data-raw/make_cces.R

In davidkane9/PPBDS.data: Data for Preceptor's Primer for Bayesian Data Science

# Script for cleaning cces data (Shiro 18). My intention here is to create two
# basic paths available for analysis. First, there's the basic analysis of whether
# different demographics have significantly different answers. Second, there's the
# analysis of approval of different levels of government official (pres, senator,
# governor).

# So, initially, I've kept most of the demographic information. Currently working
# on deciding which survey questions to keep. I discuss my choices below the
# select function.

library(tidyverse)
library(usethis)

x <- read_rds("data-raw/cumulative_2006_2018.rds") %>%
  select(year, state, gender,

    # I kept age instead of birth year because it records the age when they took
    # the survey, which is most relevant. Not sure if I should keep all these
    # variables. Are we using this data for the heavy machine learning stuff in
    # chapters 11 and 12? If not this data, then what data?

    age, race, marstat,

    # I've renamed ideo5 to ideology because I've removed every other
    # ideology based question.

    ideology = ideo5,
    education = educ,
    news = newsint,
    econ = economy_retro,
    approval_ch = approval_pres) %>%

  # I've kept three sort of groups of variables. The first is ideology and
  # news interest - I'm curious to see if there's a relationship between
  # self-reported news interest and self-reported political ideology.

  # The second is a retrospective view of the economy (how has the economy
  # been doing this past year?) with the approval of the current president. That's probably
  # correlated?

  # Below, I convert some of the numerical representations of the data into
  # consistent character variables.

  # There is a trickiness is the value 3 for approval. You need to include both
  # "Never Heard / Not Sure" and "Neither Approve Nor Disapprove" since the
  # latter seemed to be used in only one year in the survey.

  mutate(approval = case_when(
    approval_ch == 1 ~ 5,
    approval_ch == 2 ~ 4,
    approval_ch == 3 ~ 2,
    approval_ch == 4 ~ 1,
    approval_ch == 5 ~ 3,
    approval_ch == 6 ~ 3)) %>%
  mutate(approval_ch = case_when(
    approval_ch == 1 ~ "Strongly Approve",
    approval_ch == 2 ~ "Approve / Somewhat Approve",
    approval_ch == 3 ~ "Disapprove / Somewhat Disapprove",
    approval_ch == 4 ~ "Strongly Disapprove",
    approval_ch == 5 ~ "Never Heard / Not Sure",
    approval_ch == 6 ~ "Neither Approve Nor Disapprove")) %>%
  mutate(econ = case_when(
    econ == 1 ~ "Gotten Much Better",
    econ == 2 ~ "Gotten Better / Somewhat Better",
    econ == 3 ~ "Stayed About The Same",
    econ == 4 ~ "Gotten Worse / Somewhat Worse",
    econ == 5 ~ "Gotten Much Worse",
    econ == 6 ~ "Not Sure")) %>%
  mutate(news = case_when(
    news == 1 ~ "Most Of The Time",
    news == 2 ~ "Some Of The Time",
    news == 3 ~ "Only Now And Then",
    news == 4 ~ "Hardly At All",
    news == 7 ~ "Don't Know")) %>%
  mutate(gender = case_when(
    gender == 1 ~ "Male",
    gender == 2 ~ "Female")) %>%
  mutate(education = case_when(
    education == 1 ~ "No HS",
    education == 2 ~ "High School Graduate",
    education == 3 ~ "Some College",
    education == 4 ~ "2-Year",
    education == 5 ~ "4-Year",
    education == 6 ~ "Post-Grad")) %>%
  mutate(race = case_when(
    race == 1 ~ "White",
    race == 2 ~ "Black",
    race == 3 ~ "Hispanic",
    race == 4 ~ "Asian",
    race == 5 ~ "Native American",
    race == 6 ~ "Mixed",
    race == 7 ~ "Other",
    race == 8 ~ "Middle Eastern")) %>%
  mutate(marstat = case_when(
    marstat == 1 ~ "Married",
    marstat == 2 ~ "Separated",
    marstat == 3 ~ "Divorced",
    marstat == 4 ~ "Widowed",
    marstat == 5 ~ "Single / Never Married",
    marstat == 6 ~ "Domestic Partnership")) %>%

  # Making education an ordered factor.

  # I am a little confused about how ideology ends up as a factor. Should clean
  # this up.

  mutate(education = factor(education,
                            levels = c("No HS",
                                       "High School Graduate",
                                       "Some College",
                                       "2-Year",
                                       "4-Year",
                                       "Post-Grad")))




# Save the data.

cces <- x

usethis::use_data(cces, overwrite = TRUE)