The R Basics
Ryan Burge
11/21/2020
library(socsci)
library(car)
cces <- read_csv("https://raw.githubusercontent.com/ryanburge/cces/master/CCES%20for%20Methods/small_cces.csv")Taking A Quick Look At Data
Let’s start by taking a look at our dataset using the %>% and the glimpse command. You can get a pipe easily by hit CTRL + SHIFT + M.
## Rows: 64,600
## Columns: 33
## $ X1 <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1...
## $ X1_1 <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1...
## $ id <dbl> 222168628, 273691199, 284214415, 287557695, 290387662, 2...
## $ state <dbl> 33, 22, 29, 1, 8, 1, 48, 42, 13, 42, 15, 48, 12, 48, 20,...
## $ birthyr <dbl> 1969, 1994, 1964, 1988, 1982, 1963, 1962, 1991, 1963, 19...
## $ gender <dbl> 2, 2, 2, 2, 2, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2,...
## $ educ <dbl> 2, 2, 2, 2, 5, 2, 2, 1, 2, 2, 5, 3, 3, 4, 3, 3, 3, 2, 5,...
## $ race <dbl> 1, 1, 2, 2, 1, 6, 1, 1, 1, 1, 4, 1, 6, 3, 6, 7, 1, 1, 3,...
## $ marital <dbl> 1, 5, 5, 5, 1, 4, 2, 2, 1, 1, 1, 1, 1, 6, 5, 5, 5, 1, 1,...
## $ natecon <dbl> 3, 4, 5, 4, 2, 4, 3, 5, 4, 5, 2, 6, 3, 3, 3, 3, 6, 4, 6,...
## $ mymoney <dbl> 2, 3, 2, 4, 2, 4, 3, 5, 4, 3, 2, 1, 3, 3, 2, 5, 4, 5, 3,...
## $ econfuture <dbl> 6, 5, 4, 5, 6, 4, 3, 6, 6, 5, 3, 6, 6, 2, 2, 5, 6, 4, 5,...
## $ police <dbl> 2, 3, 2, 2, 2, 3, 2, 2, 2, 1, 2, 1, 3, 2, 1, 4, 2, 1, 2,...
## $ background <dbl> 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1,...
## $ registry <dbl> 2, 1, 2, 2, 2, 1, 1, 2, 1, 1, 8, 1, 1, 2, 1, 2, 1, 1, 1,...
## $ assaultban <dbl> 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 1, 1,...
## $ conceal <dbl> 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 2, 2, 1,...
## $ pathway <dbl> 2, 2, 1, 1, 1, 1, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 1, 2, 2,...
## $ border <dbl> 1, 1, 1, 2, 2, 1, 1, 2, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2,...
## $ dreamer <dbl> 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 2, 2,...
## $ deport <dbl> 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2,...
## $ prochoice <dbl> 1, 1, 2, 2, 2, 1, 2, 2, 2, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1,...
## $ prolife <dbl> 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 1,...
## $ gaym <dbl> 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1,...
## $ employ <dbl> 7, 7, 6, 6, 2, 6, 1, 4, 3, 1, 5, 1, 7, 2, 1, 4, 4, 6, 4,...
## $ pid7 <dbl> 5, 4, 1, 4, 2, 2, 6, 4, 7, 7, 2, 4, 4, 1, 4, 1, 2, 2, 2,...
## $ attend <dbl> 6, 8, 3, 4, 6, 2, 2, 5, 4, 5, 5, 5, 4, 6, 6, 6, 6, 1, 7,...
## $ religion <dbl> 11, 98, 2, 11, 10, 1, 1, 11, 11, 2, 1, 1, 10, 11, 9, 11,...
## $ vote16 <dbl> 1, 1, NA, NA, 2, 99, 1, NA, 1, 1, 2, 5, 99, NA, 5, 4, 99...
## $ ideo5 <dbl> 3, 3, 5, 4, 2, 6, 5, 3, 4, 3, 3, 4, 6, 1, 3, 1, 2, 3, 3,...
## $ union <dbl> 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3,...
## $ income <dbl> 97, 6, 4, 1, 7, 1, 3, 1, 4, 7, 10, 5, 2, 2, 3, 1, 6, 3, ...
## $ sexuality <dbl> 1, 1, 1, 1, 1, 1, 1, 9, 1, 1, 1, 1, 6, 1, 1, 1, 1, 1, 1,...Counting
One of the most helpful functions to get a simple sense of how many of each race in the dataset is to use the table command. Again, this uses cces and then the pipe. You need to take a look at the codebook to understand what one and two represents. I know that 1 = male and 2 = female.
## # A tibble: 2 x 3
## gender n pct
## <dbl> <int> <dbl>
## 1 1 29531 0.457
## 2 2 35069 0.543Filtering
R has a lot of cool functions. One of the most helpful is called filter. It will filter the data based on whatever parameters we have set up.
The racial breakdown of women in the dataset. It’s important that you do == (2 equal signs).
## # A tibble: 8 x 3
## race n pct
## <dbl> <int> <dbl>
## 1 1 24489 0.698
## 2 2 5137 0.146
## 3 3 2894 0.083
## 4 4 1115 0.032
## 5 5 248 0.007
## 6 6 858 0.024
## 7 7 261 0.007
## 8 8 67 0.002Recoding
Do you want to create a dichotomous variable? Use the case_when function. When looking at the codebook, we see that white is 1. So this race == 1 ~ 1, tells R to take the race variable and when it equals one, then convert that to the number 1. The TRUE ~ 0 means, take everything else and make it zero.
## # A tibble: 2 x 3
## white n pct
## <dbl> <int> <dbl>
## 1 0 18311 0.283
## 2 1 46289 0.717Or do you want to have multiple levels of a variable?
cces %>%
mutate(race = frcode(race == 1 ~ "White",
race == 2 ~ "Black",
race == 3 ~ "Hispanic",
race == 4 ~ "Asian")) %>%
ct(race)## # A tibble: 5 x 3
## race n pct
## <fct> <int> <dbl>
## 1 White 46289 0.717
## 2 Black 7926 0.123
## 3 Hispanic 5238 0.081
## 4 Asian 2278 0.035
## 5 <NA> 2869 0.044But, do you notice how you have some NAs values? We can still do our count but exclude them by including show_na = FALSE
cces %>%
mutate(race = frcode(race == 1 ~ "White",
race == 2 ~ "Black",
race == 3 ~ "Hispanic",
race == 4 ~ "Asian")) %>%
ct(race, show_na = FALSE)## # A tibble: 4 x 3
## race n pct
## <fct> <int> <dbl>
## 1 White 46289 0.75
## 2 Black 7926 0.128
## 3 Hispanic 5238 0.085
## 4 Asian 2278 0.037Or, you can do simple math:
## # A tibble: 80 x 3
## age n pct
## <dbl> <int> <dbl>
## 1 18 668 0.01
## 2 19 776 0.012
## 3 20 682 0.011
## 4 21 750 0.012
## 5 22 788 0.012
## 6 23 784 0.012
## 7 24 913 0.014
## 8 25 1002 0.016
## 9 26 1303 0.02
## 10 27 1126 0.017
## # ... with 70 more rowsMaking a Bar Chart
graph <- cces %>%
mutate(race = frcode(race == 1 ~ "White",
race == 2 ~ "Black",
race == 3 ~ "Hispanic",
race == 4 ~ "Asian")) %>%
ct(race, show_na = FALSE)
graph %>%
ggplot(., aes(x = race, y = pct)) +
geom_col()
If you want to make dodged bars here’s a tutorial for that:
Making a Line Graph
graph <- cces %>%
mutate(race = frcode(race == 1 ~ "White",
race == 2 ~ "Black",
race == 3 ~ "Hispanic",
race == 4 ~ "Asian",
TRUE ~ "All Others")) %>%
mutate(age = 2016 - birthyr) %>%
group_by(race) %>%
ct(age)
graph %>%
ggplot(., aes(x = age, y = pct, color = race, group = race)) +
geom_point() +
geom_line()
Let’s do smoothed lines now.
graph <- cces %>%
mutate(race = frcode(race == 1 ~ "White",
race == 2 ~ "Black",
race == 3 ~ "Hispanic",
race == 4 ~ "Asian",
TRUE ~ "All Others")) %>%
mutate(age = 2016 - birthyr) %>%
group_by(race) %>%
ct(age)
graph %>%
ggplot(., aes(x = age, y = pct, color = race, group = race)) +
geom_point() +
geom_smooth()## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
Finding the Mean + Graph
## # A tibble: 1 x 7
## mean sd n level se lower upper
## <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
## 1 47.9 16.8 64600 0.05 0.0662 47.8 48.0Finding the average age for each racial group.
cces %>%
mutate(race = frcode(race == 1 ~ "White",
race == 2 ~ "Black",
race == 3 ~ "Hispanic",
race == 4 ~ "Asian")) %>%
mutate(age = 2016- birthyr) %>%
group_by(race) %>%
mean_ci(age)## # A tibble: 5 x 8
## race mean sd n level se lower upper
## <fct> <dbl> <dbl> <int> <dbl> <dbl> <dbl> <dbl>
## 1 White 49.8 16.9 46289 0.05 0.0786 49.6 49.9
## 2 Black 44.7 15.9 7926 0.05 0.178 44.4 45.1
## 3 Hispanic 40.9 14.9 5238 0.05 0.205 40.5 41.3
## 4 Asian 39.4 14.0 2278 0.05 0.293 38.8 40.0
## 5 <NA> 45.5 16.6 2869 0.05 0.310 44.9 46.1Now, we can graph that
graph <- cces %>%
mutate(race = frcode(race == 1 ~ "White",
race == 2 ~ "Black",
race == 3 ~ "Hispanic",
race == 4 ~ "Asian")) %>%
mutate(age = 2016- birthyr) %>%
group_by(race) %>%
mean_ci(age)
graph %>%
ggplot(., aes(x = race, y = mean)) +
geom_point() +
geom_errorbar(aes(ymin = lower, ymax = upper))
Graph that without NAs
graph %>%
filter(race != "NA") %>%
ggplot(., aes(x = race, y = mean)) +
geom_point() +
geom_errorbar(aes(ymin = lower, ymax = upper))