Ahmad Mousavi

Ahmad Mousavi

Assistant Prof. of Data Science,
Dept. of Math. and Stat.,
American University

Lab 3: Graphics with ggplot2

DATA 412/612 · Week 3 · PDF · Source (.qmd)

Map, smooth, fix overplotting, and save a faceted boxplot.

Work in a Quarto document. Number the exercises. Keep the plots. Do 3.1 through 3.3 first. You will need ggplot2 (from library(tidyverse)), mpg, iris, and diamonds.

library(tidyverse)
data("mpg")
data("iris")
data("diamonds")

1. Mappings

  1. What happens when you map a categorical variable to size or alpha? Try it on mpg with drv. Write one sentence about whether the mapping is a good idea.
  2. Look at head(iris). Name the variables and say whether each is quantitative or categorical.
  3. Scatterplot petal length vs petal width, colored by species. What do you notice?
  4. What happens when you map a quantitative variable to shape? Try shape = cty on an mpg scatterplot of displ vs hwy.
  5. On iris, plot petal length vs petal width, and also map species, sepal length, and sepal width. Is the plot still readable?

2. Smooths and overplotting

  1. Scatterplot petal length vs petal width. Color the points by species. Add one linear smooth (method = lm, se = FALSE) that does not use species.
  2. Plot price vs carat in diamonds. Show the overplotted version and a version that you can actually read.

3. Distributions and boxplots

  1. Histogram of diamond price, filled by cut. Try bins = 50.
  2. Boxplot of price vs cut on a log-y scale. Does anything surprise you? Add a second plot that helps explain it.

4. Facets, theme, and save

  1. Boxplots of price vs cut, log-y, faceted by color.
  2. Same plot: orange fill, theme_bw().
  3. Save the plot from (2) with ggsave(). Put it in a folder you already have. ggsave() overwrites without asking.