Compound Figures Exercise Solutions

Coding exercise 7.3

In this worksheet, we will discuss how to combine several ggplot2 plots into one compound figure.

We will be using the R package tidyverse, which includes ggplot() and related functions. We will also be using the package patchwork for plot composition.

# load required library
library(tidyverse)
library(patchwork)

We will be working with the dataset mtcars, which contains fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

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

Combining plots

First we set up four different plots that we will subsequently combine. The plots are stored in variables p1, p2, p3, p4.

p1 <- ggplot(mtcars) + 
  geom_point(aes(mpg, disp))
p1  

p2 <- ggplot(mtcars) + 
  geom_boxplot(aes(gear, disp, group = gear))
p2

p3 <- ggplot(mtcars) + 
  geom_smooth(aes(disp, qsec))
p3

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

p4 <- ggplot(mtcars) + 
  geom_bar(aes(carb))
p4

To show plots side-by-side, we use the operator |, as in p1 | p2. Try this by making a compound plot of plots p1, p2, p3 side-by-side.

 # build all the code for this exercisew

  # solution 
 p1 | p2 | p3

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

To show plots on top of one-another, we use the operator /, as in p1 / p2. Try this by making a compound plot of plots p1, p2, p3 on top of each other.

 # build all the code for this exercise

  p1 / p2 / p3

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

We can also use parentheses to group plots with respect to the operators | and /. For example, we can place several plots side-by-side and then place this entire row of plots on top of another plot. Try putting p1, p2, p3, on the top row, and p4 on the bottom row.

(___) / ___

# solution
(p1 | p2 | p3 ) / p4

Plot annotations

The patchwork package provides a powerful annotation system via the plot_annotation() function that can be added to a plot assembly. For example, we can add plot tags (the labels in the upper left corner identifying the plots) via the plot annotation tag_levels. You can set tag_levels = "A" to generate tags A, B, C, etc. Try this out.

(p1 | p2 | p3 ) / p4

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

# solution
(p1 | p2 | p3 ) / p4 +
  plot_annotation(
    tag_levels = "A"
  )

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Try also tag levels such as "a", "i", or "1".

You can also add elements such as titles, subtitles, and captions, by setting the title, subtitle, or caption argument in plot_annotation(). Try this out by adding an overall title to the figure from the previous exercise.

 # build all the code for this exercise

 # solution
(p1 | p2 | p3 ) / p4 +
  plot_annotation(
    tag_levels = "A",
    title = "Various observations about old cars"
  )

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Also set a subtitle and a caption.

Finally, you can change the theme of all plots in the plot assembly via the & operator, as in (p1 | p2) & theme_bw(). Try this out.

 # build all the code for this exercise

# solution
(p1 | p2) & theme_bw()

What happens if you write this expression without parentheses? Do you understand why?

(Big) Challenge Problem:

If you have time this morning, or if you want to work on it this afternoon, try analyzing a new dataset to test your R skills. First, learn what the columns mean, what missing values you see, and then start to ask questions about patterns in the data by making plots.
You can browse the datasets at https://github.com/rfordatascience/tidytuesday/tree/master/data (click on a year folder and go to the README to read about the datasets). Or pick from one of the ones below:

# fish consumption in different countries 
consumption <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-12/fish-and-seafood-consumption-per-capita.csv')

Rows: 11028 Columns: 4
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (2): Entity, Code
dbl (2): Year, Fish, Seafood- Food supply quantity (kg/capita/yr) (FAO, 2020)

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# world cup Cricket matches from 1996 to 2005
matches <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-11-30/matches.csv')

Rows: 1237 Columns: 24
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (19): match_id, team1, team2, team1_away_or_home, team2_home_away, winne...
dbl  (5): score_team1, score_team2, wickets_team1, wickets_team2, margin

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# malaria deaths by age across the world and time. 
 malaria_deaths<- readr::read_csv('https://github.com/rfordatascience/tidytuesday/blob/master/data/2018/2018-11-13/malaria_deaths_age.csv')

Warning: One or more parsing issues, call `problems()` on your data frame for details,
e.g.:
  dat <- vroom(...)
  problems(dat)

Rows: 1359 Columns: 1
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (1): <!DOCTYPE html>

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

#meteorites and/or volcanos:
# note to plot a map, try the following:
countries_map <- ggplot2::map_data("world")
world_map<-ggplot() + 
  geom_map(data = countries_map, 
           map = countries_map,aes(x = long, y = lat, map_id = region, group = group),
           fill = "white", color = "black", size = 0.1) # then add geom_point() to this map to add lat/long points

Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.

Warning in geom_map(data = countries_map, map = countries_map, aes(x = long, :
Ignoring unknown aesthetics: x and y

meteorites <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-06-11/meteorites.csv")

Rows: 45716 Columns: 10
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (5): name, name_type, class, fall, geolocation
dbl (5): id, mass, year, lat, long

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

volcano <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-05-12/volcano.csv")

Rows: 958 Columns: 26
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (18): volcano_name, primary_volcano_type, last_eruption_year, country, r...
dbl  (8): volcano_number, latitude, longitude, elevation, population_within_...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# or pick any dataset from https://github.com/rfordatascience/tidytuesday/tree/master/data , 
# just click on a year folder and go to the README to read about the datasets
# if you have trouble loading a dataset there, ask for help!