challenge4.qmd

---
title: "Challenge 4"
author: "Emily Duryea"
desription: "Challenge 4"
date: "12/20/2022"
format:
  html:
    toc: true
    code-fold: true
    code-copy: true
    code-tools: true
categories:
  - challenge_4
---

# Challenge 4

```{r}
#| label: setup
#| warning: false
#| message: false

library(tidyverse)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)
```

## Read in Data

```{r}
abc <- read.csv("_data/abc_poll_2021.csv")
abc

# summary of data
summary(abc)

```

This dataset appears to be an ABC Poll, a survey conducted with 527 respondents (the number of rows). This survey contains 15 demographic variables, 10 political attitudes questions, and 5 survey administration variables.

## Cleaning the Data

```{r}
# Renaming variables
abc <-rename(abc, language = xspanish, age =  ppage, education5 = ppeduc5, education = ppeducat, gender = ppgender, ethnicity = ppethm, household_size = pphhsize, income = ppinc7, marital_status = ppmarit5, region = ppreg4, rent = pprent, state = ppstaten, work = PPWORKA, employment = ppemploy)
abc <- abc%>%
  mutate(ethnicity = str_remove (ethnicity, ", Non-Hispanic"))

# Removing values where respondents skipped
abc<-abc %>%
  mutate(across(starts_with("Q"), ~ na_if(.x, "Skipped")))
```

## Mutating Variables

```{r}
# Mutating Party ID 
abc <-abc %>%
  mutate(QPID = fct_recode(QPID, "dem" = "A Democrat",
                                "rep" = "A Republican",
                                "ind" = "An Independent",
                                "na" = "Skipped",
                              "other" = "Something else")) %>%
  mutate(QPID = fct_relevel(QPID, "dem", "ind", "rep","other", "na"))

ggplot(abc, aes(QPID)) + geom_bar()

```