Here's one way which uses the sf, tidyverse, and tidycensus packages.
library(tidyverse)
library(sf)
#> Linking to GEOS 3.9.0, GDAL 3.2.1, PROJ 7.2.1
library(tigris)
#> To enable
#> caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.
options(tigris_class = "sf")
point_tib <- tibble(
Points=c("A", "B"),
longitude=c(-83.5, -81.6),
latitude=c(32.8, 28.3)
)
point_sf = st_as_sf(point_tib, coords = c("longitude", "latitude"),
crs = 4326, agr = "constant")
states_sf <- states()
point_sf <- st_transform(point_sf, st_crs(states_sf))
states_subset_idx <- st_contains(states_sf, point_sf) %>% as.data.frame() %>% pull(row.id)
states_subset <- states_sf %>%
slice(states_subset_idx)
ggplot() +
geom_sf(aes(geometry=geometry), data=states_subset) +
geom_sf(aes(geometry=geometry), data=point_sf)

Created on 2021-11-03 by the reprex package (v2.0.1)