Last updated: 2022-04-18
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The #30DayChartChallenge is running once again over on Twitter with lots of inspirational data visualization content. One of the pros to follow is Tanya Shapiro. She poses a question here that prompted my short post today.
I think ggiraph is my new favorite #RStats library. Easy way to make your ggplots interactive. "Interactified" last week's pictogram of Supreme Court Justices. 🧑⚖️
— Tanya Shapiro (@tanya_shapiro) April 15, 2022
Anyone have any a work-around for creating tooltip hovers for geom_image?https://t.co/6JdRTFItBN
I had attempted to work on her challenge with the new Quarto document
framework, github pages, and the workflowr site management
system. More on what I discovered at the end. First, let’s load the open
source packages:
suppressPackageStartupMessages({
library(rvest)
library(tidyverse)
library(ggimage)
library(ggiraph)
})Her graphic depicts seated justices through the history of the United States Supreme Court. The visual includes Justice Ketanji Brown Jackson, who was recently confirmed on April 7th, 2022. Once sworn in, Jackson will be the 116th justice and the first Black woman to sit on the Supreme Court.
Emojis are used to denote gender and race of each justice. Colored points are based on nominating president party affiliation (e.g. red for Republican).
Let’s make a link to the data source at wikipedia:
url <- 'https://en.wikipedia.org/wiki/List_of_justices_of_the_Supreme_Court_of_the_United_States'Scraping the page html table from the wikipedia page is easy with
rvest:
sc_justices_raw <- url |>
read_html() |>
html_elements(".wikitable") |>
html_table(trim = TRUE, fill = TRUE) |>
pluck(1)
names(sc_justices_raw) <-
c(
"index",
"blank",
"justice",
"state",
"position",
"succeeded",
"confirmed",
"tenured",
"length",
"nominated_by"
)
sc_justices_raw <- sc_justices_raw |>
mutate(id = row_number()) |>
head(-1) # remove the last row of the dataframeWe need to clean the dataset a little to parse the dates and categorize justices.
justices <- sc_justices_raw |>
#some justices were promoted to chief justice later, get distinct justices
distinct(justice,
.keep_all = TRUE) |>
select(-index, -blank) |>
separate(justice, into = c("name", "born_died"), sep = "\\(") |>
mutate(
index = row_number(),
born_died = str_replace(born_died, "\\)", ""),
position = str_replace(position, "\\s*\\[[^\\)]+\\]", ""),
position = case_when(
position == "ChiefJustice" ~ "Chief Justice",
position == "AssociateJustice" ~ "Associate Justice"
),
confirmed = str_replace(confirmed, "\\([^()]*\\)", ""),
confirmed = str_replace(confirmed, "\\s*\\[[^\\)]+\\]", ""),
confirmed = as.Date(confirmed, '%B %d, %Y'),
demo = case_when(
name %in% c('Thurgood Marshall', 'Clarence Thomas') ~ 'black_male',
name %in% c(
"Sandra Day O'Connor",
"Ruth Bader Ginsburg",
"Elena Kagan",
"Amy Coney Barrett"
) ~ 'white_female',
name == 'Sonia Sotomayor' ~ 'hispanic_female',
TRUE ~ 'white_male'
)
) Similarly, getting a list of presidents with party affiliation is
easy with rvest and wikipedia:
pres_url <- 'https://en.wikipedia.org/wiki/List_of_presidents_of_the_United_States'
#scrape president data
pres_raw <- pres_url |>
read_html() |>
html_elements(".wikitable") |>
html_table(trim = TRUE, fill = TRUE) |>
pluck(1) |>
head(-1)
names(pres_raw) <-
c(
"index",
"portrait",
"name_birth_death",
"term",
"party_misc",
"party",
"election",
"vp"
)As above, the presidents data needs a little bit of cleaning for party affiliation.
presidents <- pres_raw |>
select(name_birth_death, party) |>
separate(name_birth_death, into = c("name","birth_death"), sep = "\\(") |>
mutate(party = str_replace(party, "\\s*\\[[^\\)]+\\]",""),
name = str_replace(name, "\\s*\\[[^\\)]+\\]","")
) |>
distinct(name,party) |>
mutate(index = row_number()) |>
distinct(index,
.keep_all = TRUE) |>
select(-index)A this point, we join presidents to justices and add Justice Jackson.
justices <- justices |>
left_join(presidents, by = c("nominated_by" = "name")) |>
bind_rows(
data.frame(
name = "Ketanji Brown Jackson",
born_died = NA,
state = NA,
position = "Associate Justice",
succeeded = "Breyer",
confirmed = as.Date('2022-04-07'),
tenured = NA,
length = NA,
nominated_by = "Joe Biden",
id = NA,
index = 116,
demo = "black_female",
party = "Democratic"
)
)To build the plot, position variables will be needed to give each justice a tile on a grid.
justices <- justices |>
mutate(y = (index - 1) %/% 10, # truncated division wihtout remainder
x = (index - 1) %% 10) # the remainderCredit for the trick to get png images for emojis goes to Emil Hvitfeldt, here
white_male_icon <- 'https://emojipedia-us.s3.dualstack.us-west-1.amazonaws.com/thumbs/120/apple/325/man-judge-light-skin-tone_1f468-1f3fb-200d-2696-fe0f.png'
black_male_icon <- 'https://emojipedia-us.s3.dualstack.us-west-1.amazonaws.com/thumbs/120/apple/325/man-judge-medium-dark-skin-tone_1f468-1f3fe-200d-2696-fe0f.png'
white_female_icon_2 <- 'https://emojipedia-us.s3.dualstack.us-west-1.amazonaws.com/thumbs/120/apple/325/woman-judge-medium-light-skin-tone_1f469-1f3fc-200d-2696-fe0f.png'
white_female_icon <- 'https://emojipedia-us.s3.dualstack.us-west-1.amazonaws.com/thumbs/120/apple/325/woman-judge-light-skin-tone_1f469-1f3fb-200d-2696-fe0f.png'
hispanic_female_icon <- 'https://emojipedia-us.s3.dualstack.us-west-1.amazonaws.com/thumbs/120/apple/325/woman-judge-medium-skin-tone_1f469-1f3fd-200d-2696-fe0f.png'
black_female_icon <- 'https://emojipedia-us.s3.dualstack.us-west-1.amazonaws.com/thumbs/120/apple/325/woman-judge-medium-dark-skin-tone_1f469-1f3fe-200d-2696-fe0f.png'Last, lets create the new variable in the justices data frame to match up with the icon html information.
justices <- justices |>
mutate(
icon = case_when(
name == "Sandra Day O'Connor" ~ white_female_icon_2,
demo == "white_male" ~ white_male_icon,
demo == "white_female" ~ white_female_icon,
demo == "black_male" ~ black_male_icon,
demo == "black_female" ~ black_female_icon,
demo == "hispanic_female" ~ hispanic_female_icon
),
party_group = as_factor(case_when(
party %in% c(
"Federalist",
"Whig",
"Unaffiliated",
"National Union",
"National Republican"
) ~ "Other",
TRUE ~ party
)),
party_group = fct_relevel(party_group,
c("Democratic-Republican",
"Democratic",
"Republican",
"Other"))
) Building out the ggplot is simple. Shapiro uses a Google font in her
work that I have not downloaded. I’ve added a
geom_point_interactive() tooltip from the
ggiraph package.
gg <- ggplot(data = justices,
mapping = aes(x = x + 1, y = y + 1)) +
geom_point(size = 12, aes(color = party_group), alpha = 0.9) +
scale_color_manual(
values = c("#EBB027", "#266DD3", "#CC3333", "grey"),
guide = guide_legend(
title.position = "top",
title.hjust = 0.5,
override.aes = list(size = 3)
)
) +
geom_image(aes(image = icon)) +
geom_text(aes(label = index), vjust = 4.8, size = 2) +
geom_point_interactive(aes(tooltip = glue::glue(
"{ name } \n { tenured } \n Nominated by { nominated_by }")
),
alpha = 0.002) +
scale_y_reverse(limits = c(12.5, 1)) +
labs(
title = "United States Supreme Court Justices",
subtitle = "Emoji Portraits of all 116 Justices",
color = "NOMINATING PRESIDENT PARTY AFFILIATION",
caption = "Data from Wikipedia | Original Chart by @tanya_shapiro"
) +
theme(
legend.position = "top",
legend.title = element_text(size = 8),
legend.key = element_rect(fill = NA),
plot.margin = margin(
r = 20,
l = 20,
t = 10,
b = 5
),
plot.title = element_text(hjust = 0.5, face = "bold"),
plot.subtitle = element_text(hjust = 0.5),
plot.background = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_blank()
)
girafe(ggobj = gg,
width_svg = 6,
height_svg = 8)Thank you to
workflowr, who have more work to
do make wflow_publish() work with quarto.
sessionInfo()R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22000)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggiraph_0.8.2 ggimage_0.3.0 forcats_0.5.1 stringr_1.4.0
[5] dplyr_1.0.8 purrr_0.3.4 readr_2.1.2 tidyr_1.2.0
[9] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1 rvest_1.0.2
[13] workflowr_1.7.0
loaded via a namespace (and not attached):
[1] httr_1.4.2 sass_0.4.1 jsonlite_1.8.0 modelr_0.1.8
[5] bslib_0.3.1 assertthat_0.2.1 getPass_0.2-2 yulab.utils_0.0.4
[9] selectr_0.4-2 cellranger_1.1.0 yaml_2.3.5 pillar_1.7.0
[13] backports_1.4.1 glue_1.6.2 uuid_1.0-4 digest_0.6.29
[17] promises_1.2.0.1 colorspace_2.0-3 ggfun_0.0.6 htmltools_0.5.2
[21] httpuv_1.6.5 pkgconfig_2.0.3 tweetrmd_0.0.9 broom_0.8.0
[25] haven_2.4.3 magick_2.7.3 scales_1.2.0 processx_3.5.3
[29] ggplotify_0.1.0 whisker_0.4 later_1.3.0 tzdb_0.3.0
[33] git2r_0.30.1 farver_2.1.0 generics_0.1.2 ellipsis_0.3.2
[37] withr_2.5.0 cli_3.2.0 magrittr_2.0.3 crayon_1.5.1
[41] readxl_1.4.0 evaluate_0.15 ps_1.6.0 fs_1.5.2
[45] fansi_1.0.3 xml2_1.3.3 tools_4.1.2 hms_1.1.1
[49] lifecycle_1.0.1 munsell_0.5.0 reprex_2.0.1 callr_3.7.0
[53] compiler_4.1.2 jquerylib_0.1.4 systemfonts_1.0.4 gridGraphics_0.5-1
[57] rlang_1.0.2 grid_4.1.2 rstudioapi_0.13 htmlwidgets_1.5.4
[61] labeling_0.4.2 rmarkdown_2.13 gtable_0.3.0 DBI_1.1.2
[65] curl_4.3.2 R6_2.5.1 lubridate_1.8.0 knitr_1.38
[69] fastmap_1.1.0 utf8_1.2.2 rprojroot_2.0.3 stringi_1.7.6
[73] Rcpp_1.0.8.3 vctrs_0.4.0 dbplyr_2.1.1 tidyselect_1.1.2
[77] xfun_0.30