From 74687c1e60c8b82833cf927ce366ec05eb7ef892 Mon Sep 17 00:00:00 2001 From: Kyle Walker Date: Mon, 18 Mar 2024 08:25:40 -0500 Subject: [PATCH 1/3] add support for 2023 PEP --- DESCRIPTION | 4 +- R/estimates.R | 101 +++++++++++++++++++++++++++++++------------ R/zzz.r | 1 + man/get_estimates.Rd | 12 ++--- 4 files changed, 83 insertions(+), 35 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 49759bf..edf5612 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,12 +1,12 @@ Package: tidycensus Type: Package Title: Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames -Version: 1.6.2 +Version: 1.6.3 Authors@R: c( person(given = "Kyle", family = "Walker", email="kyle@walker-data.com", role=c("aut", "cre")), person(given = "Matt", family = "Herman", email = "mfherman@gmail.com", role = "aut"), person(given = "Kris", family = "Eberwein", email = "eberwein@knights.ucf.edu", role = "ctb")) -Date: 2024-03-05 +Date: 2024-03-18 URL: https://walker-data.com/tidycensus/ BugReports: https://github.com/walkerke/tidycensus/issues Description: An integrated R interface to several United States Census Bureau diff --git a/R/estimates.R b/R/estimates.R index f40ec12..1b0efde 100644 --- a/R/estimates.R +++ b/R/estimates.R @@ -20,11 +20,9 @@ #' #' @param breakdown_labels Whether or not to label breakdown elements returned when #' \code{product = "characteristics"}. Defaults to FALSE. -#' -#' Not yet supported for 2020 and later. -#' @param year The data year (defaults to 2022). It is recommended to use the most recent vintage -#' available for a given decennial series (so, year = 2019 for the 2010s, and year = 2022 for the 2020s) -#' and use \code{time_series = TRUE} to access time-series estimates. +#' @param vintage It is recommended to use the most recent vintage +#' available for a given decennial series (so, year = 2019 for the 2010s, and year = 2023 for the 2020s). Will default to 2022 until the full PEP for 2023 is released. +#' @param year The data year (defaults to the vintage requested). Use \code{time_series = TRUE} to access time-series estimates. #' @param state The state for which you are requesting data. State #' names, postal codes, and FIPS codes are accepted. #' Defaults to NULL. @@ -64,8 +62,8 @@ get_estimates <- function(geography = c("us", "region", "division", "state", "co "metropolitan statistical area/micropolitan statistical area", "cbsa", "metropolitan division", "combined statistical area"), product = NULL, variables = NULL, - breakdown = NULL, breakdown_labels = FALSE, - year = 2022, state = NULL, county = NULL, + breakdown = NULL, breakdown_labels = FALSE, vintage = 2022, + year = vintage, state = NULL, county = NULL, time_series = FALSE, output = "tidy", geometry = FALSE, keep_geo_vars = FALSE, shift_geo = FALSE, key = NULL, show_call = FALSE, ...) { @@ -91,6 +89,10 @@ get_estimates <- function(geography = c("us", "region", "division", "state", "co rlang::abort("The only supported geographies at this time for population characteristics 2020 and later are 'state' and 'county'.") } + if (vintage > 2022) { + rlang::abort("The Characteristics dataset has not yet been released for vintages beyond 2022.") + } + if (geography == "state") { state_raw <- suppressMessages(readr::read_csv("https://www2.census.gov/programs-surveys/popest/datasets/2020-2022/state/asrh/sc-est2022-alldata6.csv")) @@ -311,15 +313,26 @@ get_estimates <- function(geography = c("us", "region", "division", "state", "co if (product == "population") { variables <- population_estimates_variables22 } else if (product == "components") { - variables <- components_estimates_variables22 + if (year == 2021) { + variables <- components_estimates_variables21 + + } else { + variables <- components_estimates_variables22 + + } } } # Get the data into a reasonable first format that is consistent for downstream use if (geography == "us") { - raw <- suppressMessages(readr::read_csv("https://www2.census.gov/programs-surveys/popest/datasets/2020-2022/state/totals/NST-EST2022-ALLDATA.csv")) %>% - dplyr::filter(SUMLEV == "010") + if (vintage == 2021) { + raw <- suppressMessages(readr::read_csv(sprintf("https://www2.census.gov/programs-surveys/popest/datasets/2020-%s/state/totals/NST-EST%s-alldata.csv", vintage, vintage))) %>% + dplyr::filter(SUMLEV == "010") + } else { + raw <- suppressMessages(readr::read_csv(sprintf("https://www2.census.gov/programs-surveys/popest/datasets/2020-%s/state/totals/NST-EST%s-ALLDATA.csv", vintage, vintage))) %>% + dplyr::filter(SUMLEV == "010") + } raw <- raw[,!(names(raw) %in% c("SUMLEV", "REGION", "DIVISION", "STATE"))] @@ -335,9 +348,15 @@ get_estimates <- function(geography = c("us", "region", "division", "state", "co } else if (geography == "region") { - raw <- suppressMessages(readr::read_csv("https://www2.census.gov/programs-surveys/popest/datasets/2020-2022/state/totals/NST-EST2022-ALLDATA.csv")) %>% - dplyr::filter(SUMLEV == "020") %>% - dplyr::mutate(GEOID = REGION) + if (vintage == 2021) { + raw <- suppressMessages(readr::read_csv(sprintf("https://www2.census.gov/programs-surveys/popest/datasets/2020-%s/state/totals/NST-EST%s-alldata.csv", vintage, vintage))) %>% + dplyr::filter(SUMLEV == "020") %>% + dplyr::mutate(GEOID = REGION) + } else { + raw <- suppressMessages(readr::read_csv(sprintf("https://www2.census.gov/programs-surveys/popest/datasets/2020-%s/state/totals/NST-EST%s-ALLDATA.csv", vintage, vintage))) %>% + dplyr::filter(SUMLEV == "020") %>% + dplyr::mutate(GEOID = REGION) + } raw <- raw[,!(names(raw) %in% c("SUMLEV", "REGION", "DIVISION", "STATE"))] @@ -350,9 +369,13 @@ get_estimates <- function(geography = c("us", "region", "division", "state", "co } else if (geography == "division") { - raw <- suppressMessages(readr::read_csv("https://www2.census.gov/programs-surveys/popest/datasets/2020-2022/state/totals/NST-EST2022-ALLDATA.csv")) %>% - dplyr::filter(SUMLEV == "030") %>% - dplyr::mutate(GEOID = DIVISION) + if (vintage == 2021) { + rlang::abort("Divisions are not available in the 2021 vintage dataset.") + } else { + raw <- suppressMessages(readr::read_csv(sprintf("https://www2.census.gov/programs-surveys/popest/datasets/2020-%s/state/totals/NST-EST%s-ALLDATA.csv", vintage, vintage))) %>% + dplyr::filter(SUMLEV == "030") %>% + dplyr::mutate(GEOID = DIVISION) + } raw <- raw[,!(names(raw) %in% c("SUMLEV", "REGION", "DIVISION", "STATE"))] @@ -365,9 +388,15 @@ get_estimates <- function(geography = c("us", "region", "division", "state", "co } else if (geography == "state") { - raw <- suppressMessages(readr::read_csv("https://www2.census.gov/programs-surveys/popest/datasets/2020-2022/state/totals/NST-EST2022-ALLDATA.csv")) %>% - dplyr::filter(SUMLEV == "040") %>% - dplyr::mutate(GEOID = STATE) + if (vintage == 2021) { + raw <- suppressMessages(readr::read_csv(sprintf("https://www2.census.gov/programs-surveys/popest/datasets/2020-%s/state/totals/NST-EST%s-alldata.csv", vintage, vintage))) %>% + dplyr::filter(SUMLEV == "040") %>% + dplyr::mutate(GEOID = STATE) + } else { + raw <- suppressMessages(readr::read_csv(sprintf("https://www2.census.gov/programs-surveys/popest/datasets/2020-%s/state/totals/NST-EST%s-ALLDATA.csv", vintage, vintage))) %>% + dplyr::filter(SUMLEV == "040") %>% + dplyr::mutate(GEOID = STATE) + } raw <- raw[,!(names(raw) %in% c("SUMLEV", "REGION", "DIVISION", "STATE"))] @@ -380,7 +409,7 @@ get_estimates <- function(geography = c("us", "region", "division", "state", "co } else if (geography == "county") { - raw <- suppressMessages(readr::read_csv("https://www2.census.gov/programs-surveys/popest/datasets/2020-2022/counties/totals/co-est2022-alldata.csv")) %>% + raw <- suppressMessages(readr::read_csv(sprintf("https://www2.census.gov/programs-surveys/popest/datasets/2020-%s/counties/totals/co-est%s-alldata.csv", vintage, vintage))) %>% dplyr::filter(SUMLEV == "050") %>% dplyr::mutate(GEOID = paste0(STATE, COUNTY), NAME = paste0(CTYNAME, ", ", STNAME)) @@ -397,9 +426,15 @@ get_estimates <- function(geography = c("us", "region", "division", "state", "co } else if (geography == "cbsa" || geography == "metropolitan statistical area/micropolitan statistical area") { - raw <- suppressMessages(readr::read_csv("https://www2.census.gov/programs-surveys/popest/datasets/2020-2022/metro/totals/cbsa-est2022.csv")) %>% - dplyr::filter(LSAD %in% c("Micropolitan Statistical Area", "Metropolitan Statistical Area")) %>% - dplyr::mutate(GEOID = CBSA) + if (vintage != 2022) { + raw <- suppressMessages(readr::read_csv(sprintf("https://www2.census.gov/programs-surveys/popest/datasets/2020-%s/metro/totals/cbsa-est%s-alldata.csv", vintage, vintage))) %>% + dplyr::filter(LSAD %in% c("Micropolitan Statistical Area", "Metropolitan Statistical Area")) %>% + dplyr::mutate(GEOID = CBSA) + } else { + raw <- suppressMessages(readr::read_csv("https://www2.census.gov/programs-surveys/popest/datasets/2020-2022/metro/totals/cbsa-est2022.csv")) %>% + dplyr::filter(LSAD %in% c("Micropolitan Statistical Area", "Metropolitan Statistical Area")) %>% + dplyr::mutate(GEOID = CBSA) + } raw <- raw[,!(names(raw) %in% c("MDIV", "STCOU", "LSAD", "CBSA"))] @@ -411,9 +446,16 @@ get_estimates <- function(geography = c("us", "region", "division", "state", "co dplyr::mutate(variable = stringr::str_remove(variable, "_")) } else if (geography == "combined statistical area") { - raw <- suppressMessages(readr::read_csv("https://www2.census.gov/programs-surveys/popest/datasets/2020-2022/metro/totals/csa-est2022.csv")) %>% - dplyr::filter(LSAD == "Combined Statistical Area") %>% - dplyr::mutate(GEOID = CSA) + + if (vintage != 2022) { + raw <- suppressMessages(readr::read_csv(sprintf("https://www2.census.gov/programs-surveys/popest/datasets/2020-%s/metro/totals/csa-est%s-alldata.csv", vintage, vintage))) %>% + dplyr::filter(LSAD == "Combined Statistical Area") %>% + dplyr::mutate(GEOID = CSA) + } else { + raw <- suppressMessages(readr::read_csv("https://www2.census.gov/programs-surveys/popest/datasets/2020-2022/metro/totals/csa-est2022.csv")) %>% + dplyr::filter(LSAD == "Combined Statistical Area") %>% + dplyr::mutate(GEOID = CSA) + } raw <- raw[,!(names(raw) %in% c("MDIV", "STCOU", "LSAD", "CBSA", "CSA"))] @@ -425,6 +467,11 @@ get_estimates <- function(geography = c("us", "region", "division", "state", "co dplyr::mutate(variable = stringr::str_remove(variable, "_")) } else if (geography == "place") { + + if (vintage > 2022) { + rlang::abort("The most recent PEP release for this geography is 2022.") + } + raw <- suppressMessages(readr::read_csv("https://www2.census.gov/programs-surveys/popest/datasets/2020-2022/cities/totals/sub-est2022.csv")) %>% dplyr::filter(SUMLEV == "162") %>% dplyr::mutate(GEOID = paste0(STATE, PLACE), @@ -554,7 +601,7 @@ get_estimates <- function(geography = c("us", "region", "division", "state", "co variables = variables, breakdown = breakdown, breakdown_labels = breakdown_labels, - year = year, + year = vintage, state = .x, county = county, time_series = time_series, diff --git a/R/zzz.r b/R/zzz.r index d05816b..f49443c 100644 --- a/R/zzz.r +++ b/R/zzz.r @@ -33,6 +33,7 @@ population_estimates_variables <- c("POP", "DENSITY") components_estimates_variables <- c("BIRTHS", "DEATHS","DOMESTICMIG","INTERNATIONALMIG","NATURALINC","NETMIG","RBIRTH","RDEATH","RDOMESTICMIG","RINTERNATIONALMIG","RNATURALINC","RNETMIG") housing_estimates_variables <- "HUEST" population_estimates_variables22 <- c("POPESTIMATE", "NPOPCHG") +components_estimates_variables21 <- c("BIRTHS", "DEATHS", "NATURALINC", "DOMESTICMIG","INTERNATIONALMIG","NETMIG","RBIRTH","RDEATH","RDOMESTICMIG","RINTERNATIONALMIG","RNATURALINC","RNETMIG","RESIDUAL") components_estimates_variables22 <- c("BIRTHS", "DEATHS", "NATURALCHG", "DOMESTICMIG","INTERNATIONALMIG","NETMIG","RBIRTH","RDEATH","RDOMESTICMIG","RINTERNATIONALMIG","RNATURALCHG","RNETMIG","RESIDUAL") diff --git a/man/get_estimates.Rd b/man/get_estimates.Rd index 8e64ac1..e4aa5a1 100644 --- a/man/get_estimates.Rd +++ b/man/get_estimates.Rd @@ -13,7 +13,8 @@ get_estimates( variables = NULL, breakdown = NULL, breakdown_labels = FALSE, - year = 2022, + vintage = 2022, + year = vintage, state = NULL, county = NULL, time_series = FALSE, @@ -45,13 +46,12 @@ a vector, returning population estimates in the \code{value} column for all combinations of these breakdowns. For years 2020 and later, \code{"AGE"} is also available for single-year age when using \code{geography = "state"}.} \item{breakdown_labels}{Whether or not to label breakdown elements returned when - \code{product = "characteristics"}. Defaults to FALSE. +\code{product = "characteristics"}. Defaults to FALSE.} - Not yet supported for 2020 and later.} +\item{vintage}{It is recommended to use the most recent vintage +available for a given decennial series (so, year = 2019 for the 2010s, and year = 2023 for the 2020s). Will default to 2022 until the full PEP for 2023 is released.} -\item{year}{The data year (defaults to 2022). It is recommended to use the most recent vintage -available for a given decennial series (so, year = 2019 for the 2010s, and year = 2022 for the 2020s) -and use \code{time_series = TRUE} to access time-series estimates.} +\item{year}{The data year (defaults to the vintage requested). Use \code{time_series = TRUE} to access time-series estimates.} \item{state}{The state for which you are requesting data. State names, postal codes, and FIPS codes are accepted. From 7e0a86ededc832593bef12842f3031897e88fa95 Mon Sep 17 00:00:00 2001 From: Kyle Walker Date: Mon, 18 Mar 2024 08:33:50 -0500 Subject: [PATCH 2/3] update docs for PEP '23 --- R/estimates.R | 3 +++ man/get_estimates.Rd | 7 ++++++- 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/R/estimates.R b/R/estimates.R index 1b0efde..29ded13 100644 --- a/R/estimates.R +++ b/R/estimates.R @@ -1,5 +1,7 @@ #' Get data from the US Census Bureau Population Estimates Program #' +#' The \code{get_estimates()} function requests data from the US Census Bureau's Population Estimates Program (PEP) datasets. The PEP datasets are defined by the US Census Bureau as follows: "The Census Bureau's Population Estimates Program (PEP) produces estimates of the population for the United States, its states, counties, cities, and towns, as well as for the Commonwealth of Puerto Rico and its municipios. Demographic components of population change (births, deaths, and migration) are produced at the national, state, and county levels of geography. Additionally, housing unit estimates are produced for the nation, states, and counties. PEP annually utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census and produce a time series of estimates of population, demographic components of change, and housing units. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously-produced estimates for those dates." +#' #' \code{get_estimates()} requests data from the Population Estimates API for years 2019 and earlier; however the Population Estimates are no longer supported on the API as of 2020. For recent years, \code{get_estimates()} reads a flat file from the Census website and parses it. This means that arguments and output for 2020 and later datasets may differ slightly from datasets acquired for 2019 and earlier. #' #' As of April 2022, variables available for 2020 and later datasets are as follows: ESTIMATESBASE, POPESTIMATE, NPOPCHG, BIRTHS, DEATHS, NATURALCHG, INTERNATIONALMIG, DOMESTICMIG, NETMIG, RESIDUAL, GQESTIMATESBASE, GQESTIMATES, RBIRTH, RDEATH, RNATURALCHG, RINTERNATIONALMIG, RDOMESTICMIG, and RNETMIG. @@ -56,6 +58,7 @@ #' @param ... other keyword arguments #' #' @return A tibble, or sf tibble, of population estimates data +#' @seealso \url{https://www.census.gov/programs-surveys/popest/about.html} #' @export get_estimates <- function(geography = c("us", "region", "division", "state", "county", "county subdivision", "place/balance (or part)", "place", "consolidated city", "place (or part)", diff --git a/man/get_estimates.Rd b/man/get_estimates.Rd index e4aa5a1..0ca3d79 100644 --- a/man/get_estimates.Rd +++ b/man/get_estimates.Rd @@ -98,8 +98,13 @@ a browser and see what is returned by the API directly. Defaults to FALSE.} A tibble, or sf tibble, of population estimates data } \description{ -\code{get_estimates()} requests data from the Population Estimates API for years 2019 and earlier; however the Population Estimates are no longer supported on the API as of 2020. For recent years, \code{get_estimates()} reads a flat file from the Census website and parses it. This means that arguments and output for 2020 and later datasets may differ slightly from datasets acquired for 2019 and earlier. +The \code{get_estimates()} function requests data from the US Census Bureau's Population Estimates Program (PEP) datasets. The PEP datasets are defined by the US Census Bureau as follows: "The Census Bureau's Population Estimates Program (PEP) produces estimates of the population for the United States, its states, counties, cities, and towns, as well as for the Commonwealth of Puerto Rico and its municipios. Demographic components of population change (births, deaths, and migration) are produced at the national, state, and county levels of geography. Additionally, housing unit estimates are produced for the nation, states, and counties. PEP annually utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census and produce a time series of estimates of population, demographic components of change, and housing units. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously-produced estimates for those dates." } \details{ +\code{get_estimates()} requests data from the Population Estimates API for years 2019 and earlier; however the Population Estimates are no longer supported on the API as of 2020. For recent years, \code{get_estimates()} reads a flat file from the Census website and parses it. This means that arguments and output for 2020 and later datasets may differ slightly from datasets acquired for 2019 and earlier. + As of April 2022, variables available for 2020 and later datasets are as follows: ESTIMATESBASE, POPESTIMATE, NPOPCHG, BIRTHS, DEATHS, NATURALCHG, INTERNATIONALMIG, DOMESTICMIG, NETMIG, RESIDUAL, GQESTIMATESBASE, GQESTIMATES, RBIRTH, RDEATH, RNATURALCHG, RINTERNATIONALMIG, RDOMESTICMIG, and RNETMIG. } +\seealso{ +\url{https://www.census.gov/programs-surveys/popest/about.html} +} From 9363bde074472ac4ba6820ede7ad1e29c86fbcc8 Mon Sep 17 00:00:00 2001 From: Kyle Walker Date: Mon, 18 Mar 2024 09:55:11 -0500 Subject: [PATCH 3/3] update docs --- R/estimates.R | 29 +--- docs/404.html | 2 +- docs/LICENSE-text.html | 2 +- docs/articles/basic-usage.html | 2 +- docs/articles/index.html | 2 +- docs/articles/margins-of-error.html | 2 +- docs/articles/other-datasets.html | 66 ++++---- .../figure-html/unnamed-chunk-4-1.png | Bin 431083 -> 408342 bytes .../figure-html/unnamed-chunk-6-1.png | Bin 83846 -> 58061 bytes docs/articles/pums-data.html | 12 +- .../figure-html/unnamed-chunk-23-1.png | Bin 139337 -> 139245 bytes docs/articles/spatial-data.html | 143 +----------------- .../figure-html/unnamed-chunk-4-1.png | Bin 484542 -> 484538 bytes .../figure-html/unnamed-chunk-5-1.png | Bin 153462 -> 153500 bytes .../figure-html/unnamed-chunk-6-1.png | Bin 145090 -> 145155 bytes docs/authors.html | 6 +- docs/index.html | 2 +- docs/news/index.html | 2 +- docs/pkgdown.yml | 2 +- docs/reference/acs5_geography.html | 2 +- docs/reference/as_dot_density.html | 2 +- docs/reference/census_api_key.html | 2 +- docs/reference/check_ddhca_groups.html | 2 +- docs/reference/county_laea.html | 2 +- docs/reference/fips_codes.html | 2 +- docs/reference/get_acs.html | 2 +- docs/reference/get_decennial.html | 2 +- docs/reference/get_estimates.html | 28 ++-- docs/reference/get_flows.html | 2 +- docs/reference/get_pop_groups.html | 2 +- docs/reference/get_pums.html | 2 +- docs/reference/index.html | 2 +- docs/reference/interpolate_pw.html | 2 +- docs/reference/load_variables.html | 2 +- docs/reference/mig_recodes.html | 2 +- docs/reference/moe_product.html | 2 +- docs/reference/moe_prop.html | 2 +- docs/reference/moe_ratio.html | 2 +- docs/reference/moe_sum.html | 2 +- docs/reference/pums_variables.html | 4 +- docs/reference/significance.html | 2 +- docs/reference/state_laea.html | 2 +- docs/reference/summary_files.html | 2 +- docs/reference/tidycensus.html | 2 +- docs/reference/to_survey.html | 2 +- vignettes/other-datasets.Rmd | 18 ++- 46 files changed, 120 insertions(+), 250 deletions(-) diff --git a/R/estimates.R b/R/estimates.R index 29ded13..8b97c1e 100644 --- a/R/estimates.R +++ b/R/estimates.R @@ -879,27 +879,14 @@ get_estimates <- function(geography = c("us", "region", "division", "state", "co } else { - geom <- try(suppressMessages(use_tigris(geography = geography, year = year, - state = state, county = county, ...))) - - # if (year > 2021) { - # geom <- try(suppressMessages(use_tigris(geography = geography, year = 2021, - # state = state, county = county, ...))) - # - # if (geography == "county" && "09" %in% stringr::str_sub(geom$GEOID, 1, 2)) { - # ct_2022 <- clean_connecticut() - # - # geom <- geom %>% - # dplyr::filter(!stringr::str_sub(GEOID, 1, 2) == "09") %>% - # dplyr::bind_rows(ct_2022) - # } - # - # } else { - # geom <- try(suppressMessages(use_tigris(geography = geography, year = year, - # state = state, county = county, ...))) - # } - - + # Handle here until 2023 CB files are released + if (year == 2023) { + geom <- try(suppressMessages(use_tigris(geography = geography, year = 2022, + state = state, county = county, ...))) + } else { + geom <- try(suppressMessages(use_tigris(geography = geography, year = year, + state = state, county = county, ...))) + } if ("try-error" %in% class(geom)) { stop("Your geometry data download failed. Please try again later or check the status of the Census Bureau website at https://www2.census.gov/geo/tiger/", call. = FALSE) diff --git a/docs/404.html b/docs/404.html index ff4f6eb..53cf7cd 100644 --- a/docs/404.html +++ b/docs/404.html @@ -39,7 +39,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index af3a11b..4c09760 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/articles/basic-usage.html b/docs/articles/basic-usage.html index 317d494..058971f 100644 --- a/docs/articles/basic-usage.html +++ b/docs/articles/basic-usage.html @@ -40,7 +40,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/articles/index.html b/docs/articles/index.html index e9ae161..b8cf552 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/articles/margins-of-error.html b/docs/articles/margins-of-error.html index 378a005..e8ee083 100644 --- a/docs/articles/margins-of-error.html +++ b/docs/articles/margins-of-error.html @@ -40,7 +40,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/articles/other-datasets.html b/docs/articles/other-datasets.html index 6493e7e..178c88c 100644 --- a/docs/articles/other-datasets.html +++ b/docs/articles/other-datasets.html @@ -40,7 +40,7 @@ tidycensus - 1.6.1 + 1.6.3 @@ -153,29 +153,29 @@

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:

+variables for US states in 2023:

 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 @@

Components of change populati in other tidycensus functions. 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")

@@ -288,7 +292,7 @@

Estimates of population charact breakdown_labels = TRUE, state = "CA", county = "Los Angeles", - year = 2022) + vintage = 2022) la_age_hisp

## # A tibble: 114 × 7
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Using get_pums() to d
## # A tibble: 6,436 × 9
 ##   SERIALNO      SPORDER  WGTP PWGTP  AGEP PUMA  ST    SCHL  SEX  
 ##   <chr>           <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr> <chr>
-## 1 2018GQ0002083       1     0    59    20 00100 50    19    2    
-## 2 2018GQ0002131       1     0     2    51 00400 50    16    1    
-## 3 2018GQ0002587       1     0    52    18 00400 50    16    1    
-## 4 2018GQ0003751       1     0    34    19 00100 50    19    2    
-## 5 2018GQ0003853       1     0   129    20 00400 50    19    1    
+## 1 2018GQ0001230       1     0     3    26 00300 50    20    1    
+## 2 2018GQ0002135       1     0    64    18 00100 50    19    2    
+## 3 2018GQ0002999       1     0    62    20 00400 50    19    2    
+## 4 2018GQ0004077       1     0    16    94 00200 50    21    2    
+## 5 2018GQ0006486       1     0    26    20 00400 50    19    1    
 ## # ℹ 6,431 more rows

We get 6436 rows and 9 columns. 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z?L?NixNrNO{^oy!mXG~o00X@Ga&!_*l$$!kTRN@t-?-br;P)gYiAG%bd++L zgNu)vGi|t@vM;L;TPRhDkT!g+hhAMjGd0!IzSj89&j2@p4p6is#L3Ef+CLrr_pATD h|9|Ws#B0G1N>1W9I tidycensus - 1.6.1 + 1.6.3 @@ -305,142 +305,9 @@

Detailed shorelin cb = FALSE ) %>% st_transform(26918) %>% - erase_water(year = 2020)

-
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-ggplot(ny_erase, aes(fill = estimate)) + 
+  erase_water(year = 2020)
+
+ggplot(ny_erase, aes(fill = estimate)) + 
   geom_sf() + 
   theme_void() + 
   scale_fill_viridis_c(labels = scales::dollar)
@@ -458,7 +325,7 @@

Writing to shapefilesst_write function in the sf package:

-
+
 library(sf)
 st_write(orange, "orange.shp")

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z|CiZ!7kEU?aug~8x=q=H;t$9C2o}Zs2oKvy!-wM!_w(-}(jg3qM4DZjYYTQRKo=ZV zx=8Qeri(@88wML8>#98$I=j;kmmRZLJf|c~Y6E<5a>O)!z_DmRw9n;1Nk+Xl_%xFK?_9aCMCEJPfms;dl7`-Hs40B zRWtnaCtgapxH0li9FjxB!ty3lK$2SI-=a0Fkdc}@X-L)wsQjIHn5}cTm$wbNf z)GwRt)LDway^!;k-$s_xF$&af!Hh0a?En2WU~FQxT^WEOjUk&86ljZQ)Q-gdrDc62 bu>%e9tSo+hR}%+52vbwhMi$<(dh-7O2 tidycensus - 1.6.1 + 1.6.3

@@ -99,13 +99,13 @@

Citation

Walker K, Herman M (2024). tidycensus: Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames. -R package version 1.6.1, https://walker-data.com/tidycensus/. +R package version 1.6.3, https://walker-data.com/tidycensus/.

@Manual{,
   title = {tidycensus: Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames},
   author = {Kyle Walker and Matt Herman},
   year = {2024},
-  note = {R package version 1.6.1},
+  note = {R package version 1.6.3},
   url = {https://walker-data.com/tidycensus/},
 }
diff --git a/docs/index.html b/docs/index.html index 795118d..cda1d00 100644 --- a/docs/index.html +++ b/docs/index.html @@ -44,7 +44,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/news/index.html b/docs/news/index.html index a317a9e..c9f6b26 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index a2669e8..f9285ac 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -7,5 +7,5 @@ articles: other-datasets: other-datasets.html pums-data: pums-data.html spatial-data: spatial-data.html -last_built: 2024-02-07T18:01Z +last_built: 2024-03-18T14:52Z diff --git a/docs/reference/acs5_geography.html b/docs/reference/acs5_geography.html index 6950888..22d059c 100644 --- a/docs/reference/acs5_geography.html +++ b/docs/reference/acs5_geography.html @@ -25,7 +25,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/as_dot_density.html b/docs/reference/as_dot_density.html index 214ee58..de8aa4e 100644 --- a/docs/reference/as_dot_density.html +++ b/docs/reference/as_dot_density.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/census_api_key.html b/docs/reference/census_api_key.html index 0fc94cd..15e8326 100644 --- a/docs/reference/census_api_key.html +++ b/docs/reference/census_api_key.html @@ -21,7 +21,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/check_ddhca_groups.html b/docs/reference/check_ddhca_groups.html index b69b310..b0c6965 100644 --- a/docs/reference/check_ddhca_groups.html +++ b/docs/reference/check_ddhca_groups.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/county_laea.html b/docs/reference/county_laea.html index 576b449..a4ea23a 100644 --- a/docs/reference/county_laea.html +++ b/docs/reference/county_laea.html @@ -18,7 +18,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/fips_codes.html b/docs/reference/fips_codes.html index b959846..e48e20a 100644 --- a/docs/reference/fips_codes.html +++ b/docs/reference/fips_codes.html @@ -26,7 +26,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/get_acs.html b/docs/reference/get_acs.html index f4b3d29..6f7578c 100644 --- a/docs/reference/get_acs.html +++ b/docs/reference/get_acs.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/get_decennial.html b/docs/reference/get_decennial.html index 36d9db3..d1f168c 100644 --- a/docs/reference/get_decennial.html +++ b/docs/reference/get_decennial.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/get_estimates.html b/docs/reference/get_estimates.html index 647f222..4fde1d9 100644 --- a/docs/reference/get_estimates.html +++ b/docs/reference/get_estimates.html @@ -1,5 +1,5 @@ -Get data from the US Census Bureau Population Estimates Program — get_estimates • tidycensusGet data from the US Census Bureau Population Estimates Program — get_estimates • tidycensus @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 @@ -77,7 +77,7 @@

Get data from the US Census Bureau Population Estimates Program

-

get_estimates() requests data from the Population Estimates API for years 2019 and earlier; however the Population Estimates are no longer supported on the API as of 2020. For recent years, get_estimates() reads a flat file from the Census website and parses it. This means that arguments and output for 2020 and later datasets may differ slightly from datasets acquired for 2019 and earlier.

+

The get_estimates() function requests data from the US Census Bureau's Population Estimates Program (PEP) datasets. The PEP datasets are defined by the US Census Bureau as follows: "The Census Bureau's Population Estimates Program (PEP) produces estimates of the population for the United States, its states, counties, cities, and towns, as well as for the Commonwealth of Puerto Rico and its municipios. Demographic components of population change (births, deaths, and migration) are produced at the national, state, and county levels of geography. Additionally, housing unit estimates are produced for the nation, states, and counties. PEP annually utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census and produce a time series of estimates of population, demographic components of change, and housing units. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously-produced estimates for those dates."

@@ -90,7 +90,8 @@

Get data from the US Census Bureau Population Estimates Program

variables = NULL, breakdown = NULL, breakdown_labels = FALSE, - year = 2022, + vintage = 2022, + year = vintage, state = NULL, county = NULL, time_series = FALSE, @@ -132,14 +133,16 @@

Arguments

breakdown_labels

Whether or not to label breakdown elements returned when - product = "characteristics". Defaults to FALSE.

-

Not yet supported for 2020 and later.

+product = "characteristics". Defaults to FALSE.

+ + +
vintage
+

It is recommended to use the most recent vintage +available for a given decennial series (so, year = 2019 for the 2010s, and year = 2023 for the 2020s). Will default to 2022 until the full PEP for 2023 is released.

year
-

The data year (defaults to 2022). It is recommended to use the most recent vintage -available for a given decennial series (so, year = 2019 for the 2010s, and year = 2022 for the 2020s) -and use time_series = TRUE to access time-series estimates.

+

The data year (defaults to the vintage requested). Use time_series = TRUE to access time-series estimates.

state
@@ -211,7 +214,12 @@

Value

Details

-

As of April 2022, variables available for 2020 and later datasets are as follows: ESTIMATESBASE, POPESTIMATE, NPOPCHG, BIRTHS, DEATHS, NATURALCHG, INTERNATIONALMIG, DOMESTICMIG, NETMIG, RESIDUAL, GQESTIMATESBASE, GQESTIMATES, RBIRTH, RDEATH, RNATURALCHG, RINTERNATIONALMIG, RDOMESTICMIG, and RNETMIG.

+

get_estimates() requests data from the Population Estimates API for years 2019 and earlier; however the Population Estimates are no longer supported on the API as of 2020. For recent years, get_estimates() reads a flat file from the Census website and parses it. This means that arguments and output for 2020 and later datasets may differ slightly from datasets acquired for 2019 and earlier.

+

As of April 2022, variables available for 2020 and later datasets are as follows: ESTIMATESBASE, POPESTIMATE, NPOPCHG, BIRTHS, DEATHS, NATURALCHG, INTERNATIONALMIG, DOMESTICMIG, NETMIG, RESIDUAL, GQESTIMATESBASE, GQESTIMATES, RBIRTH, RDEATH, RNATURALCHG, RINTERNATIONALMIG, RDOMESTICMIG, and RNETMIG.

+
+ diff --git a/docs/reference/get_flows.html b/docs/reference/get_flows.html index 14e5cbc..76b7881 100644 --- a/docs/reference/get_flows.html +++ b/docs/reference/get_flows.html @@ -20,7 +20,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/get_pop_groups.html b/docs/reference/get_pop_groups.html index 8ce9ecc..1446221 100644 --- a/docs/reference/get_pop_groups.html +++ b/docs/reference/get_pop_groups.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/get_pums.html b/docs/reference/get_pums.html index 28598df..bf08b40 100644 --- a/docs/reference/get_pums.html +++ b/docs/reference/get_pums.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/index.html b/docs/reference/index.html index 42fce35..fa3352f 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/interpolate_pw.html b/docs/reference/interpolate_pw.html index 3bb64e0..4c92df2 100644 --- a/docs/reference/interpolate_pw.html +++ b/docs/reference/interpolate_pw.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/load_variables.html b/docs/reference/load_variables.html index 391b4b1..458a76b 100644 --- a/docs/reference/load_variables.html +++ b/docs/reference/load_variables.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/mig_recodes.html b/docs/reference/mig_recodes.html index a214cd5..64dcff5 100644 --- a/docs/reference/mig_recodes.html +++ b/docs/reference/mig_recodes.html @@ -24,7 +24,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/moe_product.html b/docs/reference/moe_product.html index bdb56f4..36cf190 100644 --- a/docs/reference/moe_product.html +++ b/docs/reference/moe_product.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/moe_prop.html b/docs/reference/moe_prop.html index 993473b..e78d806 100644 --- a/docs/reference/moe_prop.html +++ b/docs/reference/moe_prop.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/moe_ratio.html b/docs/reference/moe_ratio.html index 6a1e401..711fb02 100644 --- a/docs/reference/moe_ratio.html +++ b/docs/reference/moe_ratio.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/moe_sum.html b/docs/reference/moe_sum.html index 8bf7dec..4715b5b 100644 --- a/docs/reference/moe_sum.html +++ b/docs/reference/moe_sum.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/pums_variables.html b/docs/reference/pums_variables.html index 1591c7a..4400442 100644 --- a/docs/reference/pums_variables.html +++ b/docs/reference/pums_variables.html @@ -33,7 +33,7 @@ tidycensus - 1.6.1 + 1.6.3 @@ -115,7 +115,7 @@

Dataset with PUMS variables and codes

Format

-

An object of class tbl_df (inherits from tbl, data.frame) with 58576 rows and 12 columns.

+

An object of class tbl_df (inherits from tbl, data.frame) with 58580 rows and 12 columns.

Details

diff --git a/docs/reference/significance.html b/docs/reference/significance.html index 2d316b9..2c01284 100644 --- a/docs/reference/significance.html +++ b/docs/reference/significance.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3
diff --git a/docs/reference/state_laea.html b/docs/reference/state_laea.html index fdcc53c..a18fc66 100644 --- a/docs/reference/state_laea.html +++ b/docs/reference/state_laea.html @@ -18,7 +18,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/summary_files.html b/docs/reference/summary_files.html index ba7e98e..7a69302 100644 --- a/docs/reference/summary_files.html +++ b/docs/reference/summary_files.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/tidycensus.html b/docs/reference/tidycensus.html index aca9086..f033d9f 100644 --- a/docs/reference/tidycensus.html +++ b/docs/reference/tidycensus.html @@ -17,7 +17,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/docs/reference/to_survey.html b/docs/reference/to_survey.html index 5c2305b..2da9ab1 100644 --- a/docs/reference/to_survey.html +++ b/docs/reference/to_survey.html @@ -22,7 +22,7 @@ tidycensus - 1.6.1 + 1.6.3 diff --git a/vignettes/other-datasets.Rmd b/vignettes/other-datasets.Rmd index ac3030f..3879f51 100644 --- a/vignettes/other-datasets.Rmd +++ b/vignettes/other-datasets.Rmd @@ -19,7 +19,7 @@ The post-2020 Population Estimates are no longer on the Census API, but they are ### Components of change population estimates -By default, specifying `"population"`, `"components"`, or `"housing"` (housing is not 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: +By default, specifying `"population"`, `"components"`, or `"housing"` (housing is not 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 2023: ```{r} library(tidycensus) @@ -27,7 +27,7 @@ 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 ``` @@ -40,13 +40,13 @@ unique(us_components$variable) Available geographies include `"us"`, `"state"`, `"county"`, `"metropolitan statistical area/micropolitan statistical area"` or `"cbsa"`, `"combined statistical area"`, and `"place"`. -If desired, users can request a specific component or components by supplying a character vector to the `variables` parameter, as in other tidycensus functions. `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 `shift_geometry()` function from the __tigris__ package to shift and rescale counties outside the continental US for national mapping. +If desired, users can request a specific component or components by supplying a character vector to the `variables` parameter, as in other tidycensus functions. `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 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. ```{r} net_migration <- get_estimates(geography = "county", variables = "RNETMIG", - year = 2022, + vintage = 2023, geometry = TRUE, resolution = "20m") %>% shift_geometry() @@ -58,6 +58,10 @@ net_migration 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. ```{r} +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 %>% @@ -83,9 +87,9 @@ ggplot() + 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") ``` @@ -103,7 +107,7 @@ la_age_hisp <- get_estimates(geography = "county", breakdown_labels = TRUE, state = "CA", county = "Los Angeles", - year = 2022) + vintage = 2022) la_age_hisp ```