R Data Visualization: ggplot2

Matthew DeHaven

Course Home Page

2024-01-01

Lecture Summary

• Using ggplot2 to create simple plots
  • Data, aesthetics, geoms, layers
• Scales, labels, themes
• Custom themes
• Facets
• patchwork
• Saving plots

ggplot2

One of the most used packages in R.

Developed by Hadley Wickham (we’ve seen his name before).

“gg” stands for “grammar” of “graphics”.

Loading the package

You sould already have ggplot2 installed, as it is part of the tidyverse.

If not:

renv::install("ggplot2")

We just need to load it into our system.

library(ggplot2)

An Example Dataset

We’ve seen this built in dataset mtcars before. It has values for 32 different cars.

mtcars

                     mpg cyl  disp  hp drat    wt  qsec vs am gear carb
Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

An Example Plot

ggplot(data = mtcars, mapping = aes(x = hp, y = mpg, color = cyl)) + 
  geom_point()

Let’s look in detail how this works.

Elements of a ggplot

1. Data

2. Aesthetic Mapping

3. Geoms (geometric objets)

4. Layers

We will show each of these in our example plot.

An Example Plot

ggplot(data = mtcars, mapping = aes(x = hp, y = mpg, color = cyl)) + 
  geom_point()

1. Data

data = mtcars

• Data is always the first argument for a ggplot, so you will often see,

mtcars |>
  ggplot(mapping = aes(x = hp, y = mpg, color = cyl)) + 
  geom_point()

An Example Plot

ggplot(data = mtcars, mapping = aes(x = hp, y = mpg, color = cyl)) + 
  geom_point()

2. Aesthetic Mapping

mapping = aes(x = hp, y = mpg, color = cyl)

• Aesthetics on the plot (x, y, color) are linked to columns in your dataset (hp, mpg, cyl).

• This mapping translates our data variables into the “grammar of graphics”.

Once we have that, we can add a “geom”…

An Example Plot

ggplot(data = mtcars, mapping = aes(x = hp, y = mpg, color = cyl)) + 
  geom_point()

3. Geoms

geom_point()

• “Geom”s take our aesthetic mapping and draw objects to represent them on the chart

• In this case, we draw points.

Instead, we could have drawn a line…

ggplot(data = mtcars, mapping = aes(x = hp, y = mpg, color = cyl)) + 
  geom_line()

An Example Plot

ggplot(data = mtcars, mapping = aes(x = hp, y = mpg, color = cyl)) + 
  geom_line()

But it would have looked pretty silly.

An Example Plot

ggplot(data = mtcars, mapping = aes(x = hp, y = mpg, color = cyl)) + 
  geom_point()

4. Layers

+ geom_point()

• All ggplots are built in layers, one for each geometry.

• In this case, we hav only one layer: geom_point()

What happens if we add two?

ggplot(data = mtcars, mapping = aes(x = hp, y = mpg, color = cyl)) + 
  geom_point() +
  geom_line()

Multiple layers

ggplot(data = mtcars, mapping = aes(x = hp, y = mpg, color = cyl)) + 
  geom_point() +
  geom_line()

We would get both geometric objects drawn on the chart!

Multiple layers

ggplot(data = mtcars, mapping = aes(x = hp, y = mpg, color = cyl)) + 
  geom_point() +
  geom_line()

Notice that our geom_line is also using the color aesthetic.

What if we wanted it to be black instead?

Setting aesthetics by layer

ggplot(data = mtcars) + 
  geom_point(aes(x = hp, y = mpg, color = cyl)) +
  geom_line(aes(x = hp, y = mpg))

Instead of having one plot-wide aesthetic, we can set aesthetics for each layer.

Adding additional aesthetics by layer

ggplot(data = mtcars, aes(x = hp, y = mpg)) + 
  geom_point(aes(color = cyl)) +
  geom_line()

Or we could set the common aesthetics in ggplot() call, and just add color for geom_point().

Overriding by layer

ggplot(data = mtcars, aes(x = hp, y = mpg, color = cyl)) + 
  geom_point() +
  geom_line(color = "black")

Or we can override plot-wide aesthetics for individual layers.

Notice that we don’t use aes(color = "black").

Geoms

Possible geom_s and the aesthetics they require:

• geom_point(), geom_line() x, y

• geom_histogram(), geom_denstity() x

• geom_col() x, y,

• geom_ribbon() x, ymin, ymax

• geom_text(), geom_label() x, y, label

And many more. See the ggplot2 reference page for more.

Changing Plot Defaults

Changing Plot Defaults

We have seen how to build a ggplot with data, aesthetics, geoms, and layers.

Now, we will look at how to adjust other parts of the plate:

• scales

• labels (title, axis labels, etc.)

• themes

Scales

Every ggplot aesthetic has a scale.

p <- mtcars |>
  ggplot(aes(x = hp, y = mpg, color = cyl)) + 
  geom_point()

This plot has three scales:

• x-axis

• y-axis

• color

Note: I’ve saved the plot to a variable p which we can print later and can add additional layers, scales, or themes to it without repeating the initial construction.

X and Y scales

The most used scales are for the x- and y-axes.

p + scale_y_continuous(limits = c(0, 50))

X and Y scales

Here we are using _continuous() scales because X and Y are both numeric.

p + scale_y_continuous(limits = c(0, 50)) +
  scale_x_continuous(limits = c(0, 500))

For discrete data, you would use scale_x_discrete() or scale_y_discrete().

scale_*_contionus() options

limits = c(0, 50)

• Set start and end values, can be smaller than your data

expand = c(0, 0)

• Set the buffer space at the start and end of the scale, default is c(0.05, 0.05)

breaks = c(0, 10, 20, 30, 40, 50)

• set the locations of breaks (tick marks on the axes)

labels = c(0, 10, 20, 30, 40, 50)

• set labels for each break (must match break length)

Scale options in action

p + scale_x_continuous(
  limits = c(0, 200),
  expand = c(0, 0),
  breaks = c(0, 50, 100, 200),
  labels = c("0", "50", "text", "anything you want")
)

Color scales

The default continuous color scale is

p + scale_color_gradient(low = "red", high = "black")

For low-mid-high colorscales use scale_color_gradient2().

For n-level color scales, use scale_color_gradientn().

But is a continuous scale the right one for this data?

Discrete colorscales

p_discrete <- mtcars |>
  ggplot(aes(x = hp, y = mpg, color = as.factor(cyl))) +
  geom_point()
p_discrete

For changing discrete colors, use scale_color_manual().

Discrete colorscales

Use the “values=” argument to provide your own colors.

p_discrete +
  scale_color_manual(values = c("red", "#0F0F0F", rgb(0, 1, 0)))

Discrete colorscales

Use a named vector to match them to specific color values.

p_discrete +
  scale_color_manual(values = c(
    `6` = "red", `8` = "#0F0F0F", `4` = rgb(0, 1, 0)
  ))

Scales overview

There is a scale in your plot for each aesthetic.

The defaults can always be adjusted, with the right scale function.

Important to remember if your data is contionuous or discrete.

There are many more built-in scales, see ggplot2 reference page for more.

Plot Labels

These labels default to your variable names:

• x- and y-axis labels

• color (or other aesthetic) labels

And these labels are optional:

• plot title

• plot subtitle

• plot caption

Changing Plot Labels

p_labeled <- p_discrete + labs(
  title = "Car Fuel Efficiency",
  subtitle = "More horsepower means less fuel effecient",
  caption = "Source: built-in R dataset: mtcars",
  x = "Miles Per Gallon (mpg)",
  y = "Horse Power (HP)",
  color = "Cylinders"
)
p_labeled

ggplot themes

Want to quickly change how your plot looks? Change the theme!

p_discrete + theme_bw()

ggplot built in themes

• theme_gray() the default with gray plot background

• theme_bw() black and white (my preference)

• theme_minimal() no axes lines

• theme_classic() no grid lines

• theme_dark() dark background

Editing a theme

All elements of a theme can be edited using + theme().

p_discrete + theme(legend.position = "top")

To see the full list of options, see ggplot2::theme reference or type ?theme.

Other Themes

Many other packages and organizations share their own ggplot themes.

Try installing this collection of themes:

renv::install("ggthemes")

Then loading them in the session.

library(ggthemes)

And then we can make our plot look like it’s from…

Economist Theme

The Economist!

p_labeled + theme_economist()

WSJ Theme

The Wall Street Journal!

p_labeled + theme_wsj()

STATA Theme

… or Stata??

p_labeled + theme_stata()

Theme Takeaways

Notice that a large part of what changed in each of those themes were the fonts.

With themes you can change just about any non-data part of your plot.

But that means all the options can be hard to figure out.

Using pre-built themes is a good way to get what you want without digging into the details.

My own theming preferences

• No background color

• No grid lines

  • Except for 0, which should always have a line

• Start and end ticks, if possible

• A box around the plot (i.e. top and right axis lines)

• Legends within the plot, if possible

  • Better yet, label the lines directly

• Make colors colorblind friendly

  • Also print well in black and white

My version of our example plot

Facets

Facets

Facets allow for easily plotting multiple cuts of the data.

You can think of it as adding another “z” dimension to your plot.

For example, for our Fuel Efficiency plot, instead of using color to show “cylinders” we could have used facets.

Facetted example plot

facet_wrap() constructs plot panels from one variable.

mtcars |>
  ggplot(aes(x = hp, y = mpg)) + 
  geom_point() +
  facet_wrap(vars(cyl))

Facetted example plot

Use scales="free" to let the scales vary by panel

mtcars |>
  ggplot(aes(x = hp, y = mpg)) + 
  geom_point() +
  facet_wrap(vars(cyl), scales = "free")

2-D facets

You can create a grid of facets using facet_grid and two variables

mtcars |>
  ggplot(aes(x = hp, y = mpg)) + 
  geom_point() +
  facet_grid(rows = vars(cyl), cols = vars(gear))

Facets Takeaways

Facets are very useful for looking at lots of data.

But you loose some of the control over each individual panel.

• For example, you cannot set each panel scale in a facet_grid

Combining Plots

Combining Plots

Sometimes you want to create a single image from two or more charts.

There are multiple packages that allow you to do this.

We will use patchwork.

renv::install("patchwork")

library(patchwork)

Creating multiple plots

p1 <- mtcars |>
  ggplot(aes(x = hp, y = mpg)) + geom_point()
p2 <- mtcars |>
  ggplot(aes(x = hp)) + geom_density()
p3 <- mtcars |>
  ggplot(aes(x = gear, y = mpg)) + geom_col()
p4 <- mtcars |>
  ggplot(aes(x = gear, y = hp, group = gear )) + geom_boxplot()

Combining plots

We can combine two plots side by side with |.

p1 | p2

Combining plots

We can combine two plots in a column with /.

p1 / p2

Combining plots

And we can mix and match to get complicated layouts.

p1 / (p2 | p3 | p4)

Combining plots

You can set empty spaces using plot_spacer()

p1 | plot_spacer() / p2

Even More patchwork

A super powerful package.

• Can add plot annoations (like “Panel A”, “Panel B”, etc.)

• Add a group title

• Merge common legends across plots

• Set each column/row width/height

• Non-grid layouts

See the vignettes for a great guide.

Saving Plots

Saving Plots

ggplots are easy to save with ggsave()

ggsave("filename.pdf", plot, width = 6, height = 4, units = "in")

• file extension determines format of image

• “width, height, units” determine the size of the plot

Plot formats

Two main choices:

1. Raster

2. Vector Graphics

Use vector graphic format if possible.

Plot formats

1. Raster

• A specifc grid of pixels, each with a color value

• Fixed resolution and aspect ratio

• Ex: .png, .jpeg

2. Vector Graphics

• A set of instructions to draw shapes at specified locations

• Never loses resolution as image sizing changes

• Ex: .pdf, .svg

Summary

Summary

• Using ggplot2 to create simple plots
  • Data, aesthetics, geoms, layers
• Scales, labels, themes
• Custom themes
• Facets
• patchwork
• Saving plots