Appendix D — Skill Assignment 3: EDA 1 & data management

Find Quarto themes here.

D.1 Overall Discussion

This dataset will provide me with a range of potential variables to investigate attitudes about overturning Roe v. Wade. Among them are voting in the 2020 presidential election, political party identification, ideology, where people live, gender, religiosity, and whether people follow the news.

Based on the graphs:

• fewer people favor overturning Roe.

• There are more Democrats than Republicans. (Note to self: If I want to use this variable I should filter out “other” and “not sure.)

• Suburbanites are the biggest group, but there is a balance between city, suburb, and rural Americans in the survey.

• You can talk about other variables in your codebook too!

D.1.1 Load Packages

library(tidyverse)
library(haven)
library(hrbrthemes)
library(survey)
library(srvyr)
library(labelled)
library(sjmisc)
library(sjPlot)
library(gmodels)
library(gtsummary)
library(skimr)
library(ggblanket)

D.1.2 Load Your Dataset

Need help? Go to chapter x in the webbook.

load("anes_pilot.RData")

D.2 Discussion of data management

• Select: I added a variable that tells us who the respondent voted for in the 2020 presidential election from the original dataset, presvote20post .

• Filter the variable presvote20post so the dataset now only includes people who voted for either Trump or Biden.

• Recode Roe so that there are two categories: favor and don’t favor.

• Recode urbanicity2 so that there are 3 categories: city, suburban, and rural.

Need help? Go to chapter x in the webbook.

# Select variables 

anes_pilot_variables <- anes_pilot |>
  select(roe, presvote20post, urbanicity2, pid3, ideo5, gender, pew_religimp, follow, caseid, weight)

anes_pilot_variables <- anes_pilot_variables |>
  filter(presvote20post < 3)

anes_pilot_variables <- anes_pilot_variables |>
  as_factor() |>
  mutate(roe_recode = fct_collapse(roe, 
                                   "Favor" = c("Favor"), 
                                   "Doesn't favor" = c("Oppose", "Neither favor nor oppose")))

anes_pilot_variables <- anes_pilot_variables |>
  as_factor() |>
  mutate(urbanicity2_recode = fct_collapse(urbanicity2, 
                                   "City" = c("Big city", "Smaller city"), 
                                   "Suburb" = c("Suburban area"), 
                                   "Rural" = c("Small town", "Rural area")))

D.2.1 Graph 1

Need help? Go to chapter 4 in the webbook.

anes_pilot_variables |>
   as_factor() |>
   drop_na(roe_recode) |>
  gg_bar(
  x = roe_recode,
    title = "Oppose or Favor Overturning Roe v. Wade",
  x_title = "Oppose or Favor Overturning Roe v. Wade", 
  y_title = "Number of Respondents", 
  caption = "Source: 2022 ANES Pilot Study"
)

D.2.2 Graph 2

Need help? Go to chapter 4 in the webbook.

 anes_pilot_variables |>
   as_factor() |>
   drop_na(pid3) |>
    gg_bar(
      x = pid3,
      title = "Three Category Party Identification",
  x_title = "Three Category Party Identification", 
  y_title = "Number of Respondents", 
  caption = "Source: 2022 ANES Pilot Study"
)

D.2.3 Graph 3 (add more if you want to!)

Need help? Go to chapter 4 in the webbook.

 anes_pilot_variables |>
   as_factor() |>
   drop_na(urbanicity2_recode) |>
      gg_bar(
      x = urbanicity2_recode,
      title = "Who did you vote in the 2020 election?",
  x_title = "Who did you vote for? (vote20)", 
  y_title = "Number of Respondents", 
  caption = "Source: 2022 ANES Pilot Study"
)

D.2.4 Run Univariate Statistics

Need help? Go to chapter 4 in the webbook.

anes_pilot_variables |>
  as_factor() |>
  tbl_summary()

anes_pilot_variables |>
  as_factor() |>
  skim() |>
  yank("numeric")

Characteristic	N = 1,1351
Favor/oppose - overturn Roe v. Wade
Favor	434 (38%)
Oppose	554 (49%)
Neither favor nor oppose	147 (13%)
Profile: 2020 President Vote Post Election
Joe Biden	607 (53%)
Donald Trump	528 (47%)
Jo Jorgensen	0 (0%)
Howie Hawkins	0 (0%)
Other	0 (0%)
Did not vote for President	0 (0%)
Profile: Urban-rural status
Big city	213 (19%)
Smaller city	165 (15%)
Suburban area	409 (36%)
Small town	151 (13%)
Rural area	197 (17%)
Profile: 3 point Party ID
Democrat	426 (39%)
Republican	347 (32%)
Independent	266 (24%)
Other	42 (3.8%)
Not sure	17 (1.5%)
Unknown	37
Profile: Ideology
Very liberal	144 (13%)
Liberal	195 (17%)
Moderate	328 (29%)
Conservative	271 (24%)
Very conservative	168 (15%)
Not sure	28 (2.5%)
Unknown	1
Profile: Gender
Male	519 (46%)
Female	616 (54%)
Profile: Importance of religion (Pew version)
Very important	450 (40%)
Somewhat important	283 (25%)
Not too important	154 (14%)
Not at all important	248 (22%)
Follow what’s going on in government and public affairs
Most of the time	675 (59%)
Some of the time	307 (27%)
Only now and then	116 (10%)
Hardly at all	37 (3.3%)
Case ID	674 (330, 1,060)
Analysis weight	0.84 (0.64, 1.13)
Unknown	78
Favor/oppose - overturn Roe v. Wade
Favor	434 (38%)
Doesn't favor	701 (62%)
Profile: Urban-rural status
City	378 (33%)
Suburb	409 (36%)
Rural	348 (31%)
1 n (%); Median (Q1, Q3)

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
caseid	0	1.00	709.38	440.25	1.0	330.50	674.00	1059.50	1585.00	▇▇▆▆▅
weight	78	0.93	0.94	0.43	0.3	0.64	0.84	1.13	4.33	▇▂▁▁▁

D.2.5 Save your updated dataset?

Need help? Go to chapter 4 in the webbook.

save(anes_pilot_variables, file = "anes_pilot_variables.RData")