Diverging Dot Plot and Lollipop Charts – Plotting Variance with ggplot2

Creating the Dot Plot Variance chart

The data preparation was used in the previous blog entitled: Diverging Bar Charts – Plotting Variance with ggplot2.

# 20240222 Added for qmd to run
library(ggplot2)
theme_set(theme_classic())
data("mtcars") # load data

mtcars$CarBrand <- rownames(mtcars) # Create new column for car brands and names

mtcars$mpg_z_score <- round((mtcars$mpg - mean(mtcars$mpg))/sd(mtcars$mpg), digits=2)

mtcars$mpg_type <- ifelse(mtcars$mpg_z_score <= 0, "below", "above")

mtcars <- mtcars[order(mtcars$mpg_z_score), ] #Ascending sort on Z Score
mtcars$CarBrand <- factor(mtcars$CarBrand, levels = mtcars$CarBrand)

Refer to that if you need to know how to create the data prior to this tutorial.

Setting up the Dot Plot Variance chart

library(ggplot2)
ggplot(mtcars, aes(x=CarBrand, y=mpg_z_score, label=mpg_z_score)) +
  geom_point(stat='identity', aes(col=mpg_type), size=6) +
  scale_color_manual(name="Mileage (deviation)",
                     labels = c("Above Average", "Below Average"),
                     values = c("above"="#00ba38", "below"="#0b8fd3")) +
  geom_text(color="white", size=2) +
  labs(title="Diverging Dot Plot (ggplot2)",
       subtitle="Z score showing Normalised mileage", caption="Produced by Gary Hutson") +
  ylim(-2.5, 2.5) +
  coord_flip()

This is very similar to the previous plot we created in the previous post, however there are a few differences. The main difference is that we use a geom_point() geometry and set the colour of the points based on whether the said point deviates above and below the average. In addition, we use the geom_text() to set the colour of the text in the points to white and specify the size of the text. The final difference is that I have added a Y limit (ylim) range of -2.5 standard deviation to positive 2.5 standard deviations.

Running this block of code, along with the data preparation code, will give you a chart that looks as below:

Creating the Diverging Lollipop Chart

The code below shows how to build the diverging lollipop chart in R and ggplot2:

ggplot(mtcars, aes(x=CarBrand, y=mpg_z_score, label=mpg_z_score)) +
  geom_point(stat='identity', aes(col=mpg_type), size=6) +
  scale_color_manual(name="Mileage (deviation)",
                     labels = c("Above Average", "Below Average"),
                     values = c("above"="#00ba38", "below"="#0b8fd3")) +
  geom_segment(aes(y = 0,
                   x = CarBrand,
                   yend = mpg_z_score,
                   xend = CarBrand),
               color = "black") +
  geom_text(color="white", size=2) +
  labs(title="Diverging Lollipop Chart",
       subtitle="Z score for normalised mileage",
       caption="Produced by Gary Hutson") +
  ylim(-2.5, 2.5) + coord_flip() + theme(panel.grid.major = element_blank(), panel.grid.minor =
  element_blank())

Similar geometries are used here. What has been added here is the geom_segment() this shows how the line segments need to be added. The starting y is equal to 0 on the Y scale and the starting x is the first car by the car brand. Similarly, the end of the x (xend) is also the CarBrand.

The only other difference is to add a theme constraint to the end of the code to turn off the major and minor grid lines, this is achieved by setting the panel.grid.major and panel.grid.minor equal to element_blank().

The completed graph and plot is shown below:

There – we now have some lovely looking charts that can be put into a report to report on variance between categorical variables.

This blog was written by Gary Hutson, Principal Analyst, Activity & Access Team, Information & Insight at Nottingham University Hospitals NHS Trust, and was originally posted at Hutsons-Hacks.