├── .gitignore ├── 00_whyR ├── 00_whyR.Rmd ├── 00_whyR.log ├── 00_whyR.pdf ├── exercises │ ├── 1_stats.R │ ├── 2_visual.R │ ├── 3_eda.R │ ├── 4_reproducible.Rmd │ └── covnat.rda ├── header.tex └── images │ ├── Coding-Lab.jpg │ ├── Knowledge-Scaffolding.jpg │ ├── R4art.png │ ├── RStudio-Screenshot.png │ ├── R_inventor.png │ ├── R_logo.png │ ├── R_vs_SPSS.jpg │ ├── arrival-movie.png │ ├── data-science-explore.png │ ├── data_science.png │ ├── hadley-wickham.jpg │ ├── night_king.jpg │ ├── r4ds-cover.png │ ├── rstudio-editor.png │ ├── social_science.jpg │ ├── tidyverse.png │ ├── tiobe-index.png │ ├── typesetting.png │ └── what_is_R.png ├── 01_install ├── 01_install.Rmd ├── 01_install.log ├── 01_install.pdf ├── header.tex └── images │ ├── QQgroup_PsyStats.png │ ├── QQgroup_chenglong.png │ ├── QQgroup_shizishan.png │ ├── RStudio-Screenshot.png │ ├── Rhelp.png │ ├── Rinstall.png │ ├── Rstudio_install.png │ ├── dashboard.jpg │ ├── engine.jpg │ ├── mirror1.png │ ├── mirror2.png │ ├── rstudio-editor1.png │ └── run_script.png ├── 02_basicR ├── 02_basicR.Rmd ├── 02_basicR.log ├── 02_basicR.pdf ├── header.tex └── images │ ├── Rhelp.png │ ├── data_struction1.png │ ├── data_type.png │ ├── rstudio-editor.png │ ├── script1.png │ └── script2.png ├── 03_subset ├── 03_subset.Rmd ├── 03_subset.pdf ├── header.tex └── images │ ├── R_box.png │ └── data_struction1.png ├── 04_Rmarkdown ├── 04_Rmarkdown.Rmd ├── 04_Rmarkdown.log ├── 04_Rmarkdown.pdf ├── header.tex └── images │ ├── R_logo.png │ ├── rmarkdown.png │ └── rstudio-markdown.png ├── 05_dplyr ├── 05_dplyr.Rmd ├── 05_dplyr.log ├── 05_dplyr.pdf ├── demo_data │ ├── olympics.xlsx │ └── wages.csv ├── header.tex └── images │ ├── import_datatype01.png │ ├── pipe1.png │ ├── pipe2.png │ └── tidyverse.png ├── 06_ggplot2 ├── 06_ggplot2.Rmd ├── 06_ggplot2.log ├── 06_ggplot2.pdf ├── demo_data │ └── temp_carbon.csv ├── header.tex └── images │ ├── Paradox1.pdf │ ├── Paradox2.pdf │ ├── Paradox3.pdf │ ├── a-14.png │ ├── a-20.png │ ├── a-21.png │ ├── a-3.png │ ├── cholera_a.pdf │ ├── cholera_b.pdf │ ├── cholera_c.pdf │ ├── ggplot_template.png │ ├── mapping.png │ └── tidyverse.png ├── 09_stringr ├── 09_stringr.Rmd ├── 09_stringr.pdf ├── header.tex └── images │ ├── hex-stringr.png │ └── regex_repeat.jpg ├── 15_eda02 ├── 15_reproducible.Rmd ├── 15_reproducible.pdf ├── demo_data │ └── penguins.csv ├── header.tex └── images │ ├── 01.png │ ├── 4_3.png │ ├── culmen_depth.png │ ├── lter_penguins.png │ └── penguins.png ├── R4DS_slides.Rproj ├── README.md └── data_science.jpg /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | .Ruserdata 5 | -------------------------------------------------------------------------------- /00_whyR/00_whyR.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "R能给我们生活带来什么？" 3 | author: "王敏杰" 4 | institute: "四川师范大学" 5 | date: "\\today" 6 | fontsize: 12pt 7 | output: binb::metropolis 8 | section-titles: true 9 | #toc: true 10 | header-includes: 11 | - \usepackage[fontset = fandol]{ctex} 12 | - \input{header.tex} 13 | link-citations: yes 14 | colorlinks: yes 15 | linkcolor: red 16 | classoption: "dvipsnames,UTF8" 17 | --- 18 | 19 | ```{r setup, include=FALSE} 20 | options(digits = 3) 21 | knitr::opts_chunk$set( 22 | comment = "#>", 23 | echo = TRUE, 24 | collapse = TRUE, 25 | message = FALSE, 26 | warning = FALSE, 27 | out.width = "50%", 28 | fig.align = "center", 29 | fig.asp = 0.618, # 1 / phi 30 | fig.show = "hold" 31 | ) 32 | ``` 33 | 34 | ## R能给我们生活带来什么？ 35 | 36 | 这个问题，好比人生三大终极问题： 37 | 38 | - R是什么? 39 | - R能干什么? 40 | - 为什么是R？ 41 | 42 | # R是什么 43 | 44 | ## R那些事 45 | 46 | - 1992年，新西兰奥克兰大学统计学教授 Ross Ihaka 和 Robert Gentleman，为了方便地给学生教授统计学课程，他们设计开发了R语言（他们名字的首字母都是R）。 47 | 48 | ```{r echo=FALSE, out.width = '0.8\\textwidth'} 49 | knitr::include_graphics(path = "images/R_inventor.png") 50 | ``` 51 | 52 | - 2000年，R1.0.0 发布 53 | - 2004年，第一届国际useR!会议（随后每年举办一次） 54 | - 2005年，ggplot2宏包（2018.8 - 2019.8下载量超过 1.3 亿次） 55 | - 2012年，R2.15.2 发布 56 | - 2013年，R3.0.2 发布, CRAN上的宏包数量5026个 57 | - 2016年，Rstudio公司推出 tidyverse 宏包（数据科学当前最流行的R宏包） 58 | - 2017年，R3.4.1 发布，CRAN上的宏包数量10875个 59 | - 2019年，R3.6.1 发布，CRAN上的宏包数量15102个 60 | - 2020年，R4.0.0 发布，CRAN上的宏包数量16054个 61 | 62 | [The History of R](https://blog.revolutionanalytics.com/2020/07/the-history-of-r-updated-for-2020.html) 63 | 64 | ## R是什么 65 | 66 | 官网定义： 67 | 68 | ```{r eval=FALSE, include=FALSE} 69 | knitr::include_graphics("images/what_is_R.png") 70 | ``` 71 | 72 | R语言是用于统计分析，图形表示和报告的编程语言: 73 | 74 | - R 是一个\textcolor{red}{统计编程}语言（statistical programming） 75 | - R 可运行于多种平台之上，包括Windows、UNIX 和 Mac OS X 76 | - R 拥有顶尖水准的\textcolor{red}{制图}功能 77 | - R 是免费的 78 | - R 应用广泛，拥有丰富的\textcolor{red}{库包} 79 | - 活跃的\textcolor{red}{社区} 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | ## R语言发展趋势 89 | 90 | ```{r echo=FALSE, out.width = '100%'} 91 | knitr::include_graphics("images/tiobe-index.png") 92 | ``` 93 | 94 | [TIOBE index](https://www.tiobe.com/tiobe-index/) 95 | 96 | ## 界面很友好 97 | 98 | ```{r out.width = '85%', echo = FALSE} 99 | knitr::include_graphics("images/rstudio-editor.png") 100 | ``` 101 | 102 | ## R路上的大神 103 | 104 | 2019 年 8 月，国际统计学年会将考普斯总统奖（\textcolor{red}{被誉为统计学的诺贝尔奖}）奖颁给 tidyverse 的作者 105 | 106 | ```{r echo=FALSE, out.width = '50%'} 107 | knitr::include_graphics("images/hadley-wickham.jpg") 108 | ``` 109 | 110 | - [Hadley Wickham](http://hadley.nz/) 111 | - R路上的大神 112 | - 一个改变了R语言的人 113 | 114 | # R能干什么 115 | 116 | ## 数据科学的流程 117 | 118 | Hadley Wickham 定义了数据科学的工作流程 119 | 120 | ```{r echo=FALSE, out.width = '\\textwidth'} 121 | knitr::include_graphics(path = "images/data-science-explore.png") 122 | ``` 123 | 124 | 125 | ## tidyverse套餐 126 | 127 | ```{r out.width = '80%', echo = FALSE} 128 | knitr::include_graphics("images/tidyverse.png") 129 | ``` 130 | \centering{https://www.tidyverse.org/} 131 | 132 | 133 | 134 | ## R & tidyverse 135 | 136 | | 序号 | 内容 | 代码演示 | 137 | |------ |-------------- |------------------ | 138 | | 1 | 统计 | 1_stats.R | 139 | | 2 | 可视化 | 2_visual.R | 140 | | 3 | 探索性分析 | 3_eda.R | 141 | | 4 | 可重复性报告 | 4_reproducible.R | 142 | 143 | 144 | ## 难吗？ 145 | \Huge 146 | \centering{ 感觉很难吗？ \\ 如果是，那说明你认真听了} 147 | 148 | 149 | 150 | ## 看了这些代码，可能第一眼感觉是这样的 151 | ```{r echo=FALSE, out.width = '100%', fig.cap='图片来自电影《降临》'} 152 | knitr::include_graphics("images/arrival-movie.png") 153 | ``` 154 | 155 | 156 | 157 | 158 | ## 但我更希望这门课结束后 159 | ```{r echo=FALSE, out.width = '100%', fig.cap='图片来自美剧《权利的游戏》'} 160 | knitr::include_graphics("images/night_king.jpg") 161 | ``` 162 | 163 | 164 | 165 | # 为什么是R 166 | 167 | 168 | ## 社会科学需要统计 169 | 170 | ```{r echo=FALSE, out.width = '60%'} 171 | knitr::include_graphics("images/social_science.jpg") 172 | ``` 173 | 174 | \centering{我们不是学统计的，但需要统计} 175 | 176 | 177 | 178 | 179 | 180 | ## 社会科学需要可视化 181 | 182 | ```{r echo=FALSE, out.width = '50%'} 183 | knitr::include_graphics("images/R4art.png") 184 | ``` 185 | 186 | 187 | \centering{我们不是学美术的，但要可视化} 188 | 189 | 190 | 191 | ## 社会科学需要编程 192 | 193 | ```{r echo=FALSE, out.width = '80%'} 194 | knitr::include_graphics("images/Coding-Lab.jpg") 195 | ``` 196 | 197 | \centering{我们不是学计算机的，但需要编程} 198 | 199 | 200 | 201 | 202 | ## 你的论文需要排版 203 | 204 | ```{r echo=FALSE, out.width = '60%'} 205 | knitr::include_graphics("images/typesetting.png") 206 | ``` 207 | 208 | \centering{我们不是学设计的，但要操心\textcolor{red}{交叉引用}的事} 209 | 210 | 211 | 212 | 213 | 214 | ## 挖掘机技术到底哪家强？ 215 | 216 | 217 | 218 | 219 | 220 | \centering 221 | 你有需求，而 222 | \raisebox{-.5\height}{\includegraphics[height=3\baselineskip]{images/R_logo.png}} 223 | 很专业 224 | 225 | 226 | 227 | | 序号 | 内容 | 特性 | 评价 | 228 | |------ |--------------- |---------- |------ | 229 | | 1 | 统计分析 | 看家本领 | 好用 | 230 | | 2 | ggplot2画图 | 颜值担当 | 好看 | 231 | | 3 | tidyverse语法 | 简单易懂 | 好学 | 232 | | 4 | 可重复性报告 | 方便快捷 | 好玩 | 233 | 234 | 235 | 236 | 237 | 238 | ## 一见钟情，还是相见恨晚？ 239 | 240 | ```{r echo=FALSE, out.width = '100%'} 241 | knitr::include_graphics("images/R_vs_SPSS.jpg") 242 | ``` 243 | 244 | 245 | 246 | 247 | 248 | # 关于学习 249 | 250 | ## 我们的课程不会枯燥 251 | 252 | ```{r echo=FALSE, out.width = '45%'} 253 | knitr::include_graphics("images/data_science.png") 254 | ``` 255 | 256 | - 数据科学是为社会科学服务的，我们会有很多案例 257 | - 编程是工具，统计是灵魂，专业是核心 258 | 259 | 260 | 261 | ## 关于学习 262 | 263 | 我很少使用 264 | 265 | $$ 266 | f(x)=\frac{1}{\sqrt{2 \pi}} e^{-\frac{1}{2} x^{2}} 267 | $$ 268 | 269 | 更多的是 270 | 271 | ```{r, eval = FALSE} 272 | library(tidyverse) 273 | summary_monthly_temp <- weather %>% 274 | group_by(month) %>% 275 | summarize(mean = mean(temp), 276 | std_dev = sd(temp)) 277 | ``` 278 | 279 | ## 关于学习 280 | 281 | ### 课程目标 282 | 283 | - 训练数据思维，提升编程技能，培养创新能力 284 | 285 | ### 学习方法 286 | 287 | - **问题驱动型学习** 288 | - 多实践（光看李小龙的电影，是学不会功夫的） 289 | - 不是 learning R，而是 learning with R 290 | - 把 R 看做是知识学习的**脚手架** 291 | 292 | ```{r echo=FALSE, out.width = '35%'} 293 | knitr::include_graphics("images/Knowledge-Scaffolding.jpg") 294 | ``` 295 | 296 | 297 | 298 | 299 | 300 | 301 | 302 | 303 | 304 | 305 | 306 | 307 | 308 | 309 | 310 | 311 | 312 | 313 | ## 参考书目 314 | 315 | ```{r echo=FALSE, out.width = '35%'} 316 | knitr::include_graphics("images/r4ds-cover.png") 317 | ``` 318 | 319 | - [R for Data Science](https://r4ds.had.co.nz/) 320 | - [https://bookdown.org/wangminjie/R4DS/](https://bookdown.org/wangminjie/R4DS/) 321 | -------------------------------------------------------------------------------- /00_whyR/00_whyR.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/00_whyR/00_whyR.pdf -------------------------------------------------------------------------------- /00_whyR/exercises/1_stats.R: -------------------------------------------------------------------------------- 1 | # Numeric Functions 2 | 1 + 5 3 | 1:100 4 | abs(-3.14) 5 | sqrt(3.14) 6 | floor(3.14) 7 | round(3.14) 8 | cos(3.14) 9 | log(3.14) 10 | exp(3.14) 11 | 12 | seq(1, 10, 2) 13 | rep(1:3, 2) 14 | 15 | 16 | 17 | 18 | 19 | # Character Functions 20 | substr("abcdef", 2, 4) 21 | grep("a", c("alice", "bob", "claro")) 22 | strsplit("a.b.c", "\\.") 23 | toupper("Alice") 24 | tolower("Alice") 25 | 26 | 27 | 28 | 29 | # Statistical Functions 30 | x <- 1:10 31 | sum(x) 32 | min(x) 33 | mean(x) 34 | sd(x) 35 | var(x) 36 | median(x) 37 | quantile(x, probs = 0.75) 38 | range(x) 39 | scale(x, center = TRUE, scale = TRUE) 40 | 41 | 42 | 43 | # Statistical Probability Functions 44 | rnorm(20, mean = 0, sd = 1) 45 | dnorm(0.5, mean = 0, sd = 1) 46 | rpois(100, lambda = 10) 47 | dpois(2, lambda = 10) 48 | 49 | 50 | 51 | 52 | 53 | # Regression Modeling 54 | lm(mpg ~ wt, data = mtcars) 55 | aov(mpg ~ wt, data = mtcars) 56 | t.test(extra ~ group, data = sleep) 57 | 58 | 59 | -------------------------------------------------------------------------------- /00_whyR/exercises/2_visual.R: -------------------------------------------------------------------------------- 1 | library(ggplot2) 2 | 3 | ggplot(midwest, aes(x = area, y = poptotal)) + 4 | geom_point(aes(color = state, size = popdensity)) + 5 | geom_smooth(method = "loess", se = F) + 6 | xlim(c(0, 0.1)) + 7 | ylim(c(0, 500000)) + 8 | labs( 9 | subtitle = "Area Vs Population", 10 | y = "Population", 11 | x = "Area", 12 | title = "Scatterplot", 13 | caption = "Source: midwest" 14 | ) 15 | -------------------------------------------------------------------------------- /00_whyR/exercises/3_eda.R: -------------------------------------------------------------------------------- 1 | library(tidyverse) 2 | 3 | # 案例一：飓风数据集 4 | 5 | storms %>% count(year) 6 | 7 | storms %>% 8 | group_by(year) %>% 9 | summarize( 10 | wind_mean = mean(wind), 11 | wind_sd = sd(wind) 12 | ) 13 | 14 | 15 | 16 | 17 | 18 | # 案例二：VC剂量和喂食方法对豚鼠牙齿的影响？ 19 | # 双因素方差分析 (ANOVA) 20 | 21 | my_data <- ToothGrowth %>% 22 | mutate( 23 | across(c(supp, dose), as_factor) 24 | ) 25 | 26 | 27 | my_data %>% 28 | ggplot(aes(x = supp, y = len, fill = supp)) + 29 | geom_boxplot(position = position_dodge()) + 30 | facet_wrap(vars(dose)) 31 | 32 | 33 | 34 | aov(len ~ supp + dose, data = my_data) 35 | 36 | 37 | aov(len ~ supp + dose, data = my_data) %>% 38 | TukeyHSD(which = "dose") %>% 39 | broom::tidy() 40 | 41 | 42 | 43 | 44 | 45 | 46 | -------------------------------------------------------------------------------- /00_whyR/exercises/4_reproducible.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "这是一份关于新冠肺炎的探索性分析报告" 3 | author: "王小二" 4 | date: "`r Sys.Date()`" 5 | output: 6 | pdf_document: 7 | latex_engine: xelatex 8 | extra_dependencies: 9 | ctex: UTF8 10 | number_sections: yes 11 | #toc: yes 12 | df_print: kable 13 | classoptions: "hyperref, 12pt, a4paper" 14 | --- 15 | 16 | 17 | ```{r setup, include=FALSE} 18 | knitr::opts_chunk$set(echo = TRUE, 19 | message = FALSE, 20 | warning = FALSE, 21 | fig.align = "center" 22 | ) 23 | ``` 24 | 25 | 26 | 27 | # 引言 28 | 29 | 新型冠状病毒疫情在多国蔓延，一些国家的病例确诊数量明显增多，各国防疫力度继续加强。本章通过分析疫情数据，了解疫情发展，祝愿人类早日会战胜病毒！ 30 | 31 | 32 | # 导入数据 33 | 34 | 首先，我们加载需要的宏包，其中tidyverse用于数据探索、covdata用于获取数据 35 | 36 | ```{r} 37 | # Load libraries 38 | library(tidyverse) 39 | #library(covdata) 40 | load("covnat.rda") 41 | ``` 42 | 43 | 44 | 论文的数据来源 45 | [https://kjhealy.github.io/covdata/](https://kjhealy.github.io/covdata/)，我们选取部分数据看看 46 | 47 | 48 | ```{r, echo = FALSE} 49 | covnat %>% 50 | tail(8) 51 | ``` 52 | 53 | 54 | 55 | # 数据变量 56 | 57 | 这个数据集包含8个变量，具体含义如下： 58 | 59 | | 变量 | 含义 | 60 | |----------- |-------------------- | 61 | | date | 日期 | 62 | | cname | 国家名 | 63 | | iso3 | 国家编码 | 64 | | cases | 确诊病例 | 65 | | deaths | 死亡病例 | 66 | | pop | 2019年国家人口数量 | 67 | | cu_cases | 累积确诊病例 | 68 | | cu_deaths | 累积死亡病例 | 69 | 70 | # 数据探索 71 | 72 | 找出累积确诊病例最多的几个国家 73 | 74 | ```{r} 75 | covnat %>% 76 | ungroup() %>% 77 | filter(date == max(date)) %>% 78 | slice_max(cu_cases, n = 8) 79 | ``` 80 | 81 | 82 | # 可视化 83 | 84 | 为了更好的呈现数据，我们将筛选出美国确诊病例数据，并可视化 85 | 86 | ```{r, fig.showtext = TRUE} 87 | covnat %>% 88 | filter(iso3 == "USA") %>% 89 | filter(cu_cases > 0) %>% 90 | ungroup() %>% 91 | 92 | ggplot(aes(x = date, y = cases)) + 93 | geom_path() + 94 | scale_x_date(name = NULL, breaks = "month") + 95 | labs(title = "美国新冠肺炎累积确诊病例", 96 | subtitle = "数据来源https://kjhealy.github.io/covdata/") 97 | ``` 98 | 99 | 100 | -------------------------------------------------------------------------------- /00_whyR/exercises/covnat.rda: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/00_whyR/exercises/covnat.rda -------------------------------------------------------------------------------- /00_whyR/header.tex: -------------------------------------------------------------------------------- 1 | \usepackage{ctex} 2 | 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-------------------------------------------------------------------------------- 1 | --- 2 | title: "第一章：配置R语言环境" 3 | author: "王敏杰" 4 | institute: "四川师范大学" 5 | date: "\\today" 6 | fontsize: 12pt 7 | output: binb::metropolis 8 | section-titles: true 9 | #toc: true 10 | header-includes: 11 | - \usepackage[fontset = fandol]{ctex} 12 | - \input{header.tex} 13 | link-citations: yes 14 | colorlinks: yes 15 | linkcolor: red 16 | classoption: "dvipsnames,UTF8" 17 | --- 18 | 19 | ```{r setup, include=FALSE} 20 | options(digits = 3) 21 | knitr::opts_chunk$set( 22 | comment = "#>", 23 | echo = TRUE, 24 | collapse = TRUE, 25 | message = FALSE, 26 | warning = FALSE, 27 | out.width = "50%", 28 | fig.align = "center", 29 | fig.asp = 0.618, # 1 / phi 30 | fig.show = "hold" 31 | ) 32 | ``` 33 | 34 | # 配置R语言环境 35 | 36 | ## 准备工作 37 | 38 | - \textcolor{red}{第一步}：连网（无线）办法 39 | - 用户名：学号+ @sicnu，比如 `20150956@sicnu` 40 | - 密码：出生年月日 + 身份证最后一位(如果最后一位为X，要大写)，比如 `19880923X` 41 | 42 | - \textcolor{red}{第二步}：加QQ群 43 | 44 | ```{r echo=FALSE, out.width = '25%'} 45 | #knitr::include_graphics(path = "images/QQgroup_PsyStats.png") 46 | knitr::include_graphics(path = "images/QQgroup_shizishan.png") 47 | knitr::include_graphics(path = "images/QQgroup_chenglong.png") 48 | ``` 49 | 50 | - \textcolor{red}{第三步}：在QQ群文件里下载(R-4.0.2-win.exe, RStudio-1.3.1091.exe)，点击安装 51 | 52 | 53 | ## 环境配置 54 | 55 | 主要分三步： 56 | 57 | - 安装R 58 | - 安装Rstudio 59 | - 安装必要的宏包(packages) 60 | 61 | ## 第一步安装R 62 | 63 | - 下载并安装R，官方网站 64 | 65 | ```{r echo=FALSE, out.width = '85%'} 66 | knitr::include_graphics("images/Rinstall.png") 67 | ``` 68 | 69 | ## 第二步安装RStudio 70 | 71 | - 下载并安装RStudio，官方网站 72 | - 选择`RStudio Desktop` 73 | 74 | ```{r out.width = '85%', echo = FALSE} 75 | knitr::include_graphics("images/Rstudio_install.png") 76 | ``` 77 | 78 | ## 注意事项 79 | 80 | 这里有个小小的提示： 81 | 82 | - 电脑用户名\textcolor{red}{不要有中文和空格} 83 | 84 | - 尽量安装在\textcolor{red}{非系统盘}，比如，可以选择安装在D盘 85 | 86 | - 安装路径\textcolor{red}{不要有中文和空格}。比如，这样就比较好 87 | 88 | - `D:/R` 89 | - `D:/Rstudio 90 | 91 | 92 | 93 | ## R 与 RStudio 是什么关系呢 94 | 95 | \qquad \qquad \qquad R \hspace{4cm} RStudio 96 | 97 | ```{r, fig.show="hold", out.width="49%", echo = FALSE} 98 | knitr::include_graphics(c("images/engine.jpg", "images/dashboard.jpg")) 99 | ``` 100 | 101 | \centering{R 是有趣的灵魂， Rstudio 是好看的皮囊} 102 | 103 | 104 | ## RStudio很友好 105 | 106 | 从windows开始菜单，点开rstudio，界面效果 107 | 108 | ```{r out.width = '75%', echo = FALSE} 109 | knitr::include_graphics("images/rstudio-editor1.png") 110 | ``` 111 | 112 | 113 | 114 | ## 第三步安装宏包 115 | 116 | ```{r out.width = '60%', echo = FALSE} 117 | knitr::include_graphics("images/RStudio-Screenshot.png") 118 | ``` 119 | 120 | - 命令行安装 121 | 122 | - `install.packages("tidyverse")` 123 | - 回车，安静等待 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | ## 如果宏包安装速度太慢， 方法一 143 | 144 | - 指定清华大学镜像 145 | 146 | ```{r, eval = FALSE} 147 | install.packages( 148 | "tidyverse", 149 | repos = "http://mirrors.tuna.tsinghua.edu.cn/CRAN" 150 | ) 151 | ``` 152 | 153 | 154 | - 或者，指定兰州大学镜像 155 | 156 | ```{r, eval = FALSE} 157 | install.packages( 158 | "tidyverse", 159 | repos = "https://mirror.lzu.edu.cn/CRAN/" 160 | ) 161 | ``` 162 | 163 | 164 | 165 | ## 如果宏包安装速度太慢， 方法二 166 | 167 | - `Rstudio`里设置镜像，步骤如下： 168 | 169 | 170 | 171 | 172 | ```{r image_grobs, fig.show='hold', out.width = "49%", fig.align = "default", echo=FALSE} 173 | library(cowplot) 174 | library(ggplot2) 175 | 176 | ggdraw() + draw_image("images/mirror1.png") 177 | ggdraw() + draw_image("images/mirror2.png") 178 | ``` 179 | 180 | 181 | - 然后 182 | 183 | ```{r, eval = FALSE } 184 | install.packages("tidyverse") 185 | ``` 186 | 187 | 188 | ## 测试 189 | 190 | 复制以下代码到**脚本编辑区** \footnotesize 191 | 192 | ```{r, eval=FALSE} 193 | library(ggplot2) 194 | 195 | ggplot(midwest, aes(x = area, y = poptotal)) + 196 | geom_point(aes(color = state, size = popdensity)) + 197 | geom_smooth(method = "loess", se = F) + 198 | xlim(c(0, 0.1)) + 199 | ylim(c(0, 500000)) + 200 | labs( 201 | title = "Scatterplot", 202 | subtitle = "Area Vs Population", 203 | x = "Area", 204 | y = "Population" 205 | ) 206 | ``` 207 | 208 | 209 | 210 | ## 运行 211 | 212 | ```{r out.width = '65%', echo = FALSE} 213 | knitr::include_graphics("images/run_script.png") 214 | ``` 215 | 216 | - 方法1：点击`Run`, 运行光标所在行的代码 217 | - 方法2：点击`Source`，从头到尾运行全部代码 218 | 219 | 220 | 221 | 222 | 223 | ## 如果出现这个图，说明配置成功 224 | 225 | ```{r out.width = '100%', echo = FALSE} 226 | library(ggplot2) 227 | 228 | ggplot(midwest, aes(x = area, y = poptotal)) + 229 | geom_point(aes(color = state, size = popdensity)) + 230 | geom_smooth(method = "loess", se = F) + 231 | xlim(c(0, 0.1)) + 232 | ylim(c(0, 500000)) + 233 | labs( 234 | title = "Scatterplot", 235 | subtitle = "Area Vs Population", 236 | x = "Area", 237 | y = "Population" 238 | ) 239 | ``` 240 | 241 | # 可能的问题 242 | 243 | ## 可能的问题 244 | 245 | - 我的电脑是苹果系统，怎么安装呢？ 246 | - 我的Rstudio需要哪些设置？ 247 | - 我的系统不能兼容64位的Rstudio？ 248 | - 为什么Rstudio打开是空白呢？ 249 | - 安装宏包太慢，怎么解决？ 250 | - 安装宏包，遇到报错信息"unable to access index for repository..."？ 251 | 252 | 253 | ## Happy R 254 | 255 | 课时有限，想掌握这门技术，需要课后多下功夫 256 | 257 | - 请务必配置好环境，包括安装宏包（群里有安装视频，实在不行，@我远程协助） 258 | - 学习资料 https://bookdown.org/wangminjie/R4DS/ 259 | - 参考书目《R数据科学》（群文件book文件夹中） 260 | - 我们不是孙悟空，一出生就身怀绝技。No shame in asking help 261 | - 学习曲线会比较陡，但有老司机带路，要有信心。 262 | 263 | 祝大家happy R ！ 264 | -------------------------------------------------------------------------------- /01_install/01_install.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/01_install/01_install.pdf -------------------------------------------------------------------------------- /01_install/header.tex: -------------------------------------------------------------------------------- 1 | \usepackage{ctex} 2 | \usepackage{booktabs} 3 | \usepackage{longtable} 4 | 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-------------------------------------------------------------------------------- 1 | --- 2 | title: "第二章：R语言基础" 3 | author: "王敏杰" 4 | institute: "四川师范大学" 5 | date: "\\today" 6 | fontsize: 12pt 7 | output: binb::metropolis 8 | section-titles: true 9 | #toc: true 10 | header-includes: 11 | - \usepackage[fontset = fandol]{ctex} 12 | - \input{header.tex} 13 | link-citations: yes 14 | colorlinks: yes 15 | linkcolor: red 16 | classoption: "dvipsnames,UTF8" 17 | --- 18 | 19 | ```{r setup, include=FALSE} 20 | options(digits = 3) 21 | knitr::opts_chunk$set( 22 | comment = "#>", 23 | echo = TRUE, 24 | collapse = TRUE, 25 | message = FALSE, 26 | warning = FALSE, 27 | out.width = "50%", 28 | fig.align = "center", 29 | fig.asp = 0.618, # 1 / phi 30 | fig.show = "hold" 31 | ) 32 | ``` 33 | 34 | 35 | 36 | # 开始 37 | 38 | ## 开始 39 | 40 | 安装完毕后，从windows`开始菜单`，点开`rstudio`图标，就打开了rstudio的窗口，界面效果如下 41 | 42 | ```{r out.width = '75%', echo = FALSE} 43 | knitr::include_graphics("images/rstudio-editor.png") 44 | ``` 45 | 46 | ## RStudio 非常友好 47 | 48 | 想要运行一段R代码，只需要在 RStudio 控制台面板最下面(Console)一行内键入R 代码，然后回车即可。比如 49 | ```{r } 50 | 1 + 1 51 | ``` 52 | 53 | 54 | ```{r echo=TRUE, message=TRUE, warning=TRUE} 55 | log(8) 56 | ``` 57 | 58 | 59 | 60 | 61 | ```{r echo=TRUE, message=TRUE, warning=TRUE} 62 | 1:12 63 | ``` 64 | 65 | 66 | ## 对象 67 | 68 | ### 一切都是对象 69 | 在R中存储的数据称为**对象**， R语言数据处理实际上就是不断的创建和操控这些对象。 70 | 71 | ### 创建对象 72 | 创建一个 R 对象，首先确定一个名称，然后使用 73 | 赋值操作符 `<-`，将数据赋值给它。比如，如果想给变量 x 赋值为5，在命令行中可以这样写 `x <- 5` ，然后回车. 74 | 75 | ```{r assignment operator} 76 | x <- 5 77 | ``` 78 | 79 | ### 打印对象 80 | 81 | 当键入`x` 然后回车，就打印出 x 的值 82 | ```{r} 83 | x 84 | ``` 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | ## 对象 95 | 96 | ### 创建对象 97 | ```{r} 98 | l <- "hello world" 99 | ``` 100 | 101 | ### 访问对象 102 | 103 | ```{r} 104 | l 105 | ``` 106 | 107 | 108 | ## 对象 109 | 110 | ### 创建一个序列 111 | ```{r echo=TRUE, message=TRUE, warning=TRUE} 112 | d <- 1:10 113 | ``` 114 | 115 | ### 访问对象 116 | ```{r echo=TRUE, message=TRUE, warning=TRUE} 117 | d 118 | ``` 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 | 140 | 141 | 142 | 143 | ## 数据类型 144 | 145 | 146 | ```{r out.width = '100%', echo = FALSE} 147 | knitr::include_graphics("images/data_type.png") 148 | ``` 149 | ## 数据类型 150 | - 数值型 151 | ```{r} 152 | 3 153 | 5000 154 | 3e+06 155 | class(0.0001) 156 | ``` 157 | 158 | 159 | ## 数据类型 160 | - 字符串型 161 | ```{r} 162 | "hello" 163 | "girl" 164 | "1" # 注意1 和 "1" 的区别 165 | ``` 166 | 167 | ```{r} 168 | class("1") 169 | ``` 170 | 171 | 172 | ## 数据类型 173 | 174 | - 逻辑型 175 | ```{r} 176 | TRUE 177 | FALSE 178 | 3 < 4 179 | ``` 180 | 181 | 182 | ```{r} 183 | class(T) 184 | ``` 185 | 186 | 187 | ```{r} 188 | 3 < 4 189 | ``` 190 | 191 | ## 数据类型 192 | - 因子型 193 | ```{r} 194 | fac <- factor(c("a", "b", "c")) 195 | fac 196 | ``` 197 | 198 | 199 | ```{r} 200 | class(fac) 201 | ``` 202 | 203 | 204 | 205 | 206 | 207 | 208 | ## 数据结构 209 | 210 | ### 向量 211 | - 用`c`函数将一组数据**构造**成向量，要求每个元素用逗号分隔，且每个元素的数据类型是一致的 212 | 213 | ```{r echo=TRUE, message=TRUE, warning=TRUE} 214 | d <- c(2, 4, 3, 1, 5, 7) 215 | d 216 | ``` 217 | 218 | 219 | ```{r echo=TRUE, message=TRUE, warning=TRUE} 220 | t <- c("2", "4", "3", "1", "5", "7") 221 | t 222 | ``` 223 | 224 | 225 | 长度为 1 的向量 226 | ```{r echo=TRUE, message=TRUE, warning=TRUE} 227 | x <- c(1) # 228 | x <- 1 # 偷懒的写法 229 | ``` 230 | 231 | 232 | ## 数据结构 233 | ### 矩阵 234 | - 可以用 `matrix` 函数创建 235 | ```{r echo=TRUE, message=TRUE, warning=TRUE} 236 | m <- matrix(c(2, 4, 3, 1, 5, 7), 237 | nrow = 2, 238 | ncol = 3, 239 | byrow = TRUE 240 | ) 241 | m 242 | ``` 243 | 244 | 245 | 246 | ## 数据结构 247 | ### 数组 248 | - `array` 函数生成`n`维数组 249 | ```{r echo=TRUE, message=TRUE, warning=TRUE} 250 | ar <- array(c(11:14, 21:24, 31:34), dim = c(2, 2, 3)) 251 | ar 252 | ``` 253 | 254 | 255 | 256 | 257 | 258 | ## 数据结构 259 | ### 列表 260 | - 与`c`函数创建向量的方式相似，元素之间用逗号分开。不同的是，列表允许每个元素不同的数据类型（数值型，字符型，逻辑型等），而向量要求每个元素的数据类型必须相同。 261 | 262 | ```{r echo=TRUE, message=TRUE, warning=TRUE} 263 | list1 <- list(100:110, "R", c(2, 4, 3, 1, 5, 7)) 264 | list1 265 | ``` 266 | 267 | 268 | 269 | ## 数据结构 270 | ### 数据框 271 | - `data.frame`函数构建 272 | 273 | ```{r echo=TRUE, message=TRUE, warning=TRUE} 274 | df <- data.frame( 275 | name = c("ace", "bob", "carl", "kaite"), 276 | age = c(21, 14, 13, 15), 277 | sex = c("girl", "boy", "boy", "girl") 278 | ) 279 | df 280 | ``` 281 | 282 | 283 | 284 | 285 | ## 数据结构 286 | ### 数据框 287 | R 对象的数据结构(向量、矩阵、数组、列表和数据框)，总结如下 288 | 289 | ```{r out.width = '100%', echo = FALSE} 290 | knitr::include_graphics("images/data_struction1.png") 291 | ``` 292 | 293 | 294 | 295 | 296 | 297 | 298 | ## 函数 299 | 300 | R 语言的强大在于使用**函数**操控各种对象，你可以把对象看作是名词，而函数看作是动词。 301 | 我们用一个简单的例子，`sum()`来演示函数如何工作的。`sum()`后的结果可以直接显示出来， 302 | ```{r} 303 | sum(5, 10) 304 | ``` 305 | 306 | 也可以赋名。比如下面代码，首先计算`5 + 10`然后赋给新创建的对象`y`， 并在第二行中打印出来对象`y`的值 307 | 308 | ```{r} 309 | y <- sum(5, 10) 310 | y 311 | ``` 312 | 313 | 314 | ## 更多函数 315 | 316 | 除了`sum()`求和函数，R语言有很多很多函数 317 | 318 | 319 | ```{r echo=TRUE, message=TRUE, warning=TRUE} 320 | mean(1:6) 321 | ``` 322 | 323 | 324 | ```{r echo=TRUE, message=TRUE, warning=TRUE} 325 | abs(1:6) 326 | ``` 327 | 328 | 329 | 330 | ```{r echo=TRUE, message=TRUE, warning=TRUE} 331 | round(3.14159) 332 | ``` 333 | 334 | 335 | 336 | ```{r echo=TRUE, message=TRUE, warning=TRUE} 337 | x <- seq(1, 100) 338 | sum(x) 339 | ``` 340 | 341 | 342 | 343 | 344 | ## 脚本 345 | ### 什么是脚本 346 | 如果我们已经写好了一段R程序，我们可以保存为**脚本**文件，脚本文件通常以.R作为文件的后缀名。比如我们可以将刚才创建`x`和 `y`对象的命令，保存为脚本文件`my_script.R`。 347 | 这样我们可以在其它时间修改和重新运行它。 348 | 349 | ## 脚本 350 | ### 创建脚本 351 | 在RStudio中，你可以通过菜单栏依此点击`File > New File > R Script` 来创建一个新的脚本。 352 | 强烈建议大家在运行代码之前，使用脚本的形式编写和编辑自己的程序，养成这样的习惯后，你今后所有的工作都有案可查，并且具有可重复性。 353 | 354 | 355 | ## 创建脚本 356 | ```{r out.width = '100%', echo = FALSE} 357 | knitr::include_graphics("images/script1.png") 358 | ``` 359 | 360 | 361 | 362 | ## 运行脚本 363 | 364 | - 点击 `Run` 运行光标所在行 365 | - 点击 `Source` 运行整个脚本 366 | 367 | ```{r out.width = '75%', echo = FALSE} 368 | knitr::include_graphics("images/script2.png") 369 | ``` 370 | 371 | 372 | 373 | 374 | ## 宏包 375 | 376 | R 语言的强大还在于各种宏包，一般在[The Comprehensive R Archive Network (CRAN)](https://cran.r-project.org)下载安装。 377 | 378 | 379 | 可以用如下命令安装宏包: 380 | 381 | ```{r, eval = FALSE } 382 | # 安装单个包 383 | install.packages("tidyverse") 384 | ``` 385 | 386 | 387 | ```{r, eval = FALSE } 388 | # 安装多个包 389 | install.packages(c("ggplot2", "devtools", "dplyr")) 390 | ``` 391 | 392 | 393 | 394 | ## 如何获取帮助 395 | 396 | 397 | - 记住和学习所有的函数几乎是不可能的 398 | - 打开函数的帮助页面(`Rstudio`右下面板的`Help`选项卡) 399 | 400 | ```{r, eval = FALSE } 401 | ?sqrt 402 | ?gather 403 | ?spread 404 | ?ggplot2 405 | ?scale 406 | ?map_dfr 407 | ``` 408 | 409 | ## 如何获取帮助 410 | 411 | 快速获取帮助，是R的又一个优良特性 412 | 413 | ```{r out.width = '100%', echo = FALSE} 414 | knitr::include_graphics("images/Rhelp.png") 415 | ``` 416 | 417 | 418 | 419 | -------------------------------------------------------------------------------- /02_basicR/02_basicR.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/02_basicR/02_basicR.pdf -------------------------------------------------------------------------------- /02_basicR/header.tex: -------------------------------------------------------------------------------- 1 | \usepackage{ctex} 2 | \usepackage{booktabs} 3 | \usepackage{longtable} 4 | \usepackage{array} 5 | \usepackage{multirow} 6 | \usepackage{wrapfig} 7 | \usepackage{float} 8 | \usepackage{colortbl} 9 | \usepackage{pdflscape} 10 | \usepackage{tabu} 11 | \usepackage{threeparttable} 12 | \usepackage{threeparttablex} 13 | \usepackage{makecell} 14 | \usepackage{xcolor} 15 | \usepackage{xtab} 16 | 17 | \def\begincols{ 18 | \begin{columns}} \def\begincol{\begin{column}} \def\endcol{\end{column}} \def\endcols{\end{columns} 19 | } 20 | 21 | 22 | -------------------------------------------------------------------------------- /02_basicR/images/Rhelp.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/02_basicR/images/Rhelp.png -------------------------------------------------------------------------------- /02_basicR/images/data_struction1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/02_basicR/images/data_struction1.png -------------------------------------------------------------------------------- /02_basicR/images/data_type.png: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/02_basicR/images/script2.png -------------------------------------------------------------------------------- /03_subset/03_subset.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "第三章：子集选取" 3 | author: "王敏杰" 4 | institute: "四川师范大学" 5 | date: "\\today" 6 | fontsize: 12pt 7 | output: binb::metropolis 8 | section-titles: true 9 | #toc: true 10 | header-includes: 11 | - \usepackage[fontset = fandol]{ctex} 12 | - \input{header.tex} 13 | link-citations: yes 14 | colorlinks: yes 15 | linkcolor: red 16 | classoption: "dvipsnames,UTF8" 17 | --- 18 | 19 | ```{r setup, include=FALSE} 20 | options(digits = 3) 21 | knitr::opts_chunk$set( 22 | comment = "#>", 23 | echo = TRUE, 24 | collapse = TRUE, 25 | message = FALSE, 26 | warning = FALSE, 27 | out.width = "100%", 28 | fig.align = "center", 29 | fig.asp = 0.618, # 1 / phi 30 | fig.show = "hold" 31 | ) 32 | ``` 33 | 34 | 35 | 36 | ## 子集选取 37 | 38 | **对象**就是在计算机里新建了存储空间，好比一个盒子， 39 | 我们可以往盒子里装东西，也可以从盒子里取东西。 40 | 41 | ```{r echo=FALSE, out.width = '85%'} 42 | knitr::include_graphics("images/R_box.png") 43 | ``` 44 | 45 | 46 | ## 数据结构 47 | 48 | R 对象的数据结构(向量、矩阵、数组、列表和数据框) 49 | 50 | ```{r out.width = '100%', echo = FALSE} 51 | knitr::include_graphics("images/data_struction1.png") 52 | ``` 53 | 下面依次讲解，从每一种数据结构中选取子集... 54 | 55 | 56 | # 开始 57 | 58 | ## 向量 59 | 60 | 对于原子型向量，我们有至少四种选取子集的方法 61 | ```{r} 62 | x <- c(1.1, 2.2, 3.3, 4.4, 5.5) 63 | ``` 64 | 65 | 66 | - 正整数： 指定向量元素中的位置 67 | ```{r} 68 | x[1] 69 | ``` 70 | 71 | ```{r} 72 | x[c(3,1)] 73 | ``` 74 | ```{r} 75 | x[1:3] 76 | ``` 77 | 78 | ## 向量 79 | - 负整数：删除指定位置的元素 80 | ```{r} 81 | x[-2] 82 | ``` 83 | 84 | 85 | ```{r} 86 | x[c(-3, -4)] 87 | ``` 88 | 89 | 90 | ## 向量 91 | 92 | - 逻辑向量：将`TRUE`对应位置的元素提取出来 93 | ```{r} 94 | x[c(TRUE, FALSE, TRUE, FALSE, TRUE)] 95 | ``` 96 | 97 | 常用的一种情形；筛选出大于某个值的所有元素 98 | ```{r} 99 | x > 3 100 | ``` 101 | 102 | ```{r} 103 | x[x > 3] 104 | ``` 105 | 106 | ## 向量 107 | - 如果是命名向量 108 | ```{r} 109 | y <- c("a" = 11, "b" = 12, "c" = 13, "d" = 14) 110 | y 111 | ``` 112 | 113 | 我们可以用命名向量，返回对应位置的向量 114 | ```{r} 115 | y[c("d", "c", "a")] 116 | ``` 117 | 118 | 119 | 120 | 121 | ## 列表 122 | 123 | 对列表取子集，和向量的方法一样。使用`[`总是返回列表， 124 | ```{r} 125 | l <- list("one" = c("a", "b", "c"), 126 | "two" = c(1:5), 127 | "three" = c(TRUE, FALSE) 128 | ) 129 | l 130 | ``` 131 | 132 | ```{r} 133 | l[1] # 仍然是列表喔 134 | ``` 135 | 136 | 137 | ## 列表 138 | 139 | 如果想列表中的元素，需要使用`[[` 140 | ```{r} 141 | l[[1]] 142 | ``` 143 | 144 | 145 | 也可以使用其中的元素名，比如`[["one"]]`， 146 | ```{r} 147 | l[["one"]] 148 | ``` 149 | 150 | 151 | 程序员觉得以上太麻烦了，于是用`$`来简写 152 | ```{r} 153 | l$one 154 | ``` 155 | 156 | 157 | ## 列表 158 | 159 | 所以，请记住 160 | 161 | - `[` 和 `[[` 的区别 162 | - `x$y` 是 `x[["y"]]` 的简写 163 | 164 | 165 | 166 | 167 | ## 矩阵 168 | 169 | ```{r} 170 | a <- matrix(1:9, nrow = 3, byrow = TRUE) 171 | a 172 | ``` 173 | 我们取第1到第2行的2-3列，写成`[1:2, 2:3]`. 注意，中间以逗号分隔，它得到一个新的矩阵 174 | ```{r} 175 | a[1:2, 2:3] 176 | ``` 177 | 178 | 179 | ## 矩阵 180 | 默认情况下, `[` 会将获取的数据以尽可能低的维度形式呈现。比如 181 | ```{r} 182 | a[1, 1:2] 183 | ``` 184 | 表示第1行的第1、2列，此时不再是$1 \times 2$矩阵，而是包含了两个元素的向量。 185 | 186 | \vfill 187 | **以尽可能低的维度形式呈现**，简单理解就是，这个`r a[1, 1:2]`长的像个矩阵，又有点像向量，向量的维度比矩阵低，那就是向量吧。 188 | 189 | 190 | ## 矩阵 191 | 有些时候，我们想保留所有的行或者列，比如 192 | 193 | - 行方向，只选取第1行到第2行 194 | - 列方向，选取所有列 195 | 196 | 可以这样简写 197 | 198 | ```{r} 199 | a[1:2, ] 200 | ``` 201 | 202 | 想想，这种写法，会输出什么 203 | ```{r, eval = FALSE} 204 | a[ , ] 205 | ``` 206 | 207 | 208 | ## 矩阵 209 | 210 | ```{r} 211 | a[ , ] 212 | ``` 213 | 214 | 215 | ```{r} 216 | # 可以再简化点？ 217 | a[] 218 | ``` 219 | 220 | 221 | ```{r} 222 | # 是不是可以再简化点？ 223 | a 224 | ``` 225 | 226 | 227 | ## 数据框 228 | 229 | 数据框具有`list`和`matrix`的双重属性，因此 230 | 231 | - 当选取数据框的某几列的时候，可以像list一样，指定元素位置，比如`df[1:2]`选取前两列 232 | - 也可以像矩阵一样，使用行和列的标识选取，比如`df[1:3, ]`选取前三行的所有列 233 | 234 | \small 235 | ```{r} 236 | df <- data.frame(x = 1:4, 237 | y = 4:1, 238 | z = c("a", "b", "c", "d") ) 239 | df 240 | ``` 241 | 242 | ## 数据框 243 | 244 | \small 245 | ```{r} 246 | # Like a list 247 | df[c("x", "z")] 248 | ``` 249 | 250 | 251 | 252 | ```{r} 253 | # Like a matrix 254 | df[, c("x", "z")] 255 | ``` 256 | 257 | ## 数据框 258 | 259 | 也可以通过行和列的位置 260 | ```{r} 261 | df[1:2] 262 | ``` 263 | 264 | 265 | ```{r} 266 | df[1:3, ] 267 | ``` 268 | 269 | 270 | 271 | ## 数据框 272 | 遇到单行或单列的时候，也和矩阵一样，数据会降维 273 | ```{r} 274 | df[, "x"] 275 | ``` 276 | 277 | 如果想避免降维，需要多写一句话 278 | 279 | ```{r} 280 | df[, "x", drop = FALSE] 281 | ``` 282 | 283 | 284 | 285 | 286 | 287 | 288 | 289 | 290 | 291 | 292 | ## 延伸阅读 293 | 294 | - 如何获取`matrix(1:9, nrow = 3)`上对角元? 对角元？ 295 | - 对数据框，思考`df["x"]`， `df[["x"]]`， `df$x`三者的区别? 296 | - 如果`x`是一个矩阵，请问 `x[] <- 0` 和`x <- 0` 有什么区别？ 297 | 298 | ```{r eval=FALSE, include=FALSE} 299 | m <- matrix(1:9, nrow = 3) 300 | m 301 | ``` 302 | 303 | 304 | ```{r eval=FALSE, include=FALSE} 305 | diag(m) 306 | upper.tri(m, diag = FALSE) 307 | ``` 308 | 309 | 310 | ```{r eval=FALSE, include=FALSE} 311 | m[upper.tri(m, diag = FALSE)] 312 | ``` 313 | 314 | 315 | -------------------------------------------------------------------------------- /03_subset/03_subset.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/03_subset/03_subset.pdf -------------------------------------------------------------------------------- /03_subset/header.tex: -------------------------------------------------------------------------------- 1 | \usepackage{ctex} 2 | \usepackage{booktabs} 3 | \usepackage{longtable} 4 | \usepackage{array} 5 | \usepackage{multirow} 6 | \usepackage{wrapfig} 7 | \usepackage{float} 8 | \usepackage{colortbl} 9 | \usepackage{pdflscape} 10 | \usepackage{tabu} 11 | \usepackage{threeparttable} 12 | \usepackage{threeparttablex} 13 | \usepackage{makecell} 14 | \usepackage{xcolor} 15 | \usepackage{xtab} 16 | 17 | \def\begincols{ 18 | \begin{columns}} \def\begincol{\begin{column}} \def\endcol{\end{column}} \def\endcols{\end{columns} 19 | } 20 | 21 | 22 | -------------------------------------------------------------------------------- /03_subset/images/R_box.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/03_subset/images/R_box.png -------------------------------------------------------------------------------- /03_subset/images/data_struction1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/03_subset/images/data_struction1.png -------------------------------------------------------------------------------- /04_Rmarkdown/04_Rmarkdown.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "第三章：可重复性报告" 3 | author: "王敏杰" 4 | institute: "四川师范大学" 5 | date: "\\today" 6 | fontsize: 12pt 7 | output: binb::metropolis 8 | section-titles: true 9 | #toc: true 10 | header-includes: 11 | - \usepackage[fontset = fandol]{ctex} 12 | - \input{header.tex} 13 | link-citations: yes 14 | colorlinks: yes 15 | linkcolor: red 16 | classoption: "dvipsnames,UTF8" 17 | --- 18 | 19 | ```{r setup, include=FALSE} 20 | options(digits = 3) 21 | knitr::opts_chunk$set( 22 | comment = "#>", 23 | echo = TRUE, 24 | collapse = TRUE, 25 | message = FALSE, 26 | warning = FALSE, 27 | out.width = "100%", 28 | fig.align = "center", 29 | fig.asp = 0.618, # 1 / phi 30 | fig.show = "hold" 31 | ) 32 | ``` 33 | 34 | ## 为什么要做可重复性报告 35 | 36 | 交流-理解-重复 37 | 38 | - 需要**展示和分享**我们的数据分析结果给同行、老板或者老师 39 | - 为了让老板能快速地的理解我们的分析思路和方法，最好的方法，就是将分析背景、分析过程、分析结果以及图表等形成**报告** 40 | - 让读者能重复和验证我们的结果，确保结论的真实可信 41 | 42 | 因此，本章将介绍用Rmarkdown生成分析报告（可重复性报告） 43 | 44 | 45 | ## 什么是Rmarkdown 46 | ```{r out.width = '100%', echo = FALSE} 47 | knitr::include_graphics("images/rmarkdown.png") 48 | ``` 49 | 50 | 51 | 52 | # markdown 基本语法 53 | 54 | ## markdown 基本语法 55 | 56 | - 章节 57 | ```{markdown, eval = FALSE, echo = TRUE} 58 | # 第一章 （注意 "#" 与 "第一章"之间有空格） 59 | ## 第一节 （同上， "##" 与 "第一节"之间有空格） 60 | ### 第一小节 （同上，"###" 与 "第一小节"之间有空格） 61 | ``` 62 | 63 | - 正文 64 | ```{markdown, eval = FALSE, echo = TRUE} 65 | This is a sentence. ...这是正文... 66 | ``` 67 | 68 | 69 | ## markdown 基本语法 70 | - 序列 71 | ```{markdown, eval = FALSE, echo = TRUE} 72 | Now a list begins: 73 | 74 | - no importance 75 | - again 76 | - repeat 77 | 78 | A numbered list: 79 | 80 | 1. first 81 | 2. second 82 | ``` 83 | 84 | 85 | ## markdown 基本语法 86 | 87 | - 其他标记 88 | ```{markdown, eval = FALSE, echo = TRUE} 89 | __bold__ 90 | _italic_ 91 | ~~strike through~~ 92 | ``` 93 | 94 | 95 | 96 | # 创建 RMarkdown 97 | 98 | ## 创建 RMarkdown 99 | 100 | ```{r, eval = FALSE} 101 | install.packages("rmarkdown") 102 | ``` 103 | 104 | `Rstudio`中创建： `File -> New File -> R Markdown`. 105 | 106 | 107 | 基本构成（图中绿色括号地方） 108 | 109 | - metadata 110 | - text 111 | - code 112 | 113 | 114 | ## 创建 RMarkdown 115 | ```{r out.width = '85%', echo = FALSE} 116 | knitr::include_graphics("images/rstudio-markdown.png") 117 | ``` 118 | 119 | 点击knit（图中红色地方），选择想要输出的文档格式即可。 120 | 121 | 122 | ## 生成html文档 123 | 124 | 希望html文档有章节号、目录或者更好显示表格，可以修改头文件（用下面的内容替换Rmarkdown的头文件） 125 | 126 | ```yaml 127 | --- 128 | title: Habits 129 | author: John Doe 130 | date: "`r Sys.Date()`" 131 | output: 132 | html_document: 133 | df_print: paged 134 | toc: yes 135 | number_sections: yes 136 | --- 137 | ``` 138 | 139 | 140 | 141 | 142 | 143 | 144 | ## 生成pdf文档 145 | 146 | 优雅的pdf文档 147 | 148 | - pdf文档可以插入漂亮的矢量图和优雅的数学公式，所以备受同学们的喜欢。 149 | - 但往往我们写中文的时候，编译不成功，解决方案就是使用`tinytex`，可以看这个[视频](https://www.bilibili.com/video/BV1Gf4y1R7md)。 150 | 151 | 152 | ```{r, eval = FALSE} 153 | install.packages("tinytex") 154 | tinytex::install_tinytex(dir = "D:\\Tinytex", 155 | force = T) 156 | ``` 157 | 158 | 159 | 160 | 161 | 162 | # Rmarkdown 使用方法 163 | 164 | ## 插入公式 165 | 166 | 我相信你已经熟悉了latex语法，那么我们在Rmarkdwon里输入 167 | `$$\frac{\sum (\bar{x} - x_i)^2}{n-1}$$`，那么实际输出: 168 | 169 | $$\frac{\sum (\bar{x} - x_i)^2}{n-1}$$ 170 | 171 | 172 | ## 插入公式 173 | 也可以使用latex的等式环境， 比如 174 | 175 | ```latex 176 | $$ 177 | \Theta = \begin{pmatrix}\alpha & \beta\\ 178 | \gamma & \delta 179 | \end{pmatrix} 180 | $$ 181 | ``` 182 | 输出 183 | 184 | $$ 185 | \Theta = \begin{pmatrix}\alpha & \beta\\ 186 | \gamma & \delta 187 | \end{pmatrix} 188 | $$ 189 | 190 | 191 | 192 | ## 插入图片 193 | 194 | \scriptsize 195 | ````markdown 196 | `r ''````{r, out.width='35%', fig.align='center', fig.cap='this is caption'} 197 | knitr::include_graphics("images/R_logo.png") 198 | ``` 199 | ```` 200 | 201 | 202 | ```{r out.width = '35%', fig.align='center', fig.cap='this is caption', echo = F} 203 | knitr::include_graphics("images/R_logo.png") 204 | ``` 205 | 206 | 207 | 208 | ## 运行代码 209 | 210 | ```{r, echo = T} 211 | summary(cars) 212 | ``` 213 | 214 | 215 | ## 表格 216 | ````md 217 | ```{r tables-mtcars}`r ''` 218 | knitr::kable(iris[1:5, ], caption = "A caption") 219 | ``` 220 | ```` 221 | 222 | \vskip -1cm 223 | ```{r tables-mtcars, echo = F} 224 | knitr::kable(iris[1:5, ], caption = "A caption") 225 | ``` 226 | 227 | 需要更优美的表格，可参考[这里](https://haozhu233.github.io/kableExtra/) 228 | 229 | 230 | 231 | ## 生成图片 232 | ````md 233 | ```{r}`r ''` 234 | plot(pressure) 235 | ``` 236 | ```` 237 | 238 | 239 | ```{r out.width = '85%', echo=FALSE} 240 | plot(pressure) 241 | ``` 242 | 243 | 244 | 245 | ## 把这段代码复制到你的Rmarkdown文档试试 246 | 247 | \scriptsize 248 | ````md 249 | ```{r, out.width = '85%', fig.showtext = TRUE}`r ''` 250 | library(tidyverse) 251 | library(nycflights13) 252 | library(showtext) 253 | showtext_auto() 254 | flights %>% 255 | group_by(dest) %>% 256 | summarize( 257 | count = n(), 258 | dist = mean(distance, na.rm = TRUE), 259 | delay = mean(arr_delay, na.rm = TRUE) 260 | ) %>% 261 | dplyr::filter(delay > 0, count > 20, dest != "HNL") %>% 262 | ggplot(mapping = aes(x = dist, y = delay)) + 263 | geom_point(aes(size = count), alpha = 1 / 3) + 264 | geom_smooth(se = FALSE) + 265 | ggtitle("这是我的标题") 266 | ``` 267 | ```` 268 | 269 | 270 | 271 | 272 | 273 | 274 | 275 | ## 延伸阅读 276 | 277 | * Markdown tutorial https://www.markdowntutorial.com (10分钟学完) 278 | * LaTeX tutorial https://www.latex-tutorial.com/quick-start/ 279 | * Rmarkdown 介绍 https://bookdown.org/yihui/rmarkdown/ 280 | * Rmarkdown 手册 https://bookdown.org/yihui/rmarkdown-cookbook/ 281 | -------------------------------------------------------------------------------- /04_Rmarkdown/04_Rmarkdown.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/04_Rmarkdown/04_Rmarkdown.pdf -------------------------------------------------------------------------------- /04_Rmarkdown/header.tex: -------------------------------------------------------------------------------- 1 | \usepackage{ctex} 2 | \usepackage{booktabs} 3 | \usepackage{longtable} 4 | \usepackage{array} 5 | \usepackage{multirow} 6 | \usepackage{wrapfig} 7 | \usepackage{float} 8 | \usepackage{colortbl} 9 | \usepackage{pdflscape} 10 | \usepackage{tabu} 11 | \usepackage{threeparttable} 12 | \usepackage{threeparttablex} 13 | \usepackage{makecell} 14 | \usepackage{xcolor} 15 | \usepackage{xtab} 16 | 17 | \def\begincols{ 18 | \begin{columns}} \def\begincol{\begin{column}} \def\endcol{\end{column}} \def\endcols{\end{columns} 19 | } 20 | 21 | 22 | -------------------------------------------------------------------------------- /04_Rmarkdown/images/R_logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/04_Rmarkdown/images/R_logo.png -------------------------------------------------------------------------------- /04_Rmarkdown/images/rmarkdown.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/04_Rmarkdown/images/rmarkdown.png -------------------------------------------------------------------------------- /04_Rmarkdown/images/rstudio-markdown.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/04_Rmarkdown/images/rstudio-markdown.png -------------------------------------------------------------------------------- /05_dplyr/05_dplyr.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "第四章：数据处理" 3 | author: "王敏杰" 4 | institute: "四川师范大学" 5 | date: "\\today" 6 | fontsize: 12pt 7 | output: binb::metropolis 8 | section-titles: true 9 | #toc: true 10 | header-includes: 11 | - \usepackage[fontset = fandol]{ctex} 12 | - \input{header.tex} 13 | link-citations: yes 14 | colorlinks: yes 15 | linkcolor: red 16 | classoption: "dvipsnames,UTF8" 17 | --- 18 | 19 | ```{r setup, include=FALSE} 20 | options(digits = 3) 21 | knitr::opts_chunk$set( 22 | comment = "#>", 23 | echo = TRUE, 24 | collapse = TRUE, 25 | message = FALSE, 26 | warning = FALSE, 27 | out.width = "100%", 28 | fig.align = "center", 29 | fig.asp = 0.618, # 1 / phi 30 | fig.show = "hold" 31 | ) 32 | ``` 33 | 34 | ## 正式进入tidyverse家族的学习 35 | ```{r echo=FALSE, out.width = '85%'} 36 | knitr::include_graphics("images/tidyverse.png") 37 | ``` 38 | 39 | ## tidyverse 家族 40 | 41 | tidyverse家族主要成员包括 42 | 43 | 44 | | 功能 | 宏包 | 45 | |------|-------------| 46 | 有颜值担当 | ggplot2 | 47 | 数据处理王者 | dplyr | 48 | 数据转换专家 | tidyr | 49 | 数据载入利器 | readr | 50 | 循环加速器 | purrr | 51 | 强化数据框 | tibble | 52 | 53 | # 数据读取 54 | 55 | ## 读取数据 56 | 57 | R语言提供了很多读取数据的函数。 58 | 59 | 60 | 文件格式 | **R** 函数 61 | :--------------------------- | :---------------------- 62 | .txt | read.table() 63 | .csv | read.csv() and readr::read_csv() 64 | .xls and .xlsx | readxl::read_excel() and openxlsx::read.xlsx() 65 | .sav | foreign::read.spss() 66 | .Rdata or rda | load() 67 | .rds | readRDS() and readr::read_rds() 68 | .dta | haven::read_dta() and haven::read_stata() 69 | Internet | download.file() 70 | 71 | 72 | 73 | ## 范例 74 | 75 | ```{r} 76 | library(readr) 77 | wages <- read_csv("./demo_data/wages.csv") 78 | head(wages, 6) 79 | ``` 80 | 81 | ## 范例 82 | ```{r} 83 | library(readxl) 84 | d <- read_excel("./demo_data/olympics.xlsx") 85 | tail(d, 6) 86 | ``` 87 | 88 | 89 | 90 | 91 | # 数据处理 92 | 93 | ## tidy原则 94 | 95 | Hadley Wickhamt提出了数据科学tidy原则，我结合自己的理解，tidy思想体现在: 96 | 97 | ```{r out.width = '85%', echo = FALSE} 98 | knitr::include_graphics("images/import_datatype01.png") 99 | ``` 100 | 101 | - 一切都是数据框，任何数据都可以规整 102 | - 数据框的一列代表一个**变量**，数据框的一行代表一次**观察** 103 | - 函数处理数据时，数据框进数据框出（函数的第一个参数始终为**数据框**） 104 | 105 | 106 | 107 | ## dplyr宏包 108 | 本章我们介绍tidyverse里数据处理的神器dplyr宏包。首先，我们加载该宏包 109 | ```{r message = FALSE, warning = FALSE} 110 | library(dplyr) 111 | ``` 112 | 113 | dplyr 定义了数据处理的规范语法，其中主要包含以下七个主要的函数。 114 | 115 | * `mutate() `, `select() `, `filter() ` 116 | * `summarise() `, `group_by()`, `arrange() ` 117 | * `left_join()`, `right_join()`， `full_join()` 118 | 119 | 我们将依次介绍 120 | 121 | 122 | ## 假定数据 123 | 124 | 假定我们有一数据框，包含三位学生的英语和数学科目 125 | \small 126 | ```{r} 127 | df <- data.frame( 128 | name = c("Alice", "Alice", "Bob", "Bob", "Carol", "Carol"), 129 | type = c("english", "math", "english", "math", "english", "math") 130 | ) 131 | df 132 | ``` 133 | 134 | 135 | 136 | ## `mutate() `增加一列 137 | 这里有他们的最近的考试成绩，想添加到数据框中 138 | \footnotesize 139 | ```{r} 140 | score2020 <- c(80.2, 90.5, 92.2, 90.8, 82.5, 84.6) 141 | score2020 142 | ``` 143 | 144 | 145 | \begincols[T] 146 | \begincol[T]{.48\textwidth} 147 | 使用传统的方法 148 | ```{r} 149 | df$score <- score2020 150 | df 151 | ``` 152 | 153 | ```{r include=FALSE} 154 | df <- data.frame( 155 | name = c("Alice", "Alice", "Bob", "Bob", "Carol", "Carol"), 156 | type = c("english", "math", "english", "math", "english", "math") 157 | ) 158 | ``` 159 | 160 | \endcol 161 | 162 | \begincol[T]{.48\textwidth} 163 | dplyr语法这样写 164 | 165 | ```{r} 166 | # 167 | mutate(df, score = score2020) 168 | ``` 169 | \endcol 170 | \endcols 171 | 172 | 173 | 174 | 175 | 176 | ## `mutate() `增加一列 177 | 178 | `mutate()` 函数 179 | 180 | ```{r, eval=FALSE} 181 | mutate(.data = df, score = score2020) 182 | ``` 183 | 184 | - 第一参数是我们要处理的数据框，比如这里的`df`， 185 | - 第二个参数是`score = score2020`，等号左边的`score`是我们打算创建一个新列，而取的列名； 186 | 等号右边是装着学生成绩的**向量**（注意，向量 的长度要与数据框的行数相等，比如这里长度都是6） 187 | 188 | 189 | 190 | 191 | ## `管道` %>% 192 | 193 | 这里有必要介绍下管道操作符 [ `%>%` ](https://magrittr.tidyverse.org/). 194 | 195 | ```{r} 196 | c(1:10) 197 | ``` 198 | 199 | ```{r} 200 | sum(c(1:10)) 201 | ``` 202 | 203 | 204 | 与下面的写法是等价的, 205 | ```{r} 206 | c(1:10) %>% sum() 207 | ``` 208 | 209 | 210 | 211 | ## `管道` %>% 212 | 213 | ```{r, eval=FALSE} 214 | c(1:10) %>% sum() 215 | ``` 216 | 这条语句的意思，向量`c(1:10)` 通过管道操作符 `%>%` ，传递到函数`sum()`的第一个参数位置，即`sum(c(1:10))`， 这个`%>%`管道操作符还是很形象的， 217 | 218 | ```{r out.width = '50%', echo = FALSE} 219 | knitr::include_graphics("images/pipe1.png") 220 | ``` 221 | 222 | 223 | ## `管道` %>% 224 | 当对执行多个函数操作的时候，就显得格外方便，代码可读性更强。 225 | 226 | ```{r} 227 | sqrt(sum(abs(c(-10:10)))) 228 | ``` 229 | 230 | 231 | ```{r} 232 | c(-10:10) %>% abs() %>% sum() %>% sqrt() 233 | ``` 234 | 235 | 236 | 237 | 238 | 239 | ## `管道` %>% 240 | 那么，上面增加学生成绩的语句`mutate(df, score = score2020)`就可以使用管道 241 | 242 | ```{r out.width = '75%', echo = FALSE} 243 | knitr::include_graphics("images/pipe2.png") 244 | ``` 245 | 246 | 247 | ## `管道` %>% 248 | ```{r} 249 | # 等价于 250 | df %>% mutate(score = score2020) 251 | ``` 252 | 是不是很赞？ 253 | 254 | 255 | 256 | ```{r, include=FALSE} 257 | df <- df %>% mutate(score = score2020) 258 | df 259 | ``` 260 | 261 | 262 | 263 | 264 | 265 | ## `select() ` 选择某列 266 | 267 | `select()`，就是选择数据框的某一列 268 | 269 | \bigskip 270 | 271 | \begincols 272 | \begincol{.48\textwidth} 273 | 传统的方法 274 | ```{r} 275 | df["name"] 276 | ``` 277 | 278 | \endcol 279 | 280 | \begincol{.48\textwidth} 281 | dplyr的方法 282 | ```{r} 283 | df %>% select(name) 284 | ``` 285 | \endcol 286 | \endcols 287 | 288 | 289 | 290 | ## `select() ` 选择某列 291 | 如果选取多列，就再写一个就行了 292 | ```{r} 293 | df %>% select(name, score) 294 | ``` 295 | 296 | 297 | ## `select()` 选择某列 298 | 299 | 如果不想要某列， 可以在变量前面加`-`， 300 | ```{r} 301 | df %>% select(-type) 302 | ``` 303 | 304 | 305 | 306 | 307 | 308 | 309 | ## `filter() ` 筛选 310 | 311 | 我们还可以对数据行方向的选择和筛选，比如这里把**成绩高于90分的**同学筛选出来 312 | 313 | ```{r} 314 | df %>% filter(score >= 90) 315 | ``` 316 | 317 | 318 | ## `filter()` 筛选 319 | 320 | 我们也可以限定多个条件进行筛选, 英语成绩高于90分的筛选出来 321 | ```{r} 322 | df %>% filter(type == "english", score >= 90) 323 | ``` 324 | 325 | 326 | 327 | 328 | ## `summarise() `统计 329 | 330 | `summarise() `主要用于统计，往往与其他函数配合使用 331 | 332 | \medskip 333 | 比如，计算所有同学的考试成绩的均值 334 | ```{r} 335 | df %>% summarise( mean_score = mean(score)) 336 | ``` 337 | 338 | 比如，计算所有同学的考试成绩的标准差 339 | ```{r} 340 | df %>% summarise( mean_score = sd(score)) 341 | ``` 342 | 343 | 344 | 345 | 346 | ## `summarise() `统计 347 | 还可以同时完成多个统计 348 | ```{r} 349 | df %>% summarise( 350 | mean_score = mean(score), 351 | median_score = median(score), 352 | n = n(), 353 | sum = sum(score) 354 | ) 355 | ``` 356 | 357 | 358 | 359 | 360 | 361 | ## `group_by()`分组 362 | 363 | 先分组再统计。比如，我们想统计每个学生的平均成绩，即先按学生`name`分组，然后分别求平均 364 | 365 | \small 366 | ```{r} 367 | df %>% 368 | group_by(name) %>% 369 | summarise( 370 | mean_score = mean(score), 371 | sd_score = sd(score) 372 | ) 373 | ``` 374 | 375 | 376 | 377 | 378 | 379 | ## `arrange() `排序 380 | 我们按照考试成绩从低到高排序，然后输出 381 | ```{r} 382 | df %>% arrange(score) 383 | ``` 384 | 385 | 386 | 387 | ## `arrange() `排序 388 | 389 | 如果从高到低降序排列呢，有两种方法: 390 | 391 | \small 392 | \begincols 393 | \begincol{.48\textwidth} 394 | ```{r} 395 | df %>% arrange(-score) 396 | ``` 397 | \endcol 398 | 399 | \begincol{.48\textwidth} 400 | 401 | ```{r} 402 | df %>% arrange(desc(score)) 403 | ``` 404 | \endcol 405 | \endcols 406 | 407 | 哪边可读性更强些？ 408 | 409 | 410 | ## `arrange() `排序 411 | 也可对多个变量先后排序。 412 | \medskip 413 | 比如，先按学科排，然后按照成绩从高到底排序 414 | ```{r} 415 | df %>% 416 | arrange(type, desc(score)) 417 | ``` 418 | 419 | 420 | 421 | 422 | 423 | ## `left_join` 合并 424 | 假定我们已经统计了每个同学的平均成绩，存放在数据框`df1` 425 | 426 | ```{r} 427 | df1 <- df %>% 428 | group_by(name) %>% 429 | summarise( mean_score = mean(score) ) 430 | df1 431 | ``` 432 | 433 | 434 | ## `left_join` 合并 435 | 同时，我们又有新一个数据框`df2`，它包含同学们的年龄信息 436 | ```{r} 437 | df2 <- tibble( 438 | name = c("Alice", "Bob"), 439 | age = c(12, 13) 440 | ) 441 | df2 442 | ``` 443 | 444 | 445 | 446 | ## `left_join` 左合并 447 | 448 | 通过姓名`name`把两个数据框`df1`和`df2`合并， 449 | 450 | ```{r} 451 | left_join(df1, df2, by = "name") 452 | ``` 453 | 454 | 大家注意到最后一行Carol的年龄是`NA`， 大家想想为什么呢？ 455 | 456 | 457 | 458 | 459 | 460 | ## `left_join` 左合并 461 | 462 | 当然，也可以这样写 463 | ```{r} 464 | df1 %>% left_join(df2, by = "name") 465 | ``` 466 | 467 | 468 | 469 | 470 | 471 | 472 | ## `right_join` 右合并 473 | 我们再试试`right_join()`右合并 474 | 475 | ```{r, message=FALSE} 476 | df1 %>% right_join(df2, by = "name") 477 | ``` 478 | Carol同学的信息没有了？ 大家想想又为什么呢？ 479 | 480 | 481 | 482 | 483 | 484 | 485 | 486 | 487 | ## 延伸阅读 488 | 489 | - 推荐[https://dplyr.tidyverse.org/](https://dplyr.tidyverse.org/). 490 | - [cheatsheet](https://github.com/rstudio/cheatsheets/raw/master/data-transformation.pdf) 491 | - 运行并读懂[nycflights.Rmd](https://github.com/perlatex/R_for_Data_Science/blob/master/data/nycflights.Rmd) 492 | 493 | -------------------------------------------------------------------------------- /05_dplyr/05_dplyr.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/05_dplyr/05_dplyr.pdf -------------------------------------------------------------------------------- /05_dplyr/demo_data/olympics.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/05_dplyr/demo_data/olympics.xlsx -------------------------------------------------------------------------------- /05_dplyr/demo_data/wages.csv: -------------------------------------------------------------------------------- 1 | "earn","height","sex","race","ed","age" 2 | 79571.299011024,73.89,"male","white",16,49 3 | 96396.9886433106,66.23,"female","white",16,62 4 | 48710.666947391,63.77,"female","white",16,33 5 | 80478.0961525837,63.22,"female","other",16,95 6 | 82089.3454983326,63.08,"female","white",17,43 7 | 15313.3529014342,64.53,"female","white",15,30 8 | 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95426.0144102907,71.65,"male","white",12,54 1380 | 9575.46185684499,68.22,"male","white",12,31 1381 | -------------------------------------------------------------------------------- /05_dplyr/header.tex: -------------------------------------------------------------------------------- 1 | \usepackage{ctex} 2 | \usepackage{booktabs} 3 | \usepackage{longtable} 4 | \usepackage{array} 5 | \usepackage{multirow} 6 | \usepackage{wrapfig} 7 | \usepackage{float} 8 | \usepackage{colortbl} 9 | \usepackage{pdflscape} 10 | \usepackage{tabu} 11 | \usepackage{threeparttable} 12 | \usepackage{threeparttablex} 13 | \usepackage{makecell} 14 | \usepackage{xcolor} 15 | \usepackage{xtab} 16 | 17 | \def\begincols{ 18 | \begin{columns}} \def\begincol{\begin{column}} \def\endcol{\end{column}} \def\endcols{\end{columns} 19 | } 20 | 21 | 22 | -------------------------------------------------------------------------------- /05_dplyr/images/import_datatype01.png: 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/05_dplyr/images/tidyverse.png -------------------------------------------------------------------------------- /06_ggplot2/06_ggplot2.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "第五章：数据可视化" 3 | author: "王敏杰" 4 | institute: "四川师范大学" 5 | date: "\\today" 6 | fontsize: 12pt 7 | output: binb::metropolis 8 | section-titles: true 9 | #toc: true 10 | header-includes: 11 | - \usepackage[fontset = fandol]{ctex} 12 | - \input{header.tex} 13 | link-citations: yes 14 | colorlinks: yes 15 | linkcolor: red 16 | classoption: "dvipsnames,UTF8" 17 | --- 18 | 19 | ```{r setup, include=FALSE} 20 | options(digits = 3) 21 | knitr::opts_chunk$set( 22 | comment = "#>", 23 | echo = TRUE, 24 | collapse = TRUE, 25 | message = FALSE, 26 | warning = FALSE, 27 | out.width = "75%", 28 | fig.align = "center", 29 | fig.asp = 0.618, # 1 / phi 30 | fig.show = "hold" 31 | ) 32 | ``` 33 | 34 | ## tidyverse 家族 35 | ```{r echo=FALSE, out.width = '85%'} 36 | knitr::include_graphics("images/tidyverse.png") 37 | ``` 38 | 39 | 40 | 41 | 42 | 43 | # 为什么要可视化 44 | 45 | ## 1854年伦敦霍乱 46 | ```{r out.width = '100%', echo = FALSE} 47 | knitr::include_graphics("images/cholera_a.pdf") 48 | ``` 49 | 50 | 51 | ## 1854年伦敦霍乱 52 | ```{r out.width = '100%', echo = FALSE} 53 | knitr::include_graphics("images/cholera_b.pdf") 54 | ``` 55 | 56 | 57 | ## 1854年伦敦霍乱 58 | ```{r out.width = '100%', echo = FALSE} 59 | knitr::include_graphics("images/cholera_c.pdf") 60 | ``` 61 | 62 | 63 | 64 | ## 辛普森悖论（Simpson's Paradox） 65 | ```{r out.width = '100%', echo = FALSE} 66 | knitr::include_graphics("images/Paradox1.pdf") 67 | ``` 68 | 69 | ## 辛普森悖论（Simpson's Paradox） 70 | ```{r out.width = '100%', echo = FALSE} 71 | knitr::include_graphics("images/Paradox2.pdf") 72 | ``` 73 | 74 | 75 | ## 辛普森悖论（Simpson's Paradox） 76 | ```{r out.width = '100%', echo = FALSE} 77 | knitr::include_graphics("images/Paradox3.pdf") 78 | ``` 79 | 80 | 81 | # ggplot2 宏包 82 | 83 | ## 宏包ggplot2 84 | 85 | - ggplot2是RStudio首席科学家Hadley Wickham在2005年读博士期间的作品。 86 | - 很多人学习R语言，就是因为ggplot2宏包 87 | - ggplot2已经发展成为最受欢迎的R宏包，没有之一 88 | 89 | ```{r} 90 | library(ggplot2) # install.packages("ggplot2") 91 | # or 92 | library(tidyverse) # install.packages("tidyverse") 93 | ``` 94 | 95 | 96 | 97 | 98 | 99 | ## ggplot2 的图形语法 100 | 101 | ggplot2有一套优雅的绘图语法(grammar of graphics) 102 | 103 | ```{r out.width = '70%', echo = FALSE} 104 | knitr::include_graphics("images/mapping.png") 105 | ``` 106 | 107 | Hadley Wickham将这套语法诠释为: 108 | 109 | 一张统计图形就是从数据到几何对象(geometric object，缩写geom)的图形属性(aesthetic attribute，缩写aes)的一个映射。 110 | 111 | 112 | ## ggplot2 的图形语法 113 | 114 | `ggplot()`函数包括9个部件： 115 | 116 | - **数据 (data)** 117 | - **映射 (mapping)** 118 | - **几何对象 (geom)** 119 | - 统计变换 (stats) 120 | - 标度 (scale) 121 | - 坐标系 (coord) 122 | - 分面 (facet) 123 | - 主题 (theme) 124 | - 存储和输出 (output) 125 | 126 | 其中前三个是必需的。 127 | 128 | 129 | 130 | ## 语法模板 131 | 132 | ```{r out.width = '100%', echo = FALSE} 133 | knitr::include_graphics("images/ggplot_template.png") 134 | ``` 135 | 136 | 137 | 138 | ## 案例 139 | 140 | 简单的案例（1880-2014年温度变化和二氧化碳排放量） 141 | 142 | \footnotesize 143 | ```{r, warning = FALSE, message = FALSE} 144 | d <- readr::read_csv("./demo_data/temp_carbon.csv") 145 | ``` 146 | 147 | ```{r, echo = FALSE} 148 | d %>% 149 | head(10) %>% 150 | knitr::kable() 151 | ``` 152 | 153 | ## 是不是很简单? 154 | \footnotesize 155 | ```{r, out.width="85%"} 156 | ggplot(data = d, mapping = aes(x = year, y = carbon_emissions)) + 157 | geom_line() 158 | ``` 159 | 160 | 161 | # ggplot2 语法详解 162 | 163 | ## 演示数据 164 | 165 | 我们用ggplot2宏包内置的燃油经济性数据[mpg](https://ggplot2.tidyverse.org/reference/mpg.html)演示 166 | 167 | \small 168 | |序号|变量|含义| 169 | |:---|:---|:---| 170 | |1 | manufacturer | 生产厂家| 171 | |2 | model | 类型| 172 | |3 | displ | 发动机排量，升| 173 | |4 | year | 生产年份| 174 | |5 | cyl | 气缸数量| 175 | |6 | trans | 传输类型| 176 | |7 | drv | 驱动类型| 177 | |8 | cty | 每加仑城市里程| 178 | |9 | hwy | 每加仑高速公路英里| 179 | |10 | fl | 汽油种类| 180 | |11 | class | 类型| 181 | 182 | 183 | ## 排量越大，越耗油吗？ 184 | 185 | 回答这个问题，要用到mpg数据集中的三个变量 186 | 187 | |序号|变量|含义| 188 | |:---|:---|:---| 189 | |3 | displ | **排量**| 190 | |9 | hwy | **油耗**| 191 | |11 | class | 汽车类型| 192 | 193 | 194 | ```{r} 195 | mpg %>% 196 | select(displ, hwy, class) %>% 197 | head(4) 198 | ``` 199 | 200 | 201 | 202 | ## 映射 203 | 204 | 为考察发动机排量(displ)与每加仑英里数(hwy)之间的关联，先绘制这两个变量的散点图， 205 | 206 | ```{r out.width = '100%', echo = FALSE} 207 | knitr::include_graphics("images/a-3.png") 208 | ``` 209 | 210 | 211 | 212 | 213 | 214 | 215 | 216 | 217 | 218 | 219 | 220 | 221 | 222 | 223 | 224 | ## 运行 225 | 运行脚本后生成图片： 226 | \footnotesize 227 | ```{r, out.width="85%"} 228 | ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 229 | geom_point() 230 | ``` 231 | 232 | 233 | 234 | ## 颜色映射 235 | 236 | 除了位置上的映射，ggplot2还包含了颜色、形状及透明度等图形属性的映射 237 | 238 | 239 | 240 | \footnotesize 241 | ```{r, out.width="75%"} 242 | ggplot(data = mpg, aes(x = displ, y = hwy, color = class) ) + 243 | geom_point() 244 | ``` 245 | 246 | 此图绘制不同类型的车，displ和hwy的散点图， 并用颜色来实现了分组。 247 | 248 | 249 | 250 | ## 更多映射 251 | 大家试试下面代码呢 252 | \footnotesize 253 | ```{r, eval = FALSE} 254 | ggplot(data = mpg, aes(x = displ, y = hwy, size = class)) + 255 | geom_point() 256 | ``` 257 | 258 | 259 | ```{r, eval = FALSE} 260 | ggplot(data = mpg, aes(x = displ, y = hwy, shape = class)) + 261 | geom_point() 262 | ``` 263 | 264 | 265 | ```{r, eval = FALSE} 266 | ggplot(data = mpg, aes(x = displ, y = hwy, alpha = class)) + 267 | geom_point() 268 | ``` 269 | 270 | 271 | ## 默认值 272 | 一些默认的设置 273 | 274 | ```{r out.width = '85%', echo = FALSE} 275 | knitr::include_graphics("images/a-14.png") 276 | ``` 277 | 278 | 279 | 280 | ## 映射 vs.设置 281 | 282 | 想把图中的点指定为某一种颜色，可以使用设置语句，比如 283 | 284 | ```{r out.width = '65%'} 285 | mpg %>% 286 | ggplot(aes(displ, hwy)) + 287 | geom_point(color = "blue") 288 | ``` 289 | 290 | 291 | 292 | ## 更多设置 293 | 大家也可以试试下面 294 | \footnotesize 295 | ```{r, eval = FALSE} 296 | ggplot(mpg, aes(displ, hwy)) + geom_point(size = 5) 297 | ``` 298 | 299 | 300 | ```{r, eval = FALSE} 301 | ggplot(mpg, aes(displ, hwy)) + geom_point(shape = 2) 302 | ``` 303 | 304 | 305 | ```{r, eval = FALSE} 306 | ggplot(mpg, aes(displ, hwy)) + geom_point(alpha = 0.5) 307 | ``` 308 | 309 | 310 | 311 | ## 提问 312 | ```{r out.width = '100%', echo = FALSE} 313 | knitr::include_graphics("images/a-21.png") 314 | ``` 315 | 316 | 思考下`aes(color = "blue")`为什么会红色的点？ 317 | 318 | 319 | 320 | 321 | 322 | 323 | 324 | 325 | 326 | 327 | 328 | 329 | 330 | 331 | 332 | 333 | 334 | 335 | 336 | 337 | 338 | 339 | 340 | 341 | 342 | 343 | 344 | 345 | 346 | 347 | ## 几何对象 348 | 349 | `geom_point()` 可以画散点图，也可以使用`geom_smooth()`绘制平滑曲线 350 | 351 | ```{r} 352 | ggplot(data = mpg, aes(x = displ, y = hwy)) + 353 | geom_smooth() 354 | ``` 355 | 356 | 357 | 358 | ## 图层叠加 359 | 360 | ```{r} 361 | ggplot(data = mpg, aes(x = displ, y = hwy)) + 362 | geom_point() + 363 | geom_smooth() 364 | ``` 365 | 366 | 367 | 368 | 369 | 370 | ## Global vs. Local 371 | \footnotesize 372 | 373 | ```{r, eval=FALSE} 374 | ggplot(mpg) + 375 | geom_point(aes(x = displ, y = hwy, color = class)) 376 | ``` 377 | 378 | ```{r, eval=FALSE} 379 | ggplot(mpg) + 380 | geom_point( aes(x = displ, y = hwy, color = class) ) 381 | ``` 382 | 383 | 384 | \begincols[T] 385 | \begincol[T]{.49\textwidth} 386 | ```{r, echo=FALSE, out.width= "100%"} 387 | ggplot(mpg) + 388 | geom_point(aes(x = displ, y = hwy, color = class)) 389 | ``` 390 | \endcol 391 | 392 | \begincol[T]{.49\textwidth} 393 | 394 | ```{r, echo=FALSE, out.width= "100%"} 395 | ggplot(mpg) + 396 | geom_point( aes(x = displ, y = hwy, color = class) ) 397 | ``` 398 | \endcol 399 | \endcols 400 | 401 | 大家可以看到，以上两段代码出来的图是一样，但背后的含义却不同。 402 | 403 | 404 | 405 | ## Global vs. Local 406 | 407 | - 如果映射关系`aes()` 写在`ggplot()`里, 那么`x = displ, y = hwy, color = class` 为全局变量 408 | 409 | ```{r, eval=FALSE} 410 | ggplot(mpg, aes(x = displ, y = hwy, color = class)) + 411 | geom_point() 412 | ``` 413 | 414 | - `geom_point()`中缺少所绘图所需要的映射关系，就会继承全局变量的映射关系 415 | 416 | 417 | 418 | ## Global vs. Local 419 | - 如果映射关系`aes()` 写在几何对象`geom_point()`里, 就为局部变量, 420 | 421 | ```{r, eval=FALSE} 422 | ggplot(mpg) + 423 | geom_point(aes(x = displ, y = hwy, color = class)) 424 | ``` 425 | 426 | - `geom_point()`绘图所需要的映射关系已经存在，就不会继承全局变量的映射关系 427 | 428 | 429 | 430 | 431 | ## Global vs. Local 432 | ```{r, eval=FALSE, warning=FALSE, message=FALSE} 433 | ggplot(mpg, aes(x = displ, y = hwy)) + 434 | geom_point(aes(color = class)) + 435 | geom_smooth() 436 | ``` 437 | 438 | 这里的 `geom_point()` 和 `geom_smooth()` 都会从全局变量中继承映射关系。 439 | 440 | 441 | 442 | ## Global vs. Local 443 | 444 | ```{r, out.width= "65%"} 445 | ggplot(mpg, aes(x = displ, y = hwy, color = class)) + 446 | geom_point(aes(color = factor(cyl))) 447 | ``` 448 | 局部变量中的映射关系 449 | `aes(color = )`已经存在，因此不会从全局变量中继承，沿用当前的映射关系。 450 | 451 | 452 | 453 | 454 | ## 提问 455 | 大家细细体会下，下面两段代码的区别 456 | ```{r, eval=FALSE} 457 | ggplot(mpg, aes(x = displ, y = hwy, color = class)) + 458 | geom_smooth(method = lm) + 459 | geom_point() 460 | ``` 461 | 462 | 463 | ```{r, eval=FALSE} 464 | ggplot(mpg, aes(x = displ, y = hwy)) + 465 | geom_smooth(method = lm) + 466 | geom_point(aes(color = class)) 467 | ``` 468 | 469 | 470 | 471 | 472 | ## 保存图片 473 | 474 | 可以使用`ggsave()`函数，将图片保存为所需要的格式，如".pdf", ".png"等 475 | 476 | ```{r, eval = FALSE} 477 | p <- ggplot(mpg, aes(x = displ, y = hwy)) + 478 | geom_smooth(method = lm) + 479 | geom_point(aes(color = class)) + 480 | ggtitle("This is my first plot") 481 | 482 | ggsave( 483 | filename = "myplot.pdf", 484 | plot = p, 485 | width = 8, 486 | height = 6 487 | ) 488 | ``` 489 | 490 | 491 | 492 | 493 | -------------------------------------------------------------------------------- /06_ggplot2/06_ggplot2.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/06_ggplot2/06_ggplot2.pdf -------------------------------------------------------------------------------- /06_ggplot2/demo_data/temp_carbon.csv: -------------------------------------------------------------------------------- 1 | year,temp_anomaly,land_anomaly,ocean_anomaly,carbon_emissions 2 | 1880,-0.11,-0.48,-0.01,236 3 | 1881,-0.08,-0.4,0.01,243 4 | 1882,-0.1,-0.48,0,256 5 | 1883,-0.18,-0.66,-0.04,272 6 | 1884,-0.26,-0.69,-0.14,275 7 | 1885,-0.25,-0.56,-0.17,277 8 | 1886,-0.24,-0.51,-0.17,281 9 | 1887,-0.28,-0.47,-0.23,295 10 | 1888,-0.13,-0.41,-0.05,327 11 | 1889,-0.09,-0.31,-0.02,327 12 | 1890,-0.34,-0.51,-0.29,356 13 | 1891,-0.25,-0.52,-0.15,372 14 | 1892,-0.3,-0.49,-0.23,374 15 | 1893,-0.32,-0.54,-0.24,370 16 | 1894,-0.3,-0.38,-0.27,383 17 | 1895,-0.23,-0.39,-0.17,406 18 | 1896,-0.09,-0.33,0,419 19 | 1897,-0.09,-0.26,-0.03,440 20 | 1898,-0.26,-0.37,-0.22,465 21 | 1899,-0.15,-0.21,-0.13,507 22 | 1900,-0.07,-0.15,-0.05,534 23 | 1901,-0.15,-0.12,-0.16,552 24 | 1902,-0.25,-0.26,-0.24,566 25 | 1903,-0.37,-0.37,-0.37,617 26 | 1904,-0.45,-0.44,-0.46,624 27 | 1905,-0.27,-0.33,-0.25,663 28 | 1906,-0.21,-0.17,-0.22,707 29 | 1907,-0.38,-0.62,-0.29,784 30 | 1908,-0.43,-0.44,-0.43,750 31 | 1909,-0.44,-0.43,-0.45,785 32 | 1910,-0.4,-0.36,-0.42,819 33 | 1911,-0.44,-0.48,-0.43,836 34 | 1912,-0.34,-0.48,-0.28,879 35 | 1913,-0.32,-0.31,-0.32,943 36 | 1914,-0.14,-0.06,-0.17,850 37 | 1915,-0.09,-0.08,-0.1,838 38 | 1916,-0.32,-0.46,-0.26,901 39 | 1917,-0.4,-0.63,-0.29,955 40 | 1918,-0.3,-0.5,-0.21,936 41 | 1919,-0.25,-0.33,-0.21,806 42 | 1920,-0.23,-0.36,-0.18,932 43 | 1921,-0.16,-0.15,-0.17,803 44 | 1922,-0.25,-0.27,-0.24,845 45 | 1923,-0.25,-0.29,-0.24,970 46 | 1924,-0.24,-0.25,-0.24,963 47 | 1925,-0.18,-0.15,-0.19,975 48 | 1926,-0.07,-0.02,-0.1,983 49 | 1927,-0.17,-0.22,-0.16,1062 50 | 1928,-0.18,-0.15,-0.2,1065 51 | 1929,-0.33,-0.49,-0.27,1145 52 | 1930,-0.11,-0.13,-0.11,1053 53 | 1931,-0.06,-0.02,-0.08,940 54 | 1932,-0.13,-0.03,-0.17,847 55 | 1933,-0.26,-0.36,-0.22,893 56 | 1934,-0.11,-0.06,-0.13,973 57 | 1935,-0.16,-0.17,-0.15,1027 58 | 1936,-0.12,-0.12,-0.12,1130 59 | 1937,-0.01,-0.02,-0.01,1209 60 | 1938,-0.02,0.17,-0.1,1142 61 | 1939,0.01,0.1,-0.03,1192 62 | 1940,0.16,0.07,0.2,1299 63 | 1941,0.27,0.1,0.35,1334 64 | 1942,0.11,0.06,0.13,1342 65 | 1943,0.11,0.07,0.12,1391 66 | 1944,0.28,0.19,0.32,1383 67 | 1945,0.18,-0.07,0.3,1160 68 | 1946,-0.01,-0.01,-0.01,1238 69 | 1947,-0.04,0.04,-0.07,1392 70 | 1948,-0.05,0.05,-0.1,1469 71 | 1949,-0.07,-0.07,-0.08,1419 72 | 1950,-0.15,-0.32,-0.09,1630 73 | 1951,0,-0.06,0.02,1767 74 | 1952,0.05,-0.05,0.08,1795 75 | 1953,0.13,0.2,0.1,1841 76 | 1954,-0.1,-0.12,-0.09,1865 77 | 1955,-0.13,-0.11,-0.13,2042 78 | 1956,-0.18,-0.4,-0.1,2177 79 | 1957,0.07,-0.04,0.11,2270 80 | 1958,0.13,0.15,0.12,2330 81 | 1959,0.08,0.09,0.08,2454 82 | 1960,0.05,0,0.07,2569 83 | 1961,0.1,0.12,0.09,2580 84 | 1962,0.11,0.16,0.09,2686 85 | 1963,0.12,0.21,0.08,2833 86 | 1964,-0.14,-0.22,-0.11,2995 87 | 1965,-0.07,-0.12,-0.05,3130 88 | 1966,-0.01,-0.05,0.01,3288 89 | 1967,0,0.01,-0.01,3393 90 | 1968,-0.03,-0.11,0.01,3566 91 | 1969,0.11,-0.08,0.17,3780 92 | 1970,0.06,0.05,0.06,4053 93 | 1971,-0.07,-0.02,-0.09,4208 94 | 1972,0.04,-0.17,0.11,4376 95 | 1973,0.19,0.34,0.14,4614 96 | 1974,-0.06,-0.18,-0.02,4623 97 | 1975,0.01,0.14,-0.04,4596 98 | 1976,-0.07,-0.23,-0.01,4864 99 | 1977,0.21,0.25,0.19,5016 100 | 1978,0.12,0.1,0.12,5074 101 | 1979,0.23,0.17,0.24,5357 102 | 1980,0.28,0.31,0.26,5301 103 | 1981,0.32,0.52,0.25,5138 104 | 1982,0.19,0.11,0.22,5094 105 | 1983,0.36,0.5,0.3,5075 106 | 1984,0.17,0.06,0.2,5258 107 | 1985,0.16,0.1,0.18,5417 108 | 1986,0.23,0.3,0.21,5583 109 | 1987,0.38,0.45,0.36,5725 110 | 1988,0.39,0.58,0.32,5936 111 | 1989,0.29,0.36,0.27,6066 112 | 1990,0.45,0.66,0.37,6074 113 | 1991,0.39,0.53,0.34,6142 114 | 1992,0.24,0.24,0.23,6078 115 | 1993,0.28,0.35,0.25,6070 116 | 1994,0.34,0.48,0.29,6174 117 | 1995,0.47,0.78,0.35,6305 118 | 1996,0.32,0.35,0.31,6448 119 | 1997,0.51,0.64,0.46,6556 120 | 1998,0.65,0.98,0.52,6576 121 | 1999,0.44,0.78,0.31,6561 122 | 2000,0.42,0.62,0.34,6733 123 | 2001,0.57,0.84,0.46,6893 124 | 2002,0.62,0.95,0.49,6994 125 | 2003,0.63,0.94,0.52,7376 126 | 2004,0.58,0.81,0.49,7743 127 | 2005,0.66,1.08,0.5,8042 128 | 2006,0.63,0.97,0.5,8336 129 | 2007,0.61,1.12,0.43,8503 130 | 2008,0.54,0.89,0.41,8776 131 | 2009,0.64,0.9,0.54,8697 132 | 2010,0.72,1.14,0.56,9128 133 | 2011,0.57,0.91,0.44,9503 134 | 2012,0.63,0.95,0.51,9673 135 | 2013,0.67,1.03,0.53,9773 136 | 2014,0.73,1.01,0.63,9855 137 | -------------------------------------------------------------------------------- /06_ggplot2/header.tex: -------------------------------------------------------------------------------- 1 | \usepackage{ctex} 2 | \usepackage{booktabs} 3 | \usepackage{longtable} 4 | \usepackage{array} 5 | 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| author: "王敏杰" 4 | institute: "四川师范大学" 5 | date: "\\today" 6 | fontsize: 12pt 7 | output: binb::metropolis 8 | section-titles: true 9 | #toc: true 10 | header-includes: 11 | - \usepackage[fontset = fandol]{ctex} 12 | - \input{header.tex} 13 | link-citations: yes 14 | colorlinks: yes 15 | linkcolor: red 16 | classoption: "dvipsnames,UTF8" 17 | --- 18 | 19 | ```{r setup, include=FALSE} 20 | options(digits = 3) 21 | knitr::opts_chunk$set( 22 | comment = "#>", 23 | echo = TRUE, 24 | collapse = TRUE, 25 | message = FALSE, 26 | warning = FALSE, 27 | out.width = "75%", 28 | fig.asp = 0.618, # 1 / phi 29 | fig.show = "hold", 30 | fig.showtext = TRUE 31 | ) 32 | ``` 33 | 34 | 35 | # 提问 36 | 37 | ## 问题 38 | 39 | 这是一份关于地址信息的数据 40 | ```{r echo=FALSE, message=FALSE, warning=FALSE} 41 | library(tidyverse) 42 | library(stringr) 43 | library(knitr) 44 | library(printr) 45 | 46 | d <- tibble::tribble( 47 | ~No, ~address, 48 | 1L, "Sichuan Univ, Coll Chem", 49 | 2L, "Sichuan Univ, Coll Elect Engn", 50 | 3L, "Sichuan Univ, Dept Phys", 51 | 4L, "Sichuan Univ, Coll Life Sci", 52 | 6L, "Sichuan Univ, Food Engn", 53 | 7L, "Sichuan Univ, Coll Phys", 54 | 8L, "Sichuan Univ, Sch Business", 55 | 9L, "Wuhan Univ, Mat Sci" 56 | ) 57 | 58 | d 59 | ``` 60 | 61 | - 如何提取`Sichuan Univ`后面的学院？ 62 | 63 | 64 | ```{r eval=FALSE, include=FALSE} 65 | d %>% dplyr::mutate( 66 | coll = str_extract_all(address, "(?<=Sichuan Univ,).*") 67 | ) %>% 68 | tidyr::unnest(coll, keep_empty = TRUE) 69 | ``` 70 | 71 | 72 | ```{r eval=FALSE, include=FALSE} 73 | d %>% mutate( 74 | coll = str_remove_all(address, ".*,") 75 | ) 76 | ``` 77 | 78 | ```{r eval=FALSE, include=FALSE} 79 | d %>% tidyr::separate( 80 | address, into = c("univ", "coll"), sep = ",", remove = FALSE 81 | ) 82 | ``` 83 | 84 | 85 | ```{r eval=FALSE, include=FALSE} 86 | d %>% 87 | tidyr::extract( 88 | address, c("univ", "coll"), "(Sichuan Univ), (.+)", 89 | remove = FALSE 90 | ) 91 | ``` 92 | 93 | 94 | 95 | # 正则表达式 96 | 97 | 98 | ## 什么是正则表达式 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 | 113 | 正则表达式（Regular Expression），是一种强大、便捷、高效的文本处理工具。它描述了一种字符串匹配的模式（pattern），比如： 114 | 115 | - 具有固定格式的文本 116 | - 电话号码 117 | - 网络地址、邮件地址 118 | - 日期格式 119 | - 网页解析 120 | - 等等 121 | 122 | 123 | 124 | 125 | ## stringr包 126 | - 正则表达式并不是R语言特有的，事实上，几乎所有程序语言都支持正则表达式 (e.g. Perl, Python, Java, Ruby, etc). 127 | 128 | - R语言中很多函数都需要使用正则表达式，然而大神Hadley Wickham开发的stringr包让正则表达式简单易懂，所以今天我们介绍这个包。 129 | 130 | ```{r out.width = '20%', fig.align='center', echo = FALSE} 131 | knitr::include_graphics("images/hex-stringr.png") 132 | ``` 133 | 134 | 135 | ```{r echo=TRUE, message=FALSE, warning=FALSE} 136 | library(stringr) #install.packages("stringr") 137 | ``` 138 | 139 | 140 | ## stringr包 141 | 142 | \small 143 | - 字符串处理基础 144 | - 字符串长度 145 | - 字符串组合 146 | - 字符串子串 147 | 148 | - 使用正则表达式进行模式匹配 149 | - 基础匹配 150 | - 锚点[máo][diǎn] 151 | - 字符类与字符选项 152 | - 重复 153 | - 分组与回溯引用 154 | 155 | - 解决实际问题 156 | - 判断是否匹配 157 | - 提取匹配内容 158 | 159 | 160 | 161 | 162 | 163 | # 字符串处理基础 164 | 165 | ## 字符串长度 166 | 167 | 想获取字符串的长度，使用 `str_length()`函数: 168 | ```{r} 169 | str_length("R for data science") 170 | ``` 171 | 172 | 字符串向量，也适用 173 | ```{r} 174 | str_length(c("a", "R for data science", NA)) 175 | ``` 176 | 177 | ## 字符串长度 178 | 179 | 数据框里配合dplyr函数，同样很方便 180 | ```{r} 181 | data.frame( 182 | x = c("a", "R for data science", NA) 183 | ) %>% 184 | mutate(y = str_length(x)) 185 | ``` 186 | 187 | 188 | 189 | 190 | 191 | 192 | 193 | ## 字符串组合 194 | 195 | 把字符串拼接在一起，使用`str_c()`函数 196 | ```{r} 197 | str_c("x", "y") 198 | ``` 199 | 200 | 201 | 把字符串拼接在一起，可以设置中间的间隔 202 | ```{r} 203 | str_c("x", "y", sep = ", ") 204 | ``` 205 | 206 | 207 | ```{r} 208 | str_c(c("x", "y", "z"), sep = ", ") 209 | ``` 210 | 是不是和你想象的不一样，那就试试`?str_c` 211 | 212 | 213 | 214 | ## 字符串组合 215 | ```{r} 216 | str_c(c("x", "y", "z"), c("x", "y", "z"), sep = ", ") 217 | ``` 218 | 219 | 用在数据框里 220 | ```{r} 221 | data.frame( x = c("I", "love", "you"), 222 | y = c("you", "like", "me") ) %>% 223 | mutate(z = str_c(x, y, sep = "|")) 224 | ``` 225 | 226 | 227 | ## 字符串组合 228 | 229 | 使用collapse选项，是先组合，然后再转换成单个字符串，大家对比下 230 | 231 | ```{r} 232 | str_c(c("x", "y", "z"), c("a", "b", "c"), sep = "|") 233 | ``` 234 | 235 | ```{r} 236 | str_c( 237 | c("x", "y", "z"), c("a", "b", "c"), collapse = "|" 238 | ) 239 | ``` 240 | 241 | 242 | 243 | 244 | 245 | 246 | 247 | ## 字符串取子集 248 | 249 | 截取字符串的一部分，需要指定截取的开始位置和结束位置 250 | ```{r} 251 | x <- c("Apple", "Banana", "Pear") 252 | str_sub(x, 1, 3) 253 | ``` 254 | 255 | 开始位置和结束位置如果是负整数，就表示位置是从后往前数，比如下面这段代码，截取倒数第3个至倒数第1个位置上的字符串 256 | ```{r} 257 | str_sub(x, -3, -1) 258 | ``` 259 | 260 | ## 字符串取子集 261 | 262 | 也可以进行赋值，如果该位置上有字符，就用新的字符替换旧的字符 263 | ```{r} 264 | x <- c("Apple", "Banana", "Pear") 265 | x 266 | ``` 267 | 268 | 269 | ```{r} 270 | str_sub(x, 1, 1) 271 | ``` 272 | 273 | 274 | ```{r} 275 | str_sub(x, 1, 1) <- "Q" 276 | x 277 | ``` 278 | 279 | 280 | 281 | 282 | 283 | 284 | # 使用正则表达式进行模式匹配 285 | 286 | 287 | ## 基础匹配 288 | 289 | `str_view()` 是查看string是否匹配pattern， 290 | 291 | 如果匹配，就高亮显示 292 | ```{r, out.width="300%"} 293 | x <- c("apple", "banana", "pear") 294 | str_view(string = x, pattern = "an") 295 | ``` 296 | 297 | 298 | ## 基础匹配 299 | 有时候，我们希望在字符`a`前后都有字符（即，a处在两字符中间，如rap, bad, sad, wave，spear等等） 300 | ```{r, out.width="300%"} 301 | x <- c("apple", "banana", "pear") 302 | str_view(x, ".a.") 303 | ``` 304 | 305 | 306 | ## 基础匹配 307 | 308 | \begincols[T] 309 | \begincol[T]{.49\textwidth} 310 | 311 | 这里的`.` 代表任意字符. 312 | 313 | ```{r, out.width="600%"} 314 | c("s.d") %>% 315 | str_view(".") 316 | ``` 317 | \endcol 318 | 319 | \begincol[T]{.49\textwidth} 320 | 321 | 如果想表达.本身呢？ 322 | ```{r, out.width="600%"} 323 | c("s.d") %>% 324 | str_view("\\.") 325 | ``` 326 | 327 | \endcol 328 | \endcols 329 | 330 | 331 | 332 | ## 锚点 333 | ```{r} 334 | x <- c("apple", "banana", "pear") 335 | x 336 | ``` 337 | \begincols[T] 338 | \begincol[T]{.49\textwidth} 339 | 340 | 希望`a`是字符串的开始 341 | ```{r, out.width="600%"} 342 | str_view(x, "^a") 343 | ``` 344 | \endcol 345 | 346 | \begincol[T]{.49\textwidth} 347 | 348 | 希望`a`是一字符串的末尾 349 | ```{r, out.width="600%"} 350 | str_view(x, "a$") 351 | ``` 352 | \endcol 353 | \endcols 354 | 355 | 356 | 357 | 358 | ## 锚点 359 | ```{r, out.width="300%"} 360 | x <- c("apple pie", "apple", "apple cake") 361 | str_view(x, "^apple$") 362 | ``` 363 | 364 | 365 | 366 | 367 | 368 | ## 字符类与字符选项 369 | 370 | 前面提到，`.`匹配任意字符，事实上还有很多这种**特殊含义**的字符： 371 | 372 | * `\d`: matches any digit. 373 | * `\s`: matches any whitespace (e.g. space, tab, newline). 374 | * `[abc]`: matches a, b, or c. 375 | * `[^abc]`: matches anything except a, b, or c. 376 | 377 | 378 | ```{r, out.width="300%"} 379 | str_view(c("grey", "gray"), "gr[ea]y") 380 | ``` 381 | 382 | 383 | 384 | 385 | 386 | 387 | 388 | 389 | ## 重复 390 | 391 | 控制匹配次数: 392 | 393 | * `?`: 0 or 1 394 | * `+`: 1 or more 395 | * `*`: 0 or more 396 | 397 | 398 | ```{r} 399 | x <- "Roman numerals: MDCCCLXXXVIII" 400 | ``` 401 | 402 | \begincols[T] 403 | \begincol[T]{.49\textwidth} 404 | 405 | ```{r, out.width="600%"} 406 | str_view(x, "CC?") 407 | ``` 408 | 409 | \endcol 410 | \begincol[T]{.49\textwidth} 411 | 412 | ```{r, out.width="600%"} 413 | str_view(x, "X+") 414 | ``` 415 | \endcol 416 | \endcols 417 | 418 | 419 | 420 | ## 重复 421 | 控制匹配次数: 422 | 423 | * `{n}`: exactly n 424 | * `{n,}`: n or more 425 | * `{,m}`: at most m 426 | * `{n,m}`: between n and m 427 | 428 | 429 | 430 | ## 重复 431 | ```{r, out.width="300%"} 432 | x <- "Roman numerals: MDCCCLXXXVIII" 433 | str_view(x, "C{2}") 434 | str_view(x, "C{2,}") 435 | str_view(x, "C{2,3}") 436 | ``` 437 | 438 | 439 | 440 | ## 重复 441 | - 默认的情况，`*`, `+` 匹配都是**贪婪**的，也就是它会尽可能的匹配更多 442 | - 如果想让它不贪婪，而是变得懒惰起来，可以在 `*` 或 `+` 后加个`?` 443 | 444 | 445 | ```{r} 446 | x <- "Roman numerals: MDCCCLXXXVIII" 447 | ``` 448 | 449 | \begincols[T] 450 | \begincol[T]{.49\textwidth} 451 | ```{r, out.width="600%"} 452 | str_view(x, "CLX+") 453 | ``` 454 | \endcol 455 | \begincol[T]{.49\textwidth} 456 | 457 | ```{r, out.width="600%"} 458 | str_view(x, "CLX+?") 459 | ``` 460 | \endcol 461 | \endcols 462 | 463 | 464 | 465 | ## 小结一下 466 | 467 | ```{r out.width = '100%', fig.align='center', echo = FALSE} 468 | knitr::include_graphics("images/regex_repeat.jpg") 469 | ``` 470 | 471 | 472 | 473 | 474 | ## 分组与回溯引用 475 | 476 | 477 | ```{r} 478 | ft <- fruit %>% head(10) 479 | ft 480 | ``` 481 | 482 | 我们想看看这些单词里，有哪些字母是重复两次的，比如`aa`, `pp`. 如果用上面学的方法 483 | ```{r, out.width="300%"} 484 | str_view(ft, ".{2}", match = TRUE) 485 | ``` 486 | 487 | 发现是不是和我们的预想不一样呢？ 488 | 489 | 490 | 491 | ## 分组与回溯引用 492 | 所以需要用到新技术 **分组与回溯引用**， 493 | ```{r, out.width="300%"} 494 | str_view(ft, "(.)\\1", match = TRUE) 495 | ``` 496 | 497 | 498 | ## 分组与回溯引用 499 | ```{r, eval=FALSE} 500 | str_view(ft, "(.)\\1", match = TRUE) 501 | ``` 502 | 503 | - `.` 是匹配任何字符 504 | - `(.)` 将匹配项括起来，它就用了一个名字，叫`\\1`； 如果有两个括号，就叫`\\1`和`\\2` 505 | - `\\1` 表示回溯引用，表示引用`\\1`对于的`(.)` 506 | 507 | 所以`(.)\\1`的意思就是，匹配到了字符，后面还希望有个**同样的字符** 508 | 509 | 510 | 511 | ## 分组与回溯引用 512 | 如果是匹配`abab`, `wcwc` 513 | ```{r, out.width="300%"} 514 | str_view(ft, "(..)\\1", match = TRUE) 515 | ``` 516 | 517 | 如果是匹配`abba`, `wccw`呢？ 518 | 519 | ```{r, out.width="300%"} 520 | str_view(ft, "(.)(.)\\2\\1", match = TRUE) 521 | ``` 522 | 523 | 是不是很神奇？ 524 | 525 | 526 | 527 | # 进阶部分 528 | 529 | 530 | ## look ahead 531 | 532 | 想匹配Windows，同时希望Windows右侧是`"95", "98", "NT", "2000"`中的一个 533 | ```{r, out.width="300%"} 534 | win <- c("Windows2000", "Windows", "Windows3.1") 535 | str_view(win, "Windows(?=95|98|NT|2000)") 536 | ``` 537 | 538 | ## look ahead 539 | 540 | ```{r, out.width="300%"} 541 | win <- c("Windows2000", "Windows", "Windows3.1") 542 | str_view(win, "Windows(?!95|98|NT|2000)") 543 | ``` 544 | 545 | 546 | 547 | 548 | 549 | 550 | 551 | ## look behind 552 | 553 | 554 | ```{r, out.width="300%"} 555 | win <- c("2000Windows", "Windows", "3.1Windows") 556 | str_view(win, "(?<=95|98|NT|2000)Windows") 557 | ``` 558 | 559 | ## look behind 560 | 561 | ```{r, out.width="300%"} 562 | win <- c("2000Windows", "Windows", "3.1Windows") 563 | str_view(win, "(?% mutate(has_e = str_detect(x, "e")) 592 | ``` 593 | 594 | 595 | 596 | ## 确定一个字符向量是否匹配一种模式 597 | 用去筛选也很方便 598 | ```{r echo=FALSE} 599 | d <- tibble(x = c("apple", "banana", "pear") ) 600 | d 601 | ``` 602 | 603 | ```{r} 604 | d %>% filter(str_detect(x, "e")) 605 | ``` 606 | 607 | 608 | 609 | 610 | 611 | ## 提取匹配的内容 612 | 613 | 我们希望能提取第二列中的数值，构成新的一列 614 | 615 | \begincols[T] 616 | \begincol[T]{.3\textwidth} 617 | 618 | ```{r echo=FALSE} 619 | dt <- tibble( 620 | x = 1:4, 621 | y = c("wk 3", "week-1", "7", "w#9") 622 | ) 623 | dt 624 | ``` 625 | \endcol 626 | \begincol[T]{.69\textwidth} 627 | 628 | ```{r} 629 | dt %>% mutate( 630 | z = str_extract(y, "[0-9]") 631 | ) 632 | ``` 633 | 634 | \endcol 635 | \endcols 636 | 637 | 638 | 639 | 640 | 641 | ## 提取匹配的内容 642 | 643 | 644 | 回到上课提问：如何提取`Sichuan Univ`后面的学院？ 645 | ```{r echo=FALSE, message=FALSE, warning=FALSE} 646 | d <- tibble::tribble( 647 | ~No, ~address, 648 | 1L, "Sichuan Univ, Coll Chem", 649 | 2L, "Sichuan Univ, Coll Elect Engn", 650 | 3L, "Sichuan Univ, Dept Phys", 651 | 4L, "Sichuan Univ, Coll Life Sci", 652 | 6L, "Sichuan Univ, Food Engn", 653 | 7L, "Sichuan Univ, Coll Phys", 654 | 8L, "Sichuan Univ, Sch Business", 655 | 9L, "Wuhan Univ, Mat Sci" 656 | ) 657 | 658 | d 659 | ``` 660 | 661 | 662 | ## 提取匹配的内容 663 | \footnotesize 664 | ```{r} 665 | d %>% mutate( 666 | coll = str_extract(address, "(?<=Sichuan Univ,).*") 667 | ) %>% 668 | tidyr::unnest(coll, keep_empty = TRUE) 669 | ``` 670 | 671 | 672 | -------------------------------------------------------------------------------- /09_stringr/09_stringr.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/09_stringr/09_stringr.pdf -------------------------------------------------------------------------------- /09_stringr/header.tex: -------------------------------------------------------------------------------- 1 | \usepackage{ctex} 2 | \usepackage{booktabs} 3 | \usepackage{longtable} 4 | \usepackage{array} 5 | \usepackage{multirow} 6 | \usepackage{wrapfig} 7 | \usepackage{float} 8 | \usepackage{colortbl} 9 | \usepackage{pdflscape} 10 | \usepackage{tabu} 11 | \usepackage{threeparttable} 12 | \usepackage{threeparttablex} 13 | \usepackage{makecell} 14 | \usepackage{xcolor} 15 | \usepackage{xtab} 16 | 17 | \def\begincols{ 18 | \begin{columns}} \def\begincol{\begin{column}} \def\endcol{\end{column}} \def\endcols{\end{columns} 19 | } 20 | 21 | 22 | -------------------------------------------------------------------------------- /09_stringr/images/hex-stringr.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/09_stringr/images/hex-stringr.png -------------------------------------------------------------------------------- /09_stringr/images/regex_repeat.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/09_stringr/images/regex_repeat.jpg -------------------------------------------------------------------------------- /15_eda02/15_reproducible.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Exploratory Data Analysis with the Tidyverse" 3 | subtitle: "一个关于企鹅的数据故事" 4 | author: "诗与远方" 5 | date: "`r Sys.Date()`" 6 | output: 7 | pdf_document: 8 | latex_engine: xelatex 9 | extra_dependencies: 10 | ctex: UTF8 11 | number_sections: yes 12 | #toc: yes 13 | df_print: kable 14 | classoptions: "hyperref, 12pt, a4paper" 15 | --- 16 | 17 | 18 | ```{r setup, include=FALSE} 19 | knitr::opts_chunk$set( 20 | echo = TRUE, 21 | message = FALSE, 22 | warning = FALSE, 23 | fig.align = "center" 24 | ) 25 | ``` 26 | 27 | 28 | 29 | # 数据故事 30 | 31 | 今天讲一个关于企鹅的数据故事。数据来源[这里](https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-07-28/penguins.csv)，图片来源[这里](https://github.com/allisonhorst/palmerpenguins). 32 | 33 | ```{r out.width = '100%', echo = FALSE} 34 | knitr::include_graphics("images/penguins.png") 35 | ``` 36 | 37 | 38 | 39 | # 数据 40 | 41 | ## 导入数据 42 | 43 | 可通过宏包`palmerpenguins::penguins`获取数据，也可以读取本地`penguins.csv`文件， 44 | 我们采取后面一种方法： 45 | 46 | ```{r} 47 | library(tidyverse) 48 | penguins <- read_csv("./demo_data/penguins.csv") 49 | penguins %>% head(5) 50 | ``` 51 | 52 | 53 | 54 | ## 变量含义 55 | 56 | |variable |class |description | 57 | |:-----------------|:-------|:-----------| 58 | |species |integer | 企鹅种类 (Adelie, Gentoo, Chinstrap) | 59 | |island |integer | 所在岛屿 (Biscoe, Dream, Torgersen) | 60 | |bill_length_mm |double | 嘴峰长度 (单位毫米) | 61 | |bill_depth_mm |double | 嘴峰深度 (单位毫米)| 62 | |flipper_length_mm |integer | 鰭肢长度 (单位毫米) | 63 | |body_mass_g |integer | 体重 (单位克) | 64 | |sex |integer | 性别 | 65 | |year |integer | 记录年份 | 66 | 67 | 68 | 69 | ```{r out.width = '86%', echo = FALSE} 70 | knitr::include_graphics("images/culmen_depth.png") 71 | ``` 72 | 73 | ## 数据清洗 74 | ```{r} 75 | penguins %>% filter_all(any_vars(is.na(.))) 76 | ``` 77 | 78 | ```{r} 79 | d <- penguins %>% drop_na() 80 | d %>% head() 81 | ``` 82 | 83 | # 探索性分析 84 | 85 | 86 | ## 多少种类的企鹅 87 | ```{r} 88 | d %>% count(species, sort = T) 89 | ``` 90 | 91 | ## 多少个岛屿 92 | ```{r} 93 | d %>% count(island, sort = T) 94 | ``` 95 | 96 | ## 每种类型的企鹅，他们的各个属性的均值和分布 97 | ```{r} 98 | d %>% 99 | group_by(species) %>% 100 | summarise( 101 | across(where(is.numeric), mean, na.rm = T) 102 | ) 103 | ``` 104 | ```{r} 105 | d %>% 106 | ggplot(aes( x = bill_length_mm)) + 107 | geom_density() + 108 | facet_wrap(vars(species), scale = "free") 109 | ``` 110 | 111 | ```{r} 112 | library(ggridges) 113 | d %>% 114 | ggplot(aes( x = bill_depth_mm, y = species, fill = species) ) + 115 | ggridges::geom_density_ridges() 116 | 117 | ``` 118 | 119 | 120 | 121 | ```{r} 122 | d %>% select(species, body_mass_g, ends_with("_mm")) %>% 123 | pivot_longer( 124 | cols = -species, 125 | names_to = "metric", 126 | values_to = "values" 127 | ) %>% 128 | ggplot(aes(x = values, y = species, fill = species) ) + 129 | ggridges::geom_density_ridges() + 130 | facet_wrap(vars(metric), scale = "free") 131 | ``` 132 | 133 | ## 嘴巴的长度和深度的关联？ 134 | ```{r} 135 | d %>% 136 | ggplot(aes(x = bill_length_mm, y = bill_depth_mm)) + 137 | geom_point() + 138 | geom_smooth(method = lm, aes(color = species)) + 139 | geom_smooth(method = lm) 140 | ``` 141 | 142 | 143 | 144 | ## 不同种类的宝宝，体重具有显著性差异？ 145 | ```{r} 146 | d %>% 147 | ggplot(aes(x = species, y = body_mass_g)) + 148 | geom_boxplot() + 149 | geom_jitter() 150 | ``` 151 | ```{r} 152 | aov(body_mass_g ~ species, data = d) %>% summary() 153 | ``` 154 | 155 | ```{r} 156 | library(ggstatsplot) 157 | d %>% 158 | ggbetweenstats( 159 | x = species, 160 | y = body_mass_g, 161 | pairwise.comparisons = T, 162 | pairwise.display = T 163 | ) 164 | 165 | 166 | ``` 167 | 使用这个宏包辅助我们学习统计 168 | 169 | 170 | ## 通过嘴巴的长度和深度，区分企鹅的种类？性别？ 171 | 172 | 这是机器学习的范畴 173 | ```{r} 174 | d %>% 175 | ggplot(aes(x = bill_length_mm, y = bill_depth_mm, color = species, shape = species)) + 176 | geom_point() 177 | ``` 178 | 179 | 180 | ```{r} 181 | library(tidymodels) 182 | d <- d %>% mutate(species = factor(species)) 183 | 184 | split <- initial_split(d) 185 | split 186 | training_data <- training(split) 187 | testing_data <- testing(split) 188 | 189 | model <- parsnip::nearest_neighbor() %>% 190 | set_engine("kknn") %>% 191 | set_mode("classification") %>% 192 | fit(species ~ bill_length_mm + bill_depth_mm, data = training_data) 193 | 194 | 195 | predict(model, new_data = testing_data) %>% 196 | bind_cols(testing_data) %>% 197 | count(species, .pred_class) 198 | ``` 199 | 200 | 201 | 202 | -------------------------------------------------------------------------------- /15_eda02/15_reproducible.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/15_eda02/15_reproducible.pdf -------------------------------------------------------------------------------- /15_eda02/demo_data/penguins.csv: -------------------------------------------------------------------------------- 1 | species,island,bill_length_mm,bill_depth_mm,flipper_length_mm,body_mass_g,sex,year 2 | Adelie,Torgersen,39.1,18.7,181,3750,male,2007 3 | Adelie,Torgersen,39.5,17.4,186,3800,female,2007 4 | Adelie,Torgersen,40.3,18,195,3250,female,2007 5 | Adelie,Torgersen,NA,NA,NA,NA,NA,2007 6 | Adelie,Torgersen,36.7,19.3,193,3450,female,2007 7 | Adelie,Torgersen,39.3,20.6,190,3650,male,2007 8 | Adelie,Torgersen,38.9,17.8,181,3625,female,2007 9 | Adelie,Torgersen,39.2,19.6,195,4675,male,2007 10 | Adelie,Torgersen,34.1,18.1,193,3475,NA,2007 11 | Adelie,Torgersen,42,20.2,190,4250,NA,2007 12 | Adelie,Torgersen,37.8,17.1,186,3300,NA,2007 13 | Adelie,Torgersen,37.8,17.3,180,3700,NA,2007 14 | Adelie,Torgersen,41.1,17.6,182,3200,female,2007 15 | Adelie,Torgersen,38.6,21.2,191,3800,male,2007 16 | Adelie,Torgersen,34.6,21.1,198,4400,male,2007 17 | Adelie,Torgersen,36.6,17.8,185,3700,female,2007 18 | Adelie,Torgersen,38.7,19,195,3450,female,2007 19 | Adelie,Torgersen,42.5,20.7,197,4500,male,2007 20 | Adelie,Torgersen,34.4,18.4,184,3325,female,2007 21 | Adelie,Torgersen,46,21.5,194,4200,male,2007 22 | Adelie,Biscoe,37.8,18.3,174,3400,female,2007 23 | Adelie,Biscoe,37.7,18.7,180,3600,male,2007 24 | Adelie,Biscoe,35.9,19.2,189,3800,female,2007 25 | Adelie,Biscoe,38.2,18.1,185,3950,male,2007 26 | Adelie,Biscoe,38.8,17.2,180,3800,male,2007 27 | Adelie,Biscoe,35.3,18.9,187,3800,female,2007 28 | Adelie,Biscoe,40.6,18.6,183,3550,male,2007 29 | Adelie,Biscoe,40.5,17.9,187,3200,female,2007 30 | Adelie,Biscoe,37.9,18.6,172,3150,female,2007 31 | Adelie,Biscoe,40.5,18.9,180,3950,male,2007 32 | Adelie,Dream,39.5,16.7,178,3250,female,2007 33 | Adelie,Dream,37.2,18.1,178,3900,male,2007 34 | Adelie,Dream,39.5,17.8,188,3300,female,2007 35 | Adelie,Dream,40.9,18.9,184,3900,male,2007 36 | Adelie,Dream,36.4,17,195,3325,female,2007 37 | Adelie,Dream,39.2,21.1,196,4150,male,2007 38 | Adelie,Dream,38.8,20,190,3950,male,2007 39 | Adelie,Dream,42.2,18.5,180,3550,female,2007 40 | Adelie,Dream,37.6,19.3,181,3300,female,2007 41 | Adelie,Dream,39.8,19.1,184,4650,male,2007 42 | Adelie,Dream,36.5,18,182,3150,female,2007 43 | Adelie,Dream,40.8,18.4,195,3900,male,2007 44 | Adelie,Dream,36,18.5,186,3100,female,2007 45 | Adelie,Dream,44.1,19.7,196,4400,male,2007 46 | Adelie,Dream,37,16.9,185,3000,female,2007 47 | Adelie,Dream,39.6,18.8,190,4600,male,2007 48 | Adelie,Dream,41.1,19,182,3425,male,2007 49 | Adelie,Dream,37.5,18.9,179,2975,NA,2007 50 | Adelie,Dream,36,17.9,190,3450,female,2007 51 | Adelie,Dream,42.3,21.2,191,4150,male,2007 52 | Adelie,Biscoe,39.6,17.7,186,3500,female,2008 53 | Adelie,Biscoe,40.1,18.9,188,4300,male,2008 54 | Adelie,Biscoe,35,17.9,190,3450,female,2008 55 | Adelie,Biscoe,42,19.5,200,4050,male,2008 56 | Adelie,Biscoe,34.5,18.1,187,2900,female,2008 57 | Adelie,Biscoe,41.4,18.6,191,3700,male,2008 58 | Adelie,Biscoe,39,17.5,186,3550,female,2008 59 | Adelie,Biscoe,40.6,18.8,193,3800,male,2008 60 | Adelie,Biscoe,36.5,16.6,181,2850,female,2008 61 | Adelie,Biscoe,37.6,19.1,194,3750,male,2008 62 | Adelie,Biscoe,35.7,16.9,185,3150,female,2008 63 | Adelie,Biscoe,41.3,21.1,195,4400,male,2008 64 | Adelie,Biscoe,37.6,17,185,3600,female,2008 65 | Adelie,Biscoe,41.1,18.2,192,4050,male,2008 66 | Adelie,Biscoe,36.4,17.1,184,2850,female,2008 67 | Adelie,Biscoe,41.6,18,192,3950,male,2008 68 | Adelie,Biscoe,35.5,16.2,195,3350,female,2008 69 | Adelie,Biscoe,41.1,19.1,188,4100,male,2008 70 | Adelie,Torgersen,35.9,16.6,190,3050,female,2008 71 | Adelie,Torgersen,41.8,19.4,198,4450,male,2008 72 | Adelie,Torgersen,33.5,19,190,3600,female,2008 73 | Adelie,Torgersen,39.7,18.4,190,3900,male,2008 74 | Adelie,Torgersen,39.6,17.2,196,3550,female,2008 75 | Adelie,Torgersen,45.8,18.9,197,4150,male,2008 76 | Adelie,Torgersen,35.5,17.5,190,3700,female,2008 77 | Adelie,Torgersen,42.8,18.5,195,4250,male,2008 78 | Adelie,Torgersen,40.9,16.8,191,3700,female,2008 79 | Adelie,Torgersen,37.2,19.4,184,3900,male,2008 80 | Adelie,Torgersen,36.2,16.1,187,3550,female,2008 81 | Adelie,Torgersen,42.1,19.1,195,4000,male,2008 82 | Adelie,Torgersen,34.6,17.2,189,3200,female,2008 83 | Adelie,Torgersen,42.9,17.6,196,4700,male,2008 84 | Adelie,Torgersen,36.7,18.8,187,3800,female,2008 85 | Adelie,Torgersen,35.1,19.4,193,4200,male,2008 86 | Adelie,Dream,37.3,17.8,191,3350,female,2008 87 | Adelie,Dream,41.3,20.3,194,3550,male,2008 88 | Adelie,Dream,36.3,19.5,190,3800,male,2008 89 | Adelie,Dream,36.9,18.6,189,3500,female,2008 90 | Adelie,Dream,38.3,19.2,189,3950,male,2008 91 | Adelie,Dream,38.9,18.8,190,3600,female,2008 92 | Adelie,Dream,35.7,18,202,3550,female,2008 93 | Adelie,Dream,41.1,18.1,205,4300,male,2008 94 | Adelie,Dream,34,17.1,185,3400,female,2008 95 | Adelie,Dream,39.6,18.1,186,4450,male,2008 96 | Adelie,Dream,36.2,17.3,187,3300,female,2008 97 | Adelie,Dream,40.8,18.9,208,4300,male,2008 98 | Adelie,Dream,38.1,18.6,190,3700,female,2008 99 | Adelie,Dream,40.3,18.5,196,4350,male,2008 100 | Adelie,Dream,33.1,16.1,178,2900,female,2008 101 | Adelie,Dream,43.2,18.5,192,4100,male,2008 102 | Adelie,Biscoe,35,17.9,192,3725,female,2009 103 | Adelie,Biscoe,41,20,203,4725,male,2009 104 | Adelie,Biscoe,37.7,16,183,3075,female,2009 105 | Adelie,Biscoe,37.8,20,190,4250,male,2009 106 | Adelie,Biscoe,37.9,18.6,193,2925,female,2009 107 | Adelie,Biscoe,39.7,18.9,184,3550,male,2009 108 | Adelie,Biscoe,38.6,17.2,199,3750,female,2009 109 | Adelie,Biscoe,38.2,20,190,3900,male,2009 110 | Adelie,Biscoe,38.1,17,181,3175,female,2009 111 | Adelie,Biscoe,43.2,19,197,4775,male,2009 112 | Adelie,Biscoe,38.1,16.5,198,3825,female,2009 113 | Adelie,Biscoe,45.6,20.3,191,4600,male,2009 114 | Adelie,Biscoe,39.7,17.7,193,3200,female,2009 115 | Adelie,Biscoe,42.2,19.5,197,4275,male,2009 116 | Adelie,Biscoe,39.6,20.7,191,3900,female,2009 117 | Adelie,Biscoe,42.7,18.3,196,4075,male,2009 118 | Adelie,Torgersen,38.6,17,188,2900,female,2009 119 | Adelie,Torgersen,37.3,20.5,199,3775,male,2009 120 | Adelie,Torgersen,35.7,17,189,3350,female,2009 121 | Adelie,Torgersen,41.1,18.6,189,3325,male,2009 122 | Adelie,Torgersen,36.2,17.2,187,3150,female,2009 123 | Adelie,Torgersen,37.7,19.8,198,3500,male,2009 124 | Adelie,Torgersen,40.2,17,176,3450,female,2009 125 | Adelie,Torgersen,41.4,18.5,202,3875,male,2009 126 | Adelie,Torgersen,35.2,15.9,186,3050,female,2009 127 | Adelie,Torgersen,40.6,19,199,4000,male,2009 128 | Adelie,Torgersen,38.8,17.6,191,3275,female,2009 129 | Adelie,Torgersen,41.5,18.3,195,4300,male,2009 130 | Adelie,Torgersen,39,17.1,191,3050,female,2009 131 | Adelie,Torgersen,44.1,18,210,4000,male,2009 132 | Adelie,Torgersen,38.5,17.9,190,3325,female,2009 133 | Adelie,Torgersen,43.1,19.2,197,3500,male,2009 134 | Adelie,Dream,36.8,18.5,193,3500,female,2009 135 | Adelie,Dream,37.5,18.5,199,4475,male,2009 136 | Adelie,Dream,38.1,17.6,187,3425,female,2009 137 | Adelie,Dream,41.1,17.5,190,3900,male,2009 138 | Adelie,Dream,35.6,17.5,191,3175,female,2009 139 | Adelie,Dream,40.2,20.1,200,3975,male,2009 140 | Adelie,Dream,37,16.5,185,3400,female,2009 141 | Adelie,Dream,39.7,17.9,193,4250,male,2009 142 | Adelie,Dream,40.2,17.1,193,3400,female,2009 143 | Adelie,Dream,40.6,17.2,187,3475,male,2009 144 | Adelie,Dream,32.1,15.5,188,3050,female,2009 145 | Adelie,Dream,40.7,17,190,3725,male,2009 146 | Adelie,Dream,37.3,16.8,192,3000,female,2009 147 | Adelie,Dream,39,18.7,185,3650,male,2009 148 | Adelie,Dream,39.2,18.6,190,4250,male,2009 149 | Adelie,Dream,36.6,18.4,184,3475,female,2009 150 | Adelie,Dream,36,17.8,195,3450,female,2009 151 | Adelie,Dream,37.8,18.1,193,3750,male,2009 152 | Adelie,Dream,36,17.1,187,3700,female,2009 153 | Adelie,Dream,41.5,18.5,201,4000,male,2009 154 | Gentoo,Biscoe,46.1,13.2,211,4500,female,2007 155 | Gentoo,Biscoe,50,16.3,230,5700,male,2007 156 | Gentoo,Biscoe,48.7,14.1,210,4450,female,2007 157 | Gentoo,Biscoe,50,15.2,218,5700,male,2007 158 | Gentoo,Biscoe,47.6,14.5,215,5400,male,2007 159 | Gentoo,Biscoe,46.5,13.5,210,4550,female,2007 160 | Gentoo,Biscoe,45.4,14.6,211,4800,female,2007 161 | Gentoo,Biscoe,46.7,15.3,219,5200,male,2007 162 | Gentoo,Biscoe,43.3,13.4,209,4400,female,2007 163 | Gentoo,Biscoe,46.8,15.4,215,5150,male,2007 164 | Gentoo,Biscoe,40.9,13.7,214,4650,female,2007 165 | Gentoo,Biscoe,49,16.1,216,5550,male,2007 166 | Gentoo,Biscoe,45.5,13.7,214,4650,female,2007 167 | Gentoo,Biscoe,48.4,14.6,213,5850,male,2007 168 | Gentoo,Biscoe,45.8,14.6,210,4200,female,2007 169 | Gentoo,Biscoe,49.3,15.7,217,5850,male,2007 170 | Gentoo,Biscoe,42,13.5,210,4150,female,2007 171 | Gentoo,Biscoe,49.2,15.2,221,6300,male,2007 172 | Gentoo,Biscoe,46.2,14.5,209,4800,female,2007 173 | Gentoo,Biscoe,48.7,15.1,222,5350,male,2007 174 | Gentoo,Biscoe,50.2,14.3,218,5700,male,2007 175 | Gentoo,Biscoe,45.1,14.5,215,5000,female,2007 176 | Gentoo,Biscoe,46.5,14.5,213,4400,female,2007 177 | Gentoo,Biscoe,46.3,15.8,215,5050,male,2007 178 | Gentoo,Biscoe,42.9,13.1,215,5000,female,2007 179 | Gentoo,Biscoe,46.1,15.1,215,5100,male,2007 180 | Gentoo,Biscoe,44.5,14.3,216,4100,NA,2007 181 | Gentoo,Biscoe,47.8,15,215,5650,male,2007 182 | Gentoo,Biscoe,48.2,14.3,210,4600,female,2007 183 | Gentoo,Biscoe,50,15.3,220,5550,male,2007 184 | Gentoo,Biscoe,47.3,15.3,222,5250,male,2007 185 | Gentoo,Biscoe,42.8,14.2,209,4700,female,2007 186 | Gentoo,Biscoe,45.1,14.5,207,5050,female,2007 187 | Gentoo,Biscoe,59.6,17,230,6050,male,2007 188 | Gentoo,Biscoe,49.1,14.8,220,5150,female,2008 189 | Gentoo,Biscoe,48.4,16.3,220,5400,male,2008 190 | Gentoo,Biscoe,42.6,13.7,213,4950,female,2008 191 | Gentoo,Biscoe,44.4,17.3,219,5250,male,2008 192 | Gentoo,Biscoe,44,13.6,208,4350,female,2008 193 | Gentoo,Biscoe,48.7,15.7,208,5350,male,2008 194 | Gentoo,Biscoe,42.7,13.7,208,3950,female,2008 195 | Gentoo,Biscoe,49.6,16,225,5700,male,2008 196 | Gentoo,Biscoe,45.3,13.7,210,4300,female,2008 197 | Gentoo,Biscoe,49.6,15,216,4750,male,2008 198 | Gentoo,Biscoe,50.5,15.9,222,5550,male,2008 199 | Gentoo,Biscoe,43.6,13.9,217,4900,female,2008 200 | Gentoo,Biscoe,45.5,13.9,210,4200,female,2008 201 | Gentoo,Biscoe,50.5,15.9,225,5400,male,2008 202 | Gentoo,Biscoe,44.9,13.3,213,5100,female,2008 203 | Gentoo,Biscoe,45.2,15.8,215,5300,male,2008 204 | Gentoo,Biscoe,46.6,14.2,210,4850,female,2008 205 | Gentoo,Biscoe,48.5,14.1,220,5300,male,2008 206 | Gentoo,Biscoe,45.1,14.4,210,4400,female,2008 207 | Gentoo,Biscoe,50.1,15,225,5000,male,2008 208 | Gentoo,Biscoe,46.5,14.4,217,4900,female,2008 209 | Gentoo,Biscoe,45,15.4,220,5050,male,2008 210 | Gentoo,Biscoe,43.8,13.9,208,4300,female,2008 211 | Gentoo,Biscoe,45.5,15,220,5000,male,2008 212 | Gentoo,Biscoe,43.2,14.5,208,4450,female,2008 213 | Gentoo,Biscoe,50.4,15.3,224,5550,male,2008 214 | Gentoo,Biscoe,45.3,13.8,208,4200,female,2008 215 | Gentoo,Biscoe,46.2,14.9,221,5300,male,2008 216 | Gentoo,Biscoe,45.7,13.9,214,4400,female,2008 217 | Gentoo,Biscoe,54.3,15.7,231,5650,male,2008 218 | Gentoo,Biscoe,45.8,14.2,219,4700,female,2008 219 | Gentoo,Biscoe,49.8,16.8,230,5700,male,2008 220 | Gentoo,Biscoe,46.2,14.4,214,4650,NA,2008 221 | Gentoo,Biscoe,49.5,16.2,229,5800,male,2008 222 | Gentoo,Biscoe,43.5,14.2,220,4700,female,2008 223 | Gentoo,Biscoe,50.7,15,223,5550,male,2008 224 | Gentoo,Biscoe,47.7,15,216,4750,female,2008 225 | Gentoo,Biscoe,46.4,15.6,221,5000,male,2008 226 | Gentoo,Biscoe,48.2,15.6,221,5100,male,2008 227 | Gentoo,Biscoe,46.5,14.8,217,5200,female,2008 228 | Gentoo,Biscoe,46.4,15,216,4700,female,2008 229 | Gentoo,Biscoe,48.6,16,230,5800,male,2008 230 | Gentoo,Biscoe,47.5,14.2,209,4600,female,2008 231 | Gentoo,Biscoe,51.1,16.3,220,6000,male,2008 232 | Gentoo,Biscoe,45.2,13.8,215,4750,female,2008 233 | Gentoo,Biscoe,45.2,16.4,223,5950,male,2008 234 | Gentoo,Biscoe,49.1,14.5,212,4625,female,2009 235 | Gentoo,Biscoe,52.5,15.6,221,5450,male,2009 236 | Gentoo,Biscoe,47.4,14.6,212,4725,female,2009 237 | Gentoo,Biscoe,50,15.9,224,5350,male,2009 238 | Gentoo,Biscoe,44.9,13.8,212,4750,female,2009 239 | Gentoo,Biscoe,50.8,17.3,228,5600,male,2009 240 | Gentoo,Biscoe,43.4,14.4,218,4600,female,2009 241 | Gentoo,Biscoe,51.3,14.2,218,5300,male,2009 242 | Gentoo,Biscoe,47.5,14,212,4875,female,2009 243 | Gentoo,Biscoe,52.1,17,230,5550,male,2009 244 | Gentoo,Biscoe,47.5,15,218,4950,female,2009 245 | Gentoo,Biscoe,52.2,17.1,228,5400,male,2009 246 | Gentoo,Biscoe,45.5,14.5,212,4750,female,2009 247 | Gentoo,Biscoe,49.5,16.1,224,5650,male,2009 248 | Gentoo,Biscoe,44.5,14.7,214,4850,female,2009 249 | Gentoo,Biscoe,50.8,15.7,226,5200,male,2009 250 | Gentoo,Biscoe,49.4,15.8,216,4925,male,2009 251 | Gentoo,Biscoe,46.9,14.6,222,4875,female,2009 252 | Gentoo,Biscoe,48.4,14.4,203,4625,female,2009 253 | Gentoo,Biscoe,51.1,16.5,225,5250,male,2009 254 | Gentoo,Biscoe,48.5,15,219,4850,female,2009 255 | Gentoo,Biscoe,55.9,17,228,5600,male,2009 256 | Gentoo,Biscoe,47.2,15.5,215,4975,female,2009 257 | Gentoo,Biscoe,49.1,15,228,5500,male,2009 258 | Gentoo,Biscoe,47.3,13.8,216,4725,NA,2009 259 | Gentoo,Biscoe,46.8,16.1,215,5500,male,2009 260 | Gentoo,Biscoe,41.7,14.7,210,4700,female,2009 261 | Gentoo,Biscoe,53.4,15.8,219,5500,male,2009 262 | Gentoo,Biscoe,43.3,14,208,4575,female,2009 263 | Gentoo,Biscoe,48.1,15.1,209,5500,male,2009 264 | Gentoo,Biscoe,50.5,15.2,216,5000,female,2009 265 | Gentoo,Biscoe,49.8,15.9,229,5950,male,2009 266 | Gentoo,Biscoe,43.5,15.2,213,4650,female,2009 267 | Gentoo,Biscoe,51.5,16.3,230,5500,male,2009 268 | Gentoo,Biscoe,46.2,14.1,217,4375,female,2009 269 | Gentoo,Biscoe,55.1,16,230,5850,male,2009 270 | Gentoo,Biscoe,44.5,15.7,217,4875,NA,2009 271 | Gentoo,Biscoe,48.8,16.2,222,6000,male,2009 272 | Gentoo,Biscoe,47.2,13.7,214,4925,female,2009 273 | Gentoo,Biscoe,NA,NA,NA,NA,NA,2009 274 | Gentoo,Biscoe,46.8,14.3,215,4850,female,2009 275 | Gentoo,Biscoe,50.4,15.7,222,5750,male,2009 276 | Gentoo,Biscoe,45.2,14.8,212,5200,female,2009 277 | Gentoo,Biscoe,49.9,16.1,213,5400,male,2009 278 | Chinstrap,Dream,46.5,17.9,192,3500,female,2007 279 | Chinstrap,Dream,50,19.5,196,3900,male,2007 280 | Chinstrap,Dream,51.3,19.2,193,3650,male,2007 281 | Chinstrap,Dream,45.4,18.7,188,3525,female,2007 282 | Chinstrap,Dream,52.7,19.8,197,3725,male,2007 283 | Chinstrap,Dream,45.2,17.8,198,3950,female,2007 284 | Chinstrap,Dream,46.1,18.2,178,3250,female,2007 285 | Chinstrap,Dream,51.3,18.2,197,3750,male,2007 286 | Chinstrap,Dream,46,18.9,195,4150,female,2007 287 | Chinstrap,Dream,51.3,19.9,198,3700,male,2007 288 | Chinstrap,Dream,46.6,17.8,193,3800,female,2007 289 | Chinstrap,Dream,51.7,20.3,194,3775,male,2007 290 | Chinstrap,Dream,47,17.3,185,3700,female,2007 291 | Chinstrap,Dream,52,18.1,201,4050,male,2007 292 | Chinstrap,Dream,45.9,17.1,190,3575,female,2007 293 | Chinstrap,Dream,50.5,19.6,201,4050,male,2007 294 | Chinstrap,Dream,50.3,20,197,3300,male,2007 295 | Chinstrap,Dream,58,17.8,181,3700,female,2007 296 | Chinstrap,Dream,46.4,18.6,190,3450,female,2007 297 | Chinstrap,Dream,49.2,18.2,195,4400,male,2007 298 | Chinstrap,Dream,42.4,17.3,181,3600,female,2007 299 | Chinstrap,Dream,48.5,17.5,191,3400,male,2007 300 | Chinstrap,Dream,43.2,16.6,187,2900,female,2007 301 | Chinstrap,Dream,50.6,19.4,193,3800,male,2007 302 | Chinstrap,Dream,46.7,17.9,195,3300,female,2007 303 | Chinstrap,Dream,52,19,197,4150,male,2007 304 | Chinstrap,Dream,50.5,18.4,200,3400,female,2008 305 | Chinstrap,Dream,49.5,19,200,3800,male,2008 306 | Chinstrap,Dream,46.4,17.8,191,3700,female,2008 307 | Chinstrap,Dream,52.8,20,205,4550,male,2008 308 | Chinstrap,Dream,40.9,16.6,187,3200,female,2008 309 | Chinstrap,Dream,54.2,20.8,201,4300,male,2008 310 | Chinstrap,Dream,42.5,16.7,187,3350,female,2008 311 | Chinstrap,Dream,51,18.8,203,4100,male,2008 312 | Chinstrap,Dream,49.7,18.6,195,3600,male,2008 313 | Chinstrap,Dream,47.5,16.8,199,3900,female,2008 314 | Chinstrap,Dream,47.6,18.3,195,3850,female,2008 315 | Chinstrap,Dream,52,20.7,210,4800,male,2008 316 | Chinstrap,Dream,46.9,16.6,192,2700,female,2008 317 | Chinstrap,Dream,53.5,19.9,205,4500,male,2008 318 | Chinstrap,Dream,49,19.5,210,3950,male,2008 319 | Chinstrap,Dream,46.2,17.5,187,3650,female,2008 320 | Chinstrap,Dream,50.9,19.1,196,3550,male,2008 321 | Chinstrap,Dream,45.5,17,196,3500,female,2008 322 | Chinstrap,Dream,50.9,17.9,196,3675,female,2009 323 | Chinstrap,Dream,50.8,18.5,201,4450,male,2009 324 | Chinstrap,Dream,50.1,17.9,190,3400,female,2009 325 | Chinstrap,Dream,49,19.6,212,4300,male,2009 326 | Chinstrap,Dream,51.5,18.7,187,3250,male,2009 327 | Chinstrap,Dream,49.8,17.3,198,3675,female,2009 328 | Chinstrap,Dream,48.1,16.4,199,3325,female,2009 329 | Chinstrap,Dream,51.4,19,201,3950,male,2009 330 | Chinstrap,Dream,45.7,17.3,193,3600,female,2009 331 | Chinstrap,Dream,50.7,19.7,203,4050,male,2009 332 | Chinstrap,Dream,42.5,17.3,187,3350,female,2009 333 | Chinstrap,Dream,52.2,18.8,197,3450,male,2009 334 | Chinstrap,Dream,45.2,16.6,191,3250,female,2009 335 | Chinstrap,Dream,49.3,19.9,203,4050,male,2009 336 | Chinstrap,Dream,50.2,18.8,202,3800,male,2009 337 | Chinstrap,Dream,45.6,19.4,194,3525,female,2009 338 | Chinstrap,Dream,51.9,19.5,206,3950,male,2009 339 | Chinstrap,Dream,46.8,16.5,189,3650,female,2009 340 | Chinstrap,Dream,45.7,17,195,3650,female,2009 341 | Chinstrap,Dream,55.8,19.8,207,4000,male,2009 342 | Chinstrap,Dream,43.5,18.1,202,3400,female,2009 343 | Chinstrap,Dream,49.6,18.2,193,3775,male,2009 344 | Chinstrap,Dream,50.8,19,210,4100,male,2009 345 | Chinstrap,Dream,50.2,18.7,198,3775,female,2009 346 | 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-------------------------------------------------------------------------------- 1 | # 数据科学中的 R 语言 2 | 3 | 4 |

5 | 6 | 7 | 8 | ## 课程简介 9 | 10 | - 数据科学是综合了统计学、计算机科学和专业领域的交叉学科，具体内容就是用数据的方法研究科学，用科学的方法研究数据。 11 | - R 是一种统计分析的编程语言，集统计分析与图形显示于一体。通过学习和掌握语言的语法，可以编制自己的函数来扩展现有的语言。 12 | - 2019 年国际统计学年会将考普斯总统奖（统计学界的诺贝尔奖）颁给 R 语言宏包 tidyverse 的作者 Hadley Wickham，说明 R 语言得到了学术界的充分认可。 13 | - 由于统计分析能力突出、作图功能强大、拓展与开发能力强等特点，在国际上，R 语言在自然科学和社会科学研究领域，得到了越来越广泛的应用。 14 | 15 | 本课程将以 R 语言作为数据科学学习之旅的新起点，讲解 R 语言入门基础、数据可视化、数据处理、探索性分析、统计建模、案例解析以及在代表性领域的应用，适用于研究生和博士生。 16 | 17 | 18 | ## 课程目标 19 | 训练数据思维、提升编程技能、培养创新能力 20 | 21 | 22 | ## 课程内容 23 | 24 | 25 | | 时间 | 标题 | 主要内容 | 课时 | 课件 | 26 | |-------- |------------------- |-------------------------------------------------------------------------------------------- |------ |---------------------------------------------------------------------------------------------------------------------- | 27 | | week01 | Why R? | R是什么？R能干什么？为什么是R? | 1 | [00_whyR.pdf](https://github.com/perlatex/R4DS_slides/blob/master/00_whyR/00_whyR.pdf) | 28 | | week01 | 数据科学基础 | 了解数据科学流程，配置运行环境，安装R和Rstudio，以及如何安装所需要的宏包 | 1 | [01_install.pdf](https://github.com/perlatex/R4DS_slides/blob/master/01_install/01_install.pdf) | 29 | | week02 | R语言基础 | 基本运算、数据类型、数据结构、常用统计函数、分支，循环等，了解脚本、宏包，以及如何获取帮助 | 2 | [02_basicR.pdf](https://github.com/perlatex/R4DS_slides/blob/master/02_basicR/02_basicR.pdf) | 30 | | week03 | 子集选取 | 向量、列表、矩阵、数据框 | 2 | [03_subset.pdf](https://github.com/perlatex/R4DS_slides/blob/master/03_subset/03_subset.pdf) | 31 | | week04 | 可重复性研究 | Rmarkdown语法，生成html格式报告、生成pdf格式报告、生成word格式报告 | 2 | [04_Rmarkdown.pdf](https://github.com/perlatex/R4DS_slides/blob/master/04_Rmarkdown/04_Rmarkdown.pdf) | 32 | | week05 | 数据处理 | 读取外部数据，存储数据，dplyr数据处理，案例讲解 | 2 | [05_dplyr.pdf](https://github.com/perlatex/R4DS_slides/blob/master/05_dplyr/05_dplyr.pdf) | 33 | | week06 | 数据可视化1 | ggplot2基本语法、映射、设置、图片保存 | 2 | [06_ggplot2.pdf](https://github.com/perlatex/R4DS_slides/blob/master/06_ggplot2/06_ggplot2.pdf) | 34 | | week07 | 数据可视化2 | 几何对象、主题风格、标度体系、图例系统 | 2 | [07_ggplot2.pdf](https://github.com/perlatex/R4DS_slides/blob/master/07_ggplot2/07_ggplot2.pdf) | 35 | | week08 | 探索性数据分析1 | 结合案例数据，综合运用数据处理、可视化探索技能 | 2 | [08_eda01.pdf](https://github.com/perlatex/R4DS_slides/blob/master/08_eda01/08_eda01.pdf) | 36 | | week09 | 字符串处理 | 正则表达式，文本信息提取 | 2 | [09_stringr.pdf](https://github.com/perlatex/R4DS_slides/blob/master/09_stringr/09_stringr.pdf) | 37 | | week10 | 因子类型数据 | 因子型变量的处理和应用 | 2 | [10_forcats.pdf](https://github.com/perlatex/R4DS_slides/blob/master/10_forcats/10_forcats.pdf) | 38 | | week11 | 线性回归 | 一元回归、多元回归模型，重点是分析和解释模型输出、拟合与预测 | 2 | [11_lm.pdf](https://github.com/perlatex/R4DS_slides/blob/master/11_lm/11_lm.pdf) | 39 | | week12 | 基础统计分析 | 基本描述统计，假设检验，方差分析，以及与线性回归的等价性 | 2 | [12_tidystats.pdf](https://github.com/perlatex/R4DS_slides/blob/master/12_tidystats/12_tidystats.pdf) | 40 | | week13 | 函数式编程 | 安全高效的迭代处理技术 | 2 | [13_purrr.pdf](https://github.com/perlatex/R4DS_slides/blob/master/13_purrr/13_purrr.pdf) | 41 | | week14 | tidyverse编程进阶 | 各种应用场景，常用函数和技巧 | 2 | [14_tidyverse_tips.pdf](https://github.com/perlatex/R4DS_slides/blob/master/14_tidyverse_tips/14_tidyverse_tips.pdf) | 42 | | week15 | 探索性数据分析2 | 结合具体案例，完成数据分析和建模，训练数据思维 | 2 | [15_eda02.pdf](https://github.com/perlatex/R4DS_slides/blob/master/15_eda02/15_eda02.pdf) | 43 | 44 | 45 | 46 | 47 | ## 关于考核 48 | 结合所在学科，找一篇与自己研究方向相关的文献，用课堂上学到的 R 统计编程技能，**重复**文献的数据分析和可视化过程. 49 | 50 | 51 | 52 | 53 | ## 参考书目 54 | - [https://r4ds.had.co.nz/](https://r4ds.had.co.nz/) 55 | - [https://bookdown.org/wangminjie/R4DS/](https://bookdown.org/wangminjie/R4DS/) 56 | 57 | 58 | 59 | 60 | ## 我会努力的 61 | 愿 R 语言成为你构建知识大厦的脚手架！ 62 | -------------------------------------------------------------------------------- /data_science.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/perlatex/R4DS_slides/117751057aaab7e4ebc2396641e1c32a09826e41/data_science.jpg --------------------------------------------------------------------------------