Classwork 11

Distribution of a Numeric Variable Across Groups

NoteNote

Complete the questions and submit your responses on the corresponding Brightspace page.

Complete the code in an R script and keep your brief written interpretations with it. This page contains its own setup and data definitions; no earlier classwork is required.

Setup and data

library(tidyverse)

The built-in mpg data contains 234 vehicle model configurations from model years 1999 and 2008. Each row represents a configuration, not an individual driver or purchase.

Variable	Meaning
cty	City fuel economy in miles per gallon; higher means farther per gallon
drv	Drive type: f = front-wheel, r = rear-wheel, 4 = four-wheel

Dataset reference: ggplot2 · mpg.

1. Histograms in separate panels

Make a separate histogram for each drive type. Use a bin width of 2 mpg in every panel and keep the default shared axis scales.

ggplot(data = mpg,
       mapping = aes(x = cty)) +
  geom_histogram(binwidth = 2) +
  ____(~ ____)

Use facet_wrap() and drv in the blanks. Describe one difference in typical fuel economy or spread between two drive types.

2. Counts and group sizes

These groups contain different numbers of configurations. Explain why a taller count bar alone does not establish that a larger proportion of that group falls in the bin.

3. A boxplot comparison

Compare city fuel economy across drive types.

ggplot(data = mpg,
       mapping = aes(x = ____,
                     y = ____,
                     fill = drv)) +
  ____()

Describe a difference in typical fuel economy or spread. If a point appears beyond a whisker, explain why it deserves investigation rather than automatic deletion.

4. Choose a useful comparison

The histogram shows detailed distribution shape; the boxplot provides a compact summary. Which graph better supports the comparison you described, and why? Do not treat the two graphs as interchangeable.