get_flows(). Browse the supporting documentation for information on how to analyze and map migration flows data in tidycensus.
shift_geoargument is deprecated in favor of using the new
tigris::shift_geometry()function. This allows for shifting of geometries for any geography in national mapping projects.
get_pums(). Updated variable definitions are found in the data object
get_pums()is updated to 2019.
get_acs()now supports the
stateparameter for zip code tabulation areas (ZCTAs) to allow subsetting by state.
NEWS.mdfile to track changes to the package.
get_acs()now defaults to
year = 2019, meaning that 2015-2019 5-year estimates and 2019 1-year estimates are retrieved if no year is specified.
get_estimates()now also defaults to
year = 2019.
get_decennial()as the Census Bureau has restored its API endpoint.
geometry = TRUEnow works for all geographies currently available in tidycensus.
get_acs()can now support the ACS Supplemental Estimates API.
get_acs()now defaults to the 2014-2018 five-year American Community Survey estimates, or the 2018 1-year estimates if users set
survey = "acs1".
tidycensus now includes support for the Census Bureau Population Estimates API. Please see https://walkerke.github.io/tidycensus/articles/other-datasets.html for examples of how to use these new features.
Important internal changes to ensure that tidycensus continues to work with upcoming changes to Census API endpoints.
Several internal improvements and bug fixes. Please see the closed issues at https://github.com/walkerke/tidycensus/issues for a list.
Bug fixed that was causing GEOIDs for some states to be converted to NA on certain Linux platforms
A new parameter,
shift_geo, allows tidycensus users to get US state and county geometry originally obtained with the albersusa R package with Alaska and Hawaii shifted and re-scaled for better cartographic display of the entire US.
library(tidycensus) library(tidyverse) library(viridis) us_county_income <- get_acs(geography = "county", variables = "B19013_001", shift_geo = TRUE, geometry = TRUE) ggplot(us_county_income) + geom_sf(aes(fill = estimate), color = NA) + coord_sf(datum = NA) + theme_minimal() + scale_fill_viridis_c()