Guillaume Coqueret
The following document shows the potential of datavisualization applied to political data. The examples are simple illustrations that can be generalized to much more ambitious studies.
We refer to the companion paper (Supervised learning with political data by Guillaume Coqueret) for comments on the results below, as well as for the presentation of the database.
DISCLAIMER: the data and code are meant for pedagogical and research use only. The original collector of the data, ANES, and the author bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
library(tidyverse)
load("anes.RData") # Import ANES database
beg <- 1985 # Start date for the samplefeel <- c("Feeling_blacks","Feeling_big_business","Feeling_unions","Feeling_military","Feeling_congress")
anes_feel <- anes %>% filter(Date > beg) %>% select(one_of(feel)) %>% gather()
ggplot(anes_feel, aes(x = value, y = ..density.. , fill = key)) + geom_histogram(bins = 10) + facet_grid(. ~ key)
The distribution is more symmetric for business, compared to military.
Below, we plot the distribution of ages.
ggplot(anes, aes(x = Age)) + geom_histogram(bins = 20)
demo <- c("Gender","Nb_children","Home_ownership")
# Choosing more variables, e.g.:
# demo <- c("Gender","Race","Education","Income","Work_status","Nb_children","Home_ownership","Marital_status")
# yields graphs that are harder to read
anes_demo <- anes %>% filter(Date > beg) %>% select(one_of(demo)) %>% gather()
ggplot(anes_demo, aes(x = value)) + facet_wrap(~ key, scales = "free_x", ncol = 1) + geom_histogram(stat = "count") 
Note: three (midterm) years are indeed missing.
party <- anes %>% filter(Date > 1950) %>% select(Date, Party_simple) %>% group_by(Date, Party_simple) %>% summarise(count = n())
ggplot(party, aes(x = Date, y = count, fill = Party_simple)) + geom_bar(stat="identity", position="fill")