This post has lots in common with previous posts on “the layered presentation of graphics”. It is about building up plots, but now with a focus on this incremental change for teaching ggplot2. The rational is that observing the cause and effect of incremental change is easier to digest, and that the repetition in this approach means students have more chances to learn the ggplot2 functions.

My recent tweet presented the technique:

Here, building up a #ggplot2 as slowly as possible, #rstats. Incremental adjustments. #rstatsteachingideas pic.twitter.com/nUulQl8bPh

— Gina Reynolds (@EvaMaeRey) August 13, 2018

People reacted positively. Now here is the comparison between a traditional ggplot construction and the more verbose, slow ggplot construction.

library(tidyverse)
library(gapminder)
df_2007 <- gapminder %>% filter(year == 2007)

Traditional approach:

ggplot(data = df_2007, 
       mapping = aes(x = gdpPercap, 
                     y = lifeExp, 
                     col = continent)) +
  geom_point() +
  labs(title = "Wealth and life expectancy in 2007",
       x = "GDP per capita (inflation adjusted)",
       y = "Life Expectancy",
       col = "") +
  theme_bw()

Verbose, slow approach:

ggplot(data = df_2007) +
  aes(x = gdpPercap) + 
  aes(y = lifeExp) +
  geom_point() +
  aes(col = continent) +
  labs(title = "Wealth and life expectancy in 2007") +
  labs(x = "GDP per capita (inflation adjusted)") +
  labs(y = "Life Expectancy") +
  labs(col = "") +
  theme_bw()

Same result, but students are perhaps more likely to go slow, and mentally make connections between adjustment to product and the functions for making those adjustments, given that each change gets its own line of code and that there is repetition built into this exercise. After using this approach to introduce students to ggplot, students can be alerted to the fact that they can gather up arguments into one function.