Get Census Tract from Lat/Lon using tigris - r

Question

I have a relatively large number of coordinates for which I'd like to get the census tract (in addition to the FIPS code). I know that I can look up individual lat/lon pairs using call_geolocator_latlon (as done here), but this seems impractical for my purposes as the function issues a single call to the census bureaus' API, and I imagine would take a very long time to run on my ~200,000 pairs.
Is there a faster way to do this, perhaps by downloading shapefiles for each state using the block_groups function and mapping from lat/lon to census tract from there?

Answer 1

This doesn't use tigris, but utilizes sf::st_within() to check a data frame of points for overlapping tracts.
I'm using tidycensus here to get a map of California's tracts into R.
library(sf)
ca <- tidycensus::get_acs(state = "CA", geography = "tract",
variables = "B19013_001", geometry = TRUE)
Now to sim some data:
bbox <- st_bbox(ca)
my_points <- data.frame(
x = runif(100, bbox[1], bbox[3]),
y = runif(100, bbox[2], bbox[4])
) %>%
# convert the points to same CRS
st_as_sf(coords = c("x", "y"),
crs = st_crs(ca))
I'm doing 100 points here to be able to ggplot() the results, but the overlap calculation for 1e6 is fast, only a few seconds on my laptop.
my_points$tract <- as.numeric(st_within(my_points, ca)) # this is fast for 1e6 points
The results:
head(my_points) # tract is the row-index for overlapping census tract record in 'ca'
# but part would take forever with 1e6 points
library(ggplot2)
ggplot(ca) +
geom_sf() +
geom_sf(data = my_points, aes(color = is.na(tract)))

Answer 2

Great answer above. To get Census tract IDs you could also use st_join(). NAs for the tract IDs are those points that are within California's bounding box but don't intersect the state itself.
library(tigris)
library(tidyverse)
library(sf)
ca_tracts <- tracts("CA", class = "sf") %>%
select(GEOID, TRACTCE)
bbox <- st_bbox(ca_tracts)
my_points <- data.frame(
x = runif(200000, bbox[1], bbox[3]),
y = runif(200000, bbox[2], bbox[4])
) %>%
# convert the points to same CRS
st_as_sf(coords = c("x", "y"),
crs = st_crs(ca_tracts))
my_points_tract <- st_join(my_points, ca_tracts)
> my_points_tract
Simple feature collection with 200000 features and 2 fields
geometry type: POINT
dimension: XY
bbox: xmin: -124.4819 ymin: 32.52888 xmax: -114.1312 ymax: 42.0095
epsg (SRID): 4269
proj4string: +proj=longlat +datum=NAD83 +no_defs
First 10 features:
GEOID TRACTCE geometry
1 06025012400 012400 POINT (-114.6916 33.42711)
2 <NA> <NA> POINT (-118.4255 41.81896)
3 06053990000 990000 POINT (-121.8154 36.22736)
4 06045010200 010200 POINT (-123.6909 39.70572)
5 <NA> <NA> POINT (-116.9055 37.93532)
6 06019006405 006405 POINT (-119.511 37.09383)
7 06049000300 000300 POINT (-120.7215 41.3392)
8 <NA> <NA> POINT (-115.8916 39.32392)
9 06023990100 990100 POINT (-124.2737 40.14106)
10 06071008901 008901 POINT (-117.319 35.62759)