stigma_and_bias_subreddit_content_analysis.Rmd

---
title: "stigma_and_bias_subreddits_qual_coding"
author: "Drew Walker"
date: "8/14/2022"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(irr)
library(janitor)
library(table1)
library(KappaGUI)
library(here)
library(knitr)
library(gt)
library(psych)
here()
library(table1)
```


```{r read_data}
raw_annotation_data_coder_1 <- read_csv("codesheet_random_moud_subreddit_posts_bias_lexicon_dw.csv") %>% 
  clean_names()


merging_annotation_data_coder_1 <- raw_annotation_data_coder_1 %>% 
  rename(bias_or_stigma_described_dw = bias_or_stigma_described)
merging_annotation_data_coder_1$id2 <- 1:nrow(merging_annotation_data_coder_1)


#Column names editing/cleaning 

raw_annotation_data_coder_2 <-read_csv("codesheet_random_moud_subreddit_posts_bias_lexicon_JSL.csv") %>% 
  clean_names() 

merging_annotation_data_coder_2 <- raw_annotation_data_coder_2 %>% 
  rename(bias_or_stigma_described_jsl = bias_or_stigma_described)

merging_annotation_data_coder_2$id2 <- 1:nrow(merging_annotation_data_coder_2)


combined_df <- left_join(merging_annotation_data_coder_1, merging_annotation_data_coder_2, by = "id2")
str(combined_df)



```

# Pooled Kappas

```{r pooled-kappas}
# Can change the cols = columns to specify which the range of the categories
# If we wanted to make a pooled kappa function, would need to indicate all columns for which we should elongate for the category item
pooled_kappa_df <- combined_df %>% 
  select(bias_or_stigma_described_dw,bias_or_stigma_described_jsl)

kappa2(pooled_kappa_df)

```
## List of disagreements 
```{r list-o-disagreements}
combined_df_clean <- combined_df %>% 
  select(id2,body=body.x,sentence=sentence.x,stem_word=stem_word.x,bias_or_stigma_described_dw, bias_or_stigma_described_jsl,drew_notes = notes.x,jenn_notes = notes.y) %>% 
  mutate(agree = case_when(bias_or_stigma_described_dw == 0 & bias_or_stigma_described_jsl == 0 | bias_or_stigma_described_dw == 1 & bias_or_stigma_described_jsl == 1 ~ "1", TRUE ~"0"))

agreements <- combined_df %>% 
  select(id2,body=body.x,sentence=sentence.x,stem_word=stem_word.x,bias_or_stigma_described_dw, bias_or_stigma_described_jsl,drew_notes = notes.x,jenn_notes = notes.y) %>% 
  mutate(agree = case_when(bias_or_stigma_described_dw == 0 & bias_or_stigma_described_jsl == 0 | bias_or_stigma_described_dw == 1 & bias_or_stigma_described_jsl == 1 ~ "1", TRUE ~"0"))%>% 
  filter(agree == "1") %>% 
  mutate(adjudication = bias_or_stigma_described_dw)

disagreements_df <- combined_df_clean%>% 
  filter(agree == "0")
disagreements_df
write_csv(disagreements_df, "disagreements.csv", na = "")

most_disagreed <- disagreements_df %>% 
  group_by(stem_word) %>% 
  summarize(total_disagreements = n()) %>% 
  arrange(desc(total_disagreements))
most_disagreed
```
# add in adjudications

```{r adjudications}
adjudications <- read_csv("adjudications.csv")
adjudications$agree <- as.character(adjudications$agree)

combined_final_adjudicated <- bind_rows(adjudications,agreements)

```


# Per-stem_word bias validation
```{r per-category-kappas}
stem_word_total_biased <- combined_final_adjudicated %>% 
  group_by(stem_word) %>% 
  filter(adjudication == "1") %>% 
  summarize(bias_count = n())
stem_word_total_biased %>% 
  arrange(desc(bias_count))
```

# Pooled kappas 


# Enumeration with agreements

investigate issue with multiple rows 
```{r adjudicated}
agreements_df <- combined_df_categories_coders_wide %>% 
  mutate(agree = case_when(`1` == 0 & `2` == 0 | `1` == 1 & `2` == 1 ~ "1", TRUE ~"0")) %>%
  filter(agree == "1")
combined_merge_with_adj <- agreements_df %>% 
  mutate(Adj = case_when(`1` == 0 & `2` == 0~ "0",
                         `1` == 1 & `2` == 1 ~ "1"))

adj_disagreements <- read_csv("disagreements_adjudicated.csv") %>% 
  select(id, category,Adj)

adj_disagreements$Adj <- as.character(adj_disagreements$Adj)

disagreements_adj_merge <- left_join(disagreements_df, adj_disagreements, by = c("id", "category"))

# Change percentage denominator to number of total in sample 
combined_enumeration_df <- bind_rows(combined_merge_with_adj,disagreements_adj_merge)
combined_enumeration_df$Adj <- as.integer(combined_enumeration_df$Adj)
category_counts <- combined_enumeration_df %>% 
  group_by(category) %>% 
  summarize(count = sum(Adj)) %>% 
  mutate(percentage = count/27) %>% 
  arrange(desc(percentage))


category_counts
```