library(tidycensus)
library(tidyverse)
library(janitor)
library(stringr)
library(openxlsx)
library(tigris)
library(sf)
library(ggplot2)
library(scales)
options(tigris_use_cache = TRUE)
# ---------------------------------------------------------------------------------
# parameters and file paths
# ---------------------------------------------------------------------------------
acs_year <- 2024
acs_survey <- "acs5"
pov_min <- 131
pov_max <- 165
data_dir <- "data"
output_dir <- "output"
dir.create(output_dir, showWarnings = FALSE, recursive = TRUE)
# Geocorr files
lower_crosswalk_path <- file.path(data_dir, "geocorr2022_2613906081.csv")
upper_crosswalk_path <- file.path(data_dir, "geocorr2022_2613903963.csv")
# Output files
excel_output_path <- file.path(
output_dir,
"tx_snap_lege_districts.xlsx"
)
lower_map_path <- file.path(
output_dir,
"tx_snap_lower.png"
)
upper_map_path <- file.path(
output_dir,
"tx_snap_upper.png"
)VHK grant data - Children 0-3 on SNAP, 131-165% of Poverty
READ ME
This analysis uses ACS PUMS microdata from tidycensus to estimate Texas households participating in SNAP with children and income between 131% and 165% of poverty, used here as an ACS proxy for the SNAP/FPL policy band. The ACS PUMS variable POVPIP measures income as a percentage of the Census Bureau poverty threshold. This is similar to, but not identical to, the federal poverty guidelines used for administrative program eligibility. Because Texas SNAP eligibility is commonly discussed using FPL thresholds, this analysis sometimes uses “131–165% FPL” as shorthand while interpreting estimates as ACS poverty-threshold-based approximations.
Because ACS PUMS does not directly identify Texas House or Senate districts, estimates are first calculated at the PUMA level and then allocated to state legislative districts using Geocorr 2022 population-weighted PUMA-to-district crosswalks. Results are produced separately for Texas House districts (lower) and Texas Senate districts (upper).
The main outcome, pct_snap_hh_0_3_131_165, represents the percent of SNAP households in each district that have at least one child age 0–3 and household income between 131% and 165% of poverty. Count columns are weighted ACS PUMS estimates rounded to whole numbers. Percent columns are shown as percentages. Because PUMAs and legislative districts do not perfectly align, district-level estimates should be interpreted as approximations.
You can view the 2024 ACS PUMS DATA DICTIONARY here.
Setup
# ---------------------------------------------------------------------------------
# pull Texas ACS PUMS microdata
# ---------------------------------------------------------------------------------
# Variables:
# SERIALNO = household ID
# SPORDER = person number within household
# PUMA = Public Use Microdata Area
# AGEP = age
# POVPIP = income-to-poverty ratio, as percent of Census poverty threshold
# Used here as an ACS proxy for the SNAP/FPL policy band.
# FS = household received Food Stamps/SNAP in past 12 months
# PWGTP = person weight
# WGTP = household weight
vars <- c(
"SERIALNO",
"SPORDER",
"PUMA",
"AGEP",
"POVPIP",
"FS",
"PWGTP",
"WGTP"
)
tx_pums <- get_pums(
variables = vars,
state = "TX",
survey = acs_survey,
year = acs_year,
recode = FALSE
)
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# ---------------------------------------------------------------------------------
# clean PUMS variables
# ---------------------------------------------------------------------------------
tx_clean <- tx_pums %>%
clean_names() %>%
mutate(
puma = str_pad(as.character(puma), width = 5, pad = "0"),
age = as.numeric(agep),
povpip = as.numeric(povpip),
child_0_3 = age >= 0 & age <= 3,
child_u18 = age < 18,
is_snap_hh = as.character(fs) == "1",
income_131_165 = povpip >= pov_min & povpip <= pov_max
)
# ---------------------------------------------------------------------------------
# household-level indicators
# ---------------------------------------------------------------------------------
hh_level <- tx_clean %>%
group_by(serialno, puma) %>%
summarise(
has_0_3 = any(child_0_3, na.rm = TRUE),
has_u18 = any(child_u18, na.rm = TRUE),
is_snap_hh = any(is_snap_hh, na.rm = TRUE),
povpip = first(na.omit(povpip)),
income_131_165 = povpip >= pov_min & povpip <= pov_max,
hh_wt = first(wgtp),
unwt_persons = n(),
unwt_0_3 = sum(child_0_3, na.rm = TRUE),
unwt_u18 = sum(child_u18, na.rm = TRUE),
.groups = "drop"
)
# ---------------------------------------------------------------------------------
# PUMA-level household estimates
# ---------------------------------------------------------------------------------
puma_hh <- hh_level %>%
group_by(puma) %>%
summarise(
total_hh =
sum(hh_wt, na.rm = TRUE),
snap_hh =
sum(hh_wt[is_snap_hh], na.rm = TRUE),
# Households on SNAP with at least one child age 0-3, regardless of poverty level
snap_hh_0_3 =
sum(
hh_wt[
is_snap_hh &
has_0_3
],
na.rm = TRUE
),
# Households on SNAP with income 131-165% of poverty, regardless of child status
snap_hh_131_165 =
sum(
hh_wt[
is_snap_hh &
income_131_165
],
na.rm = TRUE
),
# Main universe: households on SNAP with at least one child age 0-3
# and income 131-165% of poverty
snap_hh_0_3_131_165 =
sum(
hh_wt[
is_snap_hh &
has_0_3 &
income_131_165
],
na.rm = TRUE
),
snap_hh_u18_131_165 =
sum(
hh_wt[
is_snap_hh &
has_u18 &
income_131_165
],
na.rm = TRUE
),
snap_hh_u18 =
sum(
hh_wt[
is_snap_hh &
has_u18
],
na.rm = TRUE
),
unwt_total_hh =
n(),
unwt_snap_hh =
sum(is_snap_hh, na.rm = TRUE),
.groups = "drop"
)
# ---------------------------------------------------------------------------------
# PUMA-level population estimates
# ---------------------------------------------------------------------------------
puma_pop <- tx_clean %>%
group_by(puma) %>%
summarise(
# total pop all ages
pop_total =
sum(pwgtp, na.rm = TRUE),
# pop all ages on SNAP
pop_snap =
sum(pwgtp[is_snap_hh], na.rm = TRUE),
# pop ages 0-3 on SNAP, regardless of poverty level
pop_0_3_snap =
sum(pwgtp[is_snap_hh & child_0_3], na.rm = TRUE),
# Population ages 0-3 living in SNAP households with household income 131-165% of poverty
pop_0_3_snap_131_165 =
sum(pwgtp[is_snap_hh & child_0_3 & income_131_165], na.rm = TRUE),
.groups = "drop"
)
puma_estimates <- puma_hh %>%
left_join(puma_pop, by = "puma")Checks
# ---------------------------------------------------------------------------------
# QA checks for statewide totals
# ---------------------------------------------------------------------------------
qa_statewide <- puma_estimates %>%
summarise(
total_hh = sum(total_hh, na.rm = TRUE),
snap_hh = sum(snap_hh, na.rm = TRUE),
snap_hh_0_3 = sum(snap_hh_0_3, na.rm = TRUE),
snap_hh_131_165 = sum(snap_hh_131_165, na.rm = TRUE),
snap_hh_0_3_131_165 = sum(snap_hh_0_3_131_165, na.rm = TRUE),
pop_total = sum(pop_total, na.rm = TRUE),
pop_snap = sum(pop_snap, na.rm = TRUE),
pop_0_3_snap = sum(pop_0_3_snap, na.rm = TRUE),
pop_0_3_snap_131_165 = sum(pop_0_3_snap_131_165, na.rm = TRUE)
)
qa_statewide %>%
print(width = Inf)# A tibble: 1 × 9
total_hh snap_hh snap_hh_0_3 snap_hh_131_165 snap_hh_0_3_131_165 pop_total
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 10992823 1257339 274113 127583 29249 30188424
pop_snap pop_0_3_snap pop_0_3_snap_131_165
<dbl> <dbl> <dbl>
1 4461936 393901 41084# these should all be TRUE
qa_nested <- qa_statewide %>%
transmute(
snap_hh_lte_total_hh = snap_hh <= total_hh,
snap_hh_0_3_lte_snap_hh = snap_hh_0_3 <= snap_hh,
snap_hh_131_165_lte_snap_hh = snap_hh_131_165 <= snap_hh,
snap_hh_0_3_131_165_lte_snap_hh_0_3 = snap_hh_0_3_131_165 <= snap_hh_0_3,
snap_hh_0_3_131_165_lte_snap_hh_131_165 = snap_hh_0_3_131_165 <= snap_hh_131_165,
pop_snap_lte_pop_total = pop_snap <= pop_total,
pop_0_3_snap_lte_pop_snap = pop_0_3_snap <= pop_snap,
pop_0_3_snap_131_165_lte_pop_0_3_snap = pop_0_3_snap_131_165 <= pop_0_3_snap
)
qa_nested %>%
print(width = Inf)# A tibble: 1 × 8
snap_hh_lte_total_hh snap_hh_0_3_lte_snap_hh snap_hh_131_165_lte_snap_hh
<lgl> <lgl> <lgl>
1 TRUE TRUE TRUE
snap_hh_0_3_131_165_lte_snap_hh_0_3 snap_hh_0_3_131_165_lte_snap_hh_131_165
<lgl> <lgl>
1 TRUE TRUE
pop_snap_lte_pop_total pop_0_3_snap_lte_pop_snap
<lgl> <lgl>
1 TRUE TRUE
pop_0_3_snap_131_165_lte_pop_0_3_snap
<lgl>
1 TRUE These estimates all look to be accurate:
** Note: Income-to-poverty ratios using Census Bureau Poverty Thresholds are used as a proxy for Federal Poverty Line (FPL).
| Measure | Estimate |
|---|---|
| Total Texas households | 10,992,823 |
| SNAP households | 1,257,339 |
| SNAP households with kids ages 0–3 | 274,113 |
| SNAP households at 131–165% FPL | 127,583 |
| SNAP households with kids 0–3 and 131–165% FPL | 29,249 |
| Total Texas population | 30,188,424 |
| People in SNAP households | 4,461,936 |
| Kids ages 0–3 in SNAP households | 393,901 |
| Kids ages 0–3 in SNAP households at 131–165% FPL | 41,084 |
Geographic Crosswalk
# ---------------------------------------------------------------------------------
# function to read Geocorr crosswalks
# ---------------------------------------------------------------------------------
read_geocorr_crosswalk <- function(path, chamber = c("lower", "upper")) {
chamber <- match.arg(chamber)
raw <- read_csv(path, show_col_types = FALSE) %>%
clean_names() %>%
filter(state != "State code")
if (chamber == "lower") {
out <- raw %>%
transmute(
puma = str_pad(as.character(puma22), width = 5, pad = "0"),
district = str_pad(as.character(sldl24), width = 3, pad = "0"),
district_label = paste0("HD-", district),
chamber = "lower",
geography = "Texas House District",
state = stab,
puma_name = puma22name,
geocorr_pop20 = as.numeric(pop20),
afact = as.numeric(afact)
)
}
if (chamber == "upper") {
out <- raw %>%
transmute(
puma = str_pad(as.character(puma22), width = 5, pad = "0"),
district = str_pad(as.character(sldu24), width = 3, pad = "0"),
district_label = paste0("SD-", district),
chamber = "upper",
geography = "Texas Senate District",
state = stab,
puma_name = puma22name,
geocorr_pop20 = as.numeric(pop20),
afact = as.numeric(afact)
)
}
out %>%
filter(
!is.na(puma),
!is.na(district),
!is.na(afact)
)
}
puma_to_lower <- read_geocorr_crosswalk(
path = lower_crosswalk_path,
chamber = "lower"
)
puma_to_upper <- read_geocorr_crosswalk(
path = upper_crosswalk_path,
chamber = "upper"
)
# ---------------------------------------------------------------------------------
# check crosswalk allocation factors
# ---------------------------------------------------------------------------------
# afact should sum to about 1 within each PUMA
lower_crosswalk_check <- puma_to_lower %>%
group_by(puma) %>%
summarise(
afact_sum = sum(afact, na.rm = TRUE),
n_districts = n(),
.groups = "drop"
) %>%
arrange(desc(abs(afact_sum - 1)))
upper_crosswalk_check <- puma_to_upper %>%
group_by(puma) %>%
summarise(
afact_sum = sum(afact, na.rm = TRUE),
n_districts = n(),
.groups = "drop"
) %>%
arrange(desc(abs(afact_sum - 1)))
print(lower_crosswalk_check)# A tibble: 217 × 3
puma afact_sum n_districts
<chr> <dbl> <int>
1 02509 1.000 6
2 04621 1.000 3
3 00100 1.00 3
4 00400 1.00 3
5 01903 1.000 4
6 02302 1.00 4
7 02501 1.000 3
8 02513 1.00 5
9 02515 1.00 4
10 02600 1.00 6
# ℹ 207 more rowsprint(upper_crosswalk_check)# A tibble: 217 × 3
puma afact_sum n_districts
<chr> <dbl> <int>
1 00600 1.00 3
2 01909 1.000 2
3 02306 1.000 2
4 02312 1.000 3
5 02502 1.00 3
6 02505 1.000 3
7 02600 1.00 4
8 04602 1.00 3
9 04614 1.000 3
10 04622 1.00 3
# ℹ 207 more rows# ---------------------------------------------------------------------------------
# function to allocate PUMA estimates to legislative districts
# ---------------------------------------------------------------------------------
allocate_to_districts <- function(puma_estimates, crosswalk) {
allocated_puma_district <- puma_estimates %>%
left_join(crosswalk, by = "puma") %>%
mutate(
total_hh_alloc =
total_hh * afact,
snap_hh_alloc =
snap_hh * afact,
snap_hh_0_3_alloc =
snap_hh_0_3 * afact,
snap_hh_131_165_alloc =
snap_hh_131_165 * afact,
snap_hh_0_3_131_165_alloc =
snap_hh_0_3_131_165 * afact,
# snap_hh_u18_131_165_alloc =
# snap_hh_u18_131_165 * afact,
#
# snap_hh_u18_alloc =
# snap_hh_u18 * afact,
pop_0_3_snap_alloc =
pop_0_3_snap * afact,
pop_0_3_snap_131_165_alloc =
pop_0_3_snap_131_165 * afact,
pop_snap_alloc =
pop_snap * afact,
pop_total_alloc =
pop_total * afact
)
district_summary <- allocated_puma_district %>%
group_by(
geography,
chamber,
district,
district_label
) %>%
summarise(
pumas =
paste(sort(unique(puma)), collapse = "; "),
puma_names =
paste(sort(unique(puma_name)), collapse = "; "),
n_pumas =
n_distinct(puma),
geocorr_pop20 =
sum(geocorr_pop20, na.rm = TRUE),
total_hh =
sum(total_hh_alloc, na.rm = TRUE),
snap_hh =
sum(snap_hh_alloc, na.rm = TRUE),
snap_hh_0_3 =
sum(snap_hh_0_3_alloc, na.rm = TRUE),
snap_hh_131_165 =
sum(snap_hh_131_165_alloc, na.rm = TRUE),
snap_hh_0_3_131_165 =
sum(snap_hh_0_3_131_165_alloc, na.rm = TRUE),
# snap_hh_u18_131_165 =
# sum(snap_hh_u18_131_165_alloc, na.rm = TRUE),
#
# snap_hh_u18 =
# sum(snap_hh_u18_alloc, na.rm = TRUE),
#
pop_0_3_snap =
sum(pop_0_3_snap_alloc, na.rm = TRUE),
pop_0_3_snap_131_165 =
sum(pop_0_3_snap_131_165_alloc, na.rm = TRUE),
pop_snap =
sum(pop_snap_alloc, na.rm = TRUE),
pop_total =
sum(pop_total_alloc, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(
pct_snap_hh_0_3_131_165 =
snap_hh_0_3_131_165 / snap_hh,
pct_snap_pop_0_3_131_165 =
pop_0_3_snap_131_165 / pop_snap,
# pct_hh_u18_131_165_snap =
# snap_hh_u18_131_165 / total_hh,
#
# pct_hh_u18_snap =
# snap_hh_u18 / total_hh,
pov_level = "131-165% of Census Poverty Threshold",
note = "ACS PUMS household estimates allocated from PUMAs using Geocorr population-weighted allocation factors."
) %>%
mutate(
across(
c(
total_hh,
snap_hh,
snap_hh_0_3,
snap_hh_131_165,
snap_hh_0_3_131_165,
pop_0_3_snap,
pop_0_3_snap_131_165,
pop_snap,
pop_total,
geocorr_pop20
),
~round(.x, 0)
),
across(
starts_with("pct_"),
~round(.x * 100, 2)
)
) %>%
select(
geography,
chamber,
district,
district_label,
pct_snap_hh_0_3_131_165, ## primary measure (%) - households
snap_hh_0_3_131_165, ## primary measure (#) - households
pct_snap_pop_0_3_131_165, ## primary measure (%) - person-level
pop_0_3_snap_131_165, ## primary measure (#) - person-level
pop_0_3_snap,
snap_hh_0_3,
snap_hh_131_165,
pop_snap,
pop_total,
total_hh,
snap_hh,
pumas,
puma_names,
n_pumas,
geocorr_pop20,
pov_level,
note
) %>%
arrange(desc(pct_snap_hh_0_3_131_165))
list(
allocated_puma_district = allocated_puma_district,
district_summary = district_summary
)
}
lower_results <- allocate_to_districts(
puma_estimates = puma_estimates,
crosswalk = puma_to_lower
)
upper_results <- allocate_to_districts(
puma_estimates = puma_estimates,
crosswalk = puma_to_upper
)
lower_df <- lower_results$district_summary
upper_df <- upper_results$district_summary
# ---------------------------------------------------------------------------------
# save final Excel file
# ---------------------------------------------------------------------------------
wb <- createWorkbook()
addWorksheet(wb, "lower")
writeData(wb, "lower", lower_df)
freezePane(wb, "lower", firstRow = TRUE)
setColWidths(wb, "lower", cols = 1:ncol(lower_df), widths = "auto")
addWorksheet(wb, "upper")
writeData(wb, "upper", upper_df)
freezePane(wb, "upper", firstRow = TRUE)
setColWidths(wb, "upper", cols = 1:ncol(upper_df), widths = "auto")
# ---------------------------------------------------------------------------------
# add README sheet with notes and column definitions
# ---------------------------------------------------------------------------------
readme_overview <- tibble::tribble(
~field, ~description,
"Purpose",
"This workbook estimates Texas households participating in SNAP with at least one child age 0-3 and household income between 131% and 165% of Census Bureau Poverty Thresholds (used as proxy for FPL), by Texas legislative district.",
"Data source",
"2024 ACS PUMS microdata pulled with tidycensus, plus MCDC Geocorr 2022 PUMA-to-state-legislative-district crosswalks using 2022 PUMAs and 2024 legislative districts.",
"Geography method",
"ACS PUMS does not directly identify Texas House or Senate districts. Estimates are first calculated by PUMA and then allocated to legislative districts using Geocorr population-weighted allocation factors.",
"Unit of analysis",
"Households are used as the analytic unit. This is a household-level estimate, not an IPUMS family-unit estimate.",
"Interpretation note",
"District estimates are approximations because PUMAs and legislative districts do not perfectly align."
)
readme_columns <- tibble::tribble(
~field, ~description,
"geography",
"Legislative district type: Texas House District or Texas Senate District.",
"chamber",
"Legislative chamber: lower for Texas House, upper for Texas Senate.",
"district",
"Legislative district number, padded to three digits.",
"district_label",
"District label with chamber prefix, such as HD-001 or SD-001.",
"pct_snap_hh_0_3_131_165",
"Among SNAP households in the district, the percent with at least one child age 0-3 and household income between 131% and 165% of poverty. This is a household-level measure. Percent values are stored as percentages, not proportions; for example, 12.5 means 12.5%.",
"snap_hh_0_3_131_165",
"Estimated number of SNAP households with at least one child age 0-3 and household income between 131% and 165% of poverty threshold. This is a household-level estimate using household weights.",
"pct_snap_pop_0_3_131_165",
"Among all people living in SNAP households in the district, the percent who are ages 0-3 and live in households with income between 131% and 165% of poverty threshold. This is a person-level measure. Percent values are stored as percentages, not proportions; for example, 12.5 means 12.5%.",
"pop_0_3_snap_131_165",
"Estimated number of children ages 0-3 living in SNAP households with household income between 131% and 165% of poverty threshold. This is a person-level estimate using child records and person weights.",
"pop_0_3_snap",
"Estimated number of children ages 0-3 living in SNAP households, regardless of regardless of household income-to-poverty ratio.",
"snap_hh_0_3",
"Estimated number of SNAP households with at least one child age 0-3, regardless of household income-to-poverty ratio.",
"snap_hh_131_165",
"Estimated number of SNAP households with household income between 131% and 165% of poverty threshold, regardless of whether children are present.",
"pop_snap",
"Estimated total population of all ages living in SNAP households.",
"pop_total",
"Estimated total population of all ages in the district, calculated from ACS PUMS PUMA-level estimates allocated to districts.",
"total_hh",
"Estimated total number of households in the district, calculated from ACS PUMS PUMA-level estimates allocated to districts.",
"snap_hh",
"Estimated total number of SNAP households in the district, calculated from ACS PUMS PUMA-level estimates allocated to districts.",
"pumas",
"PUMA codes that overlap the legislative district and contribute to the allocated estimate.",
"puma_names",
"Names of PUMAs that overlap the legislative district and contribute to the allocated estimate.",
"n_pumas",
"Number of PUMAs that overlap the legislative district.",
"geocorr_pop20",
"2020 Census population from the Geocorr PUMA-to-district overlap file. This is a crosswalk/reference population used for context and QA; it is not an ACS PUMS estimate.",
"pov_level",
"Income to poverty ratio used in the analysis, measured as a person's or family's total annual income as a percentage of their official federal poverty threshold. Here, 131-165% of poverty",
"note",
"Brief methodology note describing that ACS PUMS household and person estimates were allocated from PUMAs to districts using Geocorr population-weighted allocation factors."
)
addWorksheet(wb, "README")
# Styles
header_style <- createStyle(
textDecoration = "bold",
fontSize = 14,
fgFill = "#D9EAF7",
halign = "left"
)
table_header_style <- createStyle(
textDecoration = "bold",
fgFill = "#EDEDED",
border = "Bottom"
)
wrap_style <- createStyle(
wrapText = TRUE,
valign = "top"
)
# Analysis Overview
writeData(wb, "README", "Analysis Overview", startRow = 1, startCol = 1)
addStyle(wb, "README", header_style, rows = 1, cols = 1:2, gridExpand = TRUE)
writeData(wb, "README", readme_overview, startRow = 2, startCol = 1)
addStyle(wb, "README", table_header_style, rows = 2, cols = 1:2, gridExpand = TRUE)
addStyle(
wb, "README", wrap_style,
rows = 3:(nrow(readme_overview) + 2),
cols = 1:2,
gridExpand = TRUE
)
# Column Definitions
col_def_start <- nrow(readme_overview) + 5
writeData(wb, "README", "Column Definitions", startRow = col_def_start, startCol = 1)
addStyle(wb, "README", header_style, rows = col_def_start, cols = 1:2, gridExpand = TRUE)
writeData(wb, "README", readme_columns, startRow = col_def_start + 1, startCol = 1)
addStyle(
wb, "README", table_header_style,
rows = col_def_start + 1,
cols = 1:2,
gridExpand = TRUE
)
addStyle(
wb, "README", wrap_style,
rows = (col_def_start + 2):(col_def_start + nrow(readme_columns) + 1),
cols = 1:2,
gridExpand = TRUE
)
# formatting
setColWidths(wb, "README", cols = 1, widths = 28)
setColWidths(wb, "README", cols = 2, widths = 120)
freezePane(wb, "README", firstRow = FALSE)
saveWorkbook(wb, excel_output_path, overwrite = TRUE)Mapping
# ---------------------------------------------------------------------------------
# pull district geometries for maps
# ---------------------------------------------------------------------------------
tx_lower_geom <- state_legislative_districts(
state = "TX",
house = "lower",
year = 2024,
cb = TRUE
) %>%
st_transform(3083) %>%
mutate(
district = str_pad(as.character(SLDLST), width = 3, pad = "0")
)
tx_upper_geom <- state_legislative_districts(
state = "TX",
house = "upper",
year = 2024,
cb = TRUE
) %>%
st_transform(3083) %>%
mutate(
district = str_pad(as.character(SLDUST), width = 3, pad = "0")
)
lower_map_data <- tx_lower_geom %>%
left_join(
lower_df %>%
select(
district,
district_label,
pct_snap_hh_0_3_131_165
),
by = "district"
)
upper_map_data <- tx_upper_geom %>%
left_join(
upper_df %>%
select(
district,
district_label,
pct_snap_hh_0_3_131_165
),
by = "district"
)
# ---------------------------------------------------------------------------------
# lower chamber map
# ---------------------------------------------------------------------------------
lower_map <- ggplot(lower_map_data) +
geom_sf(
aes(fill = pct_snap_hh_0_3_131_165),
color = "white",
linewidth = 0.1
) +
scale_fill_viridis_c(
option = "rocket",
direction = -1,
labels = label_percent(scale = 1),
na.value = "grey95"
) +
labs(
title = "Texas House Districts",
subtitle = "Percent of SNAP households with children ages 0-3 and income 131-165% of poverty",
fill = "Percent",
caption = "Source: 2024 ACS PUMS via tidycensus; PUMA estimates allocated to districts using MCDC Geocorr 2022."
) +
theme_void() +
theme(
plot.title = element_text(face = "bold"),
plot.subtitle = element_text(size = 10),
legend.position = "right"
)
ggsave(
filename = lower_map_path,
plot = lower_map,
width = 10,
height = 8,
dpi = 300
)
lower_map
# ---------------------------------------------------------------------------------
# upper chamber map
# ---------------------------------------------------------------------------------
upper_map <- ggplot(upper_map_data) +
geom_sf(
aes(fill = pct_snap_hh_0_3_131_165),
color = "white",
linewidth = 0.15
) +
scale_fill_viridis_c(
option = "rocket",
direction = -1,
labels = label_percent(scale = 1),
na.value = "grey95"
) +
labs(
title = "Texas Senate Districts",
subtitle = "Percent of SNAP households with children ages 0-3 and income 131-165% of poverty",
fill = "Percent",
caption = "Source: 2024 ACS PUMS via tidycensus; PUMA estimates allocated to districts using MCDC Geocorr 2022."
) +
theme_void() +
theme(
plot.title = element_text(face = "bold"),
plot.subtitle = element_text(size = 10),
legend.position = "right"
)
ggsave(
filename = upper_map_path,
plot = upper_map,
width = 10,
height = 8,
dpi = 300
)
upper_map
State-Level Estimates
statewide_pct <- puma_estimates %>%
summarise(
snap_hh_0_3_131_165 = sum(snap_hh_0_3_131_165, na.rm = TRUE),
snap_hh_0_3 = sum(snap_hh_0_3, na.rm = TRUE),
snap_hh_131_165 = sum(snap_hh_131_165, na.rm = TRUE),
snap_hh = sum(snap_hh, na.rm = TRUE),
pop_0_3_snap_131_165 = sum(pop_0_3_snap_131_165, na.rm = TRUE),
pop_0_3_snap = sum(pop_0_3_snap, na.rm = TRUE),
pop_snap = sum(pop_snap, na.rm = TRUE),
pop_total = sum(pop_total, na.rm = TRUE),
pct_snap_hh_0_3_131_165 =
snap_hh_0_3_131_165 / snap_hh * 100,
pct_snap_pop_0_3_131_165 =
pop_0_3_snap_131_165 / pop_snap * 100
) %>%
mutate(
across(
c(
snap_hh_0_3_131_165,
snap_hh_0_3,
snap_hh_131_165,
snap_hh,
pop_0_3_snap_131_165,
pop_0_3_snap,
pop_snap,
pop_total
),
~round(.x, 0)
),
across(
starts_with("pct_"),
~round(.x, 2)
)
)
statewide_pct %>%
print(width = Inf)# A tibble: 1 × 10
snap_hh_0_3_131_165 snap_hh_0_3 snap_hh_131_165 snap_hh pop_0_3_snap_131_165
<dbl> <dbl> <dbl> <dbl> <dbl>
1 29249 274113 127583 1257339 41084
pop_0_3_snap pop_snap pop_total pct_snap_hh_0_3_131_165
<dbl> <dbl> <dbl> <dbl>
1 393901 4461936 30188424 2.33
pct_snap_pop_0_3_131_165
<dbl>
1 0.92Statewide, an estimated 29,249 households receiving SNAPhave at least one child age 0–3 and income between 131% and 165% of poverty, representing 2.33% of all households receiving SNAP.
| Measure | Estimate | Interpretation |
|---|---|---|
| SNAP households with children ages 0–3 and income 131–165% of poverty | 29,249 | Estimated number of SNAP households with at least one child age 0–3 and household income between 131% and 165% of poverty. |
| SNAP households with children ages 0–3 | 274,113 | Estimated number of SNAP households with at least one child age 0–3, regardless of income band. |
| SNAP households with income 131–165% of poverty | 127,583 | Estimated number of SNAP households with household income between 131% and 165% of poverty, regardless of whether children are present. |
| Total SNAP households | 1,257,339 | Estimated total number of households receiving SNAP. |
| Children ages 0–3 in SNAP households with income 131–165% of poverty | 41,084 | Estimated number of children ages 0–3 living in SNAP households with income between 131% and 165% of poverty. |
| Children ages 0–3 in SNAP households | 393,901 | Estimated number of children ages 0–3 living in SNAP households, regardless of income band. |
| Total population living in SNAP households | 4,461,936 | Estimated number of people of all ages living in SNAP households. |
| Total population | 30,188,424 | Estimated total Texas population in the ACS PUMS data. |
| Percent of SNAP households with children ages 0–3 and income 131–165% of poverty | 2.33% | Among all SNAP households, the share with at least one child age 0–3 and income between 131% and 165% of poverty. |
| Percent of SNAP population that is ages 0–3 and in households with income 131–165% of poverty | 0.92% | Among all people living in SNAP households, the share who are ages 0–3 and live in households with income between 131% and 165% of poverty. |