#install.packages("usethis")
usethis::use_course("https://github.com/r-journalism/nicar-2024-tidycensus/archive/master.zip")
# Run this in the console of RStudio
file.edit("04_tidycensusviz.R")Mapping Census data
You can follow along with the 04_tidycensus_viz file in the nicar-2024-tidycensus project folder that you downloaded in the intro link.
The repo containing the data and scripts for this section is on Github. To install those files, run the lines of code below.
To follow along with this walkthrough, simply run the lines of code in the gray boxes in the R console. Be sure to run them in order. If you run into an error, it may be because you skipped running some preceding lines of code.
Load libraries
library(tidyverse)
library(tidycensus)
library(sf)
library(mapview)“Spatial” ACS data
One of the best features of tidycensus is the argument
geometry = TRUE, which gets you the correct Census geometries with no hassleget_acs()withgeometry = TRUEreturns a spatial Census dataset containing simple feature geometries;
Downloading “Spatial” ACS data
geometry = TRUEdoes the hard work for you of acquiring and pre-joining spatial Census data
median_value_map <- get_acs(
geography = "tract",
state= "MD",
county="Baltimore City",
variables = "B25077_001", # median values of home
year = 2022,
geometry = TRUE
)Getting data from the 2018-2022 5-year ACSDownloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.- We get back a _simple features data frame
median_value_mapSimple feature collection with 199 features and 3 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -76.71152 ymin: 39.19721 xmax: -76.52945 ymax: 39.3722
Geodetic CRS: NAD83
First 10 features:
GEOID estimate moe geometry
1 24510151200 99400 51399 MULTIPOLYGON (((-76.66506 3...
2 24510270702 218000 17102 MULTIPOLYGON (((-76.57356 3...
3 24510271802 114800 40020 MULTIPOLYGON (((-76.68352 3...
4 24510272004 210600 76149 MULTIPOLYGON (((-76.69473 3...
5 24510090700 78700 27019 MULTIPOLYGON (((-76.60149 3...
6 24510180300 209900 18838 MULTIPOLYGON (((-76.63817 3...
7 24510270703 229600 35850 MULTIPOLYGON (((-76.56192 3...
8 24510010300 338900 21159 MULTIPOLYGON (((-76.58472 3...
9 24510060300 280700 21590 MULTIPOLYGON (((-76.58773 3...
10 24510260203 152800 11899 MULTIPOLYGON (((-76.55386 3...Exploring Census data interactively
library(mapview)
mapview(median_value_map)Creating a shaded map with zcol
mapview(median_value_map, zcol = "estimate")Try all the code again in a different county
median_value_map <- get_acs(
geography = "tract",
state= "MD", # Changeme
county="Baltimore County", # Change me
variables = "B25077_001", # median values of home
year = 2022,
geometry = TRUE
)
mapview(median_value_map, zcol = "estimate")Migration data
Let’s map some county-to-county migration data from the Census! [Link]
county_migration <- get_flows(
geography = "county",
county = "Baltimore City",
state = "MD"
)county_migration# A tibble: 2,664 × 7
GEOID1 GEOID2 FULL1_NAME FULL2_NAME variable estimate moe
<chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
1 24510 <NA> Baltimore city, Maryland Africa MOVEDIN 685 194
2 24510 <NA> Baltimore city, Maryland Africa MOVEDOUT NA NA
3 24510 <NA> Baltimore city, Maryland Africa MOVEDNET NA NA
4 24510 <NA> Baltimore city, Maryland Asia MOVEDIN 1466 279
5 24510 <NA> Baltimore city, Maryland Asia MOVEDOUT NA NA
6 24510 <NA> Baltimore city, Maryland Asia MOVEDNET NA NA
7 24510 <NA> Baltimore city, Maryland Central Ameri… MOVEDIN 322 174
8 24510 <NA> Baltimore city, Maryland Central Ameri… MOVEDOUT NA NA
9 24510 <NA> Baltimore city, Maryland Central Ameri… MOVEDNET NA NA
10 24510 <NA> Baltimore city, Maryland Caribbean MOVEDIN 231 94
# ℹ 2,654 more rowsDownloading map data
county_map <- get_acs(
geography = "county",
variable = c("Population"="B03002_001"),
geometry = TRUE
)Getting data from the 2018-2022 5-year ACSDownloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.county_mapSimple feature collection with 3222 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -179.1467 ymin: 17.88328 xmax: 179.7785 ymax: 71.38782
Geodetic CRS: NAD83
First 10 features:
GEOID NAME variable estimate moe
1 01069 Houston County, Alabama Population 107040 NA
2 01023 Choctaw County, Alabama Population 12669 NA
3 01005 Barbour County, Alabama Population 24877 NA
4 01107 Pickens County, Alabama Population 18925 NA
5 01033 Colbert County, Alabama Population 57270 NA
6 04012 La Paz County, Arizona Population 16681 NA
7 04001 Apache County, Arizona Population 66054 NA
8 05081 Little River County, Arkansas Population 12024 NA
9 05121 Randolph County, Arkansas Population 18619 NA
10 06037 Los Angeles County, California Population 9936690 NA
geometry
1 MULTIPOLYGON (((-85.71209 3...
2 MULTIPOLYGON (((-88.47323 3...
3 MULTIPOLYGON (((-85.74803 3...
4 MULTIPOLYGON (((-88.34043 3...
5 MULTIPOLYGON (((-88.13925 3...
6 MULTIPOLYGON (((-114.7312 3...
7 MULTIPOLYGON (((-110.0007 3...
8 MULTIPOLYGON (((-94.48558 3...
9 MULTIPOLYGON (((-91.40687 3...
10 MULTIPOLYGON (((-118.6044 3...Prep the migration data
county_migration_moved <- county_migration |>
filter(variable=="MOVEDIN") |>
filter(!is.na(GEOID2)) |>
select(GEOID=GEOID2, migration=estimate) Join the migration data with the shapefile
county_map_migration <- county_map %>%
inner_join(county_migration_moved)
mapview(county_map_migration, zcol = "migration")Can you map migration out?
county_migration_moved <- county_migration |>
filter(variable=="MOVEDOUT") |>
filter(!is.na(GEOID2)) |>
select(GEOID=GEOID2, migration=estimate)
county_map_migration <- county_map %>%
inner_join(county_migration_moved)
mapview(county_map_migration, zcol = "migration")More resources
- R-Journalism: How to make static maps with Census data
- R-Journalism: Traffic stops case study