Chapter 8 Midpoint

Let’s try to solidify our understanding of the past concepts. These exercises will require the “ANES_isolate.tsv” file that accompanies Handout 5.

Don’t forget:

library(tidyverse)
data_isolate <- read.table("ANES_isolate.tsv")

8.1 Exercise 1

Compare the ages of females and non-females. Generate a (single) dataframe that contrasts the mean, median, max, and min ages between the two groups.

Solution:

data_summary <- data_isolate %>%
  group_by(female) %>%
  summarize(mean_age = mean(age), 
            median_age = median(age), 
            max_age = max(age), 
            min_age = min(age))

data_summary

## # A tibble: 2 × 5
##   female mean_age median_age max_age min_age
##    <int>    <dbl>      <dbl>   <int>   <int>
## 1      0     44.9         43      99      17
## 2      1     45.5         43      98      17

8.2 Exercise 2

We want to learn more about moderates (partyid = 4) and their demographics. Create a dataframe that has only moderates, and include only the columns age, income, female, and minority. Then, add a new column, retire_age, that is = 1 if an individual’s age is >= 65.

Solution:

data_moderates <- data_isolate %>%
  filter(partyid == 4) %>%
  select(c(age, income, female, minority)) %>%
  mutate(retire_age = ifelse(age >= 65, 1, 0))

head(data_moderates)

##      age income female minority retire_age
## 3701  43      2      1        1          0
## 3709  25      2      1        0          0
## 3720  52      3      0        0          0
## 3721  33      3      0        0          0
## 3726  32      4      0        0          0
## 3754  62      1      1        0          0