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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>Lecture 6</title>
<meta charset="utf-8" />
<meta name="author" content="Hu Chuan-Peng (PhD)" />
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class: center, middle, inverse, title-slide
.title[
# Lecture 6
]
.subtitle[
## Data Preprocessing
]
.author[
### Hu Chuan-Peng (PhD)
]
.institute[
### Nanjing Normal University
]
.date[
### 2024/04/03
]
---
class: center, middle
<span style="font-size: 60px;">第六讲</span> <br>
<span style="font-size: 50px;">数据预处理</span> <br>
<br>
<br>
<span style="font-size: 30px;">胡传鹏</span> <br>
<span style="font-size: 30px;">2024/04/03</span> <br>
<br>
<br>
<br>
<br>
---
<h1 lang="en" style="font-size: 60px;">Contents</h1>
<br>
<br>
<span style="font-size: 45px;">6.1 Tidyverse</span></center> <br>
<br>
<span style="font-size: 45px;">6.2 问卷数据</span></center> <br>
<span style="font-size: 30px;">&emsp;6.2.1 研究问题 & 数据情况</span></center> <br>
<span style="font-size: 30px;">&emsp;6.2.2 操作步骤</span></center> <br>
<span style="font-size: 30px;">&emsp;6.2.3 小结</span></center> <br>
<br>
<span style="font-size: 45px;">6.3 反应时数据</span></center> <br>
<span style="font-size: 30px;">&emsp;6.3.1 研究问题 & 数据情况</span></center> <br>
<span style="font-size: 30px;">&emsp;6.3.2 操作步骤</span></center> <br>
<span style="font-size: 30px;">&emsp;6.3.3 小结</span></center> <br>
---
# <h1 lang="en">6.1 Tidyverse</h1>
<img src="./picture/chp6/workflow.png" width="85%" style="display: block; margin-left: auto; margin-right: auto;">
- 本课程的数据预处理将基于tidyverse( https://www.tidyverse.org/ )，它是目前最流行的预处理工具，是由8个多功能R包组成的连贯的系统<br>
- 核心包的具体功能与工作流如图所示
---
# <h1 lang="en">6.1 Tidyverse</h1>
<img src="./picture/chp6/cheatsheet.png" width="85%" style="display: block; margin-left: auto; margin-right: auto;">
- 可以通过搜索包的cheatsheet快速了解包的使用<br>
- 官方cheatsheet: https://rstudio.github.io/cheatsheets/
---
# <h1 lang="en">6.1 Tidyverse</h1>
- **优势：共享一个底层设计哲学、语法和数据结构，具有高度的一致性**<br>
<br>
- **"整洁数据(tidy)"**：每行代表一个观察值，每列代表一个变量的值<br>
- **函数的第一个参数总是一个数据框**<br>
- **管道操作符**：连接独立代码，省去中间变量，流水线<br>
最常用的管道操作符为**%>%**，它将一个函数的输出作为下一个函数的输入<br>
<br>
假设需找到`data`中`age`大于`30`的所有行，并排序，代码如下：<br>
```r
filtered_data <- filter(data, age > 30)
filtered_sorted_data <- arrange(filtered_data, age)
```
<br>
使用管道操作符后，代码变为：<br>
```r
filtered_sorted_data <- data %>%
filter(age > 30) %>%
arrange(age)
```
---
# <h1 lang="en">6.1 Tidyverse</h1>
<br><img src="./picture/chp6/pipe.png" width="100%" style="display: block; margin-left: auto; margin-right: auto;">
- tidyverse常见的管道符如表所示，依赖于`magrittr`包<br>
- R 4.1.0 以上版本加入了原生管道操作符`|>`<br>
---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;"> 6.2.1 研究问题 & 数据情况</span></center> <br>
<br>
- 课程接下来会以重复[IJzerman et al (2018)](https://doi.org/10.1525/collabra.165)的分析进行问卷数据分析的示例<br>
<br>
- **研究问题**：社交复杂度(CSI)是否影响核心体温(CBT)，特别是在离赤道比较远的（低温）地区(DEQ)下<br>
<br>
- **研究假设**：<br>
对于在低温环境中的人来说，（在众多的变量中）社交网络复杂度能够影响个体的核心体温<br>
这一效应受个体的恋爱状态(romantic)调节<br>
<br>
- **研究方法**：路径模型，探索性监督机器学习<br>
---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;">6.2.1 研究问题 & 数据情况</span></center> <br>
- **研究结果**：<br>
<img src="./picture/chp6/pr1.png" width="65%" style="display: block; margin-left: auto; margin-right: auto;">
---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;">6.2.1 研究问题 & 数据情况</span></center> <br>
<br>
- **研究结果**：<br>
<img src="./picture/chp6/pr2.png" width="100%" style="display: block; margin-left: auto; margin-right: auto;">
---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;">6.2.1 研究问题 & 数据情况</span></center><br>
- 数据情况(Hu et al., 2019)：<br>
通过data/penguin文件夹下的penguin_full_codebook可以查看详细情况<br>
<img src="./picture/chp6/penguin.png" width="70%" style="display: block; margin-left: auto; margin-right: auto;">
---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;">6.2.1 研究问题 & 数据情况</span></center><br>
- 研究核心变量：<br>
CBT: 核心体温，测量两次，变量为Temperature_t1, Temperature_t2<br>
CSI: 变量为socialdiversity<br>
Site: 数据源站点<br>
DEQ: 距赤道的距离，变量为DEQ<br>
romantic: 是否处于恋爱关系，1 = "yes", 2 = "no"<br>
ALEX: 述情障碍，探索性监督机器学习需要的变量之一，5点量表，变量为ALEX1-16，第4, 12, 14, 16题反向计分
---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;">6.2.1 研究问题 & 数据情况</span></center><br>
```r
# 导入数据
df1 <- bruceR::import(here::here('data', 'penguin', 'penguin_rawdata.csv'))
```
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class=\"display fill-container\">\n <thead>\n <tr>\n <th> <\/th>\n <th>Site<\/th>\n <th>age<\/th>\n <th>sex<\/th>\n <th>monogamous<\/th>\n <th>romantic<\/th>\n <th>health<\/th>\n <th>exercise<\/th>\n <th>eatdrink<\/th>\n <th>gluctot<\/th>\n <th>artgluctot<\/th>\n <th>smoke<\/th>\n <th>cigs<\/th>\n <th>avgtemp<\/th>\n <th>Temperature_t1<\/th>\n <th>Temperature_t2<\/th>\n <th>DEQ<\/th>\n <th>AvgHumidity<\/th>\n <th>mintemp<\/th>\n <th>language<\/th>\n <th>langfamily<\/th>\n <th>SNI1<\/th>\n <th>SNI2<\/th>\n <th>SNI3<\/th>\n <th>SNI4<\/th>\n <th>SNI5<\/th>\n <th>SNI6<\/th>\n <th>SNI7<\/th>\n <th>SNI8<\/th>\n <th>SNI9<\/th>\n <th>SNI10<\/th>\n <th>SNI11<\/th>\n <th>SNI12<\/th>\n <th>SNI13<\/th>\n <th>SNI14<\/th>\n <th>SNI15<\/th>\n <th>SNI16<\/th>\n <th>SNI17<\/th>\n <th>SNI18<\/th>\n <th>SNI19<\/th>\n <th>SNI20<\/th>\n <th>SNI21<\/th>\n <th>SNI22<\/th>\n <th>SNI23<\/th>\n <th>SNI24<\/th>\n <th>SNI25<\/th>\n <th>SNI26<\/th>\n <th>SNI27<\/th>\n <th>SNI28<\/th>\n <th>SNI29<\/th>\n <th>SNI30<\/th>\n <th>SNI31<\/th>\n <th>SNI32<\/th>\n <th>scontrol1<\/th>\n <th>scontrol2<\/th>\n <th>scontrol3<\/th>\n <th>scontrol4<\/th>\n <th>scontrol5<\/th>\n <th>scontrol6<\/th>\n <th>scontrol7<\/th>\n <th>scontrol8<\/th>\n <th>scontrol9<\/th>\n <th>scontrol10<\/th>\n <th>scontrol11<\/th>\n <th>scontrol12<\/th>\n <th>scontrol13<\/th>\n <th>stress1<\/th>\n <th>stress2<\/th>\n <th>stress3<\/th>\n <th>stress4<\/th>\n <th>stress5<\/th>\n <th>stress6<\/th>\n <th>stress7<\/th>\n <th>stress8<\/th>\n <th>stress9<\/th>\n <th>stress10<\/th>\n <th>stress11<\/th>\n <th>stress12<\/th>\n <th>stress13<\/th>\n <th>stress14<\/th>\n <th>phone1<\/th>\n <th>phone2<\/th>\n <th>phone3<\/th>\n <th>phone4<\/th>\n <th>phone5<\/th>\n <th>phone6<\/th>\n <th>phone7<\/th>\n <th>phone8<\/th>\n <th>phone9<\/th>\n <th>onlineid1<\/th>\n <th>onlineid2<\/th>\n <th>onlineid3<\/th>\n <th>onlineid4<\/th>\n <th>onlineid5<\/th>\n <th>onlineid6<\/th>\n <th>onlineid7<\/th>\n <th>onlineid8<\/th>\n <th>onlineid9<\/th>\n <th>onlineid10<\/th>\n <th>onlineide11<\/th>\n <th>ECR1<\/th>\n <th>ECR2<\/th>\n <th>ECR3<\/th>\n <th>ECR4<\/th>\n <th>ECR5<\/th>\n <th>ECR6<\/th>\n <th>ECR7<\/th>\n <th>ECR8<\/th>\n <th>ECR9<\/th>\n <th>ECR10<\/th>\n <th>ECR11<\/th>\n <th>ECR12<\/th>\n <th>ECR13<\/th>\n <th>ECR14<\/th>\n <th>ECR15<\/th>\n <th>ECR16<\/th>\n <th>ECR17<\/th>\n <th>ECR18<\/th>\n <th>ECR19<\/th>\n <th>ECR20<\/th>\n <th>ECR21<\/th>\n <th>ECR22<\/th>\n <th>ECR23<\/th>\n <th>ECR24<\/th>\n <th>ECR25<\/th>\n <th>ECR26<\/th>\n <th>ECR27<\/th>\n <th>ECR28<\/th>\n <th>ECR29<\/th>\n <th>ECR30<\/th>\n <th>ECR31<\/th>\n <th>ECR32<\/th>\n <th>ECR33<\/th>\n <th>ECR34<\/th>\n <th>ECR35<\/th>\n <th>ECR36<\/th>\n <th>HOME1<\/th>\n <th>HOME2<\/th>\n <th>HOME3<\/th>\n <th>HOME4<\/th>\n <th>HOME5<\/th>\n <th>HOME6<\/th>\n <th>HOME7<\/th>\n <th>HOME8<\/th>\n <th>HOME9<\/th>\n <th>SNS1<\/th>\n <th>SNS2<\/th>\n <th>SNS3<\/th>\n <th>SNS4<\/th>\n <th>SNS5<\/th>\n <th>SNS6<\/th>\n <th>SNS7<\/th>\n <th>ALEX1<\/th>\n <th>ALEX2<\/th>\n <th>ALEX3<\/th>\n <th>ALEX4<\/th>\n <th>ALEX5<\/th>\n <th>ALEX6<\/th>\n <th>ALEX7<\/th>\n <th>ALEX8<\/th>\n <th>ALEX9<\/th>\n <th>ALEX10<\/th>\n <th>ALEX11<\/th>\n <th>ALEX12<\/th>\n <th>ALEX13<\/th>\n <th>ALEX14<\/th>\n <th>ALEX15<\/th>\n <th>ALEX16<\/th>\n <th>KAMF1<\/th>\n <th>KAMF2<\/th>\n <th>KAMF3<\/th>\n <th>KAMF4<\/th>\n <th>KAMF5<\/th>\n <th>KAMF6<\/th>\n <th>KAMF7<\/th>\n <th>STRAQ_1<\/th>\n <th>STRAQ_2<\/th>\n <th>STRAQ_3<\/th>\n <th>STRAQ_4<\/th>\n <th>STRAQ_5<\/th>\n <th>STRAQ_6<\/th>\n <th>STRAQ_7<\/th>\n <th>STRAQ_8<\/th>\n <th>STRAQ_9<\/th>\n <th>STRAQ_10<\/th>\n <th>STRAQ_11<\/th>\n <th>STRAQ_12<\/th>\n <th>STRAQ_13<\/th>\n <th>STRAQ_14<\/th>\n <th>STRAQ_15<\/th>\n <th>STRAQ_16<\/th>\n <th>STRAQ_17<\/th>\n <th>STRAQ_18<\/th>\n <th>STRAQ_19<\/th>\n <th>STRAQ_20<\/th>\n <th>STRAQ_21<\/th>\n <th>STRAQ_22<\/th>\n <th>STRAQ_23<\/th>\n <th>STRAQ_24<\/th>\n <th>STRAQ_25<\/th>\n <th>STRAQ_26<\/th>\n <th>STRAQ_27<\/th>\n <th>STRAQ_28<\/th>\n <th>STRAQ_29<\/th>\n <th>STRAQ_30<\/th>\n <th>STRAQ_31<\/th>\n <th>STRAQ_32<\/th>\n <th>STRAQ_33<\/th>\n <th>STRAQ_34<\/th>\n <th>STRAQ_35<\/th>\n <th>STRAQ_36<\/th>\n <th>STRAQ_37<\/th>\n <th>STRAQ_38<\/th>\n <th>STRAQ_39<\/th>\n <th>STRAQ_40<\/th>\n <th>STRAQ_41<\/th>\n <th>STRAQ_42<\/th>\n <th>STRAQ_43<\/th>\n <th>STRAQ_44<\/th>\n <th>STRAQ_45<\/th>\n <th>STRAQ_46<\/th>\n <th>STRAQ_47<\/th>\n <th>STRAQ_48<\/th>\n <th>STRAQ_49<\/th>\n <th>STRAQ_50<\/th>\n <th>STRAQ_51<\/th>\n <th>STRAQ_52<\/th>\n <th>STRAQ_53<\/th>\n <th>STRAQ_54<\/th>\n <th>STRAQ_55<\/th>\n <th>STRAQ_56<\/th>\n <th>STRAQ_57<\/th>\n <th>socialdiversity<\/th>\n <\/tr>\n 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---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;">6.2.1 研究问题 & 数据情况</span></center><br>
```r
#查看变量名（列名）
colnames(df1)
```
```
## [1] "Site" "age" "sex" "monogamous"
## [5] "romantic" "health" "exercise" "eatdrink"
## [9] "gluctot" "artgluctot" "smoke" "cigs"
## [13] "avgtemp" "Temperature_t1" "Temperature_t2" "DEQ"
## [17] "AvgHumidity" "mintemp" "language" "langfamily"
## [21] "SNI1" "SNI2" "SNI3" "SNI4"
## [25] "SNI5" "SNI6" "SNI7" "SNI8"
## [29] "SNI9" "SNI10" "SNI11" "SNI12"
## [33] "SNI13" "SNI14" "SNI15" "SNI16"
## [37] "SNI17" "SNI18" "SNI19" "SNI20"
## [41] "SNI21" "SNI22" "SNI23" "SNI24"
## [45] "SNI25" "SNI26" "SNI27" "SNI28"
## [49] "SNI29" "SNI30" "SNI31" "SNI32"
## [53] "scontrol1" "scontrol2" "scontrol3" "scontrol4"
## [57] "scontrol5" "scontrol6" "scontrol7" "scontrol8"
## [61] "scontrol9" "scontrol10" "scontrol11" "scontrol12"
## [65] "scontrol13" "stress1" "stress2" "stress3"
## [69] "stress4" "stress5" "stress6" "stress7"
## [73] "stress8" "stress9" "stress10" "stress11"
## [77] "stress12" "stress13" "stress14" "phone1"
## [81] "phone2" "phone3" "phone4" "phone5"
## [85] "phone6" "phone7" "phone8" "phone9"
## [89] "onlineid1" "onlineid2" "onlineid3" "onlineid4"
## [93] "onlineid5" "onlineid6" "onlineid7" "onlineid8"
## [97] "onlineid9" "onlineid10" "onlineide11" "ECR1"
## [101] "ECR2" "ECR3" "ECR4" "ECR5"
## [105] "ECR6" "ECR7" "ECR8" "ECR9"
## [109] "ECR10" "ECR11" "ECR12" "ECR13"
## [113] "ECR14" "ECR15" "ECR16" "ECR17"
## [117] "ECR18" "ECR19" "ECR20" "ECR21"
## [121] "ECR22" "ECR23" "ECR24" "ECR25"
## [125] "ECR26" "ECR27" "ECR28" "ECR29"
## [129] "ECR30" "ECR31" "ECR32" "ECR33"
## [133] "ECR34" "ECR35" "ECR36" "HOME1"
## [137] "HOME2" "HOME3" "HOME4" "HOME5"
## [141] "HOME6" "HOME7" "HOME8" "HOME9"
## [145] "SNS1" "SNS2" "SNS3" "SNS4"
## [149] "SNS5" "SNS6" "SNS7" "ALEX1"
## [153] "ALEX2" "ALEX3" "ALEX4" "ALEX5"
## [157] "ALEX6" "ALEX7" "ALEX8" "ALEX9"
## [161] "ALEX10" "ALEX11" "ALEX12" "ALEX13"
## [165] "ALEX14" "ALEX15" "ALEX16" "KAMF1"
## [169] "KAMF2" "KAMF3" "KAMF4" "KAMF5"
## [173] "KAMF6" "KAMF7" "STRAQ_1" "STRAQ_2"
## [177] "STRAQ_3" "STRAQ_4" "STRAQ_5" "STRAQ_6"
## [181] "STRAQ_7" "STRAQ_8" "STRAQ_9" "STRAQ_10"
## [185] "STRAQ_11" "STRAQ_12" "STRAQ_13" "STRAQ_14"
## [189] "STRAQ_15" "STRAQ_16" "STRAQ_17" "STRAQ_18"
## [193] "STRAQ_19" "STRAQ_20" "STRAQ_21" "STRAQ_22"
## [197] "STRAQ_23" "STRAQ_24" "STRAQ_25" "STRAQ_26"
## [201] "STRAQ_27" "STRAQ_28" "STRAQ_29" "STRAQ_30"
## [205] "STRAQ_31" "STRAQ_32" "STRAQ_33" "STRAQ_34"
## [209] "STRAQ_35" "STRAQ_36" "STRAQ_37" "STRAQ_38"
## [213] "STRAQ_39" "STRAQ_40" "STRAQ_41" "STRAQ_42"
## [217] "STRAQ_43" "STRAQ_44" "STRAQ_45" "STRAQ_46"
## [221] "STRAQ_47" "STRAQ_48" "STRAQ_49" "STRAQ_50"
## [225] "STRAQ_51" "STRAQ_52" "STRAQ_53" "STRAQ_54"
## [229] "STRAQ_55" "STRAQ_56" "STRAQ_57" "socialdiversity"
```
---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;">6.2.2 操作步骤</span></center><br>
- 数据预处理目标：计算感兴趣的变量，按照Site查看被试的平均体温<br>
Step1: 选择变量[select]<br>
Step2: 检查数据类型[glimpse, as族函数]<br>
Step3: 处理缺失值[filter, is.na]<br>
Step4: 计算所需变量[mutate, case when]<br>
Step5: 分组求统计量 [group_by, summarise]
```r
# 不要忘记加载包
library(tidyverse)
```
```
## Warning: 程辑包'tidyverse'是用R版本4.3.3 来建造的
```
```
## Warning: 程辑包'ggplot2'是用R版本4.3.3 来建造的
```
---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;">6.2.2 操作步骤 | Step1: 选择变量[select]</span></center><br>
```r
# 加载包后函数前不需要注明包，此处只是为了提示函数属于哪个包
# 选择我们需要的变量：Temperature_t1, Temperature_t2, SNI28-32, DEQ, romantic, ALEX1-16
df1 <- dplyr::select(df1,
Temperature_t1, Temperature_t2,
socialdiversity,
Site, DEQ,
romantic,
ALEX1:ALEX16)
```
<br>
- select()函数会按照提供的参数顺序选择列<br>
- 可以使用列名、范围（例如 starts_with()、ends_with()、contains()、matches() 等），或者使用 everything() 来选择所有列<br>
- 注意需要将函数结果赋值给一个新的变量/原始变量完成保存
---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;">6.2.2 操作步骤 | Step2: 检查数据类型[summary]</span></center><br>
```r
# 检查变量的数据类型
base::summary(df1)
```
```
## Temperature_t1 Temperature_t2 socialdiversity Site
## Min. :33.20 Min. :33.90 Min. : 0.000 Length:1517
## 1st Qu.:36.00 1st Qu.:36.28 1st Qu.: 5.000 Class :character
## Median :36.40 Median :36.50 Median : 6.000 Mode :character
## Mean :36.28 Mean :36.50 Mean : 6.599
## 3rd Qu.:36.70 3rd Qu.:36.78 3rd Qu.: 8.000
## Max. :39.40 Max. :39.30 Max. :12.000
## NA's :30 NA's :9
## DEQ romantic ALEX1 ALEX2
## Min. : 1.293 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:34.433 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000
## Median :39.912 Median :1.000 Median :2.000 Median :3.000
## Mean :39.833 Mean :1.443 Mean :2.509 Mean :2.762
## 3rd Qu.:51.317 3rd Qu.:2.000 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :60.391 Max. :2.000 Max. :7.000 Max. :7.000
## NA's :88 NA's :32 NA's :15 NA's :15
## ALEX3 ALEX4 ALEX5 ALEX6 ALEX7
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.00 Min. :1.000
## 1st Qu.:1.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.00 1st Qu.:2.000
## Median :2.00 Median :2.000 Median :2.000 Median :2.00 Median :3.000
## Mean :2.03 Mean :2.656 Mean :2.472 Mean :2.48 Mean :2.892
## 3rd Qu.:3.00 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.00 3rd Qu.:4.000
## Max. :5.00 Max. :7.000 Max. :7.000 Max. :7.00 Max. :7.000
## NA's :15 NA's :15 NA's :15 NA's :15 NA's :15
## ALEX8 ALEX9 ALEX10 ALEX11 ALEX12
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.0
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.0
## Median :2.000 Median :2.000 Median :2.000 Median :2.000 Median :2.0
## Mean :2.563 Mean :2.516 Mean :2.405 Mean :2.289 Mean :2.2
## 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:3.0
## Max. :7.000 Max. :5.000 Max. :7.000 Max. :7.000 Max. :6.0
## NA's :15 NA's :15 NA's :15 NA's :15 NA's :15
## ALEX13 ALEX14 ALEX15 ALEX16
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :3.000 Median :2.000
## Mean :2.385 Mean :2.065 Mean :2.841 Mean :2.169
## 3rd Qu.:3.000 3rd Qu.:2.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :6.000 Max. :6.000 Max. :7.000 Max. :7.000
## NA's :15 NA's :15 NA's :15 NA's :15
```
---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;">6.2.2 操作步骤 | Step2: 检查数据类型[as族函数]</span></center><br>
```r
# 转换数据类型
# 这里数据类型是正确的，只是示例
df1 <- dplyr::mutate(df1,
Temperature_t1_new = as.numeric(Temperature_t1),
Temperature_t2 = as.numeric(Temperature_t2))
```
<br>
- `mutate()`函数常用于创建新的变量或修改现有变量<br>
- 存在多种变式，如`mutate_at()`通过列名、位置或者列的类型进行选择，`mutate_if()`对数据框中满足条件的列应用指定的函数<br>
- `mutate()`内使用函数时，同样需要注意缺失值的问题<br>
- 注意`mutate()`进行转换之后需要进行核查：是否符合预期<br>
- 注意需要将函数结果赋值给一个新的变量/原始变量完成保存
---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;">6.2.2 操作步骤 | Step3: 处理缺失值[filter, is.na]</span></center><br>
```r
# 按照Temperature, DEQ处理缺失值
df1 <- filter(df1, !is.na(Temperature_t1)
& !is.na(Temperature_t2) & !is.na(DEQ))
```
<br>
- filter()函数用于从数据框中筛选**行**（观测值），可以通过逻辑运算符组合多个条件<br>
- 运算逻辑：遍历每一行，将给定的条件应用于该行，条件为真则保留，保留的行被组成一个新的数据框作为函数的返回值<br>
- 注意需要将函数结果赋值给一个新的变量/原始变量完成保存
---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;">6.2.2 操作步骤 | Step4: 计算所需变量[mutate]</span></center><br>
```r
# 计算每个被试两次核心温度的均值，保存为Temperature
df1 <- dplyr::mutate(df1,
Temperature = rowMeans(select(df1, starts_with("Temperature"))))
```
- `mean()`函数用于计算向量或数组的平均值，`colMeans()`函数用于计算矩阵或数据框的每一列的平均值，`rowMeans()`函数用于计算矩阵或数据框的每一行的平均值<br>
- 数据类型需为numeric<br>
- `starts_with()`用于在数据框中选择列名以特定字符串开头的列
---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;">6.2.2 操作步骤 | Step4: 计算所需变量[mutate, case when]</span></center><br>
```r
# 将4, 12, 14, 16题反向计分，计算ALEX，保存为ALEX
df1 <- mutate(df1,
ALEX4 = case_when(
TRUE ~ 6 - ALEX4 # 反向计分：6减去原始值
),
ALEX12 = case_when(TRUE ~ 6 - ALEX12),
ALEX14 = case_when(TRUE ~ 6 - ALEX14),
ALEX16 = case_when(TRUE ~ 6 - ALEX16)
)
#也可以写成 case_when(ALEX4 == '1' ~ '5',ALEX4 == '2' ~ '4', ALEX4 == '3' ~ '3', ALEX4 == '4' ~ '2', ALEX4 == '5' ~ '1',TRUE ~ as.character(ALEX4))
```
- `case_when()`函数是一个强大的条件判断函数，通常用于根据不同的条件生成新的变量或对现有变量进行转换<br>
- 运算逻辑：逐行评估每个条件，并根据条件的结果来确定新值，若条件为真，则用‘~’后的值替换原始值<br>
- 有多个条件时，按照条件的顺序逐个进行判断，一旦有条件满足，则返回对应的值并停止继续判断其他条件<br>
- 使用 `TRUE ~` 或者 `TRUE ~ NA`处理未匹配到任何条件的情况，这样可以确保即使所有条件都不满足时，函数也会返回一个默认值，避免产生错误
---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;">6.2.2 操作步骤 | Step4: 计算所需变量[mutate, case when]</span></center><br>
- **前后对比**：<br>
<img src="./picture/chp6/contrast.png" width="100%" style="display: block; margin-left: auto; margin-right: auto;">
---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;">6.2.2 操作步骤 | Step4: 计算所需变量[mutate, case when]</span></center><br>
- 使用case when()要确保条件中使用的值与变量的数据类型匹配<br>
```r
# age为num
case_when(
age < 18 ~ "Child",
age >= 18 & age < 65 ~ "Adult",
age >= 65 ~ "Senior"
)
# age为chr
case_when(
age < "18" ~ "Child",
age >= "18" & age < "65" ~ "Adult",
age >= "65" ~ "Senior"
)
```
---
# <h1 lang="en">6.2 问卷数据</h1>
<span style="font-size: 30px;">6.2.2 操作步骤 | Step4: 计算所需变量[mutate]</span></center><br>
```r
# 计算ALEX
df1 <- mutate(df1,
ALEX = rowSums(select(df1, starts_with("ALEX"))))
# 查看数据
df1
```
```
## Temperature_t1 Temperature_t2 socialdiversity Site DEQ
## 1 36.80000 36.70000 8 Tsinghua 26.887802
## 2 34.20000 36.60000 6 Oxford 51.750000
## 3 35.00000 35.20000 5 Oxford 51.750000
## 4 36.10000 35.80000 7 Oxford 51.750000
## 5 35.72222 36.44444 5 Chile 42.077194
## 6 35.30000 35.90000 7 Bamberg 51.656601
## 7 34.80000 36.00000 4 Oxford 51.750000
## 8 35.10000 36.50000 5 Oxford 51.750000
## 9 35.30000 36.40000 5 Oxford 51.750000
## 10 35.10000 36.10000 7 Oslo 59.912704
## 11 36.20000 35.50000 7 Chile 33.449997
## 12 35.60000 36.40000 4 Oxford 51.750000
## 13 36.50000 36.40000 7 Oxford 51.750000
## 14 36.30000 36.30000 6 Kassel 48.401505
## 15 36.20000 36.00000 10 Chile 33.449997
## 16 35.90000 36.40000 3 Oxford 51.750000
## 17 35.50000 35.50000 5 Chile 33.449997
## 18 36.50000 36.70000 5 Oxford 51.750000
## 19 36.60000 36.60000 9 Chile 33.449997
## 20 36.70000 36.90000 5 Bamberg 51.266693
## 21 35.00000 36.50000 5 Oxford 51.750000
## 22 35.80000 36.20000 8 Zurich 47.166107
## 23 36.30000 36.10000 4 Bamberg 49.897202
## 24 36.50000 36.00000 9 Bamberg 50.516693
## 25 36.60000 36.60000 7 Kassel 48.766693
## 26 38.40000 38.40000 6 Bamberg 50.157394
## 27 35.80000 36.30000 5 Bamberg 51.175995
## 28 35.80000 36.50000 5 Oxford 51.750000
## 29 36.30000 36.70000 5 Bamberg 52.516693
## 30 36.60000 36.40000 8 Portugal 38.716705
## 31 37.00000 36.50000 10 Poland 52.593094
## 32 36.30000 36.60000 5 Oxford 51.750000
## 33 36.30000 36.60000 7 Bamberg 50.116699
## 34 35.30000 35.30000 6 Oxford 51.750000
## 35 34.80000 36.50000 3 Oxford 51.750000
## 36 36.60000 36.90000 8 Poland 52.233307
## 37 35.50000 35.40000 5 Kassel 48.766693
## 38 36.40000 36.70000 7 Chile 33.449997
## 39 37.00000 36.80000 9 Portugal 38.716705
## 40 34.50000 36.00000 6 Oxford 51.750000
## 41 35.94444 36.77778 9 VCU 37.751007
## 42 36.30000 36.96000 9 Kassel 49.190201
## 43 34.90000 36.80000 7 Oxford 51.750000
## 44 36.00000 36.00000 5 Bamberg 47.000000
## 45 36.60000 36.50000 10 Tsinghua 35.000000
## 46 34.20000 36.70000 7 Oxford 51.750000
## 47 34.80000 36.20000 3 Oxford 51.750000
## 48 35.70000 36.20000 4 Oxford 51.750000
## 49 36.00000 36.70000 10 Bamberg 49.649994
## 50 36.30000 36.60000 11 Tsinghua 30.666702
## 51 36.47000 36.79000 6 Zurich 47.000000
## 52 36.55000 36.90000 7 Zurich 47.400604
## 53 36.80000 37.00000 9 Poland 52.250000
## 54 36.70000 37.80000 7 Serbia 44.818604
## 55 35.40000 36.30000 7 METU 39.923004
## 56 35.90000 36.80000 8 Chile 33.449997
## 57 36.27000 36.66000 11 Kassel 51.717102
## 58 37.00000 37.11111 8 VCU 37.751007
## 59 35.30000 36.20000 5 Oxford 51.750000
## 60 36.50000 36.40000 8 Poland 54.434692
## 61 36.80000 36.60000 8 Bamberg 50.099396
## 62 37.10000 36.60000 10 Poland 52.416702
## 63 36.00000 36.00000 7 Portugal 38.716705
## 64 35.90000 36.50000 7 Oxford 51.750000
## 65 36.33333 36.33333 8 VCU 37.399506
## 66 36.50000 36.40000 9 Poland 52.233307
## 67 36.10000 36.90000 11 Poland 52.066696
## 68 36.60000 36.70000 10 Poland 52.250000
## 69 37.00000 36.90000 9 Poland 53.233307
## 70 35.40000 36.00000 9 METU 41.013596
## 71 35.70000 36.90000 8 Oxford 51.750000
## 72 36.40000 36.60000 8 Portugal 38.716705
## 73 36.79000 36.28000 10 Poland 53.035797
## 74 36.40000 36.90000 6 Oxford 51.750000
## 75 36.80000 36.90000 9 Poland 52.593094
## 76 36.00000 35.90000 6 Oslo 59.912704
## 77 36.10000 36.40000 6 Serbia 44.818604
## 78 36.50000 36.60000 6 Oxford 51.750000
## 79 36.40000 36.90000 7 Chile 33.449997
## 80 36.90000 36.50000 8 Poland 54.360794
## 81 37.00000 36.90000 5 Serbia 44.818604
## 82 35.70000 36.80000 9 Chile 33.449997
## 83 36.40000 36.60000 7 Chile 33.449997
## 84 36.50000 36.60000 10 Poland 52.233307
## 85 34.90000 35.90000 4 Oxford 51.750000
## 86 36.30000 35.90000 8 Poland 52.250000
## 87 36.10000 36.10000 6 Chile 33.426281
## 88 35.70000 36.60000 5 METU 39.923004
## 89 36.60000 35.90000 10 Poland 53.416702
## 90 36.27778 36.22222 8 VCU 37.444901
## 91 37.00000 35.70000 10 Poland 52.250000
## 92 36.20000 36.50000 10 Poland 52.593094
## 93 36.50000 36.20000 9 Chile 33.449997
## 94 36.50000 36.40000 10 Poland 52.233307
## 95 36.50000 36.50000 9 Poland 52.250000
## 96 36.50000 36.60000 8 Bamberg 53.550003
## 97 36.74000 36.51000 9 Poland 52.233307
## 98 36.80000 37.00000 9 Poland 52.250000
## 99 36.90000 37.00000 10 Poland 53.416702
## 100 37.00000 36.90000 5 Serbia 51.000000
## 101 37.00000 36.90000 8 Oxford 51.750000
## 102 35.60000 35.90000 5 Oxford 51.750000
## 103 36.10000 36.10000 9 Bamberg 51.000000
## 104 36.40000 36.00000 10 Poland 52.593094
## 105 35.50000 37.00000 3 Oxford 51.750000
## 106 36.30000 36.30000 9 Poland 52.250000
## 107 36.40000 36.60000 12 Kassel 51.287796
## 108 36.60000 36.50000 8 Bamberg 53.550003
## 109 36.50000 36.80000 8 Poland 52.250000
## 110 36.60000 36.80000 8 Poland 52.250000
## 111 35.10000 35.10000 9 Bamberg 50.533295
## 112 36.50000 36.30000 9 Poland 52.250000
## 113 36.30000 36.50000 11 Poland 52.416702
## 114 36.90000 36.90000 7 Serbia 44.818604
## 115 36.10000 35.80000 8 Portugal 38.800995
## 116 35.90000 36.10000 9 Serbia 44.818604
## 117 36.10000 36.30000 8 Chile 33.449997
## 118 36.00000 36.70000 7 Portugal 38.716705
## 119 36.40000 36.30000 11 Poland 52.250000
## 120 36.33333 36.50000 7 VCU 37.733795
## 121 36.80000 36.10000 8 METU 39.923004
## 122 36.50000 36.50000 8 Poland 52.250000
## 123 36.50000 36.60000 6 Zurich 47.560303
## 124 36.30000 36.90000 11 Poland 54.360794
## 125 37.00000 36.70000 8 Zurich 47.000000
## 126 34.60000 36.00000 7 Kassel 50.079407
## 127 35.70000 35.70000 8 Portugal 38.716705
## 128 35.60000 35.80000 8 Oxford 51.750000
## 129 35.60000 36.20000 8 METU 39.911697
## 130 35.90000 36.30000 8 Kassel 51.316696
## 131 36.05556 36.27778 6 VCU 37.553802
## 132 36.20000 36.30000 8 Serbia 44.818604
## 133 36.60000 36.90000 10 Poland 52.250000
## 134 36.70000 37.00000 11 Poland 54.360794
## 135 34.10000 36.20000 8 Oslo 59.916702
## 136 35.90000 36.20000 10 Poland 52.250000
## 137 35.50000 36.70000 6 Oxford 51.750000
## 138 36.00000 36.40000 8 METU 39.911499
## 139 36.30000 36.20000 6 METU 38.773499
## 140 36.40000 36.40000 9 Kassel 50.983307
## 141 36.40000 36.50000 7 Portugal 38.716705
## 142 36.41000 36.57000 10 Poland 52.327805
## 143 36.70000 36.70000 6 Serbia 44.818604
## 144 36.70000 36.70000 7 Poland 52.250000
## 145 36.90000 36.60000 5 Zurich 47.351593
## 146 36.60000 36.90000 10 Poland 52.250000
## 147 37.10000 37.10000 9 Poland 51.750000
## 148 37.30000 37.30000 9 Poland 51.100006
## 149 34.70000 36.00000 7 Oxford 51.750000
## 150 36.50000 35.80000 7 Poland 52.250000
## 151 36.40000 36.10000 8 Serbia 44.000000
## 152 36.40000 36.30000 9 Poland 50.315399
## 153 36.60000 36.30000 8 Chile 33.426281
## 154 36.30000 36.70000 8 Oxford 51.750000
## 155 35.40000 37.70000 11 Poland 60.216705
## 156 36.50000 36.60000 9 Oslo 59.912704
## 157 36.40000 36.90000 6 Zurich 47.025894
## 158 36.50000 36.80000 7 Chile 33.449997
## 159 36.80000 36.70000 9 Kassel 51.491806
## 160 36.90000 37.20000 8 Poland 52.083298
## 161 37.10000 37.10000 9 Poland 52.250000
## 162 35.40000 36.60000 8 Oxford 51.750000
## 163 36.27778 35.94444 7 UCSB 34.432907
## 164 36.43000 36.05000 5 Zurich 47.366699
## 165 36.40000 36.20000 8 Chile 33.449997
## 166 36.50000 36.40000 10 Poland 53.333099
## 167 36.50000 36.60000 10 Poland 50.633301
## 168 36.40000 36.70000 6 Oxford 51.750000
## 169 36.60000 36.70000 9 Poland 52.593094
## 170 37.40000 38.10000 10 Poland 52.250000
## 171 35.30000 35.50000 5 Kassel 51.249603
## 172 35.00000 36.40000 8 METU 39.911697
## 173 36.00000 36.00000 5 METU 39.923004
## 174 36.31000 36.12000 8 Poland 52.593094
## 175 36.40000 36.50000 9 Poland 54.350006
## 176 37.00000 36.20000 10 Poland 52.416702
## 177 36.50000 36.70000 7 Zurich 47.366699
## 178 36.50000 36.90000 8 Kassel 52.516693
## 179 36.80000 36.80000 7 Chile 33.449997
## 180 36.90000 37.30000 9 Poland 53.600006
## 181 36.00000 35.90000 6 Oslo 60.391495
## 182 36.00000 36.00000 7 Kassel 51.033295
## 183 36.00000 36.20000 8 Kassel 51.249603
## 184 36.00000 36.30000 8 Chile 33.449997
## 185 36.30000 36.20000 8 Chile 33.449997
## 186 36.30000 36.30000 9 Poland 51.750000
## 187 36.50000 36.40000 6 METU 39.911697
## 188 36.60000 36.30000 6 METU 39.911697
## 189 36.30000 36.70000 5 Oxford 51.750000
## 190 36.40000 36.60000 9 Poland 53.127106
## 191 36.20000 36.90000 7 Oxford 51.750000
## 192 36.70000 36.40000 10 METU 39.911697
## 193 36.80000 36.70000 5 Chile 33.449997
## 194 37.00000 37.20000 9 Poland 52.250000
## 195 35.20000 36.10000 5 Oxford 51.750000
## 196 35.30000 36.20000 6 Oxford 51.750000
## 197 36.08000 36.03000 11 Bamberg 49.440308
## 198 36.30000 35.90000 6 METU 39.911697
## 199 36.20000 36.10000 9 Poland 52.233307
## 200 36.20000 36.30000 9 Oslo 59.267502
## 201 36.00000 36.50000 7 VCU 37.552200
## 202 36.10000 36.40000 6 Kassel 51.316696
## 203 36.33333 36.44444 8 VCU 39.185699
## 204 36.50000 36.30000 8 Poland 52.233307
## 205 36.61000 36.28000 9 Poland 52.250000
## 206 36.40000 36.50000 7 Poland 54.500000
## 207 36.50000 36.60000 7 METU 39.923004
## 208 36.60000 36.60000 7 Chile 33.449997
## 209 36.90000 36.40000 8 METU 39.911697
## 210 36.70000 36.60000 8 Kassel 51.287796
## 211 36.70000 37.20000 7 METU 39.911697
## 212 35.30000 35.50000 7 Kassel 53.550003
## 213 35.77778 35.72222 4 VCU 37.402298
## 214 35.70000 35.90000 5 Oxford 51.750000
## 215 37.10000 35.90000 7 Oxford 51.750000
## 216 36.61111 36.55556 4 VCU 37.553299
## 217 36.70000 36.70000 9 Serbia 44.818604
## 218 36.90000 36.58000 9 Poland 51.939301
## 219 36.80000 36.80000 5 Serbia 44.870804
## 220 36.94444 36.83333 9 VCU 37.552200
## 221 37.00000 37.00000 3 Zurich 47.531799
## 222 37.20000 37.08000 5 Kassel 50.170807
## 223 36.00000 35.00000 6 Oxford 51.750000
## 224 35.10000 36.10000 6 Kassel 51.316696
## 225 35.50000 35.80000 5 Zurich 47.360596
## 226 35.50000 36.40000 8 Oxford 51.750000
## 227 36.00000 36.00000 10 Poland 53.416702
## 228 35.60000 36.40000 7 Serbia 44.818604
## 229 35.50000 36.60000 8 Kassel 51.316696
## 230 36.10000 36.00000 7 Chile 33.449997
## 231 35.50000 36.90000 7 Oxford 51.750000
## 232 36.00000 36.40000 6 Kassel 51.316696
## 233 36.50000 36.40000 7 Chile 33.449997
## 234 36.40000 36.60000 8 SMU 1.293106
## 235 36.50000 36.50000 7 Serbia 44.818604
## 236 36.44444 36.72222 10 VCU 37.619202
## 237 36.60000 36.70000 5 Oxford 51.750000
## 238 36.61111 36.72222 8 VCU 37.552200
## 239 36.70000 36.70000 9 Poland 52.233307
## 240 36.70000 36.70000 6 METU 39.911697
## 241 36.80000 36.80000 9 Poland 52.250000
## 242 35.60000 35.50000 7 Bamberg 55.699997
## 243 35.30000 36.10000 5 Kassel 51.305603
## 244 35.80000 35.60000 6 Oxford 51.750000
## 245 35.60000 36.00000 6 Bamberg 51.371704
## 246 35.60000 36.40000 8 Kassel 51.287796
## 247 36.00000 36.10000 6 Kassel 50.087494
## 248 35.80000 36.40000 5 Kassel 51.305603
## 249 36.10000 36.20000 9 Zurich 46.980499
## 250 36.20000 36.40000 8 Oslo 60.141495
## 251 36.20000 36.40000 8 METU 39.911697
## 252 36.30000 36.50000 6 Kassel 51.316696
## 253 36.50000 36.30000 7 METU 39.911697
## 254 36.40000 36.50000 7 METU 39.923004
## 255 36.40000 36.59000 8 Bamberg 49.600006
## 256 36.50000 36.50000 8 Serbia 44.818604
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