Water Insecurity

Figure 1: This visualization, created using ggplot2 in R, employs geom_line() to show changes in the percentage of the population lacking plumbing across 42 California counties from 2022 to 2023. The highlighted counties—Sutter, Tehama, Yolo, and San Benito—are emphasized in blue to indicate a 100% decrease, while Los Angeles County is shown in black to reflect its 14.8% decrease. The plot features direct labels for key counties using geom_text(), a clean background theme with theme_minimal(), and subtle gray lines for all counties to provide context.

How This Graphic Was Made

1. 📦 Load Packages & Setup

# Load necessary packages using pacman for easier dependency management
pacman::p_load(
  tidyverse,   # Collection of R packages for data science (ggplot2, dplyr, etc.)
  showtext,    # Enables custom fonts for ggplot2
  ggtext,      # Adds rich text formatting to ggplot2
  skimr,       # Provides summary statistics in a readable format
  janitor,
  glue
)

# Add Google fonts
font_add_google("Roboto Condensed", family = "Roboto")
font_add_google("Oswald")

# Add local font
font_add("Font Awesome 6 Brands", here::here("fonts/otfs/Font Awesome 6 Brands-Regular-400.otf"))

# Automatically enable the use of showtext for all plots
showtext_auto()

# Set DPI for high-resolution text rendering
showtext_opts(dpi = 300)

2. 📖 Read in the Data

# Load the TidyTuesday data
tuesdata <- tidytuesdayR::tt_load(2025, week = 4)

water_insecurity_2022 <- tuesdata$water_insecurity_2022 %>% clean_names()
water_insecurity_2023 <- tuesdata$water_insecurity_2023 %>% clean_names()

tidytuesdayR::readme(tuesdata)
rm(tuesdata)

3. 🕵️ Examine the Data

glimpse(water_insecurity_2022)
skim(water_insecurity_2022)

glimpse(water_insecurity_2023)
skim(water_insecurity_2023)

4. 🤼 Wrangle Data

california_water <- water_insecurity_2022 %>%
  inner_join(
    water_insecurity_2023, 
    by = c("geoid", "name"), 
    suffix = c("_2022", "_2023")
  ) %>%
  filter(str_detect(name, "California")) %>%
  mutate(
    difference = percent_lacking_plumbing_2023 - percent_lacking_plumbing_2022,
    pct_change = case_when(
      percent_lacking_plumbing_2022 == 0 & percent_lacking_plumbing_2023 > 0 ~ 100,  # Assign 100% if increasing from 0
      percent_lacking_plumbing_2022 == 0 & percent_lacking_plumbing_2023 == 0 ~ 0,    # No change if both are 0
      TRUE ~ round((difference / percent_lacking_plumbing_2022) * 100, 1)
    )
  ) %>%
  drop_na() %>%  # Remove missing values after calculations
  pivot_longer(
    cols = starts_with("percent"), 
    names_to = "year", 
    values_to = "amount"
  ) %>%
  mutate(
    year = str_replace(year, "percent_lacking_plumbing_", "")
  ) %>%
  mutate(
    county = str_remove(name, " County, California")
  ) %>%
  select(county, year, pct_change, amount)

california_water_decrease <- california_water %>%
  slice_min(pct_change)

california_water_la <- california_water %>%
  filter(county == "Los Angeles")

5. 🔤 Text

title <- "Percentage of the Population Lacking Plumbing in California"

st <- paste("Between 2022 and 2023, an analysis of 42 California counties found that Sutter, Tehama, Yolo, and San Benito saw a ", 
            "<span style='color:#0375FC;'>**100% decrease**</span> in population lacking plumbing, while Los Angeles County—California’s largest county by population—experienced a ",
            "**14.8% decrease**.")

# Create a social media caption with customized colors and font for consistency in visualization
social <- andresutils::social_caption(font_family = "Roboto", icon_color = "black") 

# Construct the final plot caption by combining TidyTuesday details, data source, and the social caption
cap <- paste0(
  "#TidyTuesday: Week 04, 2025 | **Source**: U.S. Census Bureau, American Community Survey (ACS), via tidycensus | **Graphic**: ", social
)

6. 📊 Plot

p <- california_water %>% 
  ggplot(aes(x = year, y = amount, group = county)) +
  geom_line(data = california_water %>% filter(pct_change != min(pct_change), county != "Los Angeles"), color = "#E0E0E0") +
  geom_line(data = california_water %>% slice_min(pct_change), color = "#0375FC", linewidth = 1.1) +
  geom_line(data = california_water %>% filter(county %in% "Los Angeles"), color = "black", linewidth = 1.1) +
  geom_text(data = california_water_decrease %>% filter(year == "2022"), aes(x = year, y = amount, label = paste0(county, ": ", round(amount, 2), "%")), hjust = 1.05, family = "Roboto", size = 3.25, color = "#0375FC") +
  ggrepel::geom_text_repel(data = california_water_decrease %>% filter(year == "2023"), aes(x = year, y = amount, label = paste0(county, ": ", round(amount, 2), "%")), nudge_x = 0.15, segment.size = 0.2, direction = "y", hjust = "left", family = "Roboto", box.padding = 0.1, segment.linetype = 2, size = 3.25, color = "#0375FC") +
  geom_text(data = california_water_la, aes(x = year, y = amount, label = paste0(county, ": ", round(amount, 2), " %"),  hjust = if_else(year == "2022", 1.05, -0.05)), family = "Roboto", size = 3.25, color = "black") +
  geom_vline(xintercept = "2022", linewidth = 0.5, color = "black") + 
  geom_vline(xintercept = "2023", linewidth = 0.5, color = "black") + 
  geom_point(data = california_water_decrease, aes(x = year, y = amount), color = "#0375FC") +
  geom_point(data = california_water_la, aes(x = year, y = amount), color = "black") +
  coord_cartesian(clip = "off") +
  labs(title = title,
       subtitle = st, 
       caption = cap) +
  theme_void() +
  theme(plot.title = element_textbox_simple(family = "Oswald", size = 16, face = "bold", color = "black", margin = margin(b = 5, t = 5)),
        plot.title.position = "plot",
        plot.subtitle = element_textbox_simple(family = "Roboto", size = 10, color = "black", margin = margin(b = 15, t = 5), lineheight = 1),
        plot.caption.position = "plot",
        plot.caption = element_textbox_simple(size = 6, family = "Roboto", color = "black", margin = margin(t = 15), halign = 0.5),
        axis.text.x = element_text(family = "Roboto", size = 9, hjust = 0.5, color = "black", vjust = -2),
        plot.margin = margin(10, 10, 10, 10),
        plot.background = element_rect(color = NA, fill = "#FFFFFF")) 

7. 💾 Save

# Save plot with dimensions
andresutils::save_plot(p, type = "tidytuesday", year = 2025, week = 4, width = 6, height = 6)

8. 🚀 GitHub Repository

Expand for GitHub Repo

The complete code for this analysis is available in tt_04_2025.qmd.

For the full repository, click here.