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<!DOCTYPE html>
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<title>chapter_6.knit</title>
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class: center, middle
<span style="font-size: 50px;">**第六章**</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;">2023-04-04</span> <br>
<span style="font-size: 20px;"> Made with Rmarkdown</span> <br>
---
#回顾
##函数及用法
##-函数名及语句
##-group_by()
##for loop
##练习题
<br><br>
#本节课内容
##探索性数据分析
##--描述性统计
##--数据可视化基础
##----ggplot2介绍
##----ggplot2可视化基础
---
#回顾
##函数及用法
<font size=5>
&emsp;&emsp;重新了解函数：
</font>
```r
df.mt.raw <- read.csv('./data/match/match_raw.csv',
header = T, sep=",", stringsAsFactors = FALSE)
```
<font size=5>
&emsp;&emsp;以用的最多的read.csv()这一函数为例，它包括两部分：“read.csv”——函数名；“()”中的输入参数。<br>
&emsp;&emsp;比如，“header=…”：是否要使用第一行作为列名；<br>
&emsp;&emsp;再如，“sep=…” ：指定分割符什么 <br>
</font>
---
#回顾
##函数及用法
<font size=5>
&emsp;&emsp;重新了解函数：
</font>
```r
df.mt.raw <- read.csv('./data/match/match_raw.csv',
header = T, sep=",", stringsAsFactors = FALSE)
```
<font size=5>
&emsp;&emsp;以用的最多的read.csv()这一函数为例，它包括两部分：“read.csv”——函数名；“()”中的输入参数。<br>
&emsp;&emsp;比如，“header=…”：是否要使用第一行作为列名；<br>
&emsp;&emsp;再如，“sep=…” ：指定分割符什么 <br>
<br>
&emsp;&emsp;为什么第一个文件名没有相应的argument？。
</font>
---
#回顾
##函数及用法
<font size=5>
&emsp;&emsp;重新了解函数：
</font>
完整输入arguments
```r
result1 <- functionName(arg1 = value1, arg2 = value2, arg3 = value3)
result1 <- functionName(arg3 = value3, arg1 = value1, arg2 = value2)
```
省略arguments,按照顺序输入argument的值
```r
result2 <- functionName(value1, value2, value3)
result2 <- functionName(value3, value1, value2) # will not return expected
```
省略有默认值的arguments
```r
result3 <- functionName(arg1 = value1)
```
---
#回顾
##函数及用法
<font size=5>
&emsp;&emsp;比如，完整地写出argument应该是这样。
</font>
```r
df.mt.raw <- read.csv(file = './data/match/match_raw.csv',
header = T,
sep=",",
stringsAsFactors = FALSE)
```
---
#回顾
##函数及用法
<font size=5>
&emsp;&emsp;argument: “TRUE ~ NA_real_”?<br>
</font>
<br>
<font size=5>
&emsp;&emsp;首先，查找case_when()，但是我们并未发现对这一语句的描述，这可能是因为该argument已经在最新的语句中有了替代。<br>
&emsp;&emsp;因此，我们直接查找Na_real_，可以发现它表示这个默认值是一个缺失值（NA），并且是数值类型（real），那再结合case_when()的作用就很好理解了，那些“TRUE”，也即不在给出的任何条件中的值，被赋为NA。<br>
&emsp;&emsp;同学们也可以尝试使用case_when()文档中提供的语句重新写。
</font>
---
#回顾
##函数及用法
<font size=5>
&emsp;&emsp;关于函数“dplyr::group_by()"的作用: 比较操作前后的结果。
</font>
```r
library("tidyverse")
```
```
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 0.3.5
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
```
```r
group <- df.mt.raw %>%
group_by(Shape)
group#注意看数据框的第二行，有Groups: Shape [4]的信息
```
```
## # A tibble: 25,920 × 16
## # Groups: Shape [4]
## Date Prac Sub Age Sex Hand Block Bin Trial Shape Label Match
## <chr> <chr> <int> <int> <chr> <chr> <int> <int> <int> <chr> <chr> <chr>
## 1 02-May-201… Exp 7302 22 fema… R 1 1 1 immo… immo… mism…
## 2 02-May-201… Exp 7302 22 fema… R 1 1 2 mora… mora… mism…
## 3 02-May-201… Exp 7302 22 fema… R 1 1 3 immo… immo… mism…
## 4 02-May-201… Exp 7302 22 fema… R 1 1 4 mora… mora… mism…
## 5 02-May-201… Exp 7302 22 fema… R 1 1 5 immo… immo… match
## 6 02-May-201… Exp 7302 22 fema… R 1 1 6 immo… immo… match
## 7 02-May-201… Exp 7302 22 fema… R 1 1 7 mora… mora… match
## 8 02-May-201… Exp 7302 22 fema… R 1 1 8 mora… mora… match
## 9 02-May-201… Exp 7302 22 fema… R 1 1 9 mora… mora… mism…
## 10 02-May-201… Exp 7302 22 fema… R 1 1 10 immo… immo… mism…
## # … with 25,910 more rows, and 4 more variables: CorrResp <chr>, Resp <chr>,
## # ACC <int>, RT <dbl>
```
---
#回顾
##函数及用法
<font size=5>
&emsp;&emsp;当某一列作为group_by分类的对象时，生成的数据框具有此分类列的信息，这个信息是数据框的一个基本信息，你甚至无法删除这一列。
</font>
```r
group <- group %>%
select(-Shape)
```
```
## Adding missing grouping variables: `Shape`
```
```r
#注意返回的提示，因为数据框已经按照此列分类，所以不能再检索到此列。
```
---
# 回顾
## 函数及用法
<font size=5>
&emsp;&emsp;对于group_by()函数的作用，我们可以对比不使用它的效果。
</font>
```r
library("tidyverse")
df.mt.raw <- read.csv('./data/match/match_raw.csv',
header = T, sep=",",
stringsAsFactors = FALSE)
group <- df.mt.raw %>%
group_by(., Shape) %>%
summarise(n())
DT::datatable(group)
```
<div id="htmlwidget-5280007504acbafd7a75" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-5280007504acbafd7a75">{"x":{"filter":"none","vertical":false,"data":[["1","2","3","4"],["immoralOther","immoralSelf","moralOther","moralSelf"],[6480,6480,6480,6480]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Shape<\/th>\n <th>n()<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":2},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
---
# 回顾
## 函数及用法
<font size=5>
&emsp;&emsp;对于group_by()函数的作用，我们可以对比不使用它的效果。
</font>
```r
ungroup <- df.mt.raw %>%
summarise(n())
DT::datatable(ungroup)
```
<div id="htmlwidget-ae03e1bc3d34241f96e3" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-ae03e1bc3d34241f96e3">{"x":{"filter":"none","vertical":false,"data":[["1"],[25920]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>n()<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":1},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
---
# 回顾
## 函数及用法
<font size=5>
&emsp;&emsp;从对比中可以看出，使用了group_by()后，实际上对数据框进行了分组，summarise针对的是每一个按照Shape列拆分开的数据框。
</font>
```r
remove(group, ungroup)
```
---
# 回顾
## 函数及用法
<font size=5>
&emsp;&emsp; 理解函数的最好办法：创建函数
</font>
```r
simpleFun <- function(a = 1, b = 100){
sum <- a + b
product <- a * b
res <- c(sum, product)
return(res)
}
```
```r
simpleFun()
```
```
## [1] 101 100
```
```r
simpleResult <- simpleFun(a=1, b=5)
```
---
# 回顾
## for loop
<font size=5>
&emsp;&emsp;我们看一下for loop语句最基础的结构：<br>
&emsp;&emsp; __for (variable in sequence) {statement}__ <br>
&emsp;&emsp;其中，variable是循环变量，sequence是一个向量或列表，statement是要重复执行的语句。在每次循环中，variable会被赋值为sequence中的下一个元素，然后执行statement。<br>
比如，我如果想计算1到100的和：
</font>
```r
sum <- 0
for (i in 1:100) {
sum <- sum + i
}
print(sum)
```
```
## [1] 5050
```
---
# 回顾
## for loop
<font size=5>
&emsp;&emsp;理解了这一点我们再来看使用for loop如何批量读取文件。
</font>
```r
files <- list.files(file.path("data/match"),
pattern = "data_exp7_rep_match_.*\\.out$")
df_list <- list()
for (i in seq_along(files)) {
df <- read.table(file.path("data/match", files[i]), header = TRUE)
df_list[[i]] <- df
}
```
<font size=5>
&emsp;&emsp;1.利用list.files读取所有的文件保存成value<br>
&emsp;&emsp;2.创建新的df_list存储结果<br>
&emsp;&emsp;3.for (i in seq_along(files))……部分是，对files里的第一个值执行read.table()，并将结果放进df_list中。接着对第二个值执行，直至循环结束整个files
</font>
---
# 回顾
## 练习
定义一下函数，其有两个整数型的arguments (a, b)，第一个比第二个小，函数的输出是计算从a到b的所有整数之和。
```
# 回顾
## 练习题
<font size=5>
&emsp;&emsp;我们在实际使用这些函数时，是带着目的去使用的。想要计算击中率虚报率，我们首先要知道什么情况是击中，也要让计算机知道什么情况是击中，一旦分类了，计算的时候也需要进行判断。但在分类判断之前，我们要先告诉计算机什么是正确情况什么是错误情况，哪些是这个被试做的，哪些不是，所以也需要分组。<br>
&emsp;&emsp;梳理到这，我们有了基本的思路：1.分组，使用group_by。2.告诉计算机什么是hit，什么是miss，使用summarise函数。3.利用判断语句进行分类计算，我们这里使用了ifelse。<br>
&emsp;&emsp;当然，具体执行时，我们也需要考虑缺失值等细节，但总体的思路不会有变。
<br>
&emsp;&emsp;以上次的练习为例。首先我们需要加载数据。
</font>
```r
# 读取原始数据
df.mt.raw <- read.csv('./data/match/match_raw.csv',
header = T, sep=",", stringsAsFactors = FALSE)
```
---
# 回顾
## 练习题
<font size=5>
&emsp;&emsp;接着，我们选出需要的几列，包括自变量和因变量，以及分组变量。使用select()函数
</font>
```r
df.mt.clean <- df.mt.raw %>%
dplyr::select(Sub, Block, Bin, # block and bin
Shape, Match, # 自变量
ACC, RT, # 反应结果
)
```
<font size=5>
&emsp;&emsp;因为数据中存在缺失值，所以也需要除去它们。
</font>
```r
df.mt.clean <- df.mt.raw %>%
dplyr::select(Sub, Block, Bin, # block and bin
Shape, Match, # 自变量
ACC, RT, # 反应结果
) %>%
tidyr::drop_na()
```
---
# 回顾
## 练习题
<font size=5>
&emsp;&emsp;在计算击中率误报率等的时候，我们针对的是被试的在某一特定实验条件下的反应。所以我们需要通过bin、block、shape、sub进行分组，这样进行计算时，就是每一个条件组下分别计算。不会出现在虚报的实验条件下计算正确拒绝的情况。
</font>
```r
df.mt.clean <- df.mt.raw %>%
dplyr::select(Sub, Block, Bin, # block and bin
Shape, Match, # 自变量
ACC, RT, # 反应结果
) %>%
tidyr::drop_na() %>% #删除缺失值
dplyr::group_by(Sub, Block, Bin, Shape)
```
---
# 回顾
## 练习题
<font size=5>
&emsp;&emsp;接下来就是要使用summarise函数来给击中、虚报等分类，并使用ifelse函数根据分类计算概率
</font>
```r
df.mt.clean <- df.mt.raw %>%
dplyr::select(Sub, Block, Bin, # block and bin
Shape, Match, # 自变量
ACC, RT, # 反应结果
) %>%
tidyr::drop_na() %>% #删除缺失值
dplyr::group_by(Sub, Block, Bin, Shape) %>%
dplyr::summarise(
hit = length(ACC[Match == "match" & ACC == 1]),
fa = length(ACC[Match == "mismatch" & ACC == 0]),
miss = length(ACC[Match == "match" & ACC == 0]),
cr = length(ACC[Match == "mismatch" & ACC == 1]),
Dprime = qnorm(
ifelse(hit / (hit + miss) < 1,
hit / (hit + miss),
1 - 1 / (2 * (hit + miss))
)
) - qnorm(
ifelse(fa / (fa + cr) > 0,
fa / (fa + cr),
1 / (2 * (fa + cr))
)
))
```
```
## `summarise()` has grouped output by 'Sub', 'Block', 'Bin'. You can override
## using the `.groups` argument.
```
---
# 回顾
## 练习题
<font size=5>
&emsp;&emsp;最后使用ungroup、select等函数对数据进行清理，除去计算中的过程变量，并进行长宽转换方便查看。
</font>
```r
df.mt.clean <- df.mt.raw %>%
dplyr::select(Sub, Block, Bin, # block and bin
Shape, Match, # 自变量
ACC, RT, # 反应结果
) %>%
tidyr::drop_na() %>% #删除缺失值
dplyr::group_by(Sub, Block, Bin, Shape) %>%
dplyr::summarise(
hit = length(ACC[Match == "match" & ACC == 1]),
fa = length(ACC[Match == "mismatch" & ACC == 0]),
miss = length(ACC[Match == "match" & ACC == 0]),
cr = length(ACC[Match == "mismatch" & ACC == 1]),
Dprime = qnorm(
ifelse(hit / (hit + miss) < 1,
hit / (hit + miss),
1 - 1 / (2 * (hit + miss))
)
) - qnorm(
ifelse(fa / (fa + cr) > 0,
fa / (fa + cr),
1 / (2 * (fa + cr))
)
)) %>%
dplyr::ungroup() %>%
select(-"hit",-"fa",-"miss",-"cr") %>%
dplyr::group_by(Sub, Shape) %>%
tidyr::pivot_wider(names_from = Shape,
values_from = Dprime)
```
```
## `summarise()` has grouped output by 'Sub', 'Block', 'Bin'. You can override
## using the `.groups` argument.
```
---
class: center, middle
<span style="font-size: 50px;">**第六章**</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>
---
# 探索性数据分析
## What is exploratory data analysis?
<font size=5>
&emsp;&emsp;在介绍描述性统计和可视化之前，我们先了解一个概念：探索性数据分析(Exploratory Data Analysis, EDA)。<br>
&emsp;&emsp;In statistics, exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their maincharacteristics, often with visual methods (Wikipedia).
</font>
<div style="text-align:center;">
<img src="https://blog.escueladedatosvivos.ai/content/images/2020/12/main_img.png" alt="layer" style="width:70%; height:auto;" />
</div>
---
# 探索性数据分析
## What is exploratory data analysis?
<font size=5>
&emsp;&emsp;要进行EDA，首先要了解自己的数据，并提出有质量的问题。但是提出有质量的问题前，我们可以先从几个基础的简单问题开始：<br>
&emsp;&emsp;有哪些变量，类型如何？变量的值是如何变化的？变量之间有什么关系？
</font>
```r
# 检查是否已安装 pacman
if (!requireNamespace("pacman", quietly = TRUE)) {
install.packages("pacman") } # 如果未安装，则安装包
# 加载所需要的R包
pacman::p_load("tidyverse")
# 读取数据
df.pg.raw <- read.csv("./data/penguin/penguin_rawdata.csv",
header = TRUE, sep=",", stringsAsFactors = FALSE)
df.mt.raw <- read.csv('./data/match/match_raw.csv',
header = T, sep=",", stringsAsFactors = FALSE)
```
---
# 探索性数据分析
## 有哪些变量，类型如何？
```r
colnames(df.mt.raw) # 查看列名，观察有哪些变量
```
```
## [1] "Date" "Prac" "Sub" "Age" "Sex" "Hand"
## [7] "Block" "Bin" "Trial" "Shape" "Label" "Match"
## [13] "CorrResp" "Resp" "ACC" "RT"
```
```r
DT::datatable(head(df.mt.raw, 3)) # 了解数据内容
```
<div id="htmlwidget-62e1f1ef7922915c5aa9" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-62e1f1ef7922915c5aa9">{"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"],["immoralSelf","moralOther","immoralOther"],["mismatch","mismatch","mismatch"],["n","n","n"],["m","n","n"],[0,1,1],[0.7561,0.7043,0.9903]],"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>
---
# 探索性数据分析
## 了解数据
```r
# 前几节课提过的summary()函数
# 这里使用datatable()是为了方便在ppt呈现
DT::datatable(summary(df.mt.raw))
```
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---
# 探索性数据分析
## 了解你的数据
<font size=5>
&emsp;&emsp;更进一步，如果我想知道变量的平均数、中位数和标准差等统计量应该怎么办？
</font><br>
```r
# 使用psych包中的describe()函数
DT::datatable(psych::describe(df.mt.raw))
```
<div id="htmlwidget-fd1b9c01a7f53dc0fa1e" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-fd1b9c01a7f53dc0fa1e">{"x":{"filter":"none","vertical":false,"data":[["Date*","Prac*","Sub","Age","Sex*","Hand*","Block","Bin","Trial","Shape*","Label*","Match*","CorrResp*","Resp*","ACC","RT"],[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16],[25920,25920,25920,25920,25920,25920,25920,25920,25920,25920,25920,25920,25920,25262,25920,25920],[12218.6378086412,1,8852.5925925922,20.8333333333334,3.26851851851848,1.97685185185185,1.61111111111111,2.41666666666666,12.5,2.5,2.5,1.5,1.5,5.49564563375821,0.795949074074075,0.714950381944444],[6999.30300668651,0,9931.83282454277,2.47585664394741,0.654151598830282,0.150376806082763,0.803172843163891,1.36170415422526,6.92232008612875,1.11805555638763,1.11805555638763,0.500009645340819,0.500009645340819,0.508936839277808,0.463647210061554,0.150839365248012],[12220.5,1,7324,20,3,2,1,2,12.5,2.5,2.5,1.5,1.5,5,1,0.7019],[12219.0297550138,1,7324.23958333333,20.5277777777781,3.32638888888891,2,1.51388888888903,2.27083333333354,12.5,2.5,2.5,1.5,1.5,5.49524987629897,0.90055941358024,0.70775078125],[8920.0629,0,16.3086,1.4826,0,0,0,1.4826,8.8956,1.4826,1.4826,0.7413,0.7413,0,0,0.14336742],[1,1,7302,18,1,1,1,1,1,1,1,1,1,1,-1,0.106],[24362,1,73370,28,4,2,3,5,24,4,4,2,2,9,2,1.1831],[24361,0,66068,10,3,1,2,4,23,3,3,1,1,8,3,1.0771],[0.00220558477379076,null,6.34183273983716,1.09360106540283,-0.83747464140177,-6.34184841681397,0.815447754334393,0.670628948711956,0,0,0,0,0,-0.0870917896362933,-2.16997928633046,0.35980327619413],[-1.19185000099972,null,38.2203865539374,0.348239273835886,1.58332877329944,38.220515903065,-0.96867852976512,-0.815503330725602,-1.2043124771982,-1.36012654076884,-1.36012654076884,-2.00007715900539,-2.00007715900539,-0.333591646518246,4.12510599124667,0.237084145756972],[43.4747703279714,0,61.6895926023372,0.0153782882178081,0.00406313178492967,0.000934035446223226,0.0049887474305451,0.00845795292803207,0.0429966063183225,0.00694457840751138,0.00694457840751138,0.00310570987885453,0.00310570987885453,0.00320206411027458,0.00287985188687711,0.000936908539937426]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>vars<\/th>\n <th>n<\/th>\n <th>mean<\/th>\n <th>sd<\/th>\n <th>median<\/th>\n <th>trimmed<\/th>\n <th>mad<\/th>\n <th>min<\/th>\n <th>max<\/th>\n <th>range<\/th>\n <th>skew<\/th>\n <th>kurtosis<\/th>\n <th>se<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[1,2,3,4,5,6,7,8,9,10,11,12,13]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
```r
# 需要注意的是，describe()函数不会帮你处理缺失值，它会跳过缺失值。
```
---
# 探索性数据分析
## 了解你的数据
<font size=5>
&emsp;&emsp;更进一步，如果我想知道变量的平均数、中位数和标准差等统计量应该怎么办？
</font><br>
```r
# 使用dplyr包中的summarise()函数
df.mt.raw %>%
summarise(mean_RT = mean(RT),
sd_RT = sd(RT),
n_values = n())
```
```
## mean_RT sd_RT n_values
## 1 0.7149504 0.1508394 25920
```
```r
# summarise函数不会忽略缺失值，如果计算的列中有缺失值，会有报错。
```
---
# 探索性数据分析
## 变量的值是如何变化的？可视化
<font size=5>
&emsp;&emsp;可视化的部分我们介绍一个最常用的包：ggplot2。<br>
&emsp;&emsp;所谓gg源于“grammar of graphics”，即图形语法。<br>
&emsp;&emsp;ggplot2绘图的核心在于使用图层去描述和构建图形。<br>
&emsp;&emsp;我们在这里给出一个示例，探究体温和健康的关系，并简单了解一下ggplot2的语法。
</font>
<div style="text-align:center;">
<img src="https://picb.zhimg.com/v2-1ea8eef8abdab39c4e5cfcc0285f9d95_720w.jpg?source=172ae18b" alt="layer" style="width:60%; height:auto;" />
</div>
---
# 可视化
## 柱状图
<font size=5>
&emsp;&emsp;比方说，我们想要看看被试回答正确率的情况。
</font>
```r
ggplot2::ggplot(data=df.mt.raw, # 指定数据
aes(x=ACC)) + # 确定映射到x轴的变量
geom_bar() + # 绘制直方图
theme_classic() # 设定绘图风格
```
<!-- -->
---
# 可视化
## 柱状图
<font size=5>
&emsp;&emsp;消除图形与x轴之间的空白。
</font>
```r
ggplot2::ggplot(data=df.mt.raw, # 指定数据
aes(x=ACC)) + # 确定映射到x轴的变量
geom_bar() + # 绘制直方图
scale_y_continuous(expand=c(0, 0)) + # x轴在 y=0 处
theme_classic() # 设定绘图风格
```
<!-- -->
---
# 可视化
## 直方图
<font size=5>
&emsp;&emsp;对于连续变量，我们可以使用直方图进行可视化。比如说，我们想看看被试的反应时分布。
</font>
```r
ggplot2::ggplot(data=df.mt.raw, # 指定数据
aes(x=RT)) + # 确定映射到x轴的变量
geom_histogram() + # 绘制直方图
stat_bin(bins = 40) + # 设定连续变量分组数量
scale_x_continuous(name = "RT") + # 命名x轴
scale_y_continuous(expand=c(0, 0)) + # x轴在 y=0 处
theme_classic() # 设定绘图风格
```
<!-- -->
---
# 可视化
## 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>
```r
ggplot2::ggplot(data=df.mt.raw, # 指定数据
aes(x=RT)) + # 确定映射到x轴的变量
geom_density() + # 绘制密度曲线
scale_x_discrete(name="RT") + # 命名x轴
scale_y_continuous(expand=c(0, 0)) + # x轴在 y=0 处
theme_classic() # 设定绘图风格
```
<!-- -->
---
# 可视化
## 图层叠加
```r
ggplot2::ggplot(data=df.mt.raw, # 指定数据
aes(x=RT, # x轴的变量
y=after_stat(density), # y轴对应的是密度曲线
alpha=0.8)) + # 透明度
geom_histogram() + # 绘制直方图
geom_density() + # 绘制密度曲线
guides(alpha=FALSE) + # 隐藏透明度alpha设置带来的图例
theme_classic() # 设定绘图风格
```
<!-- -->
---
# 可视化
## 箱线图
<font size=5>
&emsp;&emsp;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。
&emsp;&emsp;这里我们利用箱线图看看不同Label的RT如何。
</font>
```r
ggplot2::ggplot(data=df.mt.raw, # 指定数据
aes(x=Label, # 确定映射到xy轴的变量
y=RT)) +
geom_boxplot() + # 绘制箱线图
theme_classic() # 设定绘图风格
```
<!-- -->
---
# 可视化
## 散点图
<font size=5>
&emsp;&emsp;而对于两个连续变量，我们可以使用散点图。比如，我们可以看看被试在做penguin问卷前后体温的关系。
</font>
```r
ggplot2::ggplot(data=df.pg.raw, # 指定数据
aes(x=Temperature_t1, # 确定映射到xy轴的变量
y=Temperature_t2)) +
geom_point() + # 绘制散点图
scale_x_continuous(name = "Temperature_t1") + # 修改X轴的名称
scale_y_continuous(name = "Temperature_t2") + # 修改Y轴的名称
theme_classic() # 设定绘图风格
```
<!-- -->
---
# 可视化
## 散点图
<font size=5>
&emsp;&emsp;事实上，当我们进行探索时往往需要先对数据进行处理，再进行可视化。以下我们想看看手机使用和焦虑是否存在关系。
</font>
```r
# 利用管道符，可以帮助我们更简洁地合并数据处理和可视化的过程。
df.pg.raw %>%
dplyr::mutate(stress_ave=rowMeans(.[,c("stress1", "stress2", "stress3","stress4", "stress5",
"stress6","stress7", "stress8", "stress9","stress10",
"stress11", "stress12","stress13", "stress14")]),
phone_ave=rowMeans(.[,c("phone1","phone2","phone3","phone4","phone5",
"phone6","phone7","phone8","phone9")])
) %>%
ggplot(aes(x=stress_ave,
y=phone_ave)) +
geom_point() +
geom_smooth(method="lm") + # 在散点图上叠加回归线，语法可以查找帮助文档
theme_classic()
```
<!-- -->
---
# 可视化
## ggplot2小组
* 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/
---
# 可视化
## Explore data with DataExplorer
<font size=5>
&emsp;&emsp;DataExplorer可能是是一个不错的工具
</font>
```r
# load DataExplorer
pacman::p_load("DataExplorer")
DataExplorer::plot_str(df.pg.raw)
```
---
# 可视化
## Explore data with DataExplorer
<font size=5>
&emsp;&emsp;DataExplorer可能是是一个不错的工具
</font>
```r
DataExplorer::plot_str(df.mt.raw)
```
---
# 可视化
## Explore data with DataExplorer
<font size=5>
&emsp;&emsp;DataExplorer可能是是一个不错的工具
</font>
```r
DataExplorer::plot_intro(df.mt.raw)
```
<!-- -->
---
# 可视化
## Explore data with DataExplorer
<font size=5>
&emsp;&emsp;DataExplorer可能是是一个不错的工具
</font>
```r
DataExplorer::plot_missing(df.mt.raw)
```
<!-- -->
---
# 可视化
## Explore data with DataExplorer
<font size=5>
&emsp;&emsp;DataExplorer可能是是一个不错的工具
</font>
```r
DataExplorer::plot_bar(df.mt.raw)
```
<!-- -->
---
# 可视化
## Explore data with DataExplorer
<font size=5>
&emsp;&emsp;DataExplorer可能是是一个不错的工具
</font>
```r
DataExplorer::plot_bar(df.mt.raw, by="Match")
```
<!-- -->
---
# 可视化
## Explore data with DataExplorer
<font size=5>
&emsp;&emsp;DataExplorer可能是是一个不错的工具
</font>
```r
DataExplorer::plot_histogram(df.mt.raw)
```
<!-- -->
---
# 可视化
## Explore data with DataExplorer
<font size=5>
&emsp;&emsp;DataExplorer可能是是一个不错的工具
</font>
```r
DataExplorer::plot_qq(df.pg.raw[,2:10])
```
<!-- -->
---
# 可视化
## Explore data with DataExplorer
<font size=5>
&emsp;&emsp;DataExplorer可能是是一个不错的工具
</font>
```r
DataExplorer::plot_correlation(na.omit(df.pg.raw[, 2:30]))
```
<!-- -->
---
#练习
## 1. 读取match数据，对自己感兴趣的变量进行描述性统计。
## 2. 读取match数据，对不同shape的击中率进行分组绘图，可使用boxplot观察差异。
## 3. 读取penguin数据，选择自己感兴趣的两个变量进行处理并画出散点图。
## 4. 对两个数据中自己感兴趣的变量们做探索性数据分析。
#探索
## 在本章的例子中，我们举例了反应时的分布。但其实我们是对所有被试的所有反应时绘制了总的分布，那么我们能不能找到一个办法绘制出每一个被试的反应时分布呢？
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