5.5 Numeric vs. categorical: Various plot types

5.5.1 Data & Packages & functions

• Data: 1 categorical variable, 1 numeric variable
• Packages & functions:
  • geomjitter() offers the same control over aesthetics geompoint() (size, color, shape)
  • geomboxplot(), geomviolin(): You can control the outline color or the internal fill color
• Strengths and weaknesses
  • Boxplots summarize the bulk of the distribution with only five numbers
  • Jittered plots show every point but only work with relatively small datasets
  • Violin plots give the richest display, but rely on the calculation of a density estimate, which can be hard to interpret
    • See (http://www.sthda.com/english/wiki/ggplot2-violin-plot-quick-start-guide-r-software-and-data-visualization)[http://www.sthda.com/english/wiki/ggplot2-violin-plot-quick-start-guide-r-software-and-data-visualization]

5.5.2 Graph

• Figure 5.9 visualizes different ways of plotting a categorical vs. a numerical variable.
• Questions:
  • What does the graph show? What are the underlying variables (and data)?
  • How many scales/mappings does it use? Could we reduce them?
  • What do you like, what do you dislike about the figure? What is good, what is bad?
  • What kind of information could we add to the graph (if any)?
  • How would you approach a replication of the graph?

## Parsed with column specification:
## cols(
##   screen_name = col_character(),
##   n_retweets = col_double(),
##   followers_count = col_double(),
##   party = col_character(),
##   party_color = col_character(),
##   first_name = col_character(),
##   account_created_at = col_datetime(format = ""),
##   account_age_months = col_double(),
##   account_age_years = col_double(),
##   last_name = col_character(),
##   female = col_double()
## )

Figure 5.9: Boxplots and jittered points

5.5.3 Lab: Data & Code

# data_twitter_influence.csv
data <- read_csv(sprintf("https://docs.google.com/uc?id=%s&export=download",
                                "1dLSTUJ5KA-BmAdS-CHmmxzqDFm2xVfv6"),
                        col_types = cols())
data_plot <- data %>% filter(n_retweets < 15000)
p1 <- ggplot(data_plot, aes(x = party, y = n_retweets)) + geom_point()+
    theme(axis.text.x = element_text(angle = 30, hjust = 1))
p2 <- ggplot(data_plot, aes(x = party, y = n_retweets)) + geom_jitter()+
    theme(axis.text.x = element_text(angle = 30, hjust = 1))
p3 <- ggplot(data_plot, aes(x = party, y = n_retweets)) + geom_boxplot()+
    theme(axis.text.x = element_text(angle = 30, hjust = 1))
p4 <- ggplot(data_plot, aes(x = party, y = n_retweets)) + geom_violin()+
    theme(axis.text.x = element_text(angle = 30, hjust = 1))
grid.arrange(p1, p2, p3, p4, ncol=2)