KEY - alternative chart types

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Your Name

Published

March 4, 2026

Note

This key accompanies the alternative chart types practice slides. Please be sure to cross-reference the slides, which contain important information and additional context!

Waffle chart (Sasquatch sightings)

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                    setup                                 ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

#..........................load packages.........................
library(tidyverse)
library(waffle)
library(showtext)

#..........................import data...........................
bigfoot <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-09-13/bigfoot.csv')

#..........................import fonts..........................
font_add_google(name = "Ultra", family = "ultra")
font_add_google(name = "Josefin Sans", family = "josefin")

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                wrangle data                              ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

ca_season_counts <- bigfoot |> 
  filter(state == "California") |> 
  group_by(season) |> 
  count(season) |> 
  ungroup() |> 
  filter(season != "Unknown") |> 
  mutate(season = fct_relevel(season, "Spring", "Summer", "Fall", "Winter")) |> # set factor levels for legend
  arrange(season, c("Spring", "Summer", "Fall", "Winter")) # order df rows; {waffle} fills color based on the order that values appear in df

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                waffle chart                              ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

#........................create palettes.........................
season_palette <- c("Spring" = "#357266", 
                    "Summer" = "#FFB813", 
                    "Fall" = "#983A06", 
                    "Winter" = "#005F71")

plot_palette <- c(gray = "#757473",
                  beige = "#EFEFEF")

#.......................create plot labels.......................
title <- "Summer is the season of Bigfoot sightings in CA"
subtitle <- "Only 10% of Sasquatch sightings occur in winter"
caption <- "Source: Bigfoot Field Researchers Organization"

#......................create waffle chart.......................
sasquatch_plot <- ggplot(ca_season_counts, aes(fill = season, values = n)) +
  geom_waffle(color = "white", size = 0.3, n_rows = 10,
              make_proportional = TRUE) +
  coord_fixed() +
  scale_fill_manual(values = season_palette) +
  labs(title = title,
       subtitle = subtitle,
       caption = caption) +
  theme_void(base_size = 17) +
  theme(
    plot.title = element_text(family = "ultra", 
                              size = rel(1), 
                              hjust = 0.5,
                              margin = margin(t = 0, r = 0, b = 0.3, l = 0, "cm")),
    plot.subtitle = element_text(family = "josefin",
                                 size = rel(0.9),
                                 hjust = 0.5,
                                 margin = margin(t = 0, r = 0, b = 0.5, l = 0, "cm")),
    plot.caption = element_text(family = "josefin",
                                size = 10,
                                color = plot_palette["gray"], 
                                hjust = 0,
                                margin = margin(t = 0.75, r = 0, b = 0, l = 0, "cm")),
    legend.position = "bottom",
    legend.title = element_blank(),
    legend.text = element_text(family = "josefin",
                               size = 12),
    plot.background = element_rect(fill = plot_palette["beige"], 
                                   color = plot_palette["beige"]),
    plot.margin = margin(t = 1, r = 8, b = 1, l = 8, "cm")
  )

#................enable {showtext} for rendering.................
showtext_auto(enable = TRUE)

#...........................print plot...........................
sasquatch_plot

#..................turn off {showtext} rendering.................
showtext_auto(enable = FALSE)

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                save as png                               ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# #..............enable {showtext} for newly opened GD.............
# showtext_auto(enable = TRUE)

# #...................set resolution to match GD...................
# showtext_opts(dpi = 300)

# #..............write plot to file (aka save as png)..............
# ggsave(
#   filename = here::here( "week9", "images", "sasquatch.png"),
#   plot = ,
#   device = "png",
#   width = 9, 
#   height = 6,
#   unit = "in",
#   dpi = 300
# )

# #...............turn off {showtext} text rendering...............
# showtext_auto(enable = FALSE)

Bump chart (highest paying occupations)

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                    setup                                 ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

#..........................load packages.........................
library(tidyverse)
library(ggbump)
library(ggtext)
library(showtext)

#..........................import data...........................
jobs <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-03-05/jobs_gender.csv")

#..........................import fonts..........................
font_add_google(name = "Passion One", family = "passion")
font_add_google(name = "Oxygen", family = "oxygen")

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                wrangle data                              ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

#...................rank occupations by salary...................
salary_rank_by_year <- jobs |> 
  select(year, occupation, total_earnings) |> 
  group_by(year) |> 
  mutate(
    rank = row_number(desc(total_earnings))
  ) |> 
  ungroup() |> 
  arrange(rank, year)

#........get top 8 occupation names for final year (2016)........
top2016 <- salary_rank_by_year |>  
  filter(year == 2016, rank <= 8) |>  
  pull(occupation) 

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                 bump chart                               ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# grab magma palette ----
magma_pal <- viridisLite::magma(12)

# view magma colors ----
# monochromeR::view_palette(magma_pal)

# assign magma colors to top 8 occupations ----
occupation_colors <- c(
  "Physicians and surgeons" = magma_pal[3],
  "Nurse anesthetists" = magma_pal[4],
  "Dentists" = magma_pal[5],
  "Architectural and engineering managers" = magma_pal[6],
  "Lawyers" = magma_pal[7], 
  "Podiatrists" = magma_pal[8],
  "Chief executives" = magma_pal[9],
  "Petroleum engineers" = magma_pal[10]
)

# create palette for additional plot theming ----
plot_palette <- c(dark_purple = "#2A114E", 
                  dark_gray = "#6D6B71",
                  light_pink = "#FFF8F4")

#.......................create plot labels.......................
title <- "Top eight highest paying occupations in 2016"
subtitle <- "Medical professionals held onto the same top three spots across years, while <span style='color:#FEA873FF;'>**petroleum engineers**</span> steadily climbed the ranks from 10^th^ in 2013 to 4^th^ in 2016"
caption <- "Data Source: Bureau of Labor Statistics"

#........................create bump chart.......................
salary_rank <- salary_rank_by_year |> 
  filter(occupation %in% top2016) |> 
  ggplot(aes(x = year, y = rank, color = occupation)) + 
  geom_point(shape = "|", size = 6) + 
  geom_bump(linewidth = 1) +
  geom_text(
    data = salary_rank_by_year |>  filter(year == 2013, occupation %in% top2016),
    aes(label = occupation),
    hjust = 1,
    nudge_x = -0.1,
    family = "oxygen",
    fontface = "bold"
  ) +
  geom_text(
    data = salary_rank_by_year |> filter(year == 2016, occupation %in% top2016),
    aes(label = rank),
    hjust = 0,
    nudge_x = 0.1,
    size = 5,
    family = "oxygen",
    fontface = "bold"
  ) +
  annotate(
    geom = "text",
    x = c(2013, 2016),
    y = c(-0.2, -0.2),
    label = c("2013", "2016"),
    hjust = c(0, 1),
    vjust = 1,
    size = 5,
    family = "oxygen",
    fontface = "bold",
    color = plot_palette["dark_gray"],
  ) +
  scale_y_reverse() +
  scale_color_manual(values = occupation_colors) +
  coord_cartesian(xlim = c(2010, 2016), 
                  ylim = c(11, 0.25), 
                  clip = "off") +
  labs(title = title,
       subtitle = subtitle,
       caption = caption) +
  theme_void() +
  theme(
    legend.position = "none",
    plot.title = element_text(family = "passion",
                              size = 25,
                              color = plot_palette["dark_purple"],
                              margin = margin(t = 0, r = 0, b = 0.3, l = 0, "cm")),
    plot.subtitle = element_textbox_simple(family = "oxygen",
                                           size = 15,
                                           color = plot_palette["dark_gray"],
                                           margin = margin(t = 0, r = 0, b = 1, l = 0, "cm")),
    plot.caption = element_text(family = "oxygen",
                                color = plot_palette["dark_gray"],
                                margin = margin(t = 0.3, r = 0, b = 0, l = 0, "cm")),
    plot.background = element_rect(fill = plot_palette["light_pink"],
                                   color = plot_palette["light_pink"]),
    plot.margin = margin(t = 1, r = 1, b = 1, l = 1, "cm")
  )

#................enable {showtext} for rendering.................
showtext_auto(enable = TRUE)

#...........................print plot...........................
salary_rank

#...............turn off {showtext} text rendering...............
showtext_auto(enable = FALSE)

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                save as png                               ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# #..............enable {showtext} for newly opened GD.............
# showtext_auto(enable = TRUE)

# #...................set resolution to match GD...................
# showtext_opts(dpi = 300)

# #..............write plot to file (aka save as png)..............
# ggsave(
#   filename = here::here("week9", "images", "salary.png"), 
#   plot = ,
#   device = "png",
#   width = 9, 
#   height = 6,
#   unit = "in",
#   dpi = 300
# )

# #...............turn off {showtext} text rendering...............
# showtext_auto(enable = FALSE)

Stream graph (energy production)

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                    setup                                 ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

#..........................load packages.........................
library(tidyverse)
library(ggstream)
library(ggtext)
library(showtext)

#..........................import data...........................
capacity <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2022/2022-05-03/capacity.csv")

#..........................import fonts..........................
font_add_google(name = "Exo 2", family = "exo")
font_add_google(name = "Open Sans", family = "open")

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                wrangle data                              ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

#......................clean capacity data.......................
capacity_clean <- capacity |> 
  mutate(type = factor(type, levels = c("Solar", "Wind", "Storage", "Gas", "Nuclear", "Coal", "Other")))

#..........create df with text labels & their positions..........
text_data <- capacity_clean |> 
  filter(year == 2020) |> 
  mutate(year_val = year + 0.1,
         y = c(225, -125, -350, -500, -560, -610, -660))

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                stream graph                              ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

#.........................create palette.........................
pal <- paletteer::paletteer_d("MetBrewer::Archambault", n = 7)

#.......................construct subtitle.......................
subtitle <- glue::glue("In large part, due to increases in 
                      <span style='color:#88A0DCFF;'>**solar**</span>,
                      <span style='color:#381A61FF;'>**wind**</span>,
                      and 
                      <span style='color:#7C4B73FF;'>**storage**</span>
                      capacities")

#......................create stream graph.......................
energy_stream <- ggplot(capacity_clean, aes(x = year, y = total_gw, fill = type)) + 
  geom_stream(bw = 0.85,
              extra_span = 0.2,
              color = "white") +
  geom_text(data = text_data, 
            aes(x = year_val, y = y, 
                label = type, 
                color = type),
            family = "exo",
            size = 4,
            hjust = 0) +
  scale_fill_manual(values = pal) +
  scale_color_manual(values = pal) +
  labs(title = "Energy Capacity in the United States has Grown Steadily",
       subtitle = subtitle,
       caption = "Data: Berkeley Lab's Utility-Scale Solar, 2021 Edition") +
  coord_cartesian(clip = "off") +
  theme_minimal(base_size = 17, 
                base_family = "open") +
  theme(
    text = element_text(color = "#333333"),
    plot.title = element_text(family = "exo", 
                              size = rel(1), 
                              hjust = 0,
                              face = "bold"),
    plot.subtitle = ggtext::element_markdown(size = rel(0.8), 
                                             hjust = 0,
                                             margin = margin(t = 3, b = 3)),
    plot.caption = element_text(size = rel(0.6), 
                                hjust = 1,
                                margin = margin(t = 20, b = 5)),
    legend.position = "none",
    panel.grid = element_blank(),
    plot.background = element_rect(fill = "#FFF5E0", color = "#FFF5E0"),
    panel.background = element_rect(fill = "#FFF5E0", color = "#FFF5E0"),
    plot.margin = margin(t = 1, r = 2, b = 1, l = 1, "cm"),
    axis.title = element_blank(),
    axis.text.y = element_blank(),
    axis.text.x = element_text(family = "exo", 
                               size = rel(0.75),
                               color = "#333333")
  )

#................enable {showtext} for rendering.................
showtext_auto(enable = TRUE)

#...........................print plot...........................
energy_stream

#...............turn off {showtext} text rendering...............
showtext_auto(enable = FALSE)

##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
##                                save as png                               ----
##~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

# #..............enable {showtext} for newly opened GD.............
# showtext_auto(enable = TRUE)

# #...................set resolution to match GD...................
# showtext_opts(dpi = 300)

# #..............write plot to file (aka save as png)..............
# ggsave(
#   filename = here::here("week9", "images", "energy.png"), 
#   plot = ,
#   device = "png",
#   width = 9, 
#   height = 6,
#   unit = "in",
#   dpi = 300
# )

# #...............turn off {showtext} text rendering...............
# showtext_auto(enable = FALSE)