From 982fb12c456411e9935d69624afeefbc1ed8bb85 Mon Sep 17 00:00:00 2001 From: perlatex Date: Sat, 6 Mar 2021 13:55:41 +0800 Subject: [PATCH] add practice chapter --- _bookdown.yml | 6 +- demo_data/tempnormals.csv | 1465 ++++++++++++++++++++++++++++++++++++ dplyr.Rmd | 3 +- forcats.Rmd | 146 +++- ggplot2_colors.Rmd | 137 +++- ggplot2_gganimate.Rmd | 2 +- ggplot2_scales.Rmd | 5 + images/make_data_tidy3.jpg | Bin 0 -> 162605 bytes images/to_reproduce.jpg | Bin 0 -> 104789 bytes index.Rmd | 13 +- pandas.Rmd | 234 ++++++ practice.rmd | 289 +++++++ tidymodels.Rmd | 2 +- tidyr.Rmd | 38 +- 14 files changed, 2325 insertions(+), 15 deletions(-) create mode 100644 demo_data/tempnormals.csv create mode 100644 images/make_data_tidy3.jpg create mode 100644 images/to_reproduce.jpg create mode 100644 pandas.Rmd create mode 100644 practice.rmd diff --git a/_bookdown.yml b/_bookdown.yml index 52d3e3d..6ce1581 100644 --- a/_bookdown.yml +++ b/_bookdown.yml @@ -34,6 +34,7 @@ rmd_files: [ "ggplot2_guides.Rmd", "ggplot2_customize.Rmd", "ggplot2_stat_layer.Rmd", + "ggplot2_colors.Rmd", "tidyverse_workflow.Rmd", "tidyverse_tips.Rmd", "adv_dplyr.Rmd", @@ -76,10 +77,11 @@ rmd_files: [ "stars.Rmd", "rowwise.Rmd", "ggplot2_academic.Rmd", - "ggplot2_colors.Rmd", + "ggplot2_gganimate.Rmd", "lazyman.Rmd", - + # "pandas.Rmd", + "practice.Rmd", "Appendix.Rmd", "references.Rmd" diff --git a/demo_data/tempnormals.csv b/demo_data/tempnormals.csv new file mode 100644 index 0000000..3f6b3ef --- /dev/null +++ b/demo_data/tempnormals.csv @@ -0,0 +1,1465 @@ +location,station_id,temperature,month_name,month,day,date,day_of_year +Death Valley,USC00042319,51,Jan,01,1,0000-01-01,1 +Death Valley,USC00042319,51.2,Jan,01,2,0000-01-02,2 +Death Valley,USC00042319,51.3,Jan,01,3,0000-01-03,3 +Death Valley,USC00042319,51.4,Jan,01,4,0000-01-04,4 +Death Valley,USC00042319,51.6,Jan,01,5,0000-01-05,5 +Death Valley,USC00042319,51.7,Jan,01,6,0000-01-06,6 +Death Valley,USC00042319,51.9,Jan,01,7,0000-01-07,7 +Death Valley,USC00042319,52,Jan,01,8,0000-01-08,8 +Death Valley,USC00042319,52.2,Jan,01,9,0000-01-09,9 +Death Valley,USC00042319,52.3,Jan,01,10,0000-01-10,10 +Death Valley,USC00042319,52.5,Jan,01,11,0000-01-11,11 +Death Valley,USC00042319,52.7,Jan,01,12,0000-01-12,12 +Death Valley,USC00042319,52.9,Jan,01,13,0000-01-13,13 +Death Valley,USC00042319,53,Jan,01,14,0000-01-14,14 +Death Valley,USC00042319,53.2,Jan,01,15,0000-01-15,15 +Death Valley,USC00042319,53.4,Jan,01,16,0000-01-16,16 +Death Valley,USC00042319,53.6,Jan,01,17,0000-01-17,17 +Death Valley,USC00042319,53.7,Jan,01,18,0000-01-18,18 +Death Valley,USC00042319,53.9,Jan,01,19,0000-01-19,19 +Death Valley,USC00042319,54.1,Jan,01,20,0000-01-20,20 +Death Valley,USC00042319,54.3,Jan,01,21,0000-01-21,21 +Death Valley,USC00042319,54.5,Jan,01,22,0000-01-22,22 +Death Valley,USC00042319,54.6,Jan,01,23,0000-01-23,23 +Death Valley,USC00042319,54.8,Jan,01,24,0000-01-24,24 +Death Valley,USC00042319,55,Jan,01,25,0000-01-25,25 +Death Valley,USC00042319,55.2,Jan,01,26,0000-01-26,26 +Death Valley,USC00042319,55.4,Jan,01,27,0000-01-27,27 +Death Valley,USC00042319,55.6,Jan,01,28,0000-01-28,28 +Death Valley,USC00042319,55.8,Jan,01,29,0000-01-29,29 +Death Valley,USC00042319,56,Jan,01,30,0000-01-30,30 +Death Valley,USC00042319,56.2,Jan,01,31,0000-01-31,31 +Death Valley,USC00042319,56.4,Feb,02,1,0000-02-01,32 +Death Valley,USC00042319,56.6,Feb,02,2,0000-02-02,33 +Death Valley,USC00042319,56.8,Feb,02,3,0000-02-03,34 +Death Valley,USC00042319,57,Feb,02,4,0000-02-04,35 +Death Valley,USC00042319,57.2,Feb,02,5,0000-02-05,36 +Death Valley,USC00042319,57.5,Feb,02,6,0000-02-06,37 +Death Valley,USC00042319,57.7,Feb,02,7,0000-02-07,38 +Death Valley,USC00042319,57.9,Feb,02,8,0000-02-08,39 +Death Valley,USC00042319,58.2,Feb,02,9,0000-02-09,40 +Death Valley,USC00042319,58.4,Feb,02,10,0000-02-10,41 +Death Valley,USC00042319,58.7,Feb,02,11,0000-02-11,42 +Death Valley,USC00042319,58.9,Feb,02,12,0000-02-12,43 +Death 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Valley,USC00042319,64.5,Mar,03,2,0000-03-02,62 +Death Valley,USC00042319,64.8,Mar,03,3,0000-03-03,63 +Death Valley,USC00042319,65.1,Mar,03,4,0000-03-04,64 +Death Valley,USC00042319,65.4,Mar,03,5,0000-03-05,65 +Death Valley,USC00042319,65.7,Mar,03,6,0000-03-06,66 +Death Valley,USC00042319,66,Mar,03,7,0000-03-07,67 +Death Valley,USC00042319,66.3,Mar,03,8,0000-03-08,68 +Death Valley,USC00042319,66.7,Mar,03,9,0000-03-09,69 +Death Valley,USC00042319,67,Mar,03,10,0000-03-10,70 +Death Valley,USC00042319,67.2,Mar,03,11,0000-03-11,71 +Death Valley,USC00042319,67.5,Mar,03,12,0000-03-12,72 +Death Valley,USC00042319,67.8,Mar,03,13,0000-03-13,73 +Death Valley,USC00042319,68.1,Mar,03,14,0000-03-14,74 +Death Valley,USC00042319,68.4,Mar,03,15,0000-03-15,75 +Death Valley,USC00042319,68.6,Mar,03,16,0000-03-16,76 +Death Valley,USC00042319,68.9,Mar,03,17,0000-03-17,77 +Death Valley,USC00042319,69.2,Mar,03,18,0000-03-18,78 +Death Valley,USC00042319,69.4,Mar,03,19,0000-03-19,79 +Death Valley,USC00042319,69.7,Mar,03,20,0000-03-20,80 +Death Valley,USC00042319,69.9,Mar,03,21,0000-03-21,81 +Death Valley,USC00042319,70.1,Mar,03,22,0000-03-22,82 +Death Valley,USC00042319,70.4,Mar,03,23,0000-03-23,83 +Death Valley,USC00042319,70.6,Mar,03,24,0000-03-24,84 +Death Valley,USC00042319,70.8,Mar,03,25,0000-03-25,85 +Death Valley,USC00042319,71.1,Mar,03,26,0000-03-26,86 +Death Valley,USC00042319,71.3,Mar,03,27,0000-03-27,87 +Death Valley,USC00042319,71.5,Mar,03,28,0000-03-28,88 +Death Valley,USC00042319,71.7,Mar,03,29,0000-03-29,89 +Death Valley,USC00042319,71.9,Mar,03,30,0000-03-30,90 +Death Valley,USC00042319,72.2,Mar,03,31,0000-03-31,91 +Death Valley,USC00042319,72.4,Apr,04,1,0000-04-01,92 +Death Valley,USC00042319,72.6,Apr,04,2,0000-04-02,93 +Death Valley,USC00042319,72.8,Apr,04,3,0000-04-03,94 +Death Valley,USC00042319,73.1,Apr,04,4,0000-04-04,95 +Death Valley,USC00042319,73.3,Apr,04,5,0000-04-05,96 +Death Valley,USC00042319,73.5,Apr,04,6,0000-04-06,97 +Death Valley,USC00042319,73.8,Apr,04,7,0000-04-07,98 +Death Valley,USC00042319,74,Apr,04,8,0000-04-08,99 +Death Valley,USC00042319,74.3,Apr,04,9,0000-04-09,100 +Death Valley,USC00042319,74.5,Apr,04,10,0000-04-10,101 +Death Valley,USC00042319,74.8,Apr,04,11,0000-04-11,102 +Death Valley,USC00042319,75.1,Apr,04,12,0000-04-12,103 +Death Valley,USC00042319,75.3,Apr,04,13,0000-04-13,104 +Death Valley,USC00042319,75.6,Apr,04,14,0000-04-14,105 +Death Valley,USC00042319,75.9,Apr,04,15,0000-04-15,106 +Death Valley,USC00042319,76.2,Apr,04,16,0000-04-16,107 +Death Valley,USC00042319,76.5,Apr,04,17,0000-04-17,108 +Death Valley,USC00042319,76.8,Apr,04,18,0000-04-18,109 +Death Valley,USC00042319,77.1,Apr,04,19,0000-04-19,110 +Death Valley,USC00042319,77.5,Apr,04,20,0000-04-20,111 +Death Valley,USC00042319,77.8,Apr,04,21,0000-04-21,112 +Death Valley,USC00042319,78.1,Apr,04,22,0000-04-22,113 +Death Valley,USC00042319,78.5,Apr,04,23,0000-04-23,114 +Death Valley,USC00042319,78.8,Apr,04,24,0000-04-24,115 +Death 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+Death Valley,USC00042319,91.3,May,05,31,0000-05-31,152 +Death Valley,USC00042319,91.5,Jun,06,1,0000-06-01,153 +Death Valley,USC00042319,91.8,Jun,06,2,0000-06-02,154 +Death Valley,USC00042319,92.1,Jun,06,3,0000-06-03,155 +Death Valley,USC00042319,92.3,Jun,06,4,0000-06-04,156 +Death Valley,USC00042319,92.6,Jun,06,5,0000-06-05,157 +Death Valley,USC00042319,92.9,Jun,06,6,0000-06-06,158 +Death Valley,USC00042319,93.1,Jun,06,7,0000-06-07,159 +Death Valley,USC00042319,93.4,Jun,06,8,0000-06-08,160 +Death Valley,USC00042319,93.7,Jun,06,9,0000-06-09,161 +Death Valley,USC00042319,94,Jun,06,10,0000-06-10,162 +Death Valley,USC00042319,94.2,Jun,06,11,0000-06-11,163 +Death Valley,USC00042319,94.5,Jun,06,12,0000-06-12,164 +Death Valley,USC00042319,94.8,Jun,06,13,0000-06-13,165 +Death Valley,USC00042319,95.1,Jun,06,14,0000-06-14,166 +Death Valley,USC00042319,95.4,Jun,06,15,0000-06-15,167 +Death Valley,USC00042319,95.7,Jun,06,16,0000-06-16,168 +Death Valley,USC00042319,95.9,Jun,06,17,0000-06-17,169 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Valley,USC00042319,98.4,Aug,08,25,0000-08-25,238 +Death Valley,USC00042319,98.1,Aug,08,26,0000-08-26,239 +Death Valley,USC00042319,97.9,Aug,08,27,0000-08-27,240 +Death Valley,USC00042319,97.6,Aug,08,28,0000-08-28,241 +Death Valley,USC00042319,97.3,Aug,08,29,0000-08-29,242 +Death Valley,USC00042319,97.1,Aug,08,30,0000-08-30,243 +Death Valley,USC00042319,96.8,Aug,08,31,0000-08-31,244 +Death Valley,USC00042319,96.5,Sep,09,1,0000-09-01,245 +Death Valley,USC00042319,96.2,Sep,09,2,0000-09-02,246 +Death Valley,USC00042319,95.9,Sep,09,3,0000-09-03,247 +Death Valley,USC00042319,95.6,Sep,09,4,0000-09-04,248 +Death Valley,USC00042319,95.2,Sep,09,5,0000-09-05,249 +Death Valley,USC00042319,94.9,Sep,09,6,0000-09-06,250 +Death Valley,USC00042319,94.6,Sep,09,7,0000-09-07,251 +Death Valley,USC00042319,94.2,Sep,09,8,0000-09-08,252 +Death Valley,USC00042319,93.8,Sep,09,9,0000-09-09,253 +Death Valley,USC00042319,93.5,Sep,09,10,0000-09-10,254 +Death Valley,USC00042319,93.1,Sep,09,11,0000-09-11,255 +Death Valley,USC00042319,92.7,Sep,09,12,0000-09-12,256 +Death Valley,USC00042319,92.3,Sep,09,13,0000-09-13,257 +Death Valley,USC00042319,91.9,Sep,09,14,0000-09-14,258 +Death Valley,USC00042319,91.5,Sep,09,15,0000-09-15,259 +Death Valley,USC00042319,91.1,Sep,09,16,0000-09-16,260 +Death Valley,USC00042319,90.7,Sep,09,17,0000-09-17,261 +Death Valley,USC00042319,90.3,Sep,09,18,0000-09-18,262 +Death Valley,USC00042319,89.8,Sep,09,19,0000-09-19,263 +Death Valley,USC00042319,89.4,Sep,09,20,0000-09-20,264 +Death Valley,USC00042319,88.9,Sep,09,21,0000-09-21,265 +Death Valley,USC00042319,88.5,Sep,09,22,0000-09-22,266 +Death Valley,USC00042319,88,Sep,09,23,0000-09-23,267 +Death Valley,USC00042319,87.6,Sep,09,24,0000-09-24,268 +Death Valley,USC00042319,87.1,Sep,09,25,0000-09-25,269 +Death Valley,USC00042319,86.6,Sep,09,26,0000-09-26,270 +Death Valley,USC00042319,86.1,Sep,09,27,0000-09-27,271 +Death Valley,USC00042319,85.7,Sep,09,28,0000-09-28,272 +Death Valley,USC00042319,85.2,Sep,09,29,0000-09-29,273 +Death Valley,USC00042319,84.7,Sep,09,30,0000-09-30,274 +Death Valley,USC00042319,84.2,Oct,10,1,0000-10-01,275 +Death Valley,USC00042319,83.7,Oct,10,2,0000-10-02,276 +Death Valley,USC00042319,83.3,Oct,10,3,0000-10-03,277 +Death Valley,USC00042319,82.8,Oct,10,4,0000-10-04,278 +Death Valley,USC00042319,82.3,Oct,10,5,0000-10-05,279 +Death Valley,USC00042319,81.8,Oct,10,6,0000-10-06,280 +Death Valley,USC00042319,81.3,Oct,10,7,0000-10-07,281 +Death Valley,USC00042319,80.9,Oct,10,8,0000-10-08,282 +Death Valley,USC00042319,80.4,Oct,10,9,0000-10-09,283 +Death Valley,USC00042319,79.9,Oct,10,10,0000-10-10,284 +Death Valley,USC00042319,79.4,Oct,10,11,0000-10-11,285 +Death Valley,USC00042319,79,Oct,10,12,0000-10-12,286 +Death Valley,USC00042319,78.5,Oct,10,13,0000-10-13,287 +Death Valley,USC00042319,78,Oct,10,14,0000-10-14,288 +Death Valley,USC00042319,77.6,Oct,10,15,0000-10-15,289 +Death Valley,USC00042319,77.1,Oct,10,16,0000-10-16,290 +Death Valley,USC00042319,76.6,Oct,10,17,0000-10-17,291 +Death Valley,USC00042319,76.2,Oct,10,18,0000-10-18,292 +Death Valley,USC00042319,75.7,Oct,10,19,0000-10-19,293 +Death Valley,USC00042319,75.3,Oct,10,20,0000-10-20,294 +Death Valley,USC00042319,74.8,Oct,10,21,0000-10-21,295 +Death Valley,USC00042319,74.3,Oct,10,22,0000-10-22,296 +Death Valley,USC00042319,73.9,Oct,10,23,0000-10-23,297 +Death Valley,USC00042319,73.4,Oct,10,24,0000-10-24,298 +Death Valley,USC00042319,73,Oct,10,25,0000-10-25,299 +Death Valley,USC00042319,72.5,Oct,10,26,0000-10-26,300 +Death Valley,USC00042319,72.1,Oct,10,27,0000-10-27,301 +Death Valley,USC00042319,71.6,Oct,10,28,0000-10-28,302 +Death Valley,USC00042319,71.1,Oct,10,29,0000-10-29,303 +Death Valley,USC00042319,70.7,Oct,10,30,0000-10-30,304 +Death Valley,USC00042319,70.2,Oct,10,31,0000-10-31,305 +Death Valley,USC00042319,69.7,Nov,11,1,0000-11-01,306 +Death Valley,USC00042319,69.3,Nov,11,2,0000-11-02,307 +Death Valley,USC00042319,68.8,Nov,11,3,0000-11-03,308 +Death Valley,USC00042319,68.3,Nov,11,4,0000-11-04,309 +Death Valley,USC00042319,67.8,Nov,11,5,0000-11-05,310 +Death Valley,USC00042319,67.3,Nov,11,6,0000-11-06,311 +Death Valley,USC00042319,66.9,Nov,11,7,0000-11-07,312 +Death Valley,USC00042319,66.4,Nov,11,8,0000-11-08,313 +Death Valley,USC00042319,65.9,Nov,11,9,0000-11-09,314 +Death Valley,USC00042319,65.4,Nov,11,10,0000-11-10,315 +Death Valley,USC00042319,64.9,Nov,11,11,0000-11-11,316 +Death Valley,USC00042319,64.4,Nov,11,12,0000-11-12,317 +Death Valley,USC00042319,63.9,Nov,11,13,0000-11-13,318 +Death Valley,USC00042319,63.3,Nov,11,14,0000-11-14,319 +Death Valley,USC00042319,62.8,Nov,11,15,0000-11-15,320 +Death Valley,USC00042319,62.3,Nov,11,16,0000-11-16,321 +Death Valley,USC00042319,61.8,Nov,11,17,0000-11-17,322 +Death Valley,USC00042319,61.3,Nov,11,18,0000-11-18,323 +Death Valley,USC00042319,60.8,Nov,11,19,0000-11-19,324 +Death Valley,USC00042319,60.3,Nov,11,20,0000-11-20,325 +Death Valley,USC00042319,59.8,Nov,11,21,0000-11-21,326 +Death Valley,USC00042319,59.3,Nov,11,22,0000-11-22,327 +Death Valley,USC00042319,58.8,Nov,11,23,0000-11-23,328 +Death Valley,USC00042319,58.3,Nov,11,24,0000-11-24,329 +Death Valley,USC00042319,57.9,Nov,11,25,0000-11-25,330 +Death Valley,USC00042319,57.4,Nov,11,26,0000-11-26,331 +Death Valley,USC00042319,56.9,Nov,11,27,0000-11-27,332 +Death Valley,USC00042319,56.5,Nov,11,28,0000-11-28,333 +Death Valley,USC00042319,56,Nov,11,29,0000-11-29,334 +Death Valley,USC00042319,55.6,Nov,11,30,0000-11-30,335 +Death Valley,USC00042319,55.2,Dec,12,1,0000-12-01,336 +Death Valley,USC00042319,54.8,Dec,12,2,0000-12-02,337 +Death Valley,USC00042319,54.4,Dec,12,3,0000-12-03,338 +Death Valley,USC00042319,54,Dec,12,4,0000-12-04,339 +Death Valley,USC00042319,53.7,Dec,12,5,0000-12-05,340 +Death Valley,USC00042319,53.3,Dec,12,6,0000-12-06,341 +Death Valley,USC00042319,53,Dec,12,7,0000-12-07,342 +Death Valley,USC00042319,52.7,Dec,12,8,0000-12-08,343 +Death Valley,USC00042319,52.4,Dec,12,9,0000-12-09,344 +Death Valley,USC00042319,52.2,Dec,12,10,0000-12-10,345 +Death Valley,USC00042319,51.9,Dec,12,11,0000-12-11,346 +Death Valley,USC00042319,51.7,Dec,12,12,0000-12-12,347 +Death Valley,USC00042319,51.5,Dec,12,13,0000-12-13,348 +Death Valley,USC00042319,51.3,Dec,12,14,0000-12-14,349 +Death Valley,USC00042319,51.1,Dec,12,15,0000-12-15,350 +Death Valley,USC00042319,51,Dec,12,16,0000-12-16,351 +Death Valley,USC00042319,50.9,Dec,12,17,0000-12-17,352 +Death Valley,USC00042319,50.8,Dec,12,18,0000-12-18,353 +Death Valley,USC00042319,50.7,Dec,12,19,0000-12-19,354 +Death Valley,USC00042319,50.6,Dec,12,20,0000-12-20,355 +Death Valley,USC00042319,50.6,Dec,12,21,0000-12-21,356 +Death Valley,USC00042319,50.5,Dec,12,22,0000-12-22,357 +Death Valley,USC00042319,50.5,Dec,12,23,0000-12-23,358 +Death Valley,USC00042319,50.5,Dec,12,24,0000-12-24,359 +Death Valley,USC00042319,50.5,Dec,12,25,0000-12-25,360 +Death Valley,USC00042319,50.6,Dec,12,26,0000-12-26,361 +Death Valley,USC00042319,50.6,Dec,12,27,0000-12-27,362 +Death Valley,USC00042319,50.7,Dec,12,28,0000-12-28,363 +Death Valley,USC00042319,50.8,Dec,12,29,0000-12-29,364 +Death Valley,USC00042319,50.8,Dec,12,30,0000-12-30,365 +Death Valley,USC00042319,50.9,Dec,12,31,0000-12-31,366 +Houston,USW00012918,53.9,Jan,01,1,0000-01-01,1 +Houston,USW00012918,53.8,Jan,01,2,0000-01-02,2 +Houston,USW00012918,53.8,Jan,01,3,0000-01-03,3 +Houston,USW00012918,53.8,Jan,01,4,0000-01-04,4 +Houston,USW00012918,53.8,Jan,01,5,0000-01-05,5 +Houston,USW00012918,53.7,Jan,01,6,0000-01-06,6 +Houston,USW00012918,53.7,Jan,01,7,0000-01-07,7 +Houston,USW00012918,53.7,Jan,01,8,0000-01-08,8 +Houston,USW00012918,53.7,Jan,01,9,0000-01-09,9 +Houston,USW00012918,53.7,Jan,01,10,0000-01-10,10 +Houston,USW00012918,53.7,Jan,01,11,0000-01-11,11 +Houston,USW00012918,53.8,Jan,01,12,0000-01-12,12 +Houston,USW00012918,53.8,Jan,01,13,0000-01-13,13 +Houston,USW00012918,53.8,Jan,01,14,0000-01-14,14 +Houston,USW00012918,53.8,Jan,01,15,0000-01-15,15 +Houston,USW00012918,53.9,Jan,01,16,0000-01-16,16 +Houston,USW00012918,53.9,Jan,01,17,0000-01-17,17 +Houston,USW00012918,53.9,Jan,01,18,0000-01-18,18 +Houston,USW00012918,54,Jan,01,19,0000-01-19,19 +Houston,USW00012918,54,Jan,01,20,0000-01-20,20 +Houston,USW00012918,54.1,Jan,01,21,0000-01-21,21 +Houston,USW00012918,54.2,Jan,01,22,0000-01-22,22 +Houston,USW00012918,54.2,Jan,01,23,0000-01-23,23 +Houston,USW00012918,54.3,Jan,01,24,0000-01-24,24 +Houston,USW00012918,54.4,Jan,01,25,0000-01-25,25 +Houston,USW00012918,54.4,Jan,01,26,0000-01-26,26 +Houston,USW00012918,54.5,Jan,01,27,0000-01-27,27 +Houston,USW00012918,54.6,Jan,01,28,0000-01-28,28 +Houston,USW00012918,54.7,Jan,01,29,0000-01-29,29 +Houston,USW00012918,54.8,Jan,01,30,0000-01-30,30 +Houston,USW00012918,54.9,Jan,01,31,0000-01-31,31 +Houston,USW00012918,55,Feb,02,1,0000-02-01,32 +Houston,USW00012918,55.2,Feb,02,2,0000-02-02,33 +Houston,USW00012918,55.3,Feb,02,3,0000-02-03,34 +Houston,USW00012918,55.4,Feb,02,4,0000-02-04,35 +Houston,USW00012918,55.6,Feb,02,5,0000-02-05,36 +Houston,USW00012918,55.7,Feb,02,6,0000-02-06,37 +Houston,USW00012918,55.8,Feb,02,7,0000-02-07,38 +Houston,USW00012918,56,Feb,02,8,0000-02-08,39 +Houston,USW00012918,56.2,Feb,02,9,0000-02-09,40 +Houston,USW00012918,56.3,Feb,02,10,0000-02-10,41 +Houston,USW00012918,56.5,Feb,02,11,0000-02-11,42 +Houston,USW00012918,56.7,Feb,02,12,0000-02-12,43 +Houston,USW00012918,56.8,Feb,02,13,0000-02-13,44 +Houston,USW00012918,57,Feb,02,14,0000-02-14,45 +Houston,USW00012918,57.2,Feb,02,15,0000-02-15,46 +Houston,USW00012918,57.4,Feb,02,16,0000-02-16,47 +Houston,USW00012918,57.6,Feb,02,17,0000-02-17,48 +Houston,USW00012918,57.8,Feb,02,18,0000-02-18,49 +Houston,USW00012918,58,Feb,02,19,0000-02-19,50 +Houston,USW00012918,58.2,Feb,02,20,0000-02-20,51 +Houston,USW00012918,58.4,Feb,02,21,0000-02-21,52 +Houston,USW00012918,58.6,Feb,02,22,0000-02-22,53 +Houston,USW00012918,58.8,Feb,02,23,0000-02-23,54 +Houston,USW00012918,59,Feb,02,24,0000-02-24,55 +Houston,USW00012918,59.3,Feb,02,25,0000-02-25,56 +Houston,USW00012918,59.5,Feb,02,26,0000-02-26,57 +Houston,USW00012918,59.7,Feb,02,27,0000-02-27,58 +Houston,USW00012918,59.9,Feb,02,28,0000-02-28,59 +Houston,USW00012918,60,Feb,02,29,0000-02-29,60 +Houston,USW00012918,60.1,Mar,03,1,0000-03-01,61 +Houston,USW00012918,60.4,Mar,03,2,0000-03-02,62 +Houston,USW00012918,60.6,Mar,03,3,0000-03-03,63 +Houston,USW00012918,60.8,Mar,03,4,0000-03-04,64 +Houston,USW00012918,61,Mar,03,5,0000-03-05,65 +Houston,USW00012918,61.2,Mar,03,6,0000-03-06,66 +Houston,USW00012918,61.5,Mar,03,7,0000-03-07,67 +Houston,USW00012918,61.7,Mar,03,8,0000-03-08,68 +Houston,USW00012918,61.9,Mar,03,9,0000-03-09,69 +Houston,USW00012918,62.1,Mar,03,10,0000-03-10,70 +Houston,USW00012918,62.3,Mar,03,11,0000-03-11,71 +Houston,USW00012918,62.5,Mar,03,12,0000-03-12,72 +Houston,USW00012918,62.8,Mar,03,13,0000-03-13,73 +Houston,USW00012918,63,Mar,03,14,0000-03-14,74 +Houston,USW00012918,63.2,Mar,03,15,0000-03-15,75 +Houston,USW00012918,63.4,Mar,03,16,0000-03-16,76 +Houston,USW00012918,63.6,Mar,03,17,0000-03-17,77 +Houston,USW00012918,63.8,Mar,03,18,0000-03-18,78 +Houston,USW00012918,64,Mar,03,19,0000-03-19,79 +Houston,USW00012918,64.2,Mar,03,20,0000-03-20,80 +Houston,USW00012918,64.4,Mar,03,21,0000-03-21,81 +Houston,USW00012918,64.6,Mar,03,22,0000-03-22,82 +Houston,USW00012918,64.8,Mar,03,23,0000-03-23,83 +Houston,USW00012918,65,Mar,03,24,0000-03-24,84 +Houston,USW00012918,65.2,Mar,03,25,0000-03-25,85 +Houston,USW00012918,65.4,Mar,03,26,0000-03-26,86 +Houston,USW00012918,65.6,Mar,03,27,0000-03-27,87 +Houston,USW00012918,65.8,Mar,03,28,0000-03-28,88 +Houston,USW00012918,66,Mar,03,29,0000-03-29,89 +Houston,USW00012918,66.2,Mar,03,30,0000-03-30,90 +Houston,USW00012918,66.4,Mar,03,31,0000-03-31,91 +Houston,USW00012918,66.6,Apr,04,1,0000-04-01,92 +Houston,USW00012918,66.8,Apr,04,2,0000-04-02,93 +Houston,USW00012918,67.1,Apr,04,3,0000-04-03,94 +Houston,USW00012918,67.3,Apr,04,4,0000-04-04,95 +Houston,USW00012918,67.5,Apr,04,5,0000-04-05,96 +Houston,USW00012918,67.7,Apr,04,6,0000-04-06,97 +Houston,USW00012918,67.9,Apr,04,7,0000-04-07,98 +Houston,USW00012918,68.1,Apr,04,8,0000-04-08,99 +Houston,USW00012918,68.3,Apr,04,9,0000-04-09,100 +Houston,USW00012918,68.5,Apr,04,10,0000-04-10,101 +Houston,USW00012918,68.8,Apr,04,11,0000-04-11,102 +Houston,USW00012918,69,Apr,04,12,0000-04-12,103 +Houston,USW00012918,69.2,Apr,04,13,0000-04-13,104 +Houston,USW00012918,69.4,Apr,04,14,0000-04-14,105 +Houston,USW00012918,69.7,Apr,04,15,0000-04-15,106 +Houston,USW00012918,69.9,Apr,04,16,0000-04-16,107 +Houston,USW00012918,70.1,Apr,04,17,0000-04-17,108 +Houston,USW00012918,70.4,Apr,04,18,0000-04-18,109 +Houston,USW00012918,70.6,Apr,04,19,0000-04-19,110 +Houston,USW00012918,70.8,Apr,04,20,0000-04-20,111 +Houston,USW00012918,71.1,Apr,04,21,0000-04-21,112 +Houston,USW00012918,71.3,Apr,04,22,0000-04-22,113 +Houston,USW00012918,71.6,Apr,04,23,0000-04-23,114 +Houston,USW00012918,71.8,Apr,04,24,0000-04-24,115 +Houston,USW00012918,72.1,Apr,04,25,0000-04-25,116 +Houston,USW00012918,72.3,Apr,04,26,0000-04-26,117 +Houston,USW00012918,72.6,Apr,04,27,0000-04-27,118 +Houston,USW00012918,72.8,Apr,04,28,0000-04-28,119 +Houston,USW00012918,73.1,Apr,04,29,0000-04-29,120 +Houston,USW00012918,73.3,Apr,04,30,0000-04-30,121 +Houston,USW00012918,73.6,May,05,1,0000-05-01,122 +Houston,USW00012918,73.8,May,05,2,0000-05-02,123 +Houston,USW00012918,74.1,May,05,3,0000-05-03,124 +Houston,USW00012918,74.3,May,05,4,0000-05-04,125 +Houston,USW00012918,74.6,May,05,5,0000-05-05,126 +Houston,USW00012918,74.8,May,05,6,0000-05-06,127 +Houston,USW00012918,75,May,05,7,0000-05-07,128 +Houston,USW00012918,75.3,May,05,8,0000-05-08,129 +Houston,USW00012918,75.5,May,05,9,0000-05-09,130 +Houston,USW00012918,75.8,May,05,10,0000-05-10,131 +Houston,USW00012918,76,May,05,11,0000-05-11,132 +Houston,USW00012918,76.3,May,05,12,0000-05-12,133 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+San Diego,USW00093107,56.3,Dec,12,2,0000-12-02,337 +San Diego,USW00093107,56.1,Dec,12,3,0000-12-03,338 +San Diego,USW00093107,56,Dec,12,4,0000-12-04,339 +San Diego,USW00093107,55.8,Dec,12,5,0000-12-05,340 +San Diego,USW00093107,55.7,Dec,12,6,0000-12-06,341 +San Diego,USW00093107,55.5,Dec,12,7,0000-12-07,342 +San Diego,USW00093107,55.4,Dec,12,8,0000-12-08,343 +San Diego,USW00093107,55.3,Dec,12,9,0000-12-09,344 +San Diego,USW00093107,55.2,Dec,12,10,0000-12-10,345 +San Diego,USW00093107,55.1,Dec,12,11,0000-12-11,346 +San Diego,USW00093107,55,Dec,12,12,0000-12-12,347 +San Diego,USW00093107,55,Dec,12,13,0000-12-13,348 +San Diego,USW00093107,54.9,Dec,12,14,0000-12-14,349 +San Diego,USW00093107,54.9,Dec,12,15,0000-12-15,350 +San Diego,USW00093107,54.8,Dec,12,16,0000-12-16,351 +San Diego,USW00093107,54.8,Dec,12,17,0000-12-17,352 +San Diego,USW00093107,54.8,Dec,12,18,0000-12-18,353 +San Diego,USW00093107,54.8,Dec,12,19,0000-12-19,354 +San Diego,USW00093107,54.8,Dec,12,20,0000-12-20,355 +San Diego,USW00093107,54.8,Dec,12,21,0000-12-21,356 +San Diego,USW00093107,54.8,Dec,12,22,0000-12-22,357 +San Diego,USW00093107,54.8,Dec,12,23,0000-12-23,358 +San Diego,USW00093107,54.9,Dec,12,24,0000-12-24,359 +San Diego,USW00093107,54.9,Dec,12,25,0000-12-25,360 +San Diego,USW00093107,54.9,Dec,12,26,0000-12-26,361 +San Diego,USW00093107,55,Dec,12,27,0000-12-27,362 +San Diego,USW00093107,55,Dec,12,28,0000-12-28,363 +San Diego,USW00093107,55.1,Dec,12,29,0000-12-29,364 +San Diego,USW00093107,55.1,Dec,12,30,0000-12-30,365 +San Diego,USW00093107,55.2,Dec,12,31,0000-12-31,366 diff --git a/dplyr.Rmd b/dplyr.Rmd index cb2411c..534860c 100644 --- a/dplyr.Rmd +++ b/dplyr.Rmd @@ -198,7 +198,8 @@ df %>% select(!type) ## `filter() ` -`select`是列方向的选择，我们还可以对数据行方向的选择和筛选，选出符合我们条件的某些行 +`select`是列方向的选择，我们还可以对数据行方向的选择和筛选，选出符合我们条件的某些行^[ +注意，这里filter()函数不是字面上“过滤掉”的意思，而是保留符合条件的行，也就说keep，不是drop的意思。 第一次会有一点点迷惑，我相信习惯就好了] ```{r dplyr-24, out.width = '65%', echo = FALSE} knitr::include_graphics("images/dplyr-filter.png") diff --git a/forcats.Rmd b/forcats.Rmd index 6ed9974..023a4ac 100644 --- a/forcats.Rmd +++ b/forcats.Rmd @@ -14,6 +14,7 @@ ## 创建因子 ```{r forcats-1} library(tidyverse) +library(palmerpenguins) ``` ```{r forcats-2} @@ -167,13 +168,154 @@ d %>% ``` -```{r forcats-20, echo = F} + +## 可视化中应用 +可能没说明白，那就看企鹅柱状图吧 +```{r forcats-20} +ggplot(penguins, aes(y = species)) + + geom_bar() +``` + + +```{r forcats-21} +ggplot(penguins, aes(y = fct_rev(species))) + + geom_bar() +``` + + + + + +```{r forcats-22a, eval=FALSE} +penguins %>% + count(species) %>% + pull(species) + +penguins %>% + count(species) %>% + mutate(species = fct_relevel(species, "Chinstrap", "Gentoo", "Adelie")) %>% + pull(species) +``` + + + +```{r forcats-22} +# Move "Chinstrap" in front, rest alphabetic +ggplot(penguins, aes(y = fct_relevel(species, "Chinstrap"))) + + geom_bar() +``` + + + +```{r forcats-23} +# Use order "Chinstrap", "Gentoo", "Adelie" +ggplot(penguins, aes(y = fct_relevel(species, "Chinstrap", "Gentoo", "Adelie"))) + + geom_bar() +``` + + +```{r forcats-24} +penguins %>% + mutate(species = fct_relevel(species, "Chinstrap", "Gentoo", "Adelie")) %>% + ggplot(aes(y = species)) + + geom_bar() +``` + + +```{r forcats-25} +ggplot(penguins, aes(y = fct_relevel(species, "Adelie", after = Inf))) + + geom_bar() +``` + + +```{r forcats-26} +# Use the order defined by the number of penguins of different species +# The order is descending, from most frequent to least frequent + +penguins %>% + mutate(species = fct_infreq(species)) %>% + ggplot(aes(y = species)) + + geom_bar() +``` + + +```{r forcats-27} +penguins %>% + mutate(species = fct_rev(fct_infreq(species))) %>% + ggplot(aes(y = species)) + + geom_bar() +``` + + +```{r forcats-28} +# Reorder based on numeric values +penguins %>% + count(species) %>% + mutate(species = fct_reorder(species, n)) %>% + ggplot(aes(n, species)) + + geom_col() +``` + +## 作业 + +- 画出的2007年美洲人口寿命的柱状图，要求从高到低排序 +```{r forcats-29} +library(gapminder) +gapminder %>% + filter( + year == 2007, + continent == "Americas" +) +``` + + +```{r forcats-30, eval=FALSE, echo = FALSE} +gapminder %>% + filter( year == 2007, continent == "Americas") %>% + mutate( country = fct_reorder(country, lifeExp)) %>% + ggplot(aes(lifeExp, country)) + + geom_point() +``` + +- 这是四个国家人口寿命的变化图 +```{r forcats-31} +gapminder %>% + filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>% + ggplot(aes(year, lifeExp)) + geom_line() + + facet_wrap(vars(country), nrow = 1) +``` + +- 要求给四个分面排序，按每个国家寿命的中位数 +```{r forcats-32, eval=FALSE, echo = FALSE} +gapminder %>% + filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>% + mutate(country = fct_reorder(country, lifeExp)) %>% # default: order by median + ggplot(aes(year, lifeExp)) + geom_line() + + facet_wrap(vars(country), nrow = 1) +``` + + + +- 要求给四个分面排序，按每个国家寿命差（最大值减去最小值） + +```{r forcats-33, eval=FALSE, echo = FALSE} +gapminder %>% + filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>% + # order by custom function: here, difference between max and min + mutate(country = fct_reorder(country, lifeExp, function(x) { max(x) - min(x) })) %>% + ggplot(aes(year, lifeExp)) + geom_line() + + facet_wrap(vars(country), nrow = 1) +``` + + + +```{r forcats-34, echo = F} # remove the objects # rm(list=ls()) rm(d, income, x) ``` -```{r forcats-21, echo = F, message = F, warning = F, results = "hide"} +```{r forcats-35, echo = F, message = F, warning = F, results = "hide"} pacman::p_unload(pacman::p_loaded(), character.only = TRUE) ``` diff --git a/ggplot2_colors.Rmd b/ggplot2_colors.Rmd index 475100d..81fd3d8 100644 --- a/ggplot2_colors.Rmd +++ b/ggplot2_colors.Rmd @@ -31,13 +31,28 @@ knitr::include_graphics("images/hcl-palettes-principles.png") 三种类型对应着三个函数 `qualitative_hcl()`, `sequential_hcl()`, 和 `diverging_hcl()`. + ## 配色模板 ```{r ggplot2-colors-4, out.width = '100%'} hcl_palettes(plot = TRUE) ``` -## 使用案例 +**colorspace** 模板使用起来很方便，有统一格式`scale___()`, + +- 这里 `` 是指定映射 (`fill`, `color`, `colour`), +- 这里 `` 是表明数据变量的类型 (`discrete`, `continuous`, `binned`), +- 这里 `colorscale` 是声明颜色标度类型 (`qualitative`, `sequential`, `diverging`, `divergingx`). + + +|Scale function | Aesthetic | Data type | Palette type | +|:----------- | :-------- | :--------- | :------------| +|`scale_color_discrete_qualitative()` | `color` | discrete | qualitative | +|`scale_fill_continuous_sequential()` | `fill` | continuous | sequential | +|`scale_colour_continous_divergingx()` | `colour` | continuous | diverging | + + +## 使用案例1 ggplot2默认 ```{r ggplot2-colors-5} @@ -67,6 +82,126 @@ penguins %>% ``` +## 使用案例2 + + +```{r ggplot2-colors-8} +temps_months <- read_csv("./demo_data/tempnormals.csv") %>% + group_by(location, month_name) %>% + summarize(mean = mean(temperature)) %>% + mutate( + month = factor( + month_name, + levels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec") + ), + location = factor( + location, levels = c("Death Valley", "Houston", "San Diego", "Chicago") + ) + ) %>% + select(-month_name) + +temps_months +``` + + + + + +```{r ggplot2-colors-9} +temps_months %>% + ggplot(aes(x = month, y = location, fill = mean)) + + geom_tile(width = 0.95, height = 0.95) + + coord_fixed(expand = FALSE) + + theme_classic() +``` + + +```{r ggplot2-colors-10} +temps_months %>% + ggplot(aes(x = month, y = location, fill = mean)) + + geom_tile(width = 0.95, height = 0.95) + + coord_fixed(expand = FALSE) + + theme_classic() + + scale_fill_gradient() +``` + + +```{r ggplot2-colors-11} +temps_months %>% + ggplot(aes(x = month, y = location, fill = mean)) + + geom_tile(width = 0.95, height = 0.95) + + coord_fixed(expand = FALSE) + + theme_classic() + + scale_fill_viridis_c() +``` + + +```{r ggplot2-colors-12} +temps_months %>% + ggplot(aes(x = month, y = location, fill = mean)) + + geom_tile(width = 0.95, height = 0.95) + + coord_fixed(expand = FALSE) + + theme_classic() + + scale_fill_viridis_c(option = "B", begin = 0.15) +``` + + +```{r ggplot2-colors-13} +temps_months %>% + ggplot(aes(x = month, y = location, fill = mean)) + + geom_tile(width = 0.95, height = 0.95) + + coord_fixed(expand = FALSE) + + theme_classic() + + scale_fill_continuous_sequential(palette = "YlGnBu", rev = FALSE) +``` + + +```{r ggplot2-colors-14} +temps_months %>% + ggplot(aes(x = month, y = location, fill = mean)) + + geom_tile(width = 0.95, height = 0.95) + + coord_fixed(expand = FALSE) + + theme_classic() + + scale_fill_continuous_sequential(palette = "Viridis", rev = FALSE) +``` + + +```{r ggplot2-colors-15} +temps_months %>% + ggplot( aes(x = month, y = location, fill = mean)) + + geom_tile(width = 0.95, height = 0.95) + + coord_fixed(expand = FALSE) + + theme_classic() + + scale_fill_continuous_sequential(palette = "Inferno", begin = 0.15, rev = FALSE) +``` + + +```{r ggplot2-colors-16} +temps_months %>% + ggplot(aes(x = month, y = location, fill = mean)) + + geom_tile(width = 0.95, height = 0.95) + + coord_fixed(expand = FALSE) + + theme_classic() + + scale_fill_continuous_sequential(palette = "Plasma", begin = 0.35, rev = FALSE) +``` + +## 配色 + +```{block ggplot2-colors-17, type="danger"} +这里有个小小的提示： + +- 尽可能不避免使用`"red"`, `"green"`, `"blue"`, `"cyan"`, `"magenta"`, `"yellow"`颜色 +- 使用相对柔和的颜色`"firebrick"`, `"springgreen4"`, `"blue3"`, `"turquoise3"`, `"darkorchid2"`, `"gold2"`，会让人觉得舒服 + +``` + + +可以对比下 +```{r ggplot2-colors-18} +colorspace::swatchplot(c("red", "green", "blue", "cyan", "magenta", "yellow")) +colorspace::swatchplot(c("firebrick", "springgreen4", "blue3", "turquoise3", "darkorchid2", "gold2")) +``` + ## color-wheel diff --git a/ggplot2_gganimate.Rmd b/ggplot2_gganimate.Rmd index a8cd76d..a0e611a 100644 --- a/ggplot2_gganimate.Rmd +++ b/ggplot2_gganimate.Rmd @@ -466,7 +466,7 @@ transition_reveal(along = ) + ### Renderer options ```{r renderer-tbl, echo=FALSE} -tribble( +tibble::tribble( ~Function, ~Description, "gifski_renderer", "Default, super fast gif renderer.", "magick_renderer", "Somewhat slower gif renderer.", diff --git a/ggplot2_scales.Rmd b/ggplot2_scales.Rmd index dd9b495..3166d40 100644 --- a/ggplot2_scales.Rmd +++ b/ggplot2_scales.Rmd @@ -1,5 +1,10 @@ # ggplot2之标度 {#ggplot2-scales} +> 用 ggplot2 画图，有种恋爱的感觉: +> “你懂我的图谋不轨，我懂你的故作矜持” + + + 这一章我们一起学习ggplot2中的scales语法，推荐大家阅读Hadley Wickham最新版的[《ggplot2: Elegant Graphics for Data Analysis》](https://ggplot2-book.org/)，但如果需要详细了解**标度**参数体系，还是要看[ggplot2官方文档](https://cran.r-project.org/web/packages/ggplot2/index.html) diff --git a/images/make_data_tidy3.jpg b/images/make_data_tidy3.jpg new file mode 100644 index 0000000000000000000000000000000000000000..d9a9bff580b207d8bf60d0d37d46435820f71498 GIT binary patch literal 162605 zcmeFZc|4SV_dhA!^XzCn|%*E`}V=X$<4{Z!NtMO&c(yU#l07JuygL?<=MM$`}ggWY=3U{?gOi z*FP}$V+c1fIrVdTX7<o! zAK+j;z_$C?={*O}U1z^_|B$#+5C`A+xHrX(oD#~WME=_ky14`-RVI#;wnf{K?C%No z`2R|>KLz`zTsY8PRu;f`tOr045S^A0Ap^>YOLcv7)s@9HBLXDzuSb?MPR1++WGj>L 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+options(digits = 4, + dplyr.print_min = 6, + dplyr.print_max = 6, + htmltools.dir.version = FALSE, + formatR.indent = 2, + width = 55 + ) knitr::opts_chunk$set( echo = TRUE, @@ -86,6 +90,7 @@ knitr::include_graphics("images/rbook1.png") - 第 \@ref(ggplot2-guides) 章ggplot2的图例 - 第 \@ref(ggplot2-customize) 章ggplot2扩展内容 - 第 \@ref(ggplot2-stat-layer) 章ggplot2统计图层 + - 第 \@ref(ggplot2-colors) 章介绍数据可视化中的配色 - 第 \@ref(workflow) 章回望tidyverse之旅 - 第 \@ref(tips) 章介绍tidyverse常用技巧 - 第 \@ref(advR) 章介绍tidyverse进阶技巧 @@ -126,9 +131,9 @@ knitr::include_graphics("images/rbook1.png") - 第 \@ref(stars) 章介绍地理数据处理 - 第 \@ref(rowwise) 章介绍tidyverse中行方向的操作 - 第 \@ref(ggplot2-academic) 章介绍科研数据可视化中的统计分布图 - - 第 \@ref(ggplot2-colors) 章介绍数据可视化中的配色 - 第 \@ref(ggplot2-gganimate) 章让你的数据骚动起来 - 第 \@ref(lazyman) 章介绍我收集的一些有用和有趣的宏包 + - 第 \@ref(practice) 章一日一练，有些题目还是很有挑战性的喔 diff --git a/pandas.Rmd b/pandas.Rmd new file mode 100644 index 0000000..0acc291 --- /dev/null +++ b/pandas.Rmd @@ -0,0 +1,234 @@ +# 谁是数据分析中最好用的宏包 {#Pandas-vs-dplyr} + +本章，我们用一些例子来比较数据科学领域Pandas vs. dplyr 两个宏包的用法 + + +```{r Pandas01, echo=FALSE} +knitr::opts_chunk$set( + engine.path = list(python = "G:\\Anaconda3\\python.exe") + ) +``` + +首先，需要加载这个宏包 +```{r Pandas02} +library(reticulate) +``` + + +## 加载数据 + +这是用 Pandas 方法， +```{python} +import pandas as pd + +gapminder = pd.read_csv("./demo_data/gapminder.csv") +``` + + + +这是用 dplyr 方法 + +```{r Pandas03} +library(dplyr) + +gapminder <- readr::read_csv("./demo_data/gapminder.csv") +``` + + +这个过程，两者没什么区别。再往下看 + + + + +## 过滤 + +### 问题1 – 找出2007年的所用记录. + +这是用 Pandas 方法， + +```{python} +gapminder[gapminder['year'] == 2007] +``` + +这是用 dplyr 方法， +```{r Pandas04} +gapminder %>% + filter(year == 2007) +``` + + +### 问题2 – 找出2007年中 continent为 Americas 的记录 + +这是用 Pandas 方法， + +```{python} +gapminder[(gapminder['year'] == 2007) & (gapminder['continent'] == 'Americas')] +``` + + +这是用 dplyr 方法 + +```{r Pandas05} +gapminder %>% + filter( + year == 2007, + continent == "Americas" + ) +``` + + + +### 问题3 – 找出2007年中 continent为 Americas，且只包含美国 的记录 + +这是用 Pandas 方法， + +```{python} +gapminder[(gapminder['year'] == 2007) & + (gapminder['continent'] == 'Americas') & + (gapminder['country'] == 'United States')] +``` + + +这是用 dplyr 方法 + +```{r Pandas06} +gapminder %>% + filter( + year == 2007, + continent == "Americas", + country == "United States" + ) +``` + + +## 统计 + +我们再看看Pandas 和 dplyr 在常用的统计分析方面的表现。 + + + +### 问题1 – 计算2007年全球寿命均值 + + +这是用 Pandas 方法， + +```{python} +gapminder[gapminder['year'] == 2007]['lifeExp'].mean() +``` + + +这是用 dplyr 方法 + +```{r Pandas07} +gapminder %>% + filter(year == 2007) %>% + summarize(mean(lifeExp)) +``` + + +### 问题2 – 计算2007年每一个洲的寿命均值 + + +这是用 Pandas 方法， + +```{python} +gapminder[gapminder['year'] == 2007].groupby(by='continent').mean()['lifeExp'] +``` + + +这是用 dplyr 方法 + +```{r Pandas08} +gapminder %>% + filter(year == 2007) %>% + group_by(continent) %>% + summarize(mean(lifeExp)) +``` + +### 问题3 – 计算2007年每一个洲的人口总数然后降序输出。 + +这是用 Pandas 方法， + +```{python} +gapminder[gapminder['year'] == 2007].groupby(by='continent').sum()['pop'].sort_values(ascending=False) +``` + + +这是用 dplyr 方法 + +```{r Pandas09} +gapminder %>% + filter(year == 2007) %>% + group_by(continent) %>% + summarize(total_pop = sum(pop)) %>% + arrange(desc(total_pop)) +``` + + +## 创建新变量 + +### 问题1 – 创建国内生产总值(GDP)变量，即人口数量乘以人均GDP + +这是用 Pandas 方法， + +```{python} +gapminder['GDP'] = gapminder['pop'] * gapminder['gdpPercap'] +gapminder.head() +``` + + +这是用 dplyr 方法 + +```{r Pandas10} +gapminder %>% + mutate(GDP = pop * gdpPercap) +``` + +### 问题2 – 人均GDP排名前90%的前十个国家 + +这是用 Pandas 方法， + +```{python} +gapminder_2007 = gapminder[gapminder['year'] == 2007] +gapminder_2007['percentile'] = gapminder_2007['gdpPercap'].rank(pct=True) +gapminder_2007.sort_values(by='percentile', ascending=False)[:10] +``` + + +这是用 dplyr 方法 + +```{r Pandas11} +gapminder %>% + filter(year == 2007) %>% + mutate(percentile = ntile(gdpPercap, 100)) %>% + arrange(desc(percentile)) %>% + top_n(10, wt = percentile) +``` + +## 小节 + +根据以上有限的对比，感觉dplyr语法要简练和清晰些。但是，这并不意味我们一定在Pandas 和 dplyr做取舍，因为解决现实问题，往往取决于我们对工具的熟练程度。以上只是个人观点（哈哈哈）。 + +## 参考 + +- +- +- + + + + +```{r Pandas99, echo = F} +# remove the objects +# ls() %>% stringr::str_flatten(collapse = ", ") + +#rm(cutoffs, d1, d2, df, mult, std, weights, replace_col_max) +``` + + + +```{r Pandas100, echo = F, message = F, warning = F, results = "hide"} +pacman::p_unload(pacman::p_loaded(), character.only = TRUE) +``` + + diff --git a/practice.rmd b/practice.rmd new file mode 100644 index 0000000..47f92ad --- /dev/null +++ b/practice.rmd @@ -0,0 +1,289 @@ +# 一天一练 {#practice} + +尽可能的在tidyverse的框架下完成 +```{r practice00} +library(tidyverse) +``` + +## day01 + +旋转数据框，要求 +```{r practice01} +d <- tibble::tribble( + ~name, ~chinese, ~math, ~physics, ~english, ~music, ~sport, + "Alice", 88L, 63L, 98L, 89L, 85L, 72L, + "Bob", 85L, 75L, 85L, 82L, 73L, 83L, + "Carlo", 95L, 98L, 75L, 75L, 68L, 84L + ) +d +``` + +变成 +```{r practice02, echo = FALSE} +d %>% + tidyr::pivot_longer( + cols = -name, + names_to = "discipline", + values_to = "score" + ) %>% + tidyr::pivot_wider( + names_from = name, + values_from = score + ) +``` + + +## day02 + +排序，要求按照score从大往小排，但希望all是最下面一行。 +```{r practice03} +d <- + tibble::tribble( + ~name, ~score, + "a1", 2, + "a2", 5, + "a3", 3, + "a4", 7, + "a5", 6, + "all", 23 + ) +``` + + +变成 +```{r practice04, echo = FALSE} +d %>% + arrange(desc(score)) %>% + arrange(name %in% c("all")) +``` + + +## day03 + +统计每位同学，成绩高于各科均值的个数， +```{r practice05} +d <- tibble::tribble( + ~name, ~chinese, ~engish, ~physics, ~sport, ~music, + "Aice", 85, 56, 56, 54, 78, + "Bob", 75, 78, 77, 56, 69, + "Cake", 69, 41, 88, 89, 59, + "Dave", 90, 66, 74, 82, 60, + "Eve", 68, 85, 75, 69, 21, + "Fod", 77, 74, 62, 74, 88, + "Gimme", 56, 88, 75, 69, 34 +) +d +``` + + +变成 +```{r practice06, echo = FALSE} +d %>% + mutate( + across(-name, list(RC = ~ . > mean(.))) + ) %>% + rowwise() %>% + mutate( + num_above_mean = sum(c_across(ends_with("_RC"))) + ) %>% + ungroup() %>% + select(-ends_with("_RC")) +``` + + +## day04 +```{r practice07} +data <- tribble( + ~id, ~corr, ~period, + 1, 0, "a", + 1, 0, "b", + 2, 0, "a", + 2, 1, "b", + 3, 1, "a", + 3, 0, "b", + 4, 1, "a", + 4, 1, "b" +) +data +``` + +先按id分组， +- 如果corr中都是0 就"none" +- 如果corr中都是1 就"both" +- 如果corr中只有一个1，就输出1对应period + + +```{r practice08, echo = FALSE} +my_function <- function(corr, period) { + sum <- sum(corr) + + if (sum == 0) { + res <- "none" + } + + if (sum == 2) { + res <- "both" + } + + if (sum == 1) { + res <- period[corr == 1] + } + return(res) +} + + +data %>% + group_by(id) %>% + summarise(resp_period = my_function(corr, period)) +``` + +## day05 + +图中柱子上的字体没有显示完整，请改进。 +```{r practice09} +d <- tibble::tribble( + ~name, ~value, + "Alice", 2.12, + "Bob", 68.45, + "Carlie", 15.84, + "Dave", 7.38, + "Eve", 0.56 + ) + + +d %>% + ggplot(aes(x = value, y = fct_reorder(name, value)) ) + + geom_col(width = 0.6, fill = "gray60") + + geom_text(aes(label = value, hjust =1)) + + theme_classic() + + scale_x_continuous(expand = c(0, 0)) + + labs(x = NULL, y = NULL) +``` + + +```{r practice10, eval=FALSE, echo = FALSE} +d %>% + ggplot(aes(x = value, y = fct_reorder(name, value)) ) + + geom_col(width = 0.6, fill = "gray60") + + geom_text(aes(label = value, hjust = ifelse(value > 50, 1, -.1)) ) + + theme_classic() + + scale_x_continuous(expand = c(0, 0)) + + labs(x = NULL, y = NULL) +``` + + +## day06 + +我看到新闻有[一张图](https://themarkup.org/news/2021/03/02/major-universities-are-using-race-as-a-high-impact-predictor-of-student-success)很漂亮，您能重复出来？ + +```{r practice11, out.width = '85%', echo = FALSE} +knitr::include_graphics("images/to_reproduce.jpg") +``` + + + +数据在下面 + +```{r practice12} +d <- tibble::tribble( + ~area, ~group, ~value, + "Texas A&M", "white Students", 0.03, + "Texas A&M", "Black Students", 0.07, + "Umass Amherst", "white Students", 0.07, + "Umass Amherst", "Black Students", 0.23, + "UW-Milwaukee", "white Students", 0.13, + "UW-Milwaukee", "Black Students", 0.31 + ) +d +``` + +提示，可以把图片拉到https://color.adobe.com/zh/create/image-gradient 获取颜色值，比如 +```{r practice13} +colorspace::swatchplot(c("#F42F5D","#252A4A")) +``` + + +```{r practice14, eval=FALSE, echo = FALSE} +## 图片拉到https://color.adobe.com/zh/create/image-gradient 获取颜色值 +colorspace::swatchplot(c("#F42F5D","#FC3762","#252A4A","#242B48")) +colorspace::swatchplot(c("#F42F5D","#252A4A")) + + +## a stupid way +text_subtitle <- glue::glue("Percentage of student body labeled as high risk to not graduate within their
    selected major
    ", + "", + str_dup("-", 100), + "" +) + + + +d %>% + mutate( + across(group, as_factor), + ) %>% + ggplot(aes(x = group, y = value, color = group, fill = group)) + + geom_col(width = 0.4) + + geom_text(aes(label = scales::label_percent(scale = 100, accuracy = 1)(value)), + vjust = -1, + size = rel(6), + fontface = "bold" + ) + + facet_wrap(vars(area), ncol = 3, scales = "free_y") + + scale_x_discrete( + labels = function(x) str_replace(x, " ", "\n"), + expand = expansion(mult = .8) + ) + + scale_y_continuous( + limits = c(0, 0.46), + breaks = c(0, 0.2, 0.4), + labels = scales::label_percent(scale = 100, accuracy = 1), + expand = expansion(mult = 0) + ) + + scale_fill_manual( + values = c("white Students" = "#252A4A", "Black Students" = "#F42F5D"), + aesthetics = c("colour", "fill") + ) + + theme( + legend.position = "none", + plot.title = element_text(size = rel(2)), + plot.subtitle = element_markdown(size = 11 ), + plot.caption = element_text(size = 12, color = "grey50", hjust = 0), + axis.text.y = element_text(size = rel(1.5)), + axis.text.x = element_text(size = rel(1.5), + face = "bold", + color = c("#252A4A", "#F42F5D")#, + #margin = margin(t = -5, unit = "pt") + ), + axis.ticks = element_blank(), + panel.background = element_rect(color = "white", fill = NA), + panel.grid.major.y = element_line(colour = "gray", + size = 0.8, + linetype = "dotted"), + strip.background = element_blank(), + strip.text = element_text(face = "bold", size = rel(1)), + panel.spacing = unit(2, "lines") + ) + + labs( + title = "Black students are regularly labeled a higher risk for failure\nthan White students", + subtitle = text_subtitle, + caption = "Sources: Texas A&M, University of Massachusetts Amherst, and University of Wisconsin–\nMilwaukee", + x = NULL, y = NULL) + +ggsave("myplot.pdf", width = 10, height = 5, units = "in") +``` + + + + +```{r practice99, echo = F} +# remove the objects +# rm(list=ls()) +rm(d, data, my_function, text_subtitle) +``` + + + +```{r practice100, echo = F, message = F, warning = F, results = "hide"} +pacman::p_unload(pacman::p_loaded(), character.only = TRUE) +``` diff --git a/tidymodels.Rmd b/tidymodels.Rmd index 9b6e3df..87cd143 100644 --- a/tidymodels.Rmd +++ b/tidymodels.Rmd @@ -64,7 +64,7 @@ bind_cols( ) -predict(model, new_data = testing_data) %>% +predict(model_logistic, new_data = testing_data) %>% bind_cols(testing_data) %>% count(.pred_class, species) ``` diff --git a/tidyr.Rmd b/tidyr.Rmd index b9730ff..50c549c 100644 --- a/tidyr.Rmd +++ b/tidyr.Rmd @@ -153,6 +153,8 @@ wide ``` + + ## 列名转换成多个变量 假定 `A`, `B`, `C` 三种植物每天生长的记录，包括三个特征（height, width, depth） @@ -207,17 +209,47 @@ knitr::include_graphics("images/make_data_tidy2.jpg") ```{r tidyr-603} -plant_record %>% +plant_record_longer <- plant_record %>% tidyr::pivot_longer( cols = !day, names_to = c("species", ".value"), names_pattern = "(.*)_(.*)" ) +plant_record_longer +``` + + +如果，反过来，又该怎么弄呢？ + +```{r tidyr-604, out.width = '100%', fig.align='left', echo = FALSE} +knitr::include_graphics("images/make_data_tidy3.jpg") +``` + + + +```{r, eval=FALSE} +us_rent_income %>% + pivot_wider( + names_from = variable, + names_glue = "{variable}_{.value}", + values_from = c(estimate, moe) + ) ``` +```{r tidyr-605} +plant_record_longer %>% + tidyr::pivot_wider( + names_from = species, + values_from = c(height, width, depth), + names_glue = "{species}_{.value}" + ) +``` + + + -```{block tidyr-604, type="danger"} +```{block tidyr-6041, type="danger"} 看明白`.value`它代表的意思了吗？ - 注意 .value 而不是value，说明这里不是单个列名，而是匹配得到的多个值做列名 @@ -263,7 +295,7 @@ long ```{r tidyr-17, echo = F} # remove the objects -rm(long, wide, plant_height, plant_record) +rm(long, wide, plant_height, plant_record, plant_record_longer) ``` ```{r tidyr-18, echo = F, message = F, warning = F, results = "hide"}