KEY - visualizing amounts / rankings

Author

Your Name

Published

January 21, 2026

Note

This key follows the visualizing amounts / rankings slides. Please be sure to cross-reference the slides, which contain important information and additional context!

Setup

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

#..........................load packages.........................
library(tidyverse)
library(janitor)
library(scales)

#..........................import data...........................
iwa_data <- read_csv(here::here("week3", "data", "combined_iwa-assessment-outputs-conus-2025_CONUS_200910-202009_long.csv"))

#..................create df of subregions names.................
# data only contain HUC codes; must manually join names if we want to include those in our viz (which we do! we'll mainly be looking at CA subregions)
# subregions (& others) identified in: https://water.usgs.gov/GIS/wbd_huc8.pdf
# there may be a downloadable dataset containing HUCs & names out there...but I couldn't find it
subregion_names <- tribble(
  ~subregion_HUC, ~subregion_name,
  "1801", "Klamath-Northern California Coastal",
  "1802", "Sacramento",
  "1803", "Tulare-Buena Vista Lakes",
  "1804", "San Joaquin",
  "1805", "San Francisco Bay",
  "1806", "Central California Coastal",
  "1807", "Southern California Coastal",
  "1808", "North Lahontan", 
  "1809", "Northern Mojave-Mono Lake",
  "1810", "Southern Mojave-Salton Sea",
)

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

#......create df with just CA water resource region (HUC 18).....
ca_region <- iwa_data |> 
  clean_names() |> 
  mutate(region_HUC = str_sub(string = huc12_id, start = 1, end = 2),
         subregion_HUC = str_sub(string = huc12_id, start = 1, end = 4)) |> 
  filter(region_HUC == "18") |> 
  separate_wider_delim(cols = year_month,
                       delim = "-",
                       names = c("year", "month")) |>                     
  mutate(year = as.numeric(year),
         month = as.numeric(month)) |> 
  left_join(subregion_names) |> 
  select(year, month, huc12_id, region_HUC, subregion_HUC, subregion_name, availab_mm_mo, consum_mm_mo, sui_frac)

Bar & Lollipop Plots

  • bar & lollipop plots are interchangeable
  • the focus is on the highest & lowest values (as well as overall rank / hierarchy)
  • best for visualizing amounts across a single categorical variable
  • encode values by LENGTH from 0

After building some basic plots, we’ll practice:

  • making space for overlapping labels
  • reordering groups
  • adding direct labels

Our goal: create bar & lollipop that shows long-term mean Surface Water-Supply and Use Index (SUI) across the ten HUC 18 subregions

#............................bar plot............................
ca_region |> 
  group_by(subregion_name) |> 
  summarise(mean_sui_2010_2020 = mean(sui_frac, na.rm = TRUE)) |> 
  mutate(subregion_name = fct_reorder(.f = subregion_name, .x = mean_sui_2010_2020)) |>
  ggplot(aes(x = mean_sui_2010_2020, y = subregion_name)) +
  geom_col() +
  geom_text(aes(label = round(mean_sui_2010_2020, 2)), hjust = 1.2, color = "white") 
#..........................lollipop plot.........................
ca_region |> 
  group_by(subregion_name) |> 
  summarise(mean_sui_2010_2020 = mean(sui_frac, na.rm = TRUE)) |> 
  mutate(subregion_name = fct_reorder(.f = subregion_name, .x = mean_sui_2010_2020)) |>
  ggplot(aes(x = mean_sui_2010_2020, y = subregion_name)) +
  geom_point() +
  geom_linerange(aes(xmin = 0, xmax = mean_sui_2010_2020)) +
  geom_text(aes(label = round(mean_sui_2010_2020, 2)), hjust = -0.3) + 
  scale_x_continuous(limits = c(0, 0.6))

Note 1: geom_col() vs. geom_bar()

Use geom_col() when you want the heights of your bars to represent values in your data:

#......................`geom_col()` example......................
ca_region |> 
  group_by(subregion_name) |> 
  summarise(mean_sui_2010_2020 = mean(sui_frac, na.rm = TRUE)) |> 
  ggplot(aes(x = mean_sui_2010_2020, y = subregion_name)) +
  geom_col()

Use geom_bar() if you want the heights of your bars to be proportional to the number of cases in each group:

#......................`geom_bar()` example......................
ggplot(ca_region, aes(x = subregion_name)) +
  geom_bar() +
  scale_y_continuous(label = scales::label_comma()) +
  coord_flip()

Stacked Bar Plots

  • great for part-to-whole comparisons
  • use a percentage stacked bar char twhen you want the focus to be on relative proportions
  • encode values by LENGTH

Our goal: create a stacked bar chart that shows how CA subwatersheds (HUC 12s) are distributed across SUI categories

First we need to do a bit of wrangling:

#.................find total # SUI obs per month.................
tot_sui_per_month <- ca_region |> 
  group_by(month) |> 
  count() |> 
  rename(tot_sui_per_month = n)

#................create df classifying SUI values................
sui_severity <- ca_region |>
  mutate(
    sui_category = case_when(
      sui_frac < 0.2 ~ "Very low",
      sui_frac >= 0.2 & sui_frac < 0.4 ~ "Low",
      sui_frac >= 0.4 & sui_frac < 0.6 ~ "Moderate",
      sui_frac >= 0.6 & sui_frac < 0.8 ~ "High",
      sui_frac >= 0.8 & sui_frac <= 1 ~ "Severe",
      TRUE ~ NA
    )) |> 
  group_by(month, sui_category) |> 
  summarize(tot_sui_per_cat = n()) |> 
  drop_na(sui_category) |> 
  left_join(tot_sui_per_month) |> 
  mutate(month = month.abb[month],
         month = factor(month, levels = c(month.abb[10:12], month.abb[1:9]))) |> 
  mutate(perc = (tot_sui_per_cat/tot_sui_per_month) * 100) |> 
  mutate(sui_category = factor(sui_category, 
                               levels = c("Very low", "Low",
                                          "Moderate", "High", "Severe")))

Then create a stacked bar plot:

#......................create color palettes.....................
# pulled hex codes from USGS map using ColorZilla Chrome extension
sui_colors <- c("Severe" = "#720C0F", 
                "High" = "#F68939", 
                "Moderate" = "#FBF7BF", 
                "Low" = "#AAD9EA", 
                "Very low" = "#217AB5")

#......................% stacked bar chart.......................
ggplot(sui_severity, aes(x = month, y = perc, fill = sui_category)) +
  geom_col() +
  scale_fill_manual(values = sui_colors) +
  scale_y_continuous(labels = scales::label_percent(scale = 1))

Heatmaps

  • great for visulizing matrices of data (e.g. 2 categorical + 1 numeric)
  • focus is on patterns rather than precise amounts
  • consider audience familiarity (may require more explanation than a basic bar plot)
  • encodes values using COLOR

Our goal: create a heatmap that shows how water stress (represented by SUI) changes through time for each of the ten HUC 18 subregions

First, we need to do a bit of wrangling:

#............df of annual mean SUI by subregion & year...........
heatmap_data <- ca_region |> 
  group_by(subregion_name, year) |> 
  summarize(annual_mean_sui = mean(sui_frac, na.rm = TRUE)) |> 
  ungroup()
`summarise()` has grouped output by 'subregion_name'. You can override using
the `.groups` argument.
#.determine order of subregions based on highest avg SUI in 2015.
order_2015 <- heatmap_data |> 
  filter(year == 2015) |> 
  arrange(annual_mean_sui) |> 
  mutate(order = row_number()) |> 
  select(annual_mean_sui, order) 

#........join order with rest of data to set factor levels.......
heatmap_order <- heatmap_data |> 
  left_join(order_2015) |> 
  mutate(name = fct_reorder(.f = subregion_name, .x = order))
Joining with `by = join_by(annual_mean_sui)`
Warning: There was 1 warning in `mutate()`.
ℹ In argument: `name = fct_reorder(.f = subregion_name, .x = order)`.
Caused by warning:
! `fct_reorder()` removing 110 missing values.
ℹ Use `.na_rm = TRUE` to silence this message.
ℹ Use `.na_rm = FALSE` to preserve NAs.

Then, create heatmap:

Question:

How else might you consider ordering these groups?

Dumbbell Plots

  • highlight magnitude and direction of change (or difference) between two values within a category
  • shows exact values, but focus is on the difference between values
  • encode values using POSITION

Our goal: create a dumbbell chart to compare mean monthly water availability and consumption in the Santa Barbara Coastal subbasin (HUC8: 18060013) across the years 2010-2020.

First, we need to do a bit of wrangling:

#.........df mean water avail vs. consum in HUC 18060013.........
sbc_subbasin_monthly <- ca_region |> 
  mutate(subbasin_HUC = str_sub(string = huc12_id, start = 1, end = 8)) |> 
  filter(subbasin_HUC == "18060013") |> 
  group_by(month) |> 
  summarize(mean_avail = mean(availab_mm_mo, na.rm = TRUE),
            mean_consum = mean(consum_mm_mo, na.rm = TRUE)) |> 
  mutate(month = month.abb[month],
          month = factor(month, levels = c(month.abb[10:12], month.abb[1:9])))

Then, create dumbbell plot:

#......................create dumbbell plot......................
ggplot(sbc_subbasin_monthly) +
  geom_linerange(aes(y = month,
                     xmin = mean_consum, xmax = mean_avail)) + 
  geom_point(aes(x = mean_avail, y = month), 
             color = "#448F9C", 
             size = 4) +
  geom_point(aes(x = mean_consum, y = month), 
             color = "#9C7344", 
             shape = 17,
             size = 4) +
  labs(x = "Water quantity (mm)") +
  theme(axis.title.y = element_blank())