Data visualization – exercises

Day 4, C

Understanding how R creates images with base graphics and ggplot2, practice using ggplot2 to customize figures, and practice creating reusable tools for plotting
Author

Michael C Sachs

Learning objectives

In this lesson you will

  1. Understand how R creates images with base graphics and ggplot2
  2. Practice using ggplot2 to customize figures
  3. Practice creating reusable tools for plotting

Create your own theme

Load the ggplot2 package and customize your own theme. Look at the built-in themes and the ggthemes package for inspiration.

Tips

  1. You can save your customization using the theme(), but that will only modify the current theme
library(ggplot2)
library(palmerpenguins)

mytheme <- theme(strip.background = element_rect(fill = "steelblue"), 
        text = element_text(family = "Comic Sans MS"), 
        plot.background = element_rect(fill = "grey81"), 
        legend.background = element_rect(fill = NA), 
        legend.position = "bottom"
        ) 

ggplot(penguins, aes(x = body_mass_g, y = bill_length_mm, color = species)) + 
  geom_point() + facet_wrap(~ year) + mytheme
Warning: Removed 2 rows containing missing values (`geom_point()`).

theme_set(theme_bw())
ggplot(penguins, aes(x = body_mass_g, y = bill_length_mm, color = species)) + 
  geom_point() + facet_wrap(~ year) + mytheme
Warning: Removed 2 rows containing missing values (`geom_point()`).

  1. To make a fully custom theme, start with an existing one, and modify it
my_fulltheme <- theme_grey() + mytheme

ggplot(penguins, aes(x = body_mass_g, y = bill_length_mm, color = species)) + 
  geom_point() + facet_wrap(~ year) + my_fulltheme
Warning: Removed 2 rows containing missing values (`geom_point()`).

  1. Save your favorite color scales as a function for easy reuse. Use discrete_scale or continuous_scale.
my_qual_scale <- function(...) {
  discrete_scale("color", scale_name = "OI", 
                 palette = function(x) {
                   res <- palette.colors(x, "Okabe-Ito")[1:x]
                   names(res) <- NULL
                   res
                 }, ...)
}

ggplot(penguins, aes(x = body_mass_g, y = bill_length_mm, color = species)) + 
  geom_point() + facet_wrap(~ year) + my_fulltheme + my_qual_scale()
Warning: Removed 2 rows containing missing values (`geom_point()`).

Adding elements to a plot

Starting with this example from the lecture:

plotbm <- ggplot(penguins, aes(x = species, y = body_mass_g)) + geom_jitter()
plotbm
Warning: Removed 2 rows containing missing values (`geom_point()`).

  1. Add solid horizontal lines at the mean of each species.
  2. Add dotted horizontal lines at the median of each species.
  3. (For keeners) Create a reusable component that adds the mean and median lines to a jitter plot. Try it out on a different plot, e.g., sex versus bill length

Make a better plot than before

Use a figure from a recent analysis or publication that you would like to reproduce or enhance. Use tidy data principles to set yourself up for success.

If you cannot think of anything, here is an example.

The following code uses the survival package to estimate survival curves in two treatment groups.

library(survival)

sfit <- survfit(Surv(time, status) ~ x, data = aml)
sfit
Call: survfit(formula = Surv(time, status) ~ x, data = aml)

                 n events median 0.95LCL 0.95UCL
x=Maintained    11      7     31      18      NA
x=Nonmaintained 12     11     23       8      NA
str(sfit)
List of 17
 $ n        : int [1:2] 11 12
 $ time     : num [1:20] 9 13 18 23 28 31 34 45 48 161 ...
 $ n.risk   : num [1:20] 11 10 8 7 6 5 4 3 2 1 ...
 $ n.event  : num [1:20] 1 1 1 1 0 1 1 0 1 0 ...
 $ n.censor : num [1:20] 0 1 0 0 1 0 0 1 0 1 ...
 $ surv     : num [1:20] 0.909 0.818 0.716 0.614 0.614 ...
 $ std.err  : num [1:20] 0.0953 0.1421 0.1951 0.2487 0.2487 ...
 $ cumhaz   : num [1:20] 0.0909 0.1909 0.3159 0.4588 0.4588 ...
 $ std.chaz : num [1:20] 0.0909 0.1351 0.1841 0.233 0.233 ...
 $ strata   : Named int [1:2] 10 10
  ..- attr(*, "names")= chr [1:2] "x=Maintained" "x=Nonmaintained"
 $ type     : chr "right"
 $ logse    : logi TRUE
 $ conf.int : num 0.95
 $ conf.type: chr "log"
 $ lower    : num [1:20] 0.754 0.619 0.488 0.377 0.377 ...
 $ upper    : num [1:20] 1 1 1 0.999 0.999 ...
 $ call     : language survfit(formula = Surv(time, status) ~ x, data = aml)
 - attr(*, "class")= chr "survfit"

How would you plot the Kaplan-Meier curves in the two treatment groups using ggplot2? What about adding confidence intervals to the plot? What about adding tick marks where the censoring times are?

Complex figures with base graphics

Check out this violin plot

ggplot(penguins, aes(x = species, y = body_mass_g)) + geom_violin() + geom_jitter()
Warning: Removed 2 rows containing non-finite values (`stat_ydensity()`).
Warning: Removed 2 rows containing missing values (`geom_point()`).

How would you produce a similar plot using base graphics? After doing so, reflect, do you appreciate to convenience and flexibility of ggplot2?

Hints

Use the density function to calculate the densities by species. The results are stored in a list with elements x and y. Then transform by rotating 90 degrees and mirroring. Plot the result using the polygon function.

dens_by_species <- split(penguins$body_mass_g, penguins$species) |> 
  lapply(density, na.rm = TRUE, cut = 0)

plot(body_mass_g ~ jitter(as.numeric(as.factor(species))), 
     data = penguins)

## rotate, scale, and mirror the densities, then add with polygon