relational_processes.Rmd

---
title: "Analyses of the Anger Experiencers' Relational 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 relational 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", "lmerTest")
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))

```


## Perception of Target's Regret/Apology

**Research Question:** Do the perceptions of the target as regretful and apologetic vary depending on whether they consider the anger event as justified or unjustified? 

```{r, regret-apol}
  #Regret 
    df1 %>% 
        t.test(person_regret ~ Condition, data = .)
    df1 %>% 
        effectsize::cohens_d(person_regret ~ Condition, data = .)
    #For reporting:
      ind_ttest_report(df1$Condition, df1$person_regret)
      
  #Apology
    df1 %>% 
        t.test(person_apologize ~ Condition, data = .)
    df1 %>% 
        effectsize::cohens_d(person_apologize ~ Condition, data = .)
    #For reporting:
      ind_ttest_report(df1$Condition, df1$person_apologize)

```

## Relationship Closeness

**Hypothesis 1:** Relationship closeness deteriorates following an anger event, both “short-term” (i.e., comparing closeness before and immediately after the event) and “long-term” (i.e., comparing closeness before the event and at the time of the study).

```{r closeness1}
closeness_df <- df1 %>% 
                      select(ResponseId, Condition, prior_closeness, immed_closeness, now_closeness)

#create a long dataframe
closeness_df_long <- closeness_df %>% 
                            tidyr::pivot_longer(cols = c(prior_closeness, immed_closeness, now_closeness),
                                                names_to = "time",
                                                values_to = "closeness") %>% 
                            mutate(time = as.factor(time)) %>% 
                            mutate(time = forcats::fct_relevel(time, "prior_closeness", "immed_closeness", "now_closeness"))
                      
  levels(closeness_df_long$time) <- c("prior", "immediate", "now")

#setup the contrasts
  contrasts(closeness_df_long$Condition) <- contr.sum
  contrasts(closeness_df_long$time) <- contr.sum  

#Overal model
  closeness_model <- lmer(closeness ~ time * Condition + (1 | ResponseId),
                          data = closeness_df_long)

  anova(closeness_model, type = "III") 
  effectsize::eta_squared(closeness_model)

#comparing closeness (a) before vs. immediately after the event 
  pbkrtest.limit = 4000
  pairwise_mod <- emmeans::emmeans(closeness_model, pairwise ~ time, pbkrtest.limit = 4000)
  pairwise_mod[["contrasts"]][1]
  #For reporting the effect size
    effectsize::cohens_d(Pair(closeness_df$prior_closeness, closeness_df$immed_closeness) ~ 1, pooled_sd = FALSE)

#comparing closeness (b) before vs. now
  pairwise_mod[["contrasts"]][2]
  #For reporting the effect size
    effectsize::cohens_d(Pair(closeness_df$prior_closeness, closeness_df$now_closeness) ~ 1, pooled_sd = FALSE)

```

**Hypothesis 2:** The long-term deterioration of relationship closeness is stronger for justified (vs. unjustified) anger experiences.

```{r, closeness2}
summary(closeness_model) #The time1:Condition1 interaction demonstrates the significant interaction between time (prior vs. now) and anger type.

longterm_mod <- emmeans::emmeans(closeness_model, pairwise ~ time | Condition, pbkrtest.limit = 4000)
  
  #justified: prior vs. now
  longterm_mod[["contrasts"]][2]
  #not justified: prior vs. now
  longterm_mod[["contrasts"]][5]
  
  #For the reporting of the effect sizes:  
  closeness_df %>% 
    filter(Condition == "justified") %>% 
    summarise(d = effectsize::cohens_d(Pair(prior_closeness, now_closeness) ~ 1, pooled_sd = FALSE, data = .))
  
  closeness_df %>% 
    filter(Condition == "nonjustified") %>% 
    summarise(d = effectsize::cohens_d(Pair(prior_closeness, now_closeness) ~ 1, pooled_sd = FALSE, data = .))
    
```

Create a figure for the manuscript:

```{r, closeness-fig}
#df with descriptive summary
      descripts <- psych::describeBy(closeness_df_long$closeness, 
                         group = list(closeness_df_long$Condition, closeness_df_long$time), mat = TRUE)   %>% 
                        select(group1, group2, mean, se) %>% 
                        transmute(Anger = as.factor(group1),
                                  Time = as.factor(group2),
                                  mean, se)
      levels(descripts$Anger) <- c("Justified", "Unjustified")
      levels(descripts$Time) <- c("Immediately After", "Now", "Prior")
      descripts$Time <- relevel(descripts$Time, ref = "Prior")
      
#figure    
  closeness_p <- ggplot(
                descripts,
                aes(x = Time, color = Anger, group = Anger, y = mean,
                    ymin = mean-se, ymax = mean+se)) + 
                geom_line(position = position_dodge(0.1)) +
                geom_errorbar(position = position_dodge(0.1), width = 0.12) +
                geom_point(aes(color = Anger), position = position_dodge(0.1), size = .8) +
                ylim(1, 5) +
                ggtitle(" ") +
                theme_bw() + 
                labs(y="Relationship Closeness", x = " ") +
                theme(plot.title = element_text(hjust = 0.5), text = element_text(size = 12),
                                         axis.text.x= element_text(size = 12)) + 
                      scale_color_manual(values=met.brewer("Nizami", 2, direction=-1)) 
  closeness_p

#code to save the figure for the manuscript        
  #ggsave(plot = closeness_p, width = 7, height = 3.5, dpi = 300, filename = "closeness_p.png")
```