Learning objectives

  • Basic plotting in R using the {ggplot2} package.

Introduction

  • {ggplot2} is very powerful, so I am just going to show you the most important and basic plots that are necessary for data analysis.

  • Before using the plotting functions from {ggplot2} in a new R session, always first load the {ggplot2} library.

    library(ggplot2)

  • In this vignette, we’ll also make some variable transformations, so we will need the {dplyr} package.

    library(dplyr)

  • I will use the mpg dataset to demonstrate plots

    data(mpg, package = "ggplot2")
glimpse(mpg)
    ## Rows: 234
## Columns: 11
## $ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "audi", "…
## $ model        <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro", "…
## $ displ        <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0, 2.0, 2.…
## $ year         <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1999, 200…
## $ cyl          <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 8, 8, …
## $ trans        <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)", "auto…
## $ drv          <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4", "4", "4…
## $ cty          <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 17, 17, 1…
## $ hwy          <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 25, 25, 2…
## $ fl           <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p…
## $ class        <chr> "compact", "compact", "compact", "compact", "compact", "c…

ggplot()

  • The first function you use in making a plot is always ggplot().

  • It takes two main arguments:

    • data: The data frame that holds the variables you want to plot.
    • mapping: The “aesthetic map”

  • An “aesthetic map” says what variables go on the \(x\)-axis, what variables go on the \(y\)-axis, what variables are represented by color, or size, or point shape, etc…

  • You place all aesthetic maps inside an aes() function.

  • E.g. here, we are mapping hwy to be on the \(x\)-axis, and different values of drv should be different colors.

    ggplot(data = mpg, mapping = aes(x = hwy, color = drv))

  • This function just sets the data and the aesthetic mapping, but it won’t produce any useful plot by itself.

  • You add additional functions to the plot to state the type of plot you want.

One Variable

Continuous

  • Histogram:

    • Variable should be on the \(x\)-axis.
    • Use the geom_histogram() function.
    ggplot(data = mpg, mapping = aes(x = hwy)) +
  geom_histogram()

  • Make the bin lines black and the fill white, and change the number of bins.

    ggplot(data = mpg, mapping = aes(x = hwy)) +
  geom_histogram(bins = 10, color = "black", fill = "white")

  • Exercise: Load in the estate data (see here for a description) and make a histogram of price with 20 bins. Make the bins red.

Discrete

  • Barplot:

    • Put the variable on the \(x\)-axis.
    • Use geom_bar().
    ggplot(data = mpg, mapping = aes(x = drv)) +
  geom_bar()

  • Exercise: What variables from the estate data are appropriately plotted using a bar plot? Plot them.

Two Variables

Continuous X, Continuous Y

  • Scatterplot:

    • Say what variables should be on the \(x\)- and \(y\)-axes.
    • Use geom_point().
    ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point()

  • Jitter points to account for overlaying points.

    • Use geom_jitter() instead of geom_point().
    ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_jitter()

  • Add a Loess Smoother by adding geom_smooth().

    ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point() +
  geom_smooth(se = FALSE)

  • Exercise: Using the estate data, make a scatterplot of number of bedrooms versus number of bathrooms. Adjust for any overplotting.

Discrete X, Continuous Y

  • Boxplot

    • Place one variable on \(x\)-axis and other on \(y\)-axis.
    • Typically, but not always, continuous goes on \(y\)-axis.
    • Use geom_boxplot().
    ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
  geom_boxplot()

  • Exercise: Using the estate data, plot sales price versus style. (hint: you need to first convert style to a factor using as.factor())

Color Coding and Legends

  • Color code a scatterplot by a categorical variable and add a legend.

    • Just add a color mapping.
    ggplot(data = mpg, mapping = aes(x = cty, y = hwy, color = drv)) +
  geom_jitter()

  • Exercise: Using the estate data, create a boxplot of price versus ac, color coding by pool.

Changing a legend title

  • Add a scale_*() call to change the name:

    ggplot(data = mpg, mapping = aes(x = cty, y = hwy, color = drv)) +
  geom_jitter() +
  scale_color_discrete(name = "New Name1")

    ggplot(data = mpg, mapping = aes(x = cty, y = hwy, shape = drv)) +
  geom_jitter() +
  scale_shape_discrete(name = "New Name2")

Faceting

  • You can facet by a categorical variable by adding a facet_grid() or facet_wrap() function.

  • The variable to the left of the tilde (“~”) indexes the row facets, the variable to the right of the tilde indexes the column facets. Using a dot (“.”) in place of a variable means that there will only be one row/column facet.

    ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point() +
  facet_grid(. ~ drv)

    ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point() +
  facet_grid(drv ~ .)

    ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point() +
  facet_grid(fl ~ drv)

  • Exercise: Using the estate data, plot price versus area, faceting by ac, color coding by pool.

Change Theme

  • Add a theme_*() function to change the theme:

    ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point() + 
  theme_classic()