Quick Plot: TidyTuesday 2022 Week 2

Revisiting bee colony stress data
TidyTuesday
Data visualization
ggplot2
Author

Seth Kasowitz

Published

September 25, 2025

This is part of a short series of posts revisiting data visualization work on TidyTuesday data sets. The code and plot were initially created on January 11th, 2022. This bee colony collapse data originally comes from the USDA. See the TidyTuesday repo for more information.

1 Setup

1.1 Packages

library(tidyverse)
library(geofacet)
library(ggokabeito)

1.2 Data

colony <- readr::read_csv(
  'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-01-11/colony.csv'
)
stressor <- readr::read_csv(
  'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-01-11/stressor.csv'
)

2 Explore the data

Peak at the data as provided…

glimpse(colony)
Rows: 1,222
Columns: 10
$ year            <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, …
$ months          <chr> "January-March", "January-March", "January-March", "Ja…
$ state           <chr> "Alabama", "Arizona", "Arkansas", "California", "Color…
$ colony_n        <dbl> 7000, 35000, 13000, 1440000, 3500, 3900, 305000, 10400…
$ colony_max      <dbl> 7000, 35000, 14000, 1690000, 12500, 3900, 315000, 1050…
$ colony_lost     <dbl> 1800, 4600, 1500, 255000, 1500, 870, 42000, 14500, 380…
$ colony_lost_pct <dbl> 26, 13, 11, 15, 12, 22, 13, 14, 4, 4, 40, 22, 18, 23, …
$ colony_added    <dbl> 2800, 3400, 1200, 250000, 200, 290, 54000, 47000, 3400…
$ colony_reno     <dbl> 250, 2100, 90, 124000, 140, NA, 25000, 9500, 760, 8000…
$ colony_reno_pct <dbl> 4, 6, 1, 7, 1, NA, 8, 9, 7, 9, 4, 1, 2, 1, NA, 13, NA,…

…quick overview of the data types and completeness

skimr::skim(colony)
Data summary
Name colony
Number of rows 1222
Number of columns 10
_______________________
Column type frequency:
character 2
numeric 8
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
months 0 1 10 16 0 4 0
state 0 1 4 14 0 47 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
year 0 1.00 2017.77 1.89 2015 2016 2018 2019 2021 ▇▃▃▃▆
colony_n 47 0.96 123578.04 437835.18 1300 8000 17500 55500 3181180 ▇▁▁▁▁
colony_max 72 0.94 79112.77 190823.42 1700 9000 21000 68750 1710000 ▇▁▁▁▁
colony_lost 47 0.96 16551.32 60544.42 20 950 2200 6500 502350 ▇▁▁▁▁
colony_lost_pct 54 0.96 11.38 7.23 1 6 10 15 52 ▇▅▁▁▁
colony_added 83 0.93 17243.20 68167.65 10 420 1800 6500 736920 ▇▁▁▁▁
colony_reno 131 0.89 15278.86 62588.04 10 260 960 4585 806170 ▇▁▁▁▁
colony_reno_pct 260 0.79 9.10 9.66 1 2 6 12 77 ▇▁▁▁▁ 
colony |>
  count(year, months) |>
  ggplot(aes(x = year, y = n, fill = months)) +
  geom_col() +
  scale_fill_okabe_ito() +
  scale_x_continuous(breaks = scales::breaks_width(1)) +
  labs(
    title = 'Quarterly rows of data per year',
    subtitle = 'Expect one per state',
    y = '',
    x = 'Year'
  ) +
  theme_bw() +
  theme(legend.position = 'bottom', )

Stacked bar plot showing number of rows of data for each year by quarter.

colony |>
  summarise(n = sum(colony_n), .by = c(year, months)) |>
  ggplot(aes(x = year, y = months, size = n / 1000000)) +
  geom_point(show.legend = FALSE) +
  scale_x_continuous(breaks = scales::breaks_width(1)) +
  theme_bw() +
  labs(title = 'Total colony sizes (n)', x = 'Year', y = 'Quarter')

The total number of bees counted in each year by quarter.

No bees in 2019Q2. That seems unlikely.

colony |>
  filter(year == 2019, months == 'April-June')
# A tibble: 47 × 10
    year months     state       colony_n colony_max colony_lost colony_lost_pct
   <dbl> <chr>      <chr>          <dbl>      <dbl>       <dbl>           <dbl>
 1  2019 April-June Alabama           NA         NA          NA              NA
 2  2019 April-June Arizona           NA         NA          NA              NA
 3  2019 April-June Arkansas          NA         NA          NA              NA
 4  2019 April-June California        NA         NA          NA              NA
 5  2019 April-June Colorado          NA         NA          NA              NA
 6  2019 April-June Connecticut       NA         NA          NA              NA
 7  2019 April-June Florida           NA         NA          NA              NA
 8  2019 April-June Georgia           NA         NA          NA              NA
 9  2019 April-June Hawaii            NA         NA          NA              NA
10  2019 April-June Idaho             NA         NA          NA              NA
# ℹ 37 more rows
# ℹ 3 more variables: colony_added <dbl>, colony_reno <dbl>,
#   colony_reno_pct <dbl>

That’s no good, we’ll likely want to drop this quarter’s data from both data sets. First, let us take a quick look at the stressor data.

glimpse(stressor)
Rows: 7,332
Columns: 5
$ year       <dbl> 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015, 2015,…
$ months     <chr> "January-March", "January-March", "January-March", "January…
$ state      <chr> "Alabama", "Alabama", "Alabama", "Alabama", "Alabama", "Ala…
$ stressor   <chr> "Varroa mites", "Other pests/parasites", "Disesases", "Pest…
$ stress_pct <dbl> 10.0, 5.4, NA, 2.2, 9.1, 9.4, 26.9, 20.5, 0.1, NA, 1.8, 3.1…

And checking 2019Q2 in this data we find the same issue!

stressor |>
  filter(year == 2019, months == 'April-June')
# A tibble: 282 × 5
    year months     state   stressor              stress_pct
   <dbl> <chr>      <chr>   <chr>                      <dbl>
 1  2019 April-June Alabama Varroa mites                  NA
 2  2019 April-June Alabama Other pests/parasites         NA
 3  2019 April-June Alabama Disesases                     NA
 4  2019 April-June Alabama Pesticides                    NA
 5  2019 April-June Alabama Other                         NA
 6  2019 April-June Alabama Unknown                       NA
 7  2019 April-June Arizona Varroa mites                  NA
 8  2019 April-June Arizona Other pests/parasites         NA
 9  2019 April-June Arizona Disesases                     NA
10  2019 April-June Arizona Pesticides                    NA
# ℹ 272 more rows

3 Data Prep

Create a date variable and filter out rows for 2019Q2

col_data <- colony |>
  mutate(
    quarter = case_when(
      months == "January-March" ~ "Q1",
      months == "April-June" ~ "Q2",
      months == "July-September" ~ "Q3",
      months == "October-December" ~ "Q4"
    )
  ) |>
  unite(yearquarter, c("year", "quarter")) |>
  mutate(date = yq(yearquarter)) |>
  filter(yearquarter != '2019_Q2')

stress_data <- stressor |>
  mutate(
    quarter = case_when(
      months == "January-March" ~ "Q1",
      months == "April-June" ~ "Q2",
      months == "July-September" ~ "Q3",
      months == "October-December" ~ "Q4"
    )
  ) |>
  unite(yearquarter, c("year", "quarter")) |>
  mutate(date = yq(yearquarter)) |>
  filter(yearquarter != '2019_Q2')

Replace missing stress_pct data with 0, break out stressors into individual columns, and correct a mispelled variable name.

stress_data <- stress_data |>
  replace_na(list(stress_pct = 0)) |>
  pivot_wider(
    names_from = stressor,
    values_from = stress_pct,
    values_fill = 0
  ) |>
  unite(id, c('state', 'yearquarter')) |>
  rename(Diseases = Disesases)

Join data sets

full_data <- col_data |>
  unite(id, c("state", "yearquarter"), remove = FALSE) |>
  left_join(
    stress_data |>
      select(-months, -date)
  ) |>
  pivot_longer(
    c("Varroa mites":"Unknown"),
    names_to = "stressor",
    values_to = "stress_pct"
  )

full_data$stressor <- factor(
  full_data$stressor,
  levels = c(
    "Varroa mites",
    "Diseases",
    "Pesticides",
    "Other pests/parasites",
    "Other",
    "Unknown"
  )
)

4 Plot

full_data |>
  ggplot(aes(x = date, y = stress_pct)) +
  geom_area(aes(fill = stressor), color = "lightgrey", alpha = 0.6) +
  facet_geo(~state, grid = "us_state_grid1") +
  scale_x_date(date_labels = "'%y", date_breaks = "1 year") +
  scale_fill_okabe_ito() +
  labs(
    title = "Bee Colony Stressors in the United States",
    x = "Year",
    y = "Colonies affected by stressor (%)",
    fill = "Stressor"
  ) +
  theme(legend.position = 'top')

A grid of plots roughly laid out like the states of the United State of America. There is one plot per state, each displaying a collection of colored areas, with the height corresponding to the percent of a bee colony affected by a particular stressor as measured over time.

Citation

BibTeX citation:
@online{kasowitz2025,
  author = {Kasowitz, Seth},
  title = {Quick {Plot:} {TidyTuesday} 2022 {Week} 2},
  date = {2025-09-25},
  url = {https://sethkasowitz.com/posts/2025-09-25_revisiting-tidytuesday-2022wk2/},
  langid = {en}
}
For attribution, please cite this work as:
Kasowitz, Seth. 2025. “Quick Plot: TidyTuesday 2022 Week 2.” September 25, 2025. https://sethkasowitz.com/posts/2025-09-25_revisiting-tidytuesday-2022wk2/.