ggplot2

Dr. Alexander Fisher

Duke University

Announcements

• exam 1 date on schedule

• teams for lab 03; see announcement on slack

included in the tidyverse package

library(tidyverse)

Basics

ggplot2 is the package. ggplot is the main function.

• the first argument is a data frame we want to plot from

• the next argument is a list of variables (columns) of our data frame that we want to visualize. These go in the aesthetic aes() function.

Example (step by step)

Blank canvas

library(palmerpenguins)

• code
• plot

penguins %>% 
  ggplot()

x and y aesthetics

• code
• plot

penguins %>%
  ggplot(aes(x = flipper_length_mm, y = bill_depth_mm))

add a geometry

• code
• plot

penguins %>%
  ggplot(aes(x = flipper_length_mm, y = bill_depth_mm)) +
  geom_point()

labels

• code
• plot

penguins %>%
  ggplot(aes(x = flipper_length_mm, y = bill_depth_mm)) +
  geom_point() + 
  labs(x = "Flipper length (mm)", y = "Bill depth (mm)",
       title = "Bill depth vs flipper length distribution",
       subtitle = "Penguins from the arctic",
       caption = "data from palmerpenguins R package")

add theme and color aesthetic

• code
• plot

penguins %>%
  ggplot(aes(x = flipper_length_mm, y = bill_depth_mm,
             color = island)) +
  geom_point() + 
  labs(x = "Flipper length (mm)", y = "Bill depth (mm)",
       color = "Island",
       title = "Bill depth vs flipper length distribution",
       subtitle = "Penguins from the arctic",
       caption = "data from palmerpenguins R package") +
  theme_bw()

faceting

• code
• plot

penguins %>%
  filter(!is.na(sex)) %>%
  ggplot(aes(x = flipper_length_mm, y = bill_depth_mm,
             color = island)) +
  geom_point() + 
  labs(x = "Flipper length (mm)", y = "Bill depth (mm)",
       color = "Island",
       title = "Bill depth vs flipper length distribution",
       subtitle = "Penguins from the arctic",
       caption = "data from palmerpenguins R package") +
  theme_bw() +
  facet_wrap(~ sex)

General ggplot

Anatomy of a ggplot

ggplot(
  data = [dataframe],
  aes(
    x = [var_x], y = [var_y], 
    color = [var_for_color], 
    fill  = [var_for_fill], 
    shape = [var_for_shape],
    size  = [var_for_size],
    alpha = [var_for_alpha],
    ...#other aesthetics
  )
) +
  geom_<some_geom>([geom_arguments]) +
  ... # other geoms
  scale_<some_axis>_<some_scale>() +
  facet_<some_facet>([formula]) +
  ... # other options

To visualize multivariate relationships we can add variables to our visualization by specifying aesthetics: color, size, shape, linetype, alpha, or fill; we can also add facets based on variable levels.

Variable mappings (aesthetics)

The name of the argument is mapping because it says how to “map” variables to a visual aesthetic.

penguins %>%
  ggplot(mapping = aes(x = bill_depth_mm, y = flipper_length_mm))

When does an aesthetic (visual) go inside function aes()?

• If you want an aesthetic to be reflective of a variable’s values, it must go inside aes().

• If you want to set an aesthetic manually and not have it convey information about a variable, use the aesthetic’s name outside of aes(), e.g. in the geometry, and set it to your desired value.

• code
• plot

penguins %>%
  ggplot(aes(x = flipper_length_mm)) + 
  geom_histogram(color = "gold", fill = "steelblue")

Continuous and discrete variables

Aesthetics for continuous and discrete variables are measured on continuous and discrete scales, respectively.

• glimpse
• code 1
• plot 1
• code 2
• plot 2

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…

mpg %>%
  ggplot(aes(x = cyl, y = cty)) +
  geom_boxplot() +
  labs(x = "Vehicle cylinders", y = "City MPG",
       title = "Fuel efficiency by year") +
  theme_bw()

mpg %>%
  ggplot(aes(x = as.factor(cyl), y = cty)) +
  geom_boxplot() +
  labs(x = "Vehicle cylinders", y = "City MPG",
       title = "Fuel efficiency by year") +
  theme_bw()

Common geometries

geometry	description
geom_point()	scatter plot
geom_histogram()	histogram
geom_boxplot()	box plot
geom_density()	density plot
geom_violin()	violin plot
geom_raster()	heat map
geom_line()	connect observations in a line
geom_bar()	bar plot (try with argument position = fill)
geom_smooth()	add a smooth trend line (try with argument method = lm
geom_abline()	add an algebraic line

• See https://ggplot2.tidyverse.org/reference/ for more geometries.

• Some geometries are in additional packages, e.g. see geom_density_ridges() within the package ggridges to create plots like these

Themes

• image credit: tvthemes package by Ryo Nakagawra

• See https://ggplot2.tidyverse.org/reference/ggtheme.html for a list of default themes.

Plotting functions

stat_function() is a powerful tool

• code
• plot

x = -10:10 # range
df = data.frame(x)

f = function(x) {
  dnorm(x)
}

df %>%
  ggplot(aes(x = x)) +
  stat_function(fun=f) +
  labs(y = "Density", 
       title = "Gaussian density")

Save the plot

• Save a plot as a file on your computer with ggsave()

penguins %>%
  ggplot(aes(x = bill_depth_mm, y = species)) + 
  geom_boxplot() +
  labs(x = "Bill depth (mm)", y = "Species") +
  theme_bw()

ggsave("penguin-box-plot.png", 
       plot = last_plot())

Advanced ggplot

Annotate

ggplot() +
  annotate(geom = "text", x = 10, y = 10, 
           label = "Text at (10, 10)", size = 10)

Patchwork

library(patchwork)

• code
• plot

p1 = penguins %>%
  ggplot(aes(x = species, y = bill_depth_mm)) +
  geom_violin() +
  labs(x = "Species", y = "Bill depth (mm)",
       title = "Violin plots")

p2 = penguins %>%
  ggplot(aes(x = bill_depth_mm, y = flipper_length_mm, color = island)) +
  geom_point() +
  labs(x ="Bill depth (mm)",
       y = "Flipper length (mm)",
       color = "Island",
       title = "Flipper length vs bill depth")

p3 = penguins %>%
  ggplot(aes(x = body_mass_g)) +
  geom_histogram(fill = "steelblue") +
  labs(x = "Body mass (g)",
       y = "Count",
       title = "Distribution of penguin body mass")

(p1 + p2) / p3

Patchwork layout

• horizontal
• vertical

p1 + p2 + 
  plot_annotation(title = "Palmer Penguins", tag_levels = c("A"))

p1 + p2 + plot_layout(ncol = 1) + 
  plot_annotation(title = "Palmer Penguins", tag_levels = c("i"))

Custom ggplot functions with ggproto

Encircle the data points that have the minimum x-value

• Output
• code
• stat_min

cars %>%
  ggplot(aes(x = speed, y = dist)) +
  geom_point() +
  stat_min(size = 5) +
  labs(title = "Car speed and stopping distance",
       x = "Speed", y = "Distance") 

# create ggproto object
StatMin = ggproto("StatMin", Stat,
                       compute_group = function(data, scales) {
                          xvar = data$x
                          yvar = data$y
                          
                          data[xvar == min(xvar), ,drop = FALSE]
                       },
                       
                       required_aes = c("x", "y")
                       
                       )

# create stat function
stat_min = function(mapping = NULL, data = NULL, geom = "point",
                         position = "identity", na.rm = FALSE, show.legend = NA, 
                         inherit.aes = TRUE,
                         shape = 21, size = 5, color = "red",
                         alpha = 1, ...) {
  layer(
    stat = StatMin, data = data, mapping = mapping, geom = geom, 
    position = position, show.legend = show.legend, inherit.aes = inherit.aes,
    params = list(color = color, shape = shape, size = size, alpha = alpha, 
                  na.rm = na.rm, ...)
  )
}

gganimate example

library(gganimate) # animate
library(gapminder) # data

• code-plot
• plot
• code-animate
• animation

ggplot(gapminder, aes(x = gdpPercap, y = lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  facet_wrap(~continent) +
  theme_bw(base_size = 16)

ggplot(gapminder, aes(x = gdpPercap, y = lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  facet_wrap(~continent) +
  theme_bw(base_size = 16) +
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'Life expectancy') +
  transition_time(year) +
  ease_aes('linear')

gganimate summary

Core functions

• transition_*() defines how the data should be spread out and how it relates to itself across time.

• view_*() defines how the positional scales should change along the animation.

• shadow_*() defines how data from other points in time should be presented in the given point in time.

• enter_*() / exit_*() defines how new data should appear and how old data should disappear during the course of the animation.

• ease_aes() defines how different aesthetics should be eased during transitions.

• Label variables

  • function dependent, use { } to access their values.

• See https://gganimate.com

LaTeX labels

library(latex2exp)

ggplot() +
  ylab(TeX("Mean: $y = \\beta_0 + \\beta_1 x_1 + \\beta_2 x_2$")) +
  xlab(TeX("$\\frac{\\alpha}{\\gamma}$")) +
  theme_grey(base_size = 20)

Style

• Keep plots simple
• Don’t redundantly encode information
• Use descriptive titles
• Always label units
• Use color and/or annotations to draw attention to a region of your plot
• Never make a pie chart
• Don’t use the default ggplot background. When in doubt, theme_bw() is always better.

Further exploring

• ggplot2 documentation

• ggplot2 extensions: https://exts.ggplot2.tidyverse.org/gallery/

• top 50 ggplot2 visualizations with code!

• extending ggplot2 with ggproto

• patchwork