11 Basic: data analysis

理论上，学至本章节，已经掌握的 R 语言知识就已经足够应对相当大一部分的数据分析任务了，只要同时具备相应的专业知识即可。以 Rudert 等人共享在 OSF 平台的一份中介效应数据分析代码为例：

#######Mediation script - Experimental studies (3-7)#######
rm(list=ls())
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
install.packages("Hmisc")
##load data
data_S3 <- read.csv(file.choose(), header = TRUE, sep = ",") #Study3_data_complete_220120.csv
data_S4 <- read.csv(file.choose(), header = TRUE, sep = ",") #Study4_data_complete_220125.csv
data_S5 <- read.csv(file.choose(), header = TRUE, sep = ",") #Study5_data_complete_220127.csv
data_S6 <- read.csv(file.choose(), header = TRUE, sep = ",") #Study6_data_complete_220127.csv
data_S7 <- read.csv(file.choose(), header = TRUE, sep = ",") #Study7_data_complete_220204.csv

##run process.R

###Study 3###
#IV and DV as numeric variable
data_S3$target_exclusion_numeric <- ifelse(data_S3$target_exclusion == "target excluded", 1, 0)
data_S3$TargetBehavior_numeric <- ifelse(data_S3$TargetBehavior == "norm-violating", 1, 0)

##mediation analysis via relatedness goals
#random seed: 659534
process(data = data_S3, y = "target_exclusion_numeric", x = "TargetBehavior_numeric",
        m = "Reason_Choice_relational_mean", model = 4, seed = 659534)
##mediation analysis via task_approach goals
#random seed: 
process(data = data_S3, y = "target_exclusion_numeric", x = "TargetBehavior_numeric",
        m = "Reason_Choice_task_approach_mean", model = 4, seed = 981522)


###Study 4###
#IV and DV as numeric variable
data_S4$target_exclusion_numeric <- ifelse(data_S4$target_exclusion == "target_excluded", 1, 0)
data_S4$TargetBehavior_numeric <- ifelse(data_S4$TargetBehavior == "control", 0, 
                                          ifelse(data_S4$TargetBehavior == "inept", 1,2))

##mediation analysis via relatedness goals
#Random seed: 503169
process(data = data_S4[data_S4$TargetBehavior != "inept",], y = "target_exclusion_numeric", x = "TargetBehavior_numeric",
        m = "Reason_Choice_relational_mean", model = 4, seed = 503169) #p = .4051
##mediation analysis via task_approach goals
#Random seed: 341173
process(data = data_S4[data_S4$TargetBehavior != "norm-violating",], y = "target_exclusion_numeric", x = "TargetBehavior_numeric",
        m = "Reason_Choice_task_approach_mean", model = 4, seed = 341173) #p = .7234


###Study 5###

##Mediation analysis via relational goals or task approach goals
process(data = data_S5[data_S5$TargetBehavior != "inept" & data_S5$understood_task == 1,], y = "target_exclusion_numeric", x = "TargetBehavior_numeric",
        m = "relational_goal_mean", model = 4) 
process(data = data_S5[data_S5$TargetBehavior != "norm-violating" & data_S5$understood_task == 1,], y = "target_exclusion_numeric", x = "TargetBehavior_numeric",
        m = "task_approach_goal_mean", model = 4) 

##Moderated Mediation analysis via relational goals or task approach goals
#relational goals
#Random seed: 115112
process(data = data_S5[data_S5$TargetBehavior != "inept" & data_S5$understood_task == 1,], y = "target_exclusion_numeric", x = "TargetBehavior_numeric",
        m = "relational_goal_mean", w = "Task_numeric", model = 14, seed = 115112) 
Hmisc::rcorr(data_S5[data_S5$TargetBehavior != "inept" & data_S5$understood_task == 1,]$Task_numeric,
      +       +       data_S5[data_S5$TargetBehavior != "inept" & data_S5$understood_task == 1,]$relational_goal_mean)
#Random seed: 515036
process(data = data_S5[data_S5$TargetBehavior != "inept" & data_S5$understood_task == 1,], y = "target_exclusion_numeric", x = "TargetBehavior_numeric",
        m = "relational_goal_mean", w = "Task_numeric", model = 8, seed = 515036) 

#task approach goals
#Random seed: 32352
process(data = data_S5[data_S5$TargetBehavior != "norm-violating" & data_S5$understood_task == 1,], y = "target_exclusion_numeric", x = "TargetBehavior_numeric",
        m = "task_approach_goal_mean", w = "Task_numeric", model = 14, seed = 32352) 
rcorr(data_S5[data_S5$TargetBehavior != "norm-violating" & data_S5$understood_task == 1,]$Task_numeric,
      +       +       data_S5[data_S5$TargetBehavior != "norm-violating" & data_S5$understood_task == 1,]$task_approach_goal_mean)
#Random seed: 469059
process(data = data_S5[data_S5$TargetBehavior != "norm-violating" & data_S5$understood_task == 1,], y = "target_exclusion_numeric", x = "TargetBehavior_numeric",
        m = "task_approach_goal_mean", w = "Task_numeric", model = 8, 469059) 


##mediated moderation analysis via burden and threat
#Random seed: 713678
process(data = data_S5[data_S5$TargetBehavior != "norm-violating" & data_S5$understood_task == 1,], y = "target_exclusion_numeric", x = "TargetBehavior_numeric",
        m = "target_burdensome", w = "Task_numeric", model = 14, seed = 713678)
#Random seed: 206890
process(data = data_S5[data_S5$TargetBehavior != "norm-violating" & data_S5$understood_task == 1,], y = "target_exclusion_numeric", x = "TargetBehavior_numeric",
        m = "target_burdensome", w = "Task_numeric", model = 8, seed = 206890)
#Random seed: 19079
process(data = data_S5[data_S5$TargetBehavior != "inept" & data_S5$understood_task == 1,], y = "target_exclusion_numeric", x = "TargetBehavior_numeric",
        m = "target_threat", w = "Task_numeric", model = 14, seed = 19079)
#Random seed: 9411
process(data = data_S5[data_S5$TargetBehavior != "inept" & data_S5$understood_task == 1,], y = "target_exclusion_numeric", x = "TargetBehavior_numeric",
        m = "target_threat", w = "Task_numeric", model = 8, seed = 9411)


###Study 6###

##mediation analysis: relevance of incompetence via perceived burdensomeness of target
#Random seed: 101606
process(data = data_S6, y = "target_exclusion", x = "Relevance_numeric",
        m = "target_burdensome", model = 4, seed = 101606)

##mediation analysis: relevance of incompetence via perceived competence of target
#Random seed: 994718
process(data = data_S6, y = "target_exclusion", x = "Relevance_numeric",
        m = "target_competence", model = 4, seed = 994718)

##mediation analysis: amount of competence information via perceived competence in relevant ability
#Random seed: 57561
process(data = data_S6, y = "target_exclusion", x = "Amount_numeric",
        m = "target_competence", model = 4, seed = 57561)

##mediation analysis: amount of competence information via perceived burdensomeness of target
#Random seed: 101606
process(data = data_S6, y = "target_exclusion", x = "Amount_numeric",
        m = "target_burdensome", model = 4, seed = 101606)

##mediation analysis: relevance of incompetence via perceived warmth - big 2
#Random seed: 57561
process(data = data_S6, y = "target_exclusion", x = "Relevance_numeric",
        m = "target_warmth", model = 4, seed = 57561)


###Study 7###

##mediation analysis 1a: TargetBehavior of incompetence via perceived burden for performance
#Random seed: 659534
process(data = data_S7, y = "target_exclusion", x = "TargetBehavior_numeric",
        m = "target_burdensome", model = 4, seed = 659534)

##mediation analysis 1b: TargetBehavior of incompetence via perceived disruptiveness
#Random seed: 593528
process(data = data_S7, y = "target_exclusion", x = "TargetBehavior_numeric",
        m = "target_threat", model = 4, seed = 593528)


##mediation analysis 2a: TargetBehavior of incompetence via perceived burden for performance
#Random seed: 659534
process(data = data_S7, y = "target_exclusion", x = "TargetBehavior_numeric",
        m = "target_competence", model = 4, seed = 659534)

##mediation analysis 2b: TargetBehavior of incompetence via perceived disruptiveness
#Random seed: 593528
process(data = data_S7, y = "target_exclusion", x = "TargetBehavior_numeric",
        m = "target_warmth", model = 4, seed = 539528) 


##mediated moderation analysis via burden and threat - For nature of task: "performance" = 1
#Random seed: 224761
process(data = data_S7, y = "target_exclusion", x = "TargetBehavior_numeric",
        m = "target_burdensome", w = "Task_numeric", model = 14, seed = 224761)
#Random seed: 799346
process(data = data_S7, y = "target_exclusion", x = "TargetBehavior_numeric",
        m = "target_burdensome", w = "Task_numeric", model = 8, seed = 799346)

#Random seed: 827710
process(data = data_S7, y = "target_exclusion", x = "TargetBehavior_numeric",
        m = "target_threat", w = "Task_numeric", model = 14, seed = 827710)
#Random seed: 549736
process(data = data_S7, y = "target_exclusion", x = "TargetBehavior_numeric",
        m = "target_threat", w = "Task_numeric", model = 8, seed = 549736)

#warmth and competence
#Random seed: 438899
process(data = data_S7, y = "target_exclusion", x = "TargetBehavior_numeric",
        m = "target_competence", w = "Task_numeric", model = 14, seed = 438899)
#Random seed: 588533
process(data = data_S7, y = "target_exclusion", x = "TargetBehavior_numeric",
        m = "target_competence", w = "Task_numeric", model = 8, seed = 588533)

#Random seed: 612730
process(data = data_S7, y = "target_exclusion", x = "TargetBehavior_numeric",
        m = "target_warmth", w = "Task_numeric", model = 14, seed = 612730)
#Random seed: 906077
process(data = data_S7, y = "target_exclusion", x = "TargetBehavior_numeric",
        m = "target_warmth", w = "Task_numeric", model = 8, seed = 906077)

以上代码的基本结构如下：

先读取数据；
与数据互动（使用数据执行中介效应分析）。

其中除了一些相对陌生的函数，如rstudioapi::getActiveDocumentContext()、dirname()、process()等之外，并不存在编程上的难点，都是非常直接的代码。所以，要完成这个数据分析任务的难点其实是关于中介效应分析的统计知识（例如，如何通过 bootstrap 置信区间来实现中介效应的显著性检验）。

但只掌握目前学过的 R 语言基本知识无法成为相对高阶的使用者，无法完成更加灵活的任务（如自动化数据分析），甚至会缺乏编程最核心的技术——debug，所以还需要继续学剩下的章节。

10 Basic: regular operations

12 Basic: control flow