library(tidyverse)
library(palmerpenguins)
library(waffle)
library(viridis)
# fix seed value for reproducibility
set.seed(123)
theme_set(theme_void())AE 05: Waffle charts for visualizing proportions
Suggested answers
Application exercise
Answers
Important
These are suggested answers. This document should be used as reference only, it’s not designed to be an exhaustive key.
Waffle charts
{waffle} provides a {ggplot2} implementation of waffle plots. The typical workflow consists of preparing the data by tabulating in advance and then plotting it with {ggplot2} and geom_waffle().
Basic waffle chart
Demonstration: Prepare the penguins data frame to visualize the number of penguins by species.
penguins |>
count(species)# A tibble: 3 × 2
species n
<fct> <int>
1 Adelie 152
2 Chinstrap 68
3 Gentoo 124Demonstration: Use the prepared data to draw a basic color-coded waffle chart
penguins |>
count(species) |>
ggplot(mapping = aes(fill = species, values = n)) +
geom_waffle() +
labs(
title = "Penguins by species",
x = NULL, y = NULL, fill = NULL
)
Improve the waffle chart
Your turn: Adjust the waffle chart to use a fixed aspect ratio so the symbols are squares. Rotate the chart so the squares are stacked vertically.
penguins |>
count(species) |>
ggplot(mapping = aes(fill = species, values = n)) +
geom_waffle(
n_rows = 20,
size = 1,
color = "white",
flip = TRUE
) +
labs(
title = "Penguins by species",
x = NULL, y = NULL, fill = NULL
) +
coord_equal()
Demonstration: {waffle} will draw all observations on the chart. For larger datasets, this is problematic. Instead, we might want to visualize the proportion of observations in each category. Use geom_waffle() to represent the data as proportions instead.
penguins |>
count(species) |>
ggplot(mapping = aes(fill = species, values = n)) +
geom_waffle(
size = 1,
color = "white",
make_proportional = TRUE
) +
labs(
title = "Penguins by species",
x = NULL, y = NULL, fill = NULL
) +
coord_equal()
Your turn: Adjust the waffle chart to use a better color palette and move the legend to the top.
penguins |>
count(species) |>
ggplot(mapping = aes(fill = species, values = n)) +
geom_waffle(
size = 1,
color = "white",
make_proportional = TRUE
) +
scale_fill_viridis_d(end = 0.8) +
labs(
title = "Penguins by species",
x = NULL, y = NULL, fill = NULL
) +
coord_equal() +
theme(legend.position = "top")
Bonus: Adjust the waffle chart to symbolize each penguin using a penguin symbol.
penguins |>
# sample 100 rows since we cannot calculate proportions using this method
slice_sample(n = 100) |>
count(species) |>
# use waffle() instead of ggplot()
waffle(
rows = 10,
# get the linux logo for a penguin
use_glyph = "linux",
glyph_font_family = "FontAwesome5Brands-Regular"
) +
labs(
title = "Penguins by species",
x = NULL, y = NULL, fill = NULL
) +
theme(legend.position = "top")
Session information
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.4.2 (2024-10-31)
os macOS Sonoma 14.6.1
system aarch64, darwin20
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2025-02-07
pandoc 3.4 @ /usr/local/bin/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
cli 3.6.3 2024-06-21 [1] CRAN (R 4.4.0)
curl 6.2.0 2025-01-23 [1] CRAN (R 4.4.1)
dichromat 2.0-0.1 2022-05-02 [1] CRAN (R 4.3.0)
digest 0.6.37 2024-08-19 [1] CRAN (R 4.4.1)
dplyr * 1.1.4 2023-11-17 [1] CRAN (R 4.3.1)
DT 0.33 2024-04-04 [1] CRAN (R 4.4.0)
evaluate 1.0.3 2025-01-10 [1] CRAN (R 4.4.1)
extrafont 0.19 2023-01-18 [1] CRAN (R 4.3.0)
extrafontdb 1.0 2012-06-11 [1] CRAN (R 4.3.0)
farver 2.1.2 2024-05-13 [1] CRAN (R 4.3.3)
fastmap 1.2.0 2024-05-15 [1] CRAN (R 4.4.0)
forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.3.0)
generics 0.1.3 2022-07-05 [1] CRAN (R 4.3.0)
ggplot2 * 3.5.1 2024-04-23 [1] CRAN (R 4.3.1)
glue 1.8.0 2024-09-30 [1] CRAN (R 4.4.1)
gridExtra 2.3 2017-09-09 [1] CRAN (R 4.3.0)
gtable 0.3.6 2024-10-25 [1] CRAN (R 4.4.1)
here 1.0.1 2020-12-13 [1] CRAN (R 4.3.0)
hms 1.1.3 2023-03-21 [1] CRAN (R 4.3.0)
htmltools 0.5.8.1 2024-04-04 [1] CRAN (R 4.3.1)
htmlwidgets 1.6.4 2023-12-06 [1] CRAN (R 4.3.1)
jsonlite 1.8.9 2024-09-20 [1] CRAN (R 4.4.1)
knitr 1.49 2024-11-08 [1] CRAN (R 4.4.1)
labeling 0.4.3 2023-08-29 [1] CRAN (R 4.3.0)
lifecycle 1.0.4 2023-11-07 [1] CRAN (R 4.3.1)
lubridate * 1.9.3 2023-09-27 [1] CRAN (R 4.3.1)
magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.3.0)
palmerpenguins * 0.1.1 2022-08-15 [1] CRAN (R 4.3.0)
pillar 1.10.1 2025-01-07 [1] CRAN (R 4.4.1)
pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.3.0)
plyr 1.8.9 2023-10-02 [1] CRAN (R 4.3.1)
purrr * 1.0.2 2023-08-10 [1] CRAN (R 4.3.0)
R6 2.5.1 2021-08-19 [1] CRAN (R 4.3.0)
RColorBrewer 1.1-3 2022-04-03 [1] CRAN (R 4.3.0)
Rcpp 1.0.14 2025-01-12 [1] CRAN (R 4.4.1)
readr * 2.1.5 2024-01-10 [1] CRAN (R 4.3.1)
rlang 1.1.5 2025-01-17 [1] CRAN (R 4.4.1)
rmarkdown 2.29 2024-11-04 [1] CRAN (R 4.4.1)
rprojroot 2.0.4 2023-11-05 [1] CRAN (R 4.3.1)
rstudioapi 0.17.0 2024-10-16 [1] CRAN (R 4.4.1)
Rttf2pt1 1.3.12 2023-01-22 [1] CRAN (R 4.3.0)
scales 1.3.0.9000 2024-11-14 [1] Github (r-lib/scales@ee03582)
sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.3.0)
stringi 1.8.4 2024-05-06 [1] CRAN (R 4.3.1)
stringr * 1.5.1 2023-11-14 [1] CRAN (R 4.3.1)
tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.3.0)
tidyr * 1.3.1 2024-01-24 [1] CRAN (R 4.3.1)
tidyselect 1.2.1 2024-03-11 [1] CRAN (R 4.3.1)
tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.3.0)
timechange 0.3.0 2024-01-18 [1] CRAN (R 4.3.1)
tzdb 0.4.0 2023-05-12 [1] CRAN (R 4.3.0)
utf8 1.2.4 2023-10-22 [1] CRAN (R 4.3.1)
vctrs 0.6.5 2023-12-01 [1] CRAN (R 4.3.1)
viridis * 0.6.5 2024-01-29 [1] CRAN (R 4.4.0)
viridisLite * 0.4.2 2023-05-02 [1] CRAN (R 4.3.0)
waffle * 1.0.2 2025-02-06 [1] Github (hrbrmstr/waffle@767875b)
withr 3.0.2 2024-10-28 [1] CRAN (R 4.4.1)
xfun 0.50.5 2025-01-15 [1] https://yihui.r-universe.dev (R 4.4.2)
yaml 2.3.10 2024-07-26 [1] CRAN (R 4.4.0)
[1] /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/library
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