25 Vis: Boxplots and Counts

Purpose: Boxplots are a key tool for EDA. Like histograms, boxplots give us a sense of “shape” for a distribution. However, a boxplot is a careful summary of shape. This helps us pick out key features of a distribution, and enables easier comparison of different distributions.

Reading: Boxplots and Counts Topics: (All topics) Reading Time: ~20 minutes

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──

## ✔ ggplot2 3.4.0      ✔ purrr   1.0.1 
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.5.0 
## ✔ readr   2.1.3      ✔ forcats 0.5.2

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

A subtle point from the primer is that we can use dplyr to generate new facts about our data, then use ggplot2 to visualize those facts. We’ll practice this idea in a number of ways.

25.0.1 q1 Use a cut_* verb to create a categorical variable out of carat. Tweak

the settings in your cut and document your observations.

Hint: Recall that we learned about cut_interval, cut_number, cut_width. Take your pick!

diamonds %>%
  mutate(carat_cut = cut_width(carat, width = 0.5, boundary = 0)) %>%
  ggplot(aes(x = carat_cut, y = price)) +
  geom_boxplot()

Observations - Price tends to increase with carat - Median price rises dramatically across carat [0, 2] - Median price is roughly constant across carat [2, 4.5] - Across carat [2, 4.5], the whiskers have essentially the same max price - The IQR is quite small at low carat, but increases with carat; the prices become more variable at higher carat

25.0.2 q2 The following code visualizes the count of diamonds of all carats according to their cut and color. Modify the code to consider only diamonds with carat >= 2. Does the most common group of cut and color change?

## NOTE: No need to modify; run and inspect
diamonds %>%
  count(cut, color) %>%
  ggplot(aes(cut, color, fill = n)) +
  geom_tile()

Modify the following code:

diamonds %>%
  filter(carat >= 2) %>%
  count(cut, color) %>%
  ggplot(aes(cut, color, fill = n)) +
  geom_tile()

Observations: - Yes, it used to be G, Ideal, but is now I, Premium

25.0.3 q3 The following plot has overlapping x-axis labels. Use a verb from the reading to flip the coordinates and improve readability.

mpg %>%
  ggplot(aes(manufacturer, hwy)) +
  geom_boxplot() +
  coord_flip()

This is a simple—but important—trick to remember when visualizing data with many categories.