13  Cleveland dot plots


This graph requires:

✅ a numeric (continuous) variable

✅ at least one categorical variable (with two levels)

13.1 Description

Cleveland dot plots compare numbers with dots on a line and are more efficient than bar graphs. The graph lists the categories on the side and shows the data with dots along a line.

Typically, the graph contains two points representing the numerical value on the y axis, differentiated by color. A line connecting the two points represents the difference between the two categorical levels (the width of the line is the size of the difference).

13.2 Set up

PACKAGES:

Install packages.

show/hide 
install.packages("palmerpenguins")
library(palmerpenguins)
library(ggplot2)

DATA:

Artwork by Allison Horst

Remove missing values from sex and flipper_length_mm and group on sex and island to the calculate the median flipper length (med_flip_length_mm).

show/hide 
peng_clev_dots <- palmerpenguins::penguins |>
    dplyr::filter(!is.na(sex) & !is.na(flipper_length_mm)) |>
    dplyr::group_by(sex, island) |>
    dplyr::summarise(
        med_flip_length_mm = median(flipper_length_mm)
        ) |>
    dplyr::ungroup()
#> `summarise()` has grouped output by 'sex'. You
#> can override using the `.groups` argument.
glimpse(peng_clev_dots)
#> Rows: 6
#> Columns: 3
#> $ sex                <fct> female, female, femal…
#> $ island             <fct> Biscoe, Dream, Torger…
#> $ med_flip_length_mm <dbl> 210, 190, 189, 219, 1…

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13.3 Grammar

CODE:

  • Create labels with labs()

  • Initialize the graph with ggplot() and provide data

  • Map the med_flip_length_mm to the x axis, and island to the y axis, but wrap island in forcats::fct_rev().

  • Add geom_line(), and map island to the group aesthetic. Set the linewidth to 0.75

  • Add geom_point() and map sex to color aesthetic. Set the size to 2.25

show/hide 
labs_clev_dots <- labs(
    title = "Flipper Length Differences",
    subtitle = "Male and female penguins",
    x = "Median Flipper Length",
    y = "Island",
    color = "Sex")

ggp2_clev_dots <- ggplot(data = peng_clev_dots,
                mapping = aes(x = med_flip_length_mm, 
                              y = fct_rev(island))) +
                geom_line(aes(group = island), 
                    linewidth = 0.75) + 
                geom_point(aes(color = sex), 
                    size = 2.25) 

ggp2_clev_dots + 
    labs_clev_dots

GRAPH:

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13.4 More info

Cleveland dot plots are also helpful when comparing multiple differences on a common scale.

13.4.1 Common scale

SCALE:

  • Remove missing values from sex, bill_length_mm and bill_depth_mm, and group on sex and island to the calculate the median bill length and median bill depth. These variables need to have ‘showtime-ready’ names because they’ll be used in our facets.

  • After un-grouping the data, pivot the new columns into a long (tidy) format with median_measure containing the name of the variable, and median_value containing the numbers.

  • Finally, convert median_measure into a factor.

show/hide 
peng_clev_dots2 <- palmerpenguins::penguins |>
    dplyr::filter(!is.na(sex) & 
            !is.na(bill_length_mm) & 
                !is.na(bill_depth_mm)) |>
    dplyr::group_by(sex, island) |>
    dplyr::summarise(
        `Median Bill Length` = median(bill_length_mm),
        `Median Bill Depth` = median(bill_depth_mm)) |>
    dplyr::ungroup() |> 
    tidyr::pivot_longer(cols = starts_with("Med"), 
        names_to = "median_measure", 
        values_to = "median_value") |> 
    dplyr::mutate(median_measure = factor(median_measure))
#> `summarise()` has grouped output by 'sex'. You
#> can override using the `.groups` argument.
glimpse(peng_clev_dots2)
#> Rows: 12
#> Columns: 4
#> $ sex            <fct> female, female, female, f…
#> $ island         <fct> Biscoe, Biscoe, Dream, Dr…
#> $ median_measure <fct> Median Bill Length, Media…
#> $ median_value   <dbl> 44.90, 14.50, 42.50, 17.8…

13.4.2 Scales

scales:

  • Re-create labels

  • Initialize the graph with ggplot() and provide data

  • Map the median_value to the x axis, and island to the y axis, but wrap island in forcats::fct_rev().

  • Add geom_line(), and map island to the group aesthetic. Set the linewidth to 0.75

  • Add geom_point() and map sex to color aesthetic. Set the size to 2.25

  • Add facet_wrap() and facet by median_measure, setting shrink to TRUE and scales to "free_x"

  • Move the legend with theme(legend.position = "top")

show/hide 
labs_clev_dots2 <- labs(
    title = "Penguin Measurements Differences",
    subtitle = "Male and female penguins",
    x = "Median Bill Length/Depth (mm)",
    y = "Island",
    color = "Sex")

ggp2_clev_dots2 <- ggplot(data = peng_clev_dots2,
    mapping = aes(x = median_value,
        y = fct_rev(island))) +
    geom_line(aes(group = island),
        linewidth = 0.55) +
    geom_point(aes(color = sex),
        size = 2) +
    facet_wrap(. ~ median_measure,
        shrink = TRUE, nrow = 2) +
    theme(legend.position = "top")

ggp2_clev_dots2 +
    labs_clev_dots2

CAUTION when using scales = "free_x": The graph below shows that the median bill length and depth is larger for male penguins on all three islands, but the magnitude of the differences should be interpreted with caution because the length of the lines can’t be directly compared!

show/hide 
labs_clev_dots2 <- labs(
    title = "Penguin Measurements Differences",
    subtitle = "Male and female penguins",
    x = "Median Bill Length/Depth (mm)",
    y = "Island",
    color = "Sex")

ggp2_clev_dots2_free_x <- ggplot(data = peng_clev_dots2,
    mapping = aes(x = median_value,
        y = fct_rev(island))) +
    geom_line(aes(group = island),
        linewidth = 0.55) +
    geom_point(aes(color = sex),
        size = 2) +
    facet_wrap(. ~ median_measure,
        shrink = TRUE, nrow = 2,
        scales = "free_x") +
    theme(legend.position = "top")

ggp2_clev_dots2_free_x +
    labs_clev_dots2