Using the {tidycensus} package in R to estimate number of four Connecticut Hispanic subgroups populations
Ramon Rodriguez-Santana, MBA, MPH
2024-04-21
Basic usage of {tidycensus} package.
tidycensus enables users to interact with specific US Census Bureau data APIs. It retrieves data frames compatible with tidyverse and integrates a straightforward geography feature.
Here is an example on how to use this package.
- Load libraries.
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.0 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── 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 errorslibrary(tidycensus)- Get a U.S. Census data API key from https://api.census.gov/data/key_signup.html.
After, enter your key alpha numeric value in census_api_key() function.
Here is an example of the code that you need to add to your program: census_api_key(“x2772902ex7ea12x13x671x12b76ab93f229x8xx”)
- Now, lets load the variables from American Community Survey (ACS), Demographic and Housing Characteristics summary file (DHC) and the (PL) redistricting data file created by the United States Census Bureau.
# Getting variables from the ACS
acs5_v20 <- load_variables(2020, "acs5", cache = TRUE)
View(acs5_v20)
# Getting variables from the DHC
dhc_v20 <- load_variables(2020, "dhc", cache = TRUE)
View(dhc_v20)
# Getting variables from the PL
pl_v20 <- load_variables(2020, "pl", cache = TRUE)
View(pl_v20)- For this example, let’s look at the estimated numbers of Connecticut Hispanic householders (i.e., Variable name: H11_004N) by county. We are going to be using data drawn from the 2020 Demographic and Housing Characteristics file (DHC).
ct_hisp_county <- get_decennial(geography = "county",
variables = "H11_004N",
year = 2020,
state = "CT",
sumfile = "dhc")## Getting data from the 2020 decennial Census## Using the Demographic and Housing Characteristics File## Note: 2020 decennial Census data use differential privacy, a technique that
## introduces errors into data to preserve respondent confidentiality.
## ℹ Small counts should be interpreted with caution.
## ℹ See https://www.census.gov/library/fact-sheets/2021/protecting-the-confidentiality-of-the-2020-census-redistricting-data.html for additional guidance.
## This message is displayed once per session.head(ct_hisp_county)## # A tibble: 6 × 4
## GEOID NAME variable value
## <chr> <chr> <chr> <dbl>
## 1 09001 Fairfield County, Connecticut H11_004N 21571
## 2 09003 Hartford County, Connecticut H11_004N 17019
## 3 09005 Litchfield County, Connecticut H11_004N 2065
## 4 09007 Middlesex County, Connecticut H11_004N 1567
## 5 09009 New Haven County, Connecticut H11_004N 17406
## 6 09011 New London County, Connecticut H11_004N 3111- Here is a plot of the estimated number of Connecticut Hispanic householders by county.
ct_hisp_county %>%
ggplot(aes(x = value, y = reorder(NAME, value))) +
geom_point() +
labs(title = "Estimated Number of Connecticut Hispanic Householders by County",
subtitle = "2020 Census Demographic and Housing Characteristics file (DHC)")
Spatial data in {tidycensus} package.
- Using the {tidycensus} package spatial data features, the following example shows median household income (i.e., Variable name: B19013_001) from the 2016-2020 ACS file. The spatial data will be displayed for Hartford county census tracts.
hartford <- get_acs(
state = "CT",
county = "Hartford",
geography = "tract",
variables = "B19013_001",
geometry = TRUE,
year = 2020
)## Getting data from the 2016-2020 5-year ACS## 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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|======================================================================| 100%head(hartford)## Simple feature collection with 6 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -72.90295 ymin: 41.59734 xmax: -72.51552 ymax: 41.83613
## Geodetic CRS: NAD83
## GEOID NAME variable
## 1 09003524700 Census Tract 5247, Hartford County, Connecticut B19013_001
## 2 09003420700 Census Tract 4207, Hartford County, Connecticut B19013_001
## 3 09003430602 Census Tract 4306.02, Hartford County, Connecticut B19013_001
## 4 09003514700 Census Tract 5147, Hartford County, Connecticut B19013_001
## 5 09003416500 Census Tract 4165, Hartford County, Connecticut B19013_001
## 6 09003471200 Census Tract 4712, Hartford County, Connecticut B19013_001
## estimate moe geometry
## 1 73550 16658 MULTIPOLYGON (((-72.71513 4...
## 2 82250 18694 MULTIPOLYGON (((-72.8654 41...
## 3 108632 23767 MULTIPOLYGON (((-72.90295 4...
## 4 31006 11568 MULTIPOLYGON (((-72.53319 4...
## 5 54220 6207 MULTIPOLYGON (((-72.82533 4...
## 6 43650 13376 MULTIPOLYGON (((-72.72104 4...- Now, let’s visualize the median household income from the 2016-2020 ACS by Hartford county census tracts.
hartford %>%
ggplot(aes(fill = estimate)) +
geom_sf(color = NA) +
scale_fill_viridis_c(option = "magma") +
labs(fill = "Median household income\nby census tracts,\nHartford County\n(2016-2020 ACS)")
- After, lets create faceted maps by census tracts for four Connecticut Hispanic subgroups populations.
# Select Hispanic subgroups
hispvars <- c(`Puerto Ricans` = "B03001_005",
Mexicans = "B03001_004",
Cubans = "B03001_006",
Peruvians = "B03001_023")
# Hartford County Hispanics subgroups by census tracts
hartford_county_hispanics <- get_acs(
state = "CT",
county = "Hartford",
geography = "tract",
variables = hispvars, # list of Hispanic groups
geometry = TRUE,
year = 2020
)## Getting data from the 2016-2020 5-year ACS## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.head(hartford_county_hispanics)## Simple feature collection with 6 features and 5 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: -72.86621 ymin: 41.65109 xmax: -72.70292 ymax: 41.7435
## Geodetic CRS: NAD83
## GEOID NAME variable
## 1 09003524700 Census Tract 5247, Hartford County, Connecticut Mexicans
## 2 09003524700 Census Tract 5247, Hartford County, Connecticut Puerto Ricans
## 3 09003524700 Census Tract 5247, Hartford County, Connecticut Cubans
## 4 09003524700 Census Tract 5247, Hartford County, Connecticut Peruvians
## 5 09003420700 Census Tract 4207, Hartford County, Connecticut Mexicans
## 6 09003420700 Census Tract 4207, Hartford County, Connecticut Puerto Ricans
## estimate moe geometry
## 1 0 13 MULTIPOLYGON (((-72.71513 4...
## 2 1586 253 MULTIPOLYGON (((-72.71513 4...
## 3 140 144 MULTIPOLYGON (((-72.71513 4...
## 4 170 140 MULTIPOLYGON (((-72.71513 4...
## 5 0 13 MULTIPOLYGON (((-72.8654 41...
## 6 189 147 MULTIPOLYGON (((-72.8654 41...- Now, let’s view the faceted maps for the for four Connecticut Hispanic subgroups populations.
# Plot the Hispanic subgroups
hartford_county_hispanics %>%
ggplot(aes(fill = estimate)) +
facet_wrap(~variable) +
geom_sf(color = NA) +
theme_void() +
scale_fill_viridis_c() +
labs(fill = "Hispanic subgroups\nestimated populations\nby census tract,\nHartford County\n(2016-2020 5-year ACS)")
Disclaimer. All presentation contents are the responsibility of the author and do not represent the official views of any organization.
A.M.D.G.
ite, inflammate omnia