From 74687c1e60c8b82833cf927ce366ec05eb7ef892 Mon Sep 17 00:00:00 2001
From: Kyle Walker Components of change populati
available for 2020 and later) as the
product
in
get_estimates()
returns all variables associated with that
component. For example, we can request all components of change
-variables for US states in 2022:
library(tidycensus)
library(tidyverse)
library(tigris)
options(tigris_use_cache = TRUE)
-us_components <- get_estimates(geography = "state", product = "components", year = 2022)
+us_components <- get_estimates(geography = "state", product = "components", vintage = 2023)
us_components
## # A tibble: 676 × 5
-## GEOID NAME variable year value
-## <chr> <chr> <chr> <int> <dbl>
-## 1 01 Alabama BIRTHS 2022 58280
-## 2 01 Alabama DEATHS 2022 66870
-## 3 01 Alabama NATURALCHG 2022 -8590
-## 4 01 Alabama INTERNATIONALMIG 2022 4597
-## 5 01 Alabama DOMESTICMIG 2022 28609
-## 6 01 Alabama NETMIG 2022 33206
-## 7 01 Alabama RESIDUAL 2022 -166
-## 8 01 Alabama RBIRTH 2022 11.5
-## 9 01 Alabama RDEATH 2022 13.2
-## 10 01 Alabama RNATURALCHG 2022 -1.70
+## GEOID NAME variable year value
+## <chr> <chr> <chr> <int> <dbl>
+## 1 01 Alabama BIRTHS 2023 58251
+## 2 01 Alabama DEATHS 2023 59813
+## 3 01 Alabama NATURALCHG 2023 -1562
+## 4 01 Alabama INTERNATIONALMIG 2023 5384
+## 5 01 Alabama DOMESTICMIG 2023 30744
+## 6 01 Alabama NETMIG 2023 36128
+## 7 01 Alabama RESIDUAL 2023 -1
+## 8 01 Alabama RBIRTH 2023 11.4
+## 9 01 Alabama RDEATH 2023 11.7
+## 10 01 Alabama RNATURALCHG 2023 -0.307
## # ℹ 666 more rows
The variables included in the components of change product consist of both estimates of counts and rates. Rates are preceded @@ -198,14 +198,14 @@
get_estimates()
also
supports simple feature geometry integration to facilitate mapping. In
the example below, we’ll acquire data on the net migration rate between
-2021 and 2022 for all counties in the United States. We’ll also use the
+2022 and 2023 for all counties in the United States. We’ll also use the
shift_geometry()
function from the tigris
package to shift and rescale counties outside the continental US for
national mapping.
net_migration <- get_estimates(geography = "county",
variables = "RNETMIG",
- year = 2022,
+ vintage = 2023,
geometry = TRUE,
resolution = "20m") %>%
shift_geometry()
@@ -219,22 +219,26 @@ Components of change populati
## # A tibble: 3,144 × 6
## GEOID NAME variable year value geometry
## <chr> <chr> <chr> <int> <dbl> <MULTIPOLYGON [m]>
-## 1 17127 Massac County, Illinois RNETMIG 2022 2.01 (((620306.9 994.4699, 62…
-## 2 27017 Carlton County, Minnes… RNETMIG 2022 9.57 (((225299.4 1038545, 283…
-## 3 37181 Vance County, North Ca… RNETMIG 2022 4.13 (((1544259 32180.06, 154…
-## 4 47079 Henry County, Tennessee RNETMIG 2022 12.6 (((663474.2 -85746.62, 6…
-## 5 06021 Glenn County, Californ… RNETMIG 2022 -14.1 (((-2253309 578922.1, -2…
-## 6 17093 Kendall County, Illino… RNETMIG 2022 12.5 (((610436.7 496523.1, 63…
-## 7 19095 Iowa County, Iowa RNETMIG 2022 -2.30 (((305057 494717.2, 3435…
-## 8 22003 Allen Parish, Louisiana RNETMIG 2022 -9.26 (((274501 -766852.4, 289…
-## 9 18055 Greene County, Indiana RNETMIG 2022 10.8 (((748725.5 221905.9, 76…
-## 10 33001 Belknap County, New Ha… RNETMIG 2022 8.18 (((1931026 926777.5, 193…
+## 1 17127 Massac County, Illinois RNETMIG 2023 -0.582 (((620306.9 994.4699, 62…
+## 2 27017 Carlton County, Minnes… RNETMIG 2023 9.57 (((225299.4 1038545, 283…
+## 3 37181 Vance County, North Ca… RNETMIG 2023 6.06 (((1544259 32180.06, 154…
+## 4 47079 Henry County, Tennessee RNETMIG 2023 11.6 (((663474.2 -85746.62, 6…
+## 5 06021 Glenn County, Californ… RNETMIG 2023 -9.17 (((-2253309 578922.1, -2…
+## 6 17093 Kendall County, Illino… RNETMIG 2023 12.1 (((610436.7 496523.1, 63…
+## 7 19095 Iowa County, Iowa RNETMIG 2023 -5.60 (((305057 494717.2, 3435…
+## 8 22003 Allen Parish, Louisiana RNETMIG 2023 -3.69 (((274501 -766852.4, 289…
+## 9 18055 Greene County, Indiana RNETMIG 2023 6.84 (((748725.5 221905.9, 76…
+## 10 33001 Belknap County, New Ha… RNETMIG 2023 10.7 (((1931026 926777.5, 193…
## # ℹ 3,134 more rows
We’ll next use tidyverse tools to generate a groups
column that bins the net migration rates into comprehensible categories,
and plot the result using geom_sf()
and ggplot2.
-order = c("-15 and below", "-15 to -5", "-5 to +5", "+5 to +15", "+15 and up")
+library(showtext)
+font_add_google("Roboto")
+showtext_auto()
+
+order = c("-15 and below", "-15 to -5", "-5 to +5", "+5 to +15", "+15 and up")
net_migration <- net_migration %>%
mutate(groups = case_when(
@@ -259,9 +263,9 @@ Components of change populati
scale_fill_brewer(palette = "PuOr", direction = -1) +
scale_color_brewer(palette = "PuOr", direction = -1, guide = FALSE) +
coord_sf(datum = NA) +
- theme_minimal(base_family = "Roboto") +
+ theme_minimal(base_family = "Roboto", base_size = 18) +
labs(title = "Net migration per 1000 residents by county",
- subtitle = "US Census Bureau 2022 Population Estimates",
+ subtitle = "US Census Bureau 2023 Population Estimates",
fill = "Rate",
caption = "Data acquired with the R tidycensus package | @kyle_e_walker")
## # A tibble: 114 × 7
diff --git a/docs/articles/other-datasets_files/figure-html/unnamed-chunk-4-1.png b/docs/articles/other-datasets_files/figure-html/unnamed-chunk-4-1.png
index 01e462d224a35f403f9bdc526551946df9262ee3..e83f9ed6e2632421dc9db4dc9fcaa3ea99f3bd42 100644
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