Creating dumbbell plots in R

rstats

ggplot2

Illustrating {ggplot2} code to produce dumbbell plots in R.

Published

January 10, 2023

Hi everyone,

Happy new year! I hope you had a relaxing holiday season and that 2023 is treating you well so far.

Well, here’s another treat for you: today we are going to make a dumbbell plot from scratch, using our dear old friend {ggplot2}. Something quick and easy to get going in 2023, but fun nonetheless - and hopefully, useful too. Let’s start by defining what a dumbbell plot actually is:

A dumbbell plot (also known as a dumbbell chart, or connected dot plot) is great for displaying changes between two points in time, two conditions, or differences between two groups.

Source: amcharts.com

You might have seen this before in one of the nice visualisations that the OECD publishes from time to time:

As you can see, this is an intuitive way of showing how a certain metric has changed between two points in time. Let’s get going then, shall we?

For this example, we will use data on monthly step counts that yours truly logged in 2021 and 2022. One of the things I wanted to do more of in 2022, compared to 2021, was walking; will I have succeeded with that? Well, we’ll find out soon.

I extracted this data from Garmin Connect, as I have been wearing a Garmin watch for the past few years now, and this is stored in a dataset named dt:

head(dt)

# A tibble: 6 × 3
  Month     X2021  X2022
  <fct>     <dbl>  <dbl>
1 January  114171 194624
2 February 118548 223310
3 March    105853 224946
4 April    172499 206213
5 May      158913 246563
6 June     166119 244314

A very simple dataset, not much to see here. Let’s start building our plot: first, we create a ggplot object and put the different months on the vertical axis:

library(ggplot2)

db_plot <- ggplot(dt, aes(y = Month))
db_plot

Not much to see yet. Then, we add a set of points for 2021 data:

db_plot <- db_plot +
  geom_point(aes(x = X2021, color = "2021"))
db_plot

Same as before, but we add data for 2022:

db_plot <- db_plot +
  geom_point(aes(x = X2022, color = "2022"))
db_plot

It’s coming together nicely, isn’t it? Now, we add a segment to join the two sets of points:

db_plot <- db_plot +
  geom_segment(aes(yend = Month, x = X2021, xend = X2022))
db_plot

And there you have it. Thank you for reading and… wait! We are not done here, of course - now we need to turn this into a nice plot.

One problem here is that the segment overlaps the data points: that looks ugly. To solve this, we need to rebuild the plot but add the segment geometry first:

ggplot(dt, aes(y = Month)) +
  geom_segment(aes(yend = Month, x = X2021, xend = X2022)) +
  geom_point(aes(x = X2021, color = "2021")) +
  geom_point(aes(x = X2022, color = "2022"))

Already better! Then, let’s make the data points larger and change the colour of the bar to grey:

ggplot(dt, aes(y = Month)) +
  geom_segment(aes(yend = Month, x = X2021, xend = X2022), color = "grey50") +
  geom_point(aes(x = X2021, color = "2021"), size = 3) +
  geom_point(aes(x = X2022, color = "2022"), size = 3)

Let’s add a better scale for the horizontal axis; for this, we use the comma() function from the {scales} package:

library(scales)

ggplot(dt, aes(y = Month)) +
  geom_segment(aes(yend = Month, x = X2021, xend = X2022), color = "grey50") +
  geom_point(aes(x = X2021, color = "2021"), size = 3) +
  geom_point(aes(x = X2022, color = "2022"), size = 3) +
  scale_x_continuous(labels = comma)

Let’s label the plot correctly:

ggplot(dt, aes(y = Month)) +
  geom_segment(aes(yend = Month, x = X2021, xend = X2022), color = "grey50") +
  geom_point(aes(x = X2021, color = "2021"), size = 3) +
  geom_point(aes(x = X2022, color = "2022"), size = 3) +
  scale_x_continuous(labels = comma) +
  labs(x = "Steps", y = "", color = "Year")

Now, the final touches: let’s change the theme of the plot and tidy things up a little:

ggplot(dt, aes(y = Month)) +
  geom_segment(aes(yend = Month, x = X2021, xend = X2022), color = "grey50") +
  geom_point(aes(x = X2021, color = "2021"), size = 3) +
  geom_point(aes(x = X2022, color = "2022"), size = 3) +
  scale_x_continuous(labels = comma) +
  theme_bw(base_size = 12) +
  theme(legend.position = "bottom", plot.margin = unit(x = rep(1, 4), units = "lines")) +
  labs(x = "Steps", y = "", color = "Year", title = "Monthly Steps Walked, 2022 vs 2021")

We can also simplify the above by turning our data into long format:

library(tidyr)

dt_long <- pivot_longer(data = dt, cols = starts_with("X"))
dt_long$name <- factor(dt_long$name, levels = c("X2021", "X2022"), labels = c("2021", "2022"))
head(dt_long)

# A tibble: 6 × 3
  Month    name   value
  <fct>    <fct>  <dbl>
1 January  2021  114171
2 January  2022  194624
3 February 2021  118548
4 February 2022  223310
5 March    2021  105853
6 March    2022  224946

The required code is very similar to what was used above, but we can now easily modify the colour palette too, and improve the title using {ggtext}:

library(ggtext)

ggplot(dt, aes(y = Month)) +
  geom_segment(aes(yend = Month, x = X2021, xend = X2022), color = "grey50") +
  geom_point(data = dt_long, aes(x = value, color = name), size = 3) +
  scale_x_continuous(labels = comma) +
  scale_color_manual(values = c("#F5DF4D", "#6667AB")) +
  theme_bw(base_size = 12) +
  theme(
    legend.position = "none",
    plot.title = element_markdown(),
    plot.margin = unit(x = rep(1, 4), units = "lines")
  ) +
  labs(
    x = "Steps",
    y = "",
    color = "Year",
    title = "Monthly Steps Walked,
    <span style='color:#6667AB;'>2022</span>
    vs
    <span style='color:#F5DF4D;'>2021</span>"
  )

And yes, for all you colours nerds out there: the two hex codes are Pantone’s colours of the year for 2021 and 2022, “Illuminating” and “Very Peri”. Fitting, right?

And yes, I did walk more in 2022 compared to 2021, it turns out! Well, except for October, but to be fair, I did run 120 km that month in 2021 compared to a shameful 0 km in 2022, so it could have been worse…

So there you have it: a short tutorial on building a dumbbell plot from scratch using {ggplot2} and other freely available tools, and making it nice (subjectively, of course). Other options for making dumbbell plots in R do exist, of course, such as the {ggalt} package - make sure you check that out too. And until next time, take care!