Tiana’s #TidyTuesday

Quarto

Quarto enables you to weave together content and executable code into a finished document. To learn more about Quarto see https://quarto.org.

# Clean data compiled from code referenced in article (https://waterdata.usgs.gov/blog/acs-maps/). 
# Code was revised to pull data for all US counties for years 2022 - 2023.

# Load packages -----
library(tidycensus)
library(janitor)

Attaching package: 'janitor'
The following objects are masked from 'package:stats':

    chisq.test, fisher.test
library(sf) 
Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE
library(tidyverse)
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.2     ✔ tibble    3.3.0
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.1.0     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
# Helper functions -----
get_census_data <- function(geography, var_names, year, proj, survey_var) {
  df <- get_acs(
    geography = geography,
    variable = var_names,
    year = year,
    geometry = TRUE,
    survey = survey_var) |>
    clean_names() |>
    st_transform(proj) |>
    mutate(year = year)
  
  return(df) 
}

# Grab relevant variables - B01003_001: total population, B25049_004: households lacking plumbing----
vars <- c("B01003_001", "B25049_004")

# Pull data for 2023 and 2022 for all US counties ------
water_insecurity_2023 <- get_census_data(
  geography = 'county', 
  var_names = vars, 
  year = "2023", 
  proj = "EPSG:5070", 
  survey_var = "acs1"
) |>
  mutate(
    variable_long = case_when(
      variable == "B01003_001" ~ "total_pop",
      variable == "B25049_004" ~ "plumbing",
      .default = NA_character_  
    )
  ) |> 
  select(geoid, name, variable_long, estimate, geometry, year) |> 
  pivot_wider(
    names_from = variable_long,
    values_from = estimate
  ) |> 
  mutate(
    percent_lacking_plumbing = (plumbing / total_pop) * 100
  )
Getting data from the 2023 1-year ACS
The 1-year ACS provides data for geographies with populations of 65,000 and greater.
Warning: • You have not set a Census API key. Users without a key are limited to 500
queries per day and may experience performance limitations.
ℹ For best results, get a Census API key at
http://api.census.gov/data/key_signup.html and then supply the key to the
`census_api_key()` function to use it throughout your tidycensus session.
This warning is displayed once per session.
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.

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water_insecurity_2022 <- get_census_data(
  geography = 'county', 
  var_names = vars, 
  year = "2022", 
  proj = "EPSG:5070", 
  survey_var = "acs1"
) |>
  mutate(
    variable_long = case_when(
      variable == "B01003_001" ~ "total_pop",
      variable == "B25049_004" ~ "plumbing",
      .default = NA_character_  
    )
  ) |> 
  select(geoid, name, variable_long, estimate, geometry, year) |> 
  pivot_wider(
    names_from = variable_long,
    values_from = estimate
  ) |> 
  mutate(
    percent_lacking_plumbing = (plumbing / total_pop) * 100
  )
Getting data from the 2022 1-year ACS
The 1-year ACS provides data for geographies with populations of 65,000 and greater.
Downloading feature geometry from the Census website.  To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.

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water_combined <- bind_rows(water_insecurity_2022, 
                            water_insecurity_2023)
state_avg <- water_combined %>%
  mutate(state = sub(".*, ", "", name)) %>%    # extract state name
  group_by(state) %>%
  summarize(mean_lacking = mean(percent_lacking_plumbing, na.rm = TRUE)) %>%
  arrange(desc(mean_lacking))

Data Summary

ggplot(water_combined, 
       aes(x = total_pop, y = percent_lacking_plumbing)) +
  geom_point(alpha = 0.5, color = "steelblue") +
  scale_x_log10(labels = scales::comma) +
  geom_smooth(method = "lm", color = "darkred", se = FALSE) +
  labs(
    title = "Population Size and Plumbing Insecurity",
    subtitle = "Smaller counties tend to have higher plumbing insecurity",
    x = "Total Population (log scale)",
    y = "% Households Lacking Plumbing"
  ) +
  theme_minimal(base_size = 12) +
  theme(text = element_text(family = "serif"))
`geom_smooth()` using formula = 'y ~ x'
Warning: Removed 3 rows containing non-finite outside the scale range
(`stat_smooth()`).
Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_point()`).

ggplot(state_avg, aes(x = fct_reorder(state, mean_lacking), 
                      y = mean_lacking)) +
  geom_col(fill = "steelblue") +
  coord_flip() +
  labs(
    title = "Average Plumbing Insecurity by State",
    x = "State",
    y = "Average % Lacking Plumbing"
  ) +
  theme_minimal(base_size = 15) +
  theme(
  axis.text.y = element_text(size = 4),  
  plot.margin = margin(10, 40, 10, 10),
  text = element_text(family = "serif")
)