temp > median(dat$temp, na.rm=TRUE) & + dat$wind.sp > median(dat$wind.sp, na.rm=TRUE)] <- 'warm & windy' + +head(wind.temp) +``` + +``` +## [1] "warm & windy" "warm & windy" "warm & windy" "warm & windy" "warm & still" +## [6] "warm & still" +``` + + --- ## Merging data diff --git a/website/static/slides/05-data-wrangling/slides_files/figure-html/unnamed-chunk-1-1.png b/website/static/slides/05-data-wrangling/slides_files/figure-html/unnamed-chunk-1-1.png index 903f092ebffd49d14cf2f856000716ff2f2c0ea3..23f4f3a9a043a8d30c414790bff9949f6ff58ab5 100644 GIT binary patch delta 110650 zcmYiNbyytF^97793j|AWNq_`*cL+{!PjF9ghXBE0&=B0+-Q8V+ySuwD&a&9e=lkA! z-`{`p^wTrlRo!*E=A7!r={GCeZ>mBO04;mtCAR|?cHiEF#m1fvT;-zUlH>E^_EalJ z9IB9AW#T~2w-JIyQp#T@lGI22nfQB=P9)H0yf6ImQlf}WlQMotb6^h;MiZ9wl@IAJ zt({hsTe0AxK6cPVB@fEKzR>(EvCYO2p^j41JoaAGN*-*xUv=zsJbwTlQSwN4fY=GR zOn4zAPjo2p1)qHpFQ$O1%g#pF|84!ww+Y`SjP?I?*>vnW=px(o!MF&rSAN#H;`^_` z=YQS19{>N3|Gq~ohK_q4^GSX?yk@9+OqLZ|xm z>krB(L3vd*HIsvbtE;Oojw|4n?QO$cv+EyZYmRz)>B&h+u9~(>>j&N*1tY`5o<0*Z zZ*LTC+zj*nuWgG;0PzK!w{ZCH4TAsI3sE%aP*5g~By8+IpZq`e`9Amhw!t=of`Z1L zp4!?wfB$}yyRf{R((HPfV_|FCAawHVbnR{Me|+>~Peh*!YUg#Lvro_o4fI;;@LG}l z1YDjqb4=S$1;6B2NN#O?me;3xISxT=PRncMCb zuRI+i;Yv`j(?nokpdvOod-!8W_KQk&f4IfCMTn! zgI`uQUK*OcpIaVa3!Szz3dSh}ANd@5!y+R$niD%Zgj&sy(F9ee%x}i+khc8s;rA>* z)pNbeQ+oa6?30 zjrH}(0L1fvUy?5j>}qjH@(LX|>nS3NQY~oHmQoUauA7p}YnzFW$Ge~LbzLx2*3xp@ zgthDJTTTg^8`ZCP%;!{U#snR8)DIrJc`cr6%?|_}W!zeyHkxU_-&XPFjp89`Yj_8jSbiB?Jakgt)=Dg(;S2EVaa zc;e0Q|BJ2v1&-;zwGCd^>q0x%@jCFn-tYqc7kB^PJ@({CYDeh#(z@Sx=(xGH?_;QC z{6PgXjzsIpegg6J+zb6Q0A8pI5@7YhCxr9~ghWk4V^dA*>#3qL^Cdp$6|{I_&~wn0 zeIT-~a1)ea^zsfy?CL{|ImjTv@+nfds}z>_U$}0v+IKB%V*I6r-B(otysR98Hv825 z@>NxXbtHl<4A<`T@in3)|HdgLyW`(++!V3zLW07x$$Qgapd!72*`*9Jbg~Y8_eD`& zJ(~7_B;$16Qav5=vpANH;^G+;my~>_wZ3>f`VS|BHoLx{T`b>@tChthP%c9|)}Ucw zVT-6<>pQwc=ll;!w}3<5M*F}a&vW^(E3C}LC9LH22r@QUf3|G@2xsl=Db%4+rNLaE z^NZy;lLE2C8$1X1-77FGHCU1lZ?A?9fd4^Pv;Af^A~Y}%5hqyu zd^X)Vw9bP7r#;j0M+v`DXtU1b?Rm#S>mwtmvbdNnG&FR6VtIVNC48YWNre8#yyeMg zAAzuqA?Vq37Xf6Kn))&I?A~(ZmVd>On}2*Iq1Ei;33Th3S%v7_#ii~3y_bf*zHK0B zcp-&L#3SHQd+)iHYbD%LGd$oU-}Y$Zh2-#w!h>`f!ak}}c5DaS>GC!c3N&!&0Y zHMgH{ceHo7(vYqa2w8?PU1GM5XAfDul|b}y1aj&|$2sEd)(M_h8*`6bM*m^CucyOz z$45J%%9@Gg7z=&=ZVMIvf50mUNuN>FDPG7#{+?4PMVKURQCyssv3mXT@NqB#Ui9g_ zAa^9Lio+1ux7=sJQ!kxvK#YQERZFMF>g@ch;-8=Dg}Sm3xbxk~4?L z&s{1gSb+O(ExP`#pm)D`vKeBNK@YM8xy3oy^0_1SOuCTmT25N|ZNOc=MxQiZ*VoN- z1o1H|t-Mt`?C)Oxi-E2Et}loe)1E+_2Jyl3Fi~Z4%HAKX??0L6a>l6n&}br3hxNqy zA`^l?gatceS_T)%Y|B6PPZdTih zjqh9ANgpiHx^IhryQ>KEnard5n;y?vI36vcz;8n&646h&{x)&JnA@!8{Q{|dvStNb zSBO7+Vy~XYuGQ5tsStR{lL6qOnq&M9{m@tW_)yGg7PhGy>a0U7Az;q}XT+YIHPg>U z9}}O+vPHX?OZ`UgvBBaFh~@-RdU7J2Dhoa~qpt)1Z5a3w-!JeN)B47+va+(ef)RO& z9K>q$%tO{tL-s+i-NNck50P}I$>*~UpwRQ^{C=IyDmYH7yo}oj$p3-E!pzPvA!Ik& zlY?==7a?-c#T3gaZ{wrreWA=atc)c8wyHEanZ2E0BH5~tWTILv^KR-RHK8kKSt zPc|dirJXy*XAK3ewzgKX+iP5~>QeL7?!V(4odxO6)T9ODDGuXX=-T_}gDVMJNp`J_ zqcg7R5<(T?zDIPRDhS(ZUf!TnDo{KGp8!NT+UV0_t{|k&_*Me!lIgH7@@@H{r5E2Y ztlErd$PvaY`Ky^;eobODQMVX3)-^9@8Mki{;SDB}Sk`R)a7js{jV$#ol8ifkLP7%L z-5+~3SQT_ z-H-7oupNkxFpREFeVPGPoxekgI!6T6?AvIhS-9B2ez8`5USBp96$pOAGNz~Pyc%Jn zo$-!cnEQ%HwWp5CV56_SKWMbs>bJHGIo#0 z9%GzL{9w=-U$ZHEJIcY0Eb}6VxN(Npr>XlzRT#%y+Jj|RAFPSDJr_rvp1x9Q`*HDP zoSQrOMGCuMrymc82Fe8Lk{XsRcWX!Zq{QUQ49PN1;p~@!TRSvOMgP&x&g$OS0H`NW@Q3*@|$a+l(B$zg`0hJM|2}yjh#7 zo)NXSwnF-Ga_#%aIDRB*ZJluaoRnge8LuO3-(Pebi(a#DZv|sK{96USbd@|%6HKWy z=cVfxq&2T|@_x@j4#776HtN`%b^K_TG*yw?pf%>flX>GgIGsP}iPGaPNj)eqS-T0+ zACSYuJaQ4%oT%yf7dKtLTV2@zoGa3{{P!l}`*v-E~EagpY= zm4ma6S3V`;q;fmXT9UO#KgpU;&8Q)6E%p3kMO6_sqNun;K8OravV_g#mc`fqMnRHm zimI0rVg8=DNftv5p`t+^jI5k+*RP{#SNh-4L5xnELdtI;xs-=s0f^2R791Kj{*l2P z#0_s-_)Gh4WWs3uUIpc|CeKs@}m-vue*#@5P2mnWN~i=_=Vx%(t4OU`0Osd($Uw z*iDc754yaEBqxsF0sBp5vLJk_1Ce2jx!m3_ug{CblWDVZ z-Zse{*9s+zJo8hweh7;e!Z2R9*I{g}5%#P}xYi5|lo9qJU?dIlzh5AvSok1Ge}cz) z5t)@+*a8oFC?^&H@9y}L>!>$Or>nx_imT|Elb#$T;&tsmczpf`kNMr9W6U2F_b25k$Hc$WDHy!i zSX@4?0SFaBvgTO4P^T(B(s0dc^7~LzLc+0-JB>e#sl1{tBOvFNoSuCRRbaQ~TfCI; zzLBHQLnoA&*PM8f0FS^MO_1XYsHDT3u%=S;5GTE1O z!vD>Q#m0jn(~Y^Y1ksek#|5K`)AgZ#Z)g6XTpre}`{@0#)SIDQHc-ZMSrAEV*9B;d zCCLymkEv|2Oc?X3iVAC}-*CBDQwmMokMGiB5#(8y$A}w+9~YBquKuT?$~Y&=VN8FW9MKa`Cer0BY5-Lg}>i& z*Il`O()D^{jf+$CcG{4V!*m3A@dZ$r##8Y>o!$%Gu(f_k;&<+BD;>@tPl0II&nrS4 z-;F68X_C2mMM)El7yWLNT?$bj9#(khNuvwl`TD@zPSPdXl9_&|CRU-V&9A3?ON_D_ z?_6T0;O&tNb6H+jg!~m|0f?jGl~Mn}5HnHqi@Ibar$=~g#FXY^-7BAc6`;P)2&J

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