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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>chapter_7.knit</title>
<meta charset="utf-8" />
<meta name="author" content="Pac_B" />
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<body>
<textarea id="source">
class: center, middle
<span style="font-size: 50px;">**第七章**</span> <br>
<span style="font-size: 50px;">__如何探索数据: __</span> <br>
<span style="font-size: 40px;">描述性统计与数据可视化基础</span><br>
<span style="font-size: 30px;">胡传鹏</span> <br>
<span style="font-size: 20px;"> </span> <br>
<span style="font-size: 30px;">2024-04-19</span> <br>
<span style="font-size: 20px;"> Made with Rmarkdown</span> <br>
<style type="text/css">
/* ---- extra.css ---- */
.bigfont {
font-size: 30px;
}
.size5{
font-size: 20px;
}
.tit_font{
font-size: 60px;
}
</style>
---
# Packages
```r
if (!requireNamespace('pacman', quietly = TRUE)) {
install.packages('pacman')
}
pacman::p_load(
# 本节课需要用到的 packages
here,skimr,quartets,GGally,showtext,bruceR,tidyverse,DataExplorer,
# 生成课件
xaringan,xaringanthemer,xaringanExtra)
```
---
class: inverse center middle
.tit_font[
回顾
]
???
- 回顾
- 数据探索与描述统计
- 为什么可视化
- 为什么用 ggplot 画图
- ggplot 绘图的原理
- 基本图形简介
- 散点图
- 柱状图
- 密度图
-
---
## 7.0.1 批量导入数据
.panelset[
.panel[.panel-name[获取地址]
```r
# 所有数据路径
*files <- list.files(
## <- & =
here::here("data", "match"),
pattern = "data_exp7_rep_match_.*\\.out$",
full.names = TRUE)
```
.panel[.panel-name[数据类型转换]
```r
convert_data_types <- function(df) {
df <- df %>%
dplyr::mutate(Date = as.character(Date),Prac = as.character(Prac),
Sub = as.numeric(Sub),Age = as.numeric(Age),
Sex = as.character(Sex),Hand = as.character(Hand),
Block = as.numeric(Block),Bin = as.numeric(Bin),
Trial = as.numeric(Trial),Shape = as.character(Shape),
Label = as.character(Label),Match = as.character(Match),
CorrResp = as.character(CorrResp),Resp = as.character(Resp),
ACC = as.numeric(ACC),RT = as.numeric(RT))
return(df)
}
```
.panel[.panel-name[批量合并]
```r
df3 <- data.frame()
for (i in seq_along(files)) {
# 读取
df <- read.table(files[i], header = TRUE) %>%
dplyr::filter(Date != "Date") %>%
convert_data_types()
# 合并
df3 <- dplyr::bind_rows(df3, df)
}
# 删除临时变量
rm(df, files, i)
```
.panel[.panel-name[保存数据]
```r
## NOT RUN
## 上节课介绍了write.csv,也可使用bruceR::export
bruceR::export(
df3,
file = here::here("data", "match","match_raw.csv"))
## 当然，export 不仅可以保存数据，也可以输出模型结果
```
.panel[.panel-name[修改列名-rename]
```r
## 修改第一列列名 Date 为小写 date
df3 %>% dplyr::rename( ## new_name = old_name
date = Date
) %>% colnames()
```
```
## [1] "date" "Prac" "Sub" "Age" "Sex" "Hand"
## [7] "Block" "Bin" "Trial" "Shape" "Label" "Match"
## [13] "CorrResp" "Resp" "ACC" "RT"
```
```r
## 将全部列名都变成小写
df3 %>% dplyr::rename_with(
## 将字符向量全部变成小写； ~ 声明这是一个函数，.代表前面的数据(df3)传到.所在的位置
* ~tolower(.)
## 即使用 tolower()对所有列名进行批量处理
##
) %>% colnames()
```
```
## [1] "date" "prac" "sub" "age" "sex" "hand"
## [7] "block" "bin" "trial" "shape" "label" "match"
## [13] "corrresp" "resp" "acc" "rt"
```
]
]
]
]
]
]
???
- 解释 变量赋值与参数接受符号差别
- 代码规范性，R 对缩进换行不敏感
- export 函数
---
## 7.0.2 代码书写规范
```r
## 看起来如何？
iris %>% group_by(Species) %>% summarize_all(mean) %>%
ungroup %>% gather(measure, value, -Species) %>%
arrange(value)
```
--
```r
### 是不是更整洁一些
iris %>%
dplyr::group_by(Species) %>%
dplyr::summarize_if(is.numeric, mean) %>%
dplyr::ungroup() %>%
tidyr::gather(measure, value, -Species) %>%
dplyr::arrange(value)
## 有没有自动挡…
```
.footnote[
-----
参考链接：[tidyverse style guide](https://style.tidyverse.org/index.html)
]
---
## 7.0.2 代码书写规范
```r
## 使用 Ctrl + Shift + A (cmd+shift+A in Mac)
## 或者点击 Code --> Reformat Code (快捷键冲突)
## 自动增加空格与缩进
## 选中下面代码看看效果，尽管效果有限
## 还是平时养成书写习惯更好
files<-list.files(
here::here("data","match"),
pattern="data_exp7_rep_match_.*\\.out$",
full.names=TRUE)
```
.footnote[
-----
参考链接：[tidyverse style guide](https://style.tidyverse.org/index.html)
]
---
## 7.0.3 数据清洗
.pull-left[
### 列操作
- 增加、修改、删除(mutate)
- 列筛选(select & tidyseletion)
### 行操作
- 条件过滤(filter)
- 行索引(slice)
### 数据框
- 列/行合并：
bind_cols & bind_rows
- 匹配合并：
left_join, right_join, full_join
- 修改列名：
rownames or dplyr::rename
- 长/宽数据转换：
pivot_longer, pivot_wider
]
.pull-right[
### 分组计算
- group_by/ungroup
- summarise
- rowMeans or bruceR::MEAN(or SUM)
- case_when()
- 多列选取：across()
<br>
### 字符串(stringr package)
- str_xxx()系列
<br>
### 函数式编程(purrr package)
- map()系列，类似 apply() 系列
]
---
class: center,middle
<span style="font-size: 50px;">**第七章**</span> <br>
<span style="font-size: 50px;">__如何探索数据: __</span> <br>
<span style="font-size: 40px;">描述性统计与数据可视化基础</span><br>
---
class: inverse,center,middle
.bigfont[
数据已经清洗完成，下面应该做的是……]
--
.bigfont[
<br>
查看数据内容？
<br>
建模分析？
<br>
......
]
--
.bigfont[
先看一个数据(quartets::datasaurus_dozen)
]
---
.panelset[
.panel[.panel-name[view]
```r
quartets::datasaurus_dozen %>% ## 包中的数据
head(10)
```
```
## # A tibble: 10 × 3
## dataset x y
## <chr> <dbl> <dbl>
## 1 dino 55.4 97.2
## 2 dino 51.5 96.0
## 3 dino 46.2 94.5
## 4 dino 42.8 91.4
## 5 dino 40.8 88.3
## 6 dino 38.7 84.9
## 7 dino 35.6 79.9
## 8 dino 33.1 77.6
## 9 dino 29.0 74.5
## 10 dino 26.2 71.4
```
.panel[.panel-name[`str()`]
```r
quartets::datasaurus_dozen %>% ## 包中的数据
str()
```
```
## tibble [1,846 × 3] (S3: tbl_df/tbl/data.frame)
## $ dataset: chr [1:1846] "dino" "dino" "dino" "dino" ...
## $ x : num [1:1846] 55.4 51.5 46.2 42.8 40.8 ...
## $ y : num [1:1846] 97.2 96 94.5 91.4 88.3 ...
## - attr(*, "spec")=
## .. cols(
## .. dataset = col_character(),
## .. x = col_double(),
## .. y = col_double()
## .. )
```
.panel[.panel-name[`summary()`]
```r
summary(datasaurus_dozen)
```
```
## dataset x y
## Length:1846 Min. :15.56 Min. : 0.01512
## Class :character 1st Qu.:41.07 1st Qu.:22.56107
## Mode :character Median :52.59 Median :47.59445
## Mean :54.27 Mean :47.83510
## 3rd Qu.:67.28 3rd Qu.:71.81078
## Max. :98.29 Max. :99.69468
```
]]]]
---
class: middle,center
.tit_font[
好像没什么问题…
]
--
.tit_font[
<br>
作图看看呢
]
---
##
<img src="picture/chp7/dino.gif" width="50%" style="display: block; margin: auto;" />
---
layout: true
# 7.1 探索性数据分析
---
.bigfont[了解原始数据的特点，做到心中有数，属于一个更广泛的概念：
**探索性数据分析(Exploratory Data Analysis, EDA)**
]
> In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods (Wikipedia).
<img src="https://blog.escueladedatosvivos.ai/content/images/2020/12/main_img.png" width="50%" style="display: block; margin: auto;" />
---
.bigfont[进行EDA是为了更加了解自己的数据，从而做出基本的判断，但每一次探索背后都对应着特定的问题。我们可以先从几个基础的简单问题开始:]
.pull-left[
.bigfont[
- 有哪些变量？
- 变量的类型？
- 变量的分布？
- 变量间关系？
]
]
.pull-right[
```r
# 读取数据
pg_raw <- bruceR::import(here::here(
"data", "penguin","penguin_rawdata.csv"))
mt_raw <- bruceR::import(here::here(
"data", "match","match_raw.csv"))
```
]
---
.panelset[
.panel[.panel-name[`head`]
```r
DT::datatable(head(mt_raw, 3)) # 注：datatable 这里只为了在网页中输出
```
<div class="datatables html-widget html-fill-item-overflow-hidden html-fill-item" id="htmlwidget-26dcb7370cc352016475" style="width:100%;height:auto;"></div>
<script type="application/json" data-for="htmlwidget-26dcb7370cc352016475">{"x":{"filter":"none","vertical":false,"data":[["1","2","3"],["02-May-2018_14:23:06","02-May-2018_14:23:08","02-May-2018_14:23:10"],["Exp","Exp","Exp"],[7302,7302,7302],[22,22,22],["female","female","female"],["R","R","R"],[1,1,1],[1,1,1],[1,2,3],["immoralSelf","moralOther","immoralOther"],["immoralOther","moralSelf","moralOther"],["mismatch","mismatch","mismatch"],["n","n","n"],["m","n","n"],[0,1,1],[0.7561,0.7043,0.9903000000000001]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Date<\/th>\n <th>Prac<\/th>\n <th>Sub<\/th>\n <th>Age<\/th>\n <th>Sex<\/th>\n <th>Hand<\/th>\n <th>Block<\/th>\n <th>Bin<\/th>\n <th>Trial<\/th>\n <th>Shape<\/th>\n <th>Label<\/th>\n <th>Match<\/th>\n <th>CorrResp<\/th>\n <th>Resp<\/th>\n <th>ACC<\/th>\n <th>RT<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[3,4,7,8,9,15,16]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
.panel[.panel-name[`str`]
```r
mt_raw %>%
str()
```
```
## 'data.frame': 25920 obs. of 16 variables:
## $ Date : chr "02-May-2018_14:23:06" "02-May-2018_14:23:08" "02-May-2018_14:23:10" "02-May-2018_14:23:13" ...
## $ Prac : chr "Exp" "Exp" "Exp" "Exp" ...
## $ Sub : int 7302 7302 7302 7302 7302 7302 7302 7302 7302 7302 ...
## $ Age : int 22 22 22 22 22 22 22 22 22 22 ...
## $ Sex : chr "female" "female" "female" "female" ...
## $ Hand : chr "R" "R" "R" "R" ...
## $ Block : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Bin : int 1 1 1 1 1 1 1 1 1 1 ...
## $ Trial : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Shape : chr "immoralSelf" "moralOther" "immoralOther" "moralSelf" ...
## $ Label : chr "immoralOther" "moralSelf" "moralOther" "immoralSelf" ...
## $ Match : chr "mismatch" "mismatch" "mismatch" "mismatch" ...
## $ CorrResp: chr "n" "n" "n" "n" ...
## $ Resp : chr "m" "n" "n" NA ...
## $ ACC : int 0 1 1 -1 1 1 1 1 1 1 ...
## $ RT : num 0.756 0.704 0.99 1.042 0.821 ...
```
]]]
---
## 常用函数
.panelset[
.panel[.panel-name[`summary`]
```r
summary(mt_raw) %>%
knitr::kable() # 注：kable函数只为了输出
```
| | Date | Prac | Sub | Age | Sex | Hand | Block | Bin | Trial | Shape | Label | Match | CorrResp | Resp | ACC | RT |
|:--|:----------------|:----------------|:-------------|:-------------|:----------------|:----------------|:-------------|:-------------|:-------------|:----------------|:----------------|:----------------|:----------------|:----------------|:---------------|:--------------|
| |Length:25920 |Length:25920 |Min. : 7302 |Min. :18.00 |Length:25920 |Length:25920 |Min. :1.000 |Min. :1.000 |Min. : 1.00 |Length:25920 |Length:25920 |Length:25920 |Length:25920 |Length:25920 |Min. :-1.0000 |Min. :0.1060 |
| |Class :character |Class :character |1st Qu.: 7313 |1st Qu.:19.00 |Class :character |Class :character |1st Qu.:1.000 |1st Qu.:1.000 |1st Qu.: 6.75 |Class :character |Class :character |Class :character |Class :character |Class :character |1st Qu.: 1.0000 |1st Qu.:0.6104 |
| |Mode :character |Mode :character |Median : 7324 |Median :20.00 |Mode :character |Mode :character |Median :1.000 |Median :2.000 |Median :12.50 |Mode :character |Mode :character |Mode :character |Mode :character |Mode :character |Median : 1.0000 |Median :0.7019 |
| |NA |NA |Mean : 8853 |Mean :20.83 |NA |NA |Mean :1.611 |Mean :2.417 |Mean :12.50 |NA |NA |NA |NA |NA |Mean : 0.7959 |Mean :0.7150 |
| |NA |NA |3rd Qu.: 7336 |3rd Qu.:22.00 |NA |NA |3rd Qu.:2.000 |3rd Qu.:3.000 |3rd Qu.:18.25 |NA |NA |NA |NA |NA |3rd Qu.: 1.0000 |3rd Qu.:0.8053 |
| |NA |NA |Max. :73370 |Max. :28.00 |NA |NA |Max. :3.000 |Max. :5.000 |Max. :24.00 |NA |NA |NA |NA |NA |Max. : 2.0000 |Max. :1.1831 |
.panel[.panel-name[`skimr::skim()--1`]
```r
skimr::skim(mt_raw) %>%
capture.output() %>%
.[1:12]
```
```
## [1] "── Data Summary ────────────────────────"
## [2] " Values"
## [3] "Name mt_raw"
## [4] "Number of rows 25920 "
## [5] "Number of columns 16 "
## [6] "_______________________ "
## [7] "Column type frequency: "
## [8] " character 9 "
## [9] " numeric 7 "
## [10] "________________________ "
## [11] "Group variables None "
## [12] ""
```
.panel[.panel-name[`skimr::skim()--2`]
```r
skimr::skim(mt_raw) %>%
capture.output() %>%
.[13:24]
```
```
## [1] "── Variable type: character ────────────────────────────────────────────────────"
## [2] " skim_variable n_missing complete_rate min max empty n_unique whitespace"
## [3] "1 Date 0 1 20 20 0 24362 0"
## [4] "2 Prac 0 1 3 3 0 1 0"
## [5] "3 Sex 0 1 1 6 0 4 0"
## [6] "4 Hand 0 1 1 1 0 2 0"
## [7] "5 Shape 0 1 9 12 0 4 0"
## [8] "6 Label 0 1 9 12 0 4 0"
## [9] "7 Match 0 1 5 8 0 2 0"
## [10] "8 CorrResp 0 1 1 1 0 2 0"
## [11] "9 Resp 658 0.975 1 5 0 9 0"
## [12] ""
```
.panel[.panel-name[`skimr::skim()--3`]
```r
skimr::skim(mt_raw) %>%
capture.output() %>%
.[25:41]
```
```
## [1] "── Variable type: numeric ──────────────────────────────────────────────────────"
## [2] " skim_variable n_missing complete_rate mean sd p0 p25"
## [3] "1 Sub 0 1 8853. 9932. 7302 7313 "
## [4] "2 Age 0 1 20.8 2.48 18 19 "
## [5] "3 Block 0 1 1.61 0.803 1 1 "
## [6] "4 Bin 0 1 2.42 1.36 1 1 "
## [7] "5 Trial 0 1 12.5 6.92 1 6.75 "
## [8] "6 ACC 0 1 0.796 0.464 -1 1 "
## [9] "7 RT 0 1 0.715 0.151 0.106 0.610"
## [10] " p50 p75 p100 hist "
## [11] "1 7324 7336 73370 ▇▁▁▁▁"
## [12] "2 20 22 28 ▇▂▁▁▁"
## [13] "3 1 2 3 ▇▁▃▁▃"
## [14] "4 2 3 5 ▇▇▃▃▃"
## [15] "5 12.5 18.2 24 ▇▇▆▇▇"
## [16] "6 1 1 2 ▁▂▁▇▁"
## [17] "7 0.702 0.805 1.18 ▁▂▇▅▁"
```
.panel[.panel-name[`bruceR::Describe()`]
```r
bruceR::Describe(mt_raw) %>%
capture.output() %>%
.[2:17] ## 可以使用 file参数输出 Word
```
```
## [1] "────────────────────────────────────────────────────────────────────────────────────"
## [2] " N (NA) Mean SD | Median Min Max Skewness Kurtosis"
## [3] "────────────────────────────────────────────────────────────────────────────────────"
## [4] "Date* 25920 12218.64 6999.30 | 12220.50 1.00 24362.00 0.00 -1.19"
## [5] "Prac* 25920 1.00 0.00 | 1.00 1.00 1.00 NaN NaN"
## [6] "Sub 25920 8852.59 9931.83 | 7324.00 7302.00 73370.00 6.34 38.22"
## [7] "Age 25920 20.83 2.48 | 20.00 18.00 28.00 1.09 0.35"
## [8] "Sex* 25920 3.27 0.65 | 3.00 1.00 4.00 -0.84 1.58"
## [9] "Hand* 25920 1.98 0.15 | 2.00 1.00 2.00 -6.34 38.22"
## [10] "Block 25920 1.61 0.80 | 1.00 1.00 3.00 0.82 -0.97"
## [11] "Bin 25920 2.42 1.36 | 2.00 1.00 5.00 0.67 -0.82"
## [12] "Trial 25920 12.50 6.92 | 12.50 1.00 24.00 0.00 -1.20"
## [13] "Shape* 25920 2.50 1.12 | 2.50 1.00 4.00 0.00 -1.36"
## [14] "Label* 25920 2.49 1.11 | 2.00 1.00 4.00 0.02 -1.35"
## [15] "Match* 25920 1.50 0.50 | 1.50 1.00 2.00 0.00 -2.00"
## [16] "CorrResp* 25920 1.50 0.50 | 1.50 1.00 2.00 0.00 -2.00"
```
]]]]]]
---
layout: false
class: inverse middle center
.tit_font[
但是...<br>还不够
]
---
# 7.2 数据可视化
## 7.2.1 可视化的重要性
.pull-left[
.size5[
Before we do any statistical analyses or present any summary statistics, we should visualise our data as it is:
- A quick and easy way to check our data make sense, and to identify any unusual trends.
- A way to honestly present the features of our data to anyone who reads our research.
]
]
--
.pull-right[
<img src="chapter_7_files/figure-html/unnamed-chunk-25-1.png" width="90%" />
]
<br>
<br>
.size5[
绘图的方式有很多，如 Base graphics, grid, lattice,plotly...为什么使用<span style="color: red;">ggplot2</span>?
]
---
# 7.2 数据可视化
## 7.2.2 为何使用 ggplot2?
<br>
.size5[
* 化繁为简：大量的默认值
* 精准定制：所有元素均可控
* 易于叠加：丰富的信息
* 日益丰富的生态系统 [https://r-graph-gallery.com/](https://r-graph-gallery.com/)
]
.bigfont[
总之，好看、业界标杆，举个例子:
]
---
## 7.2.2 为何使用 ggplot?
<img src="picture/chp7/exmp2.png" width="63%" style="display: block; margin: auto;" />
---
## 7.2.2 为何使用 ggplot?
<img src="picture/chp7/exmp1.png" width="63%" style="display: block; margin: auto;" />
.footnote[
-----
Falster, G., Konecky, B., Coats, S. et al. Forced changes in the Pacific Walker circulation over the past millennium. Nature 622, 93–100 (2023). https://doi.org/10.1038/s41586-023-06447-0
]
---
## 7.2.3 ggplot2 的逻辑
.left-column[
.bigfont[
- 数据映射
]
]
.right-column[
<img src="picture/chp7/mapping.jpg" width="75%" />
]
---
## 7.2.3 ggplot2 的逻辑
.left-column[
.bigfont[
- 数据映射
- 图层叠加
]
]
.right-column[
<img src="https://psyteachr.github.io/data-skills-v1/images/layers.png" width="70%" />
]
---
## 7.2.3 ggplot2 的逻辑
.left-column[
.bigfont[
- 数据映射
- 图层叠加
- Code展示
]]
.right-column[
.panelset[
.panel[.panel-name[数据映射]
```r
# 以penguin问卷中前后体温为例
p1 <- pg_raw %>%
ggplot(aes(x = Temperature_t1, # 确定映射到xy轴的变量
y = Temperature_t2))
p1 ## 坐标轴名称已对应，虽然图片为空
```
<img src="chapter_7_files/figure-html/unnamed-chunk-30-1.png" width="50%" style="display: block; margin: auto;" />
.panel[.panel-name[添加图层-散点]
```r
p1 + geom_point()
```
<img src="chapter_7_files/figure-html/unnamed-chunk-31-1.png" width="60%" style="display: block; margin: auto;" />
.panel[.panel-name[添加图层-拟合曲线]
```r
p1 + geom_point() + geom_smooth(method = 'lm')
```
<img src="chapter_7_files/figure-html/unnamed-chunk-32-1.png" width="60%" style="display: block; margin: auto;" />
.panel[.panel-name[改变映射]
```r
pg_raw %>%
drop_na(Temperature_t1,Temperature_t2,sex) %>%
ggplot(aes(x = Temperature_t1,
y = Temperature_t2,
color = factor(sex))) +
geom_point() + geom_smooth()
```
<img src="chapter_7_files/figure-html/unnamed-chunk-33-1.png" width="50%" style="display: block; margin: auto;" />
]]]]]]
???
- 映射与添加图层是两个概念，映射后可以不进行绘图
- 图层的添加使用 +
- 除了 x 和 y，仍然有其他变量进行映射，如 color
---
## 7.2.4 单个图片的组成
<img src="picture/chp7/comps1.png" width="70%" style="display: block; margin: auto;" />
---
## 7.2.4 单个图片的组成
<img src="picture/chp7/comps2.png" width="75%" style="display: block; margin: auto;" />
---
layout: true
# 7.3 常用图形
---
## 直方图
<font size=5>
对于连续变量，我们可以使用直方图进行可视化。
以认知实验中被试的反应时数据为例。
</font>
<br>
.pull-left[
```r
pic_his <- mt_raw %>%
# 确定映射到x轴的变量
ggplot(aes(x = RT)) +
geom_histogram(bins = 40) +
theme_classic()
```
]
.pull-right[
<img src="chapter_7_files/figure-html/unnamed-chunk-37-1.png" width="100%" />
]
---
## 密度图
<font size=5>
&emsp;&emsp;同样的我们可以使用密度图来描述反应时的分布情况。
</font>
.pull-left[
```r
pic_dens <- mt_raw %>% ggplot()+
# 绘制密度曲线
geom_density(aes(x = RT)) +
theme_classic()
```
]
.pull-right[
<img src="chapter_7_files/figure-html/unnamed-chunk-39-1.png" width="100%" />
]
---
## 直方图 + 密度图
.pull-left[
```r
## 尝试将两个图层叠加在一起
mt_raw %>% ggplot(aes(x = RT))+
geom_histogram(bins = 40) +
geom_density() +
theme_classic()
```
]
.pull-right[
<img src="chapter_7_files/figure-html/unnamed-chunk-41-1.png" width="100%" />
似乎密度曲线没有显示…
原因在于密度图和直方图Y轴单位并不相同，其实已经映射上了，
如果仔细观察 x 坐标轴还是能看到细微差异的。
]
---
## 直方图 + 密度图
.pull-left[
```r
pic_mix <- mt_raw %>%
ggplot(aes(x = RT,
## 直方图的统计结果通过after_stat(density)传递给了密度图
* y = after_stat(density))) +
geom_histogram() +
geom_density() +
theme_classic()
# 设定绘图风格
```
]
.pull-right[
<img src="chapter_7_files/figure-html/unnamed-chunk-43-1.png" width="100%" />
]
---
## 箱线图
除了单个变量的可视化，我们可以尝试将两个变量的关系可视化。这里我们利用箱线图看看不同Label的RT如何。
.pull-left[
```r
pic_box <- mt_raw %>% ggplot(aes(
x = Label,
y = RT)) +
geom_boxplot(staplewidth = 1) +
# 绘制箱线图并添加上下边缘线
theme_classic()
```
]
.pull-right[
<img src="chapter_7_files/figure-html/unnamed-chunk-45-1.png" width="100%" />
]
.footnote[
----
矩形中间线为中位数，上下两条线分别为上四分位数和下四分位数；
1.5个四分位距(Q3-Q1)以外的值为离群值；geom_boxplot默认使用1.5IQR
]
---
layout: true
# 7.4 Explore data with DataExplorer
---
```r
DataExplorer::plot_str(mt_raw)
```
<img src="picture/chp7/plotstr.png" width="65%" />
---
```r
DataExplorer::plot_intro(mt_raw)
```
<img src="chapter_7_files/figure-html/unnamed-chunk-48-1.png" width="70%" />