emotional_processes.Rmd

---
title: "Analyses of the Anger Experiencers' Emotional Processes"
author: "Pooya Razavi"
date: "last knitted: `r Sys.time()`"
output: 
  html_document:
    theme: cosmo
    highlight: textmate
    toc: TRUE
    toc_float: TRUE
editor_options: 
  chunk_output_type: console
---

This script represents the test of the hypotheses and research questions about the emotional processes of the participants during their anger experience, and how the different aspects of these processes vary between justified and unjustified anger prototypes.

```{r setup, include=FALSE, warning=FALSE}
#load libraries
package_list <- c("dplyr", "tidyr", "ggplot2", "MetBrewer")
lapply(package_list, require, character.only = TRUE)

#read in the data
df <- readxl::read_xlsx("C:/Users/pooya/Dropbox (University of Oregon)/Anger Dissertation/Prototype study analysis/ProcessedData_F21_W22_S22_F22.xlsx")

#Function to report correlation
cor_report <- function(cor_output) {
          r <- cor_output[["estimate"]] %>% round(2)
          df <- cor_output[["parameter"]] %>% round(1)
          ci_lb <- (cor_output[["conf.int"]])[1] %>% round(2)
          ci_ub <- (cor_output[["conf.int"]])[2] %>% round(2)
          original_p <- cor_output[["p.value"]] %>% round(3)
          p <- if_else(original_p >= .001, paste0("= ", as.character(original_p)), "< .001")
          
          print(paste0("r(", df, ") = ", r, " [", ci_lb, ", ", ci_ub, "], p ", p))
    }

#Function to report independent-samples t-test
  ind_ttest_report <- function(iv, dv) {
      ttest <- t.test(dv ~ iv)
      effect_size <- effectsize::cohens_d(dv ~ iv, pooled_sd = FALSE)
      t <- ttest[["statistic"]] %>% round(2)
      df <- ttest[["parameter"]] %>% round(1)
      original_p <- ttest[["p.value"]] %>% round(3)
      p <- if_else(original_p >= .001, paste0("= ", as.character(original_p)), "< .001")
      d <- effect_size[1,1] %>% round(2)    
      
      print(paste0("t(", df, ") = ", t, ", p ", p, ", d = ", d))
      
  }
  
#Function to report paired-samples t-test
    paired_ttest_report <- function(t1, t2) {
      ttest <- t.test(Pair(t1, t2) ~ 1)
      effect_size <- effectsize::cohens_d(Pair(t1, t2) ~ 1, pooled_sd = FALSE)
      t <- ttest[["statistic"]] %>% round(2)
      df <- ttest[["parameter"]] %>% round(1)
      original_p <- ttest[["p.value"]] %>% round(3)
      p <- if_else(original_p >= .001, paste0("= ", as.character(original_p)), "< .001")
      d <- effect_size[1,1] %>% round(2)    
      
      print(paste0("t(", df, ") = ", t, ", p ", p, ", d = ", d))
      
  }
  
#Function to calculate percentages for each category of a Factor variable
    percentage <- function(var, includeNA = TRUE) {
          tabb <- table(var) %>% as.data.frame()
              if (includeNA == TRUE) {
                   tabb$percentage <- (tabb$Freq * 100 / length(var))
              } else {
                    tabb$percentage <- (tabb$Freq * 100 / sum(tabb$Freq))
              }
          colnames(tabb)[1] <- c("category")
          print(tabb)
    }
    
knitr::opts_chunk$set(echo = TRUE)
```


```{r, data-exclusion}
#assigning values to factor levels
  df$NarrativeWritten <- as.factor(df$NarrativeWritten)
  df$NarrativeRelevant <- as.factor(df$NarrativeRelevant)
  df$Condition <- as.factor(df$Condition)
  
  levels(df$NarrativeWritten) <- c("No", "Yes")
  levels(df$NarrativeRelevant) <- c("No", "Yes", NA, NA) 
  levels(df$Condition) <- c("justified", "nonjustified", NA)

#drop cases following preregistration
  df1 <- df %>% 
    filter(NarrativeWritten != "No") %>% 
    filter(NarrativeRelevant != "No") %>% 
    filter(!is.na(Condition))

```


# Affective Experience

**Research Question:** What are the differences between justified vs. unjustified anger in terms of the intensity of affective experiences? <br>

## Dimensionality Analyses

To compare the broader affective experiences of participants across the justified and unjustified anger events, participants were asked to rate their emotional experience during the anger eliciting event using 27 positive and negative emotions. First, following the preregistration, the structure of the affective experiences will be determined using dimensionality reduction analysis (i.e., PCA). 


```{r, affec-pca}
df_affect <- df1 %>% 
                    select(ResponseId, Condition, starts_with("em_"))

  #scree plot
  df_affect %>% 
    select(starts_with("em_")) %>% 
    psych::scree(hline = -1) #suggests 4 components
  
  #parallel analysis
  df_affect %>% 
    select(starts_with("em_")) %>% 
    psych::fa.parallel() #suggests 4 components
  
  #Velicor's MAP
  df_affect %>% 
    select(starts_with("em_")) %>% 
    psych::nfactors(n = 27) #Velicor's MAP suggests 4 factors


#PCA with 4 components 
  pca_4_component <- df_affect %>% 
                        select(starts_with("em_")) %>%  
                        psych::principal(., nfactors = 4, rotate = "varimax") 
        
  four_comp_outcome <- psych::kaiser(pca_4_component, rotate = "Varimax") %>% psych::fa.sort()

  four_comp_outcome[["loadings"]] %>% 
                    knitr::kable(digits = 2) %>%
                    kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>% 
                    kableExtra::kable_paper(full_width = F)

  #"Strong" has the strongest cross-loading. It will be dropped before the next iteration.
  pca_4_component_i2 <- df_affect %>% 
                        select(starts_with("em_"), -em_strong) %>%  
                        psych::principal(., nfactors = 4, rotate = "varimax") 
        
  four_comp_outcome_i2 <- psych::kaiser(pca_4_component_i2, rotate = "Varimax") %>% psych::fa.sort()

  four_comp_outcome_i2[["loadings"]] %>% 
                    knitr::kable(digits = 2) %>%
                    kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>% 
                    kableExtra::kable_paper(full_width = F)
  #"Surprised" does not have a high loading (i.e., > .40) on any of the components. It will be dropped before the next iteration.
  pca_4_component_i3 <- df_affect %>% 
                        select(starts_with("em_"), -em_strong, -em_surprised) %>%  
                        psych::principal(., nfactors = 4, rotate = "varimax") 
        
  four_comp_outcome_i3 <- psych::kaiser(pca_4_component_i3, rotate = "Varimax") %>% psych::fa.sort()

  four_comp_outcome_i3[["loadings"]] %>% 
                    as.data.frame() %>% 
                    tibble::rownames_to_column("item") %>% 
                    mutate(Item = gsub("^.{0,3}", "", item)) %>% 
                    mutate(Item = stringr::str_replace_all(Item, "_", " ")) %>% 
                    select(Item, starts_with("RC")) %>% 
                    knitr::kable(digits = 2) %>%
                    kableExtra::kable_styling(bootstrap_options = c("striped", "hover")) %>% 
                    kableExtra::kable_paper(full_width = F)


  #to save the output in a .csv (for the manuscript table)
  #four_comp_outcome_i3[["loadings"]] %>% 
  #                  as.data.frame() %>% 
  #                  tibble::rownames_to_column("item") %>% 
  #                  mutate(Item = gsub("^.{0,3}", "", item)) %>% 
  #                  select(Item, starts_with("RC")) %>% write.csv("affective_experience_pca.csv")

```

Create scree plot, parallel analyses, and Velicor's MAP figures for the manuscript:

```{r}
#figure for the manuscript
  df_affect %>% 
            select(starts_with("em_")) %>%  
            psych::scree(factors = FALSE, hline = -1)
  df_affect %>% 
            select(starts_with("em_")) %>%
                      psych::fa.parallel(fa = "pc")
  
  velicor_map <- df_affect %>% 
                      select(starts_with("em_")) %>%
                      psych::nfactors(n = 27)
  plot(velicor_map[["map"]],
       main = "Velicor's MAP",
       xlab = "Number of Factos",
       ylab = "MAP Index",
       xlim=c(0,20), ylim=c(0,0.10),
       type = "b")
  abline(h=0.011, col="blue")
```

Based on the PCA results, scale scores will be created. 

```{r, affect-scoring}

#Hypothetical scenario: code for calculating scale scores for 2 components
  keys.list <- list(self_blame = c("em_disgusted_w_self","em_ashamed","em_guilty","em_angry_at_self", "em_embarrassed"),
                    hostility = c("em_hostile","em_loathing_hateful","em_frustrated","em_irritable", "em_upset", "em_disgusted", "em_alert", "em_daring"),
                    forlorn = c("em_depressed", "em_sad", "em_lonely", "em_anxious", "em_afraid", "em_shy"),
                    joy = c("em_excited", "em_happy", "em_enthusiastic", "em_proud", "em_relaxed", "em_lively"))

  affect_scores <- psych::scoreItems(keys.list, df_affect)
  affect_scores
  df_affect <- cbind(df_affect, affect_scores$scores)

  #The 3 lines below will save the APA-style correlation table in a word document:
  #df_affect %>% 
  #  select(self_blame, hostility, forlorn, joy) %>% 
  #  apaTables::apa.cor.table(.,filename = "affect_cor.doc", table.number = 1, show.conf.interval = T)  

```

## Comparison of Anger Variants

```{r, affective-comparison}

#descriptives  
  df_affect %>% 
    group_by(Condition) %>% 
    select(self_blame, hostility, forlorn, joy) %>% 
    summarise_all(list(mean, sd))


#Compare conditions for each of the 4 components
  #self-blame
    df_affect %>% 
        t.test(self_blame ~ Condition, data = .)
    df_affect %>% 
        effectsize::cohens_d(self_blame ~ Condition, data = .)
    #for reporting
      ind_ttest_report(df_affect$Condition, df_affect$self_blame)

  #hostility
    df_affect %>% 
        t.test(hostility ~ Condition, data = .)
    df_affect %>% 
        effectsize::cohens_d(hostility ~ Condition, data = .)
    #for reporting
      ind_ttest_report(df_affect$Condition, df_affect$hostility)

  #forlorn
    df_affect %>% 
        t.test(forlorn ~ Condition, data = .)
    df_affect %>% 
        effectsize::cohens_d(forlorn ~ Condition, data = .)
    #for reporting
      ind_ttest_report(df_affect$Condition, df_affect$forlorn)
      
  #joy
    df_affect %>% 
        t.test(joy ~ Condition, data = .)
    df_affect %>% 
        effectsize::cohens_d(joy ~ Condition, data = .)
    #for reporting
      ind_ttest_report(df_affect$Condition, df_affect$joy)

```


# Expressivity

**Hypothesis 1:** Participants are more likely to evaluate their own anger expression as “exaggerated” in the unjustified (vs. justified) anger condition.

```{r, exag-express}
df1$behav_reac <- as.factor(df1$behav_reac)
levels(df1$behav_reac) <- c("fully_concealed", "partly_concealed", "fully_expressed", "exaggerated")

  percentage(df1$behav_reac)

#Compare the reactions based on Condition
  #cross-tabs
    expressivity_table1 <- xtabs( ~ Condition + behav_reac,
                               data=df1)
    prop.table(expressivity_table1, 1) %>% 
                                    round(2) 
  #overall chi-square
    chisq.test(df1$Condition, 
               df1$behav_reac)

  #Since the overall chi-square is significant, follow-up analyses will be conducted. Of these four analyses, the first one corresponds to the hypothesis (stated above), and the rest are exploratory:
  df_exaggerated <- df1 %>% 
                        mutate(exaggerated = if_else(behav_reac == "exaggerated", "Yes", "No")) %>% 
                        select(Condition, exaggerated)
    chisq.test(df_exaggerated$Condition, 
               df_exaggerated$exaggerated)
    
  df_fully_expressed <- df1 %>% 
                        mutate(fully_expressed = if_else(behav_reac == "fully_expressed", "Yes", "No")) %>% 
                        select(Condition, fully_expressed)
    chisq.test(df_fully_expressed$Condition, 
               df_fully_expressed$fully_expressed)  
    
  df_partly_concealed <- df1 %>% 
                        mutate(partly_concealed = if_else(behav_reac == "partly_concealed", "Yes", "No")) %>% 
                        select(Condition, partly_concealed)
    chisq.test(df_partly_concealed$Condition, 
               df_partly_concealed$partly_concealed)
    
  df_fully_concealed <- df1 %>% 
                        mutate(fully_concealed = if_else(behav_reac == "fully_concealed", "Yes", "No")) %>% 
                        select(Condition, fully_concealed)
    chisq.test(df_fully_concealed$Condition, 
               df_fully_concealed$fully_concealed)
    
```


**Hypothesis 2:** The strength of the experience-expression relation tamps down as the anger intensity increases, and this deceleration is stronger for justified anger. 

```{r, feel-exp-relation}
df_feel_exp <- df1 %>% 
                select(ResponseId, Condition, anger_feel, anger_express) %>% 
                mutate(anger_feel_c = as.double(scale(anger_feel, scale = FALSE)),
                       anger_feel_c_sq = anger_feel_c^2)

#A linear model testing the feel-express relation
  mod1_linear <- lm(anger_express ~ anger_feel_c,
                    data = df_feel_exp)
  summary(mod1_linear)
  confint(mod1_linear)

#A curvilinear model testing the feel-express relation: This is the test of the first part of the hypothesis. 
 
  mod1_nonlinear <- lm(anger_express ~ anger_feel_c + anger_feel_c_sq,
                    data = df_feel_exp)
  summary(mod1_nonlinear)
  confint(mod1_nonlinear)
  anova(mod1_linear, mod1_nonlinear)

#If the curvilinear relation is significant, the moderating role of Condition for this curvilinear relation will be tested, which will correspond to the second part of the hypothesis.   
  mod2_nonlinear <- lm(anger_express ~ anger_feel_c * Condition + anger_feel_c_sq * Condition,
                    data = df_feel_exp)
  summary(mod2_nonlinear)
  confint(mod2_nonlinear)

```