Update CCA xwalk examples with 2020 census data

R/data.R

@@ -990,33 +990,31 @@
 #' @examples
 #' suppressPackageStartupMessages(library(dplyr))
 #'
-#' # View the tracts with population not fully contained in a single CCA
+#' # View the tracts with population split between multiple CCAs
 #' filter(xwalk_tract2cca, pop_pct < 1)
 #'
-#' # Estimate CCA-level transit mode share from tract-level ACS data
-#' df_tract <- tidycensus::get_acs(
-#'   "tract", state = "IL", county = "031", table = "B08006",
-#'   year = 2019, survey = "acs5", output = "wide", cache_table = TRUE
+#' # Estimate CCA-level population density from tract-level Census data
+#' df_tract <- tidycensus::get_decennial(
+#'   geography = "tract", variables = c("P1_001N"),
+#'   year = 2020, state = "IL", county = c("031", "043"), output = "wide"
 #' ) %>%
-#'   rename(workers = B08006_001E, transit = B08006_008E) %>%
-#'   select(GEOID, workers, transit)
+#'   suppressMessages() %>%  # Hide tidycensus messages
+#'   select(geoid_tract = GEOID, pop = P1_001N)
 #'
-#' df_cca <- xwalk_tract2cca_2010 %>%  # Note use of 2010 version with 2019 ACS
-#'   left_join(df_tract, by = c("geoid_tract" = "GEOID")) %>%
-#'   mutate(transit = transit * pop_pct,
-#'          workers = workers * pop_pct) %>%
+#' df_cca <- xwalk_tract2cca %>%
+#'   left_join(df_tract, by = "geoid_tract") %>%
+#'   mutate(pop = pop * pop_pct) %>%
 #'   group_by(cca_num) %>%
-#'   summarize_at(vars(transit, workers), sum) %>%
-#'   mutate(transit_commute_pct = transit / workers)
+#'   summarize(pop = sum(pop))
 #' df_cca
 #'
 #' # Join to cca_sf for mapping
 #' library(ggplot2)
 #' cca_sf %>%
 #'   left_join(df_cca, by = "cca_num") %>%
 #'   ggplot() +
-#'     geom_sf(aes(fill = transit_commute_pct), lwd = 0.1) +
-#'     scale_fill_viridis_c() +
+#'     geom_sf(aes(fill = pop / sqmi), lwd = 0.1) +
+#'     scale_fill_viridis_c(direction = -1) +
 #'     theme_void()
 "xwalk_tract2cca"
 
@@ -1091,32 +1089,32 @@
 #' @examples
 #' suppressPackageStartupMessages(library(dplyr))
 #'
-#' # View the block groups with households not fully contained in a single CCA
-#' filter(xwalk_blockgroup2cca, hh_pct < 1)
+#' # View the block groups with housing units split between multiple CCAs
+#' filter(xwalk_blockgroup2cca, hu_pct < 1)
 #'
-#' # Estimate CCA-level unemployment rate from block group-level ACS data
-#' df_blkgrp <- tidycensus::get_acs(
-#'   "block group", state = "IL", county = "031", table = "B23025",
-#'   year = 2019, survey = "acs5", output = "wide", cache_table = TRUE
+#' # Estimate CCA-level housing vacancy rates from block group-level Census data
+#' df_blkgrp <- tidycensus::get_decennial(
+#'   geography = "block group", variables = c("H1_001N", "H1_003N"),
+#'   year = 2020, state = "IL", county = c("031", "043"), output = "wide"
 #' ) %>%
-#'   rename(civ_lf = B23025_003E, unemp = B23025_005E) %>%
-#'   select(GEOID, civ_lf, unemp)
+#'   suppressMessages() %>%  # Hide tidycensus messages
+#'   select(geoid_blkgrp = GEOID, hu_tot = H1_001N, hu_vac = H1_003N)
 #'
-#' df_cca <- xwalk_blockgroup2cca_2010 %>%  # Note use of 2010 version with 2019 ACS
-#'   left_join(df_blkgrp, by = c("geoid_blkgrp" = "GEOID")) %>%
-#'   mutate(civ_lf = civ_lf * pop_pct,
-#'          unemp = unemp * pop_pct) %>%
+#' df_cca <- xwalk_blockgroup2cca %>%
+#'   left_join(df_blkgrp, by = "geoid_blkgrp") %>%
+#'   mutate(hu_tot = hu_tot * hu_pct,
+#'          hu_vac = hu_vac * hu_pct) %>%
 #'   group_by(cca_num) %>%
-#'   summarize_at(vars(civ_lf, unemp), sum) %>%
-#'   mutate(unemp_rate = unemp / civ_lf)
+#'   summarize_at(vars(hu_tot, hu_vac), sum) %>%
+#'   mutate(vac_rate = hu_vac / hu_tot)
 #' df_cca
 #'
 #' # Join to cca_sf for mapping
 #' library(ggplot2)
 #' cca_sf %>%
 #'   left_join(df_cca, by = "cca_num") %>%
 #'   ggplot() +
-#'     geom_sf(aes(fill = unemp_rate), lwd = 0.1) +
+#'     geom_sf(aes(fill = vac_rate), lwd = 0.1) +
 #'     scale_fill_viridis_c(direction = -1) +
 #'     theme_void()
 "xwalk_blockgroup2cca"