Problem Set 2: Task 1

packages <- c("tidyverse", "fst", "modelsummary", "viridis", "kableExtra", "flextable", "officer")

new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) install.packages(new_packages)

lapply(packages, library, character.only = TRUE)

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gss <- load("gss2022.RData")
gss <- df

Task 1: Data Cleaning and Recoding

Objective: Clean and recode the variables to ensure they are ready for analysis. Recode polviews into three categories: “Liberal”, “Moderate”, and “Conservative”. Clean sex, degree, and race but retain the relevant categories.

gss <- gss %>%
  mutate(polviews = case_when(
    polviews %in% c("Extremely liberal", "Liberal", "Slightly liberal") ~ "Liberal",
    polviews %in% c("Moderate, middle of the road") ~ "Moderate",
    polviews %in% c("Slightly conservative", "Conservative", "Extremely conservative") ~ "Conservative",
    TRUE ~ NA_character_ 
  ))

gss <- gss %>%
  filter(!is.na(sex), !is.na(degree), !is.na(race)) %>%
  mutate(
    sex = factor(sex, levels = c("Male", "Female")),
    degree = factor(degree, levels = c("Less than high school", "High school", "Junior college", "Bachelor's", "Graduate")),
    race = factor(race, levels = c("White", "Black", "Other"))
  )