| Title: | Load US Census Boundary and Attribute Data as 'tidyverse' and 'sf'-Ready Data Frames |
|---|---|
| Description: | An integrated R interface to several United States Census Bureau APIs (<https://www.census.gov/data/developers/data-sets.html>) and the US Census Bureau's geographic boundary files. Allows R users to return Census and ACS data as tidyverse-ready data frames, and optionally returns a list-column with feature geometry for mapping and spatial analysis. |
| Authors: | Kyle Walker [aut, cre], Matt Herman [aut], Kris Eberwein [ctb] |
| Maintainer: | Kyle Walker <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.6.7 |
| Built: | 2024-10-17 18:20:55 UTC |
| Source: | https://github.com/walkerke/tidycensus |
Built-in dataset for use by load_variables() to identify the smallest
geography at which 5-year ACS data are available
table: The ACS Table ID
geography: The smallest geography at which a given table is available
for a given year
year: The endyear of the 5-year ACS dataset
data(acs5_geography)data(acs5_geography)
An object of class tbl_df (inherits from tbl, data.frame) with 12228 rows and 3 columns.
Dataset used to identify geography availability in the 5-year ACS Detailed Tables
Built-in dataset that includes information on the smallest geography at which
5-year ACS Detailed Tables data are available, by table, since 2011. This dataset
is used internally by load_variables() to add a geography column
when variables are retrieved for a 5-year ACS Detailed Tables dataset.
Dot-density maps are a compelling alternative to choropleth maps for cartographic visualization of demographic data as they allow for representation of the internal heterogeneity of geographic units. This function helps users generate dots from an input polygon dataset intended for dot-density mapping. Dots are placed randomly within polygons according to a given data:dots ratio; for example, a ratio of 100:1 for an input population value column will place approximately 1 dot in the polygon for every 100 people in the geographic unit. Users can then map the dots using tools like ggplot2::geom_sf() or tmap::tm_dots().
as_dot_density( input_data, value, values_per_dot, group = NULL, erase_water = FALSE, area_threshold = NULL, water_year = 2020 )as_dot_density( input_data, value, values_per_dot, group = NULL, erase_water = FALSE, area_threshold = NULL, water_year = 2020 )
input_data |
An input sf object of geometry type |
value |
The value column to be used to determine the number of dots to generate. For tidycensus users, this will typically be the |
values_per_dot |
The number of values per dot; used to determine the output data:dots ratio. A value of 100 means that each dot will represent approximately 100 values in the value column. |
group |
A column in the dataset that identifies salient groups within which dots should be generated. For a long-form tidycensus dataset, this will typically be the |
erase_water |
If |
area_threshold |
The area percentile threshold to be used when erasing water; ranges from 0 (all water area included) to 1 (no water area included) |
water_year |
The year of the TIGER/Line water area shapefiles to use if erasing water. Defaults to 2020; ignore if not using the |
as_dot_density() uses terra::dots() internally for fast creation of dots. As terra is not a hard dependency of the tidycensus package, users must first install terra before using this function.
The erase_water parameter will internally call tigris::erase_water() to fetch water area for a given location in the United States and remove that water area from the polygons before placing dots in polygons. This will slow down performance of the function, but can improve cartographic accuracy in locations with significant water area. It is recommended that users transform their data into a projected coordinate reference system with sf::st_transform() prior to using this option in order to improve performance.
The original dataset but of geometry type POINT, with the number of point features corresponding to the given value:dot ratio for a given group.
## Not run: library(tidycensus) library(ggplot2) # Identify variables for mapping race_vars <- c( Hispanic = "P2_002N", White = "P2_005N", Black = "P2_006N", Asian = "P2_008N" ) # Get data from tidycensus baltimore_race <- get_decennial( geography = "tract", variables = race_vars, state = "MD", county = "Baltimore city", geometry = TRUE, year = 2020 ) # Convert data to dots baltimore_dots <- as_dot_density( baltimore_race, value = "value", values_per_dot = 100, group = "variable" ) # Use one set of polygon geometries as a base layer baltimore_base <- baltimore_race[baltimore_race$variable == "Hispanic", ] # Map with ggplot2 ggplot() + geom_sf(data = baltimore_base, fill = "white", color = "grey") + geom_sf(data = baltimore_dots, aes(color = variable), size = 0.01) + theme_void() ## End(Not run)## Not run: library(tidycensus) library(ggplot2) # Identify variables for mapping race_vars <- c( Hispanic = "P2_002N", White = "P2_005N", Black = "P2_006N", Asian = "P2_008N" ) # Get data from tidycensus baltimore_race <- get_decennial( geography = "tract", variables = race_vars, state = "MD", county = "Baltimore city", geometry = TRUE, year = 2020 ) # Convert data to dots baltimore_dots <- as_dot_density( baltimore_race, value = "value", values_per_dot = 100, group = "variable" ) # Use one set of polygon geometries as a base layer baltimore_base <- baltimore_race[baltimore_race$variable == "Hispanic", ] # Map with ggplot2 ggplot() + geom_sf(data = baltimore_base, fill = "white", color = "grey") + geom_sf(data = baltimore_dots, aes(color = variable), size = 0.01) + theme_void() ## End(Not run)
.Renviron File for Repeated UseThis function will add your CENSUS API key to your .Renviron file so it can be called securely without being stored
in your code. After you have installed your key, it can be called any time by typing Sys.getenv("CENSUS_API_KEY") and can be
used in package functions by simply typing CENSUS_API_KEY If you do not have an .Renviron file, the function will create on for you.
If you already have an .Renviron file, the function will append the key to your existing file, while making a backup of your
original file for disaster recovery purposes.
census_api_key(key, overwrite = FALSE, install = FALSE)census_api_key(key, overwrite = FALSE, install = FALSE)
key |
The API key provided to you from the Census formated in quotes. A key can be acquired at http://api.census.gov/data/key_signup.html |
overwrite |
If this is set to TRUE, it will overwrite an existing CENSUS_API_KEY that you already have in your |
install |
if TRUE, will install the key in your |
## Not run: census_api_key("111111abc", install = TRUE) # First time, reload your environment so you can use the key without restarting R. readRenviron("~/.Renviron") # You can check it with: Sys.getenv("CENSUS_API_KEY") ## End(Not run) ## Not run: # If you need to overwrite an existing key: census_api_key("111111abc", overwrite = TRUE, install = TRUE) # First time, relead your environment so you can use the key without restarting R. readRenviron("~/.Renviron") # You can check it with: Sys.getenv("CENSUS_API_KEY") ## End(Not run)## Not run: census_api_key("111111abc", install = TRUE) # First time, reload your environment so you can use the key without restarting R. readRenviron("~/.Renviron") # You can check it with: Sys.getenv("CENSUS_API_KEY") ## End(Not run) ## Not run: # If you need to overwrite an existing key: census_api_key("111111abc", overwrite = TRUE, install = TRUE) # First time, relead your environment so you can use the key without restarting R. readRenviron("~/.Renviron") # You can check it with: Sys.getenv("CENSUS_API_KEY") ## End(Not run)
Check to see if a given geography / population group combination is available in the Detailed DHC-A file.
check_ddhca_groups(geography, pop_group, state = NULL, county = NULL)check_ddhca_groups(geography, pop_group, state = NULL, county = NULL)
geography |
The requested geography. |
pop_group |
The code representing the population group you'd like to check. |
state |
The state (optional) |
county |
The county (optional) |
Built-in dataset for use with shift_geo = TRUE
Dataset of US counties with Alaska and Hawaii shifted and re-scaled
data(county_laea) data(county_laea)data(county_laea) data(county_laea)
An object of class sf (inherits from data.frame) with 3143 rows and 2 columns.
Dataset with county geometry for use when shifting Alaska and Hawaii
Built-in dataset for use with the shift_geo parameter, with the continental United States in a
Lambert azimuthal equal area projection and Alaska and Hawaii counties and Census areas shifted and re-scaled.
The data were originally obtained from the albersusa R package (https://github.com/hrbrmstr/albersusa).
Built-in dataset for smart state and county lookup.
To access the data directly, issue the command data(fips_codes).
county: County name, title-case
county_code: County code. (3-digit, 0-padded, character)
state: Upper-case abbreviation of state
state_code: State FIPS code (2-digit, 0-padded, character)
state_name: Title-case name of state
data(fips_codes)data(fips_codes)
An object of class data.frame with 3256 rows and 5 columns.
Dataset with FIPS codes for US states and counties
Built-in dataset for use with the lookup_code function.
To access the data directly, issue the command data(fips_codes).
Note: this dataset includes FIPS codes for all counties that have appeared in the decennial Census or American Community Survey from 2010 to the present. This means that counties that have been renamed or absorbed into other geographic entities since 2010 remain in this dataset along with newly added or renamed counties.
If you need the FIPS codes and names for counties for a particular Census year, you can use the counties function from the tigris package and set the year parameter as required.
Obtain data and feature geometry for the American Community Survey
get_acs( geography, variables = NULL, table = NULL, cache_table = FALSE, year = 2022, output = "tidy", state = NULL, county = NULL, zcta = NULL, geometry = FALSE, keep_geo_vars = FALSE, shift_geo = FALSE, summary_var = NULL, key = NULL, moe_level = 90, survey = "acs5", show_call = FALSE, ... )get_acs( geography, variables = NULL, table = NULL, cache_table = FALSE, year = 2022, output = "tidy", state = NULL, county = NULL, zcta = NULL, geometry = FALSE, keep_geo_vars = FALSE, shift_geo = FALSE, summary_var = NULL, key = NULL, moe_level = 90, survey = "acs5", show_call = FALSE, ... )
geography |
The geography of your data. |
variables |
Character string or vector of character strings of variable IDs. tidycensus automatically returns the estimate and the margin of error associated with the variable. |
table |
The ACS table for which you would like to request all
variables. Uses lookup tables to identify the variables; performs faster
when variable table already exists through |
cache_table |
Whether or not to cache table names for faster future
access. Defaults to FALSE; if TRUE, only needs to be called once per
dataset. If variables dataset is already cached via the
|
year |
The year, or endyear, of the ACS sample. 5-year ACS data is available from 2009 through 2022; 1-year ACS data is available from 2005 through 2022, with the exception of 2020. Defaults to 2022. |
output |
One of "tidy" (the default) in which each row represents an enumeration unit-variable combination, or "wide" in which each row represents an enumeration unit and the variables are in the columns. |
state |
An optional vector of states for which you are requesting data. State names, postal codes, and FIPS codes are accepted. Defaults to NULL. |
county |
The county for which you are requesting data. County names and FIPS codes are accepted. Must be combined with a value supplied to 'state'. Defaults to NULL. |
zcta |
The zip code tabulation area(s) for which you are requesting data. Specify a single value or a vector of values to get data for more than one ZCTA. Numeric or character ZCTA GEOIDs are accepted. When specifying ZCTAs, geography must be set to '"zcta"' and 'state' must be specified with 'county' left as 'NULL'. Defaults to NULL. |
geometry |
if FALSE (the default), return a regular tibble of ACS data. if TRUE, uses the tigris package to return an sf tibble with simple feature geometry in the 'geometry' column. |
keep_geo_vars |
if TRUE, keeps all the variables from the Census shapefile obtained by tigris. Defaults to FALSE. |
shift_geo |
(deprecated) if TRUE, returns geometry with Alaska and Hawaii shifted for
thematic mapping of the entire US. Geometry was originally obtained from
the albersusa R package. As of May 2021, we recommend using |
summary_var |
Character string of a "summary variable" from the ACS to be included in your output. Usually a variable (e.g. total population) that you'll want to use as a denominator or comparison. |
key |
Your Census API key. Obtain one at https://api.census.gov/data/key_signup.html |
moe_level |
The confidence level of the returned margin of error. One of 90 (the default), 95, or 99. |
survey |
The ACS contains one-year, three-year, and five-year surveys expressed as "acs1", "acs3", and "acs5". The default selection is "acs5." |
show_call |
if TRUE, display call made to Census API. This can be very useful in debugging and determining if error messages returned are due to tidycensus or the Census API. Copy to the API call into a browser and see what is returned by the API directly. Defaults to FALSE. |
... |
Other keyword arguments |
A tibble or sf tibble of ACS data
## Not run: library(tidycensus) library(tidyverse) library(viridis) census_api_key("YOUR KEY GOES HERE") tarr <- get_acs(geography = "tract", variables = "B19013_001", state = "TX", county = "Tarrant", geometry = TRUE, year = 2020) ggplot(tarr, aes(fill = estimate, color = estimate)) + geom_sf() + coord_sf(crs = 26914) + scale_fill_viridis(option = "magma") + scale_color_viridis(option = "magma") vt <- get_acs(geography = "county", variables = "B19013_001", state = "VT", year = 2019) vt %>% mutate(NAME = gsub(" County, Vermont", "", NAME)) %>% ggplot(aes(x = estimate, y = reorder(NAME, estimate))) + geom_errorbar(aes(xmin = estimate - moe, xmax = estimate + moe), width = 0.3, size = 0.5) + geom_point(color = "red", size = 3) + labs(title = "Household income by county in Vermont", subtitle = "2015-2019 American Community Survey", y = "", x = "ACS estimate (bars represent margin of error)") ## End(Not run)## Not run: library(tidycensus) library(tidyverse) library(viridis) census_api_key("YOUR KEY GOES HERE") tarr <- get_acs(geography = "tract", variables = "B19013_001", state = "TX", county = "Tarrant", geometry = TRUE, year = 2020) ggplot(tarr, aes(fill = estimate, color = estimate)) + geom_sf() + coord_sf(crs = 26914) + scale_fill_viridis(option = "magma") + scale_color_viridis(option = "magma") vt <- get_acs(geography = "county", variables = "B19013_001", state = "VT", year = 2019) vt %>% mutate(NAME = gsub(" County, Vermont", "", NAME)) %>% ggplot(aes(x = estimate, y = reorder(NAME, estimate))) + geom_errorbar(aes(xmin = estimate - moe, xmax = estimate + moe), width = 0.3, size = 0.5) + geom_point(color = "red", size = 3) + labs(title = "Household income by county in Vermont", subtitle = "2015-2019 American Community Survey", y = "", x = "ACS estimate (bars represent margin of error)") ## End(Not run)
Obtain data and feature geometry for the decennial US Census
get_decennial( geography, variables = NULL, table = NULL, cache_table = FALSE, year = 2020, sumfile = NULL, state = NULL, county = NULL, geometry = FALSE, output = "tidy", keep_geo_vars = FALSE, shift_geo = FALSE, summary_var = NULL, pop_group = NULL, pop_group_label = FALSE, key = NULL, show_call = FALSE, ... )get_decennial( geography, variables = NULL, table = NULL, cache_table = FALSE, year = 2020, sumfile = NULL, state = NULL, county = NULL, geometry = FALSE, output = "tidy", keep_geo_vars = FALSE, shift_geo = FALSE, summary_var = NULL, pop_group = NULL, pop_group_label = FALSE, key = NULL, show_call = FALSE, ... )
geography |
The geography of your data. |
variables |
Character string or vector of character strings of variable IDs. |
table |
The Census table for which you would like to request all variables. Uses
lookup tables to identify the variables; performs faster when variable
table already exists through |
cache_table |
Whether or not to cache table names for faster future access.
Defaults to FALSE; if TRUE, only needs to be called once per
dataset. If variables dataset is already cached via the
|
year |
The year for which you are requesting data. Defaults to 2020; 2000, 2010, and 2020 are available. |
sumfile |
The Census summary file; if |
state |
The state for which you are requesting data. State names, postal codes, and FIPS codes are accepted. Defaults to NULL. |
county |
The county for which you are requesting data. County names and FIPS codes are accepted. Must be combined with a value supplied to 'state'. Defaults to NULL. |
geometry |
if FALSE (the default), return a regular tibble of ACS data. if TRUE, uses the tigris package to return an sf tibble with simple feature geometry in the 'geometry' column. |
output |
One of "tidy" (the default) in which each row represents an enumeration unit-variable combination, or "wide" in which each row represents an enumeration unit and the variables are in the columns. |
keep_geo_vars |
if TRUE, keeps all the variables from the Census shapefile obtained by tigris. Defaults to FALSE. |
shift_geo |
(deprecated) if TRUE, returns geometry with Alaska and Hawaii
shifted for thematic mapping of the entire US.
Geometry was originally obtained from the albersusa R package. As of May 2021,
we recommend using |
summary_var |
Character string of a "summary variable" from the decennial Census to be included in your output. Usually a variable (e.g. total population) that you'll want to use as a denominator or comparison. |
pop_group |
The population group code for which you'd like to request data. Applies to summary files for which population group breakdowns are available like the Detailed DHC-A file. |
pop_group_label |
If |
key |
Your Census API key. Obtain one at https://api.census.gov/data/key_signup.html |
show_call |
if TRUE, display call made to Census API. This can be very useful in debugging and determining if error messages returned are due to tidycensus or the Census API. Copy to the API call into a browser and see what is returned by the API directly. Defaults to FALSE. |
... |
Other keyword arguments |
a tibble or sf tibble of decennial Census data
## Not run: # Plot of race/ethnicity by county in Illinois for 2010 library(tidycensus) library(tidyverse) library(viridis) census_api_key("YOUR KEY GOES HERE") vars10 <- c("P005003", "P005004", "P005006", "P004003") il <- get_decennial(geography = "county", variables = vars10, year = 2010, summary_var = "P001001", state = "IL", geometry = TRUE) %>% mutate(pct = 100 * (value / summary_value)) ggplot(il, aes(fill = pct, color = pct)) + geom_sf() + facet_wrap(~variable) ## End(Not run)## Not run: # Plot of race/ethnicity by county in Illinois for 2010 library(tidycensus) library(tidyverse) library(viridis) census_api_key("YOUR KEY GOES HERE") vars10 <- c("P005003", "P005004", "P005006", "P004003") il <- get_decennial(geography = "county", variables = vars10, year = 2010, summary_var = "P001001", state = "IL", geometry = TRUE) %>% mutate(pct = 100 * (value / summary_value)) ggplot(il, aes(fill = pct, color = pct)) + geom_sf() + facet_wrap(~variable) ## End(Not run)
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."
get_estimates( geography = c("us", "region", "division", "state", "county", "county subdivision", "place/balance (or part)", "place", "consolidated city", "place (or part)", "metropolitan statistical area/micropolitan statistical area", "cbsa", "metropolitan division", "combined statistical area"), product = NULL, variables = 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, ... )get_estimates( geography = c("us", "region", "division", "state", "county", "county subdivision", "place/balance (or part)", "place", "consolidated city", "place (or part)", "metropolitan statistical area/micropolitan statistical area", "cbsa", "metropolitan division", "combined statistical area"), product = NULL, variables = 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, ... )
geography |
The geography of your data. Available geographies for the most recent data vintage are listed
here. |
product |
The data product (optional). For 2020 and later, the only supported product is |
variables |
A character string or vector of character strings of requested variables. For years 2020 and later, use |
breakdown |
The population breakdown used when |
breakdown_labels |
Whether or not to label breakdown elements returned when
|
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 the vintage requested). Use |
state |
The state for which you are requesting data. State names, postal codes, and FIPS codes are accepted. Defaults to NULL. |
county |
The county for which you are requesting data. County names and FIPS codes are accepted. Must be combined with a value supplied to 'state'. Defaults to NULL. |
time_series |
If |
output |
One of "tidy" (the default) in which each row represents an enumeration unit-variable combination, or "wide" in which each row represents an enumeration unit and the variables are in the columns. |
geometry |
if FALSE (the default), return a regular tibble of ACS data. if TRUE, uses the tigris package to return an sf tibble with simple feature geometry in the 'geometry' column. |
keep_geo_vars |
if TRUE, keeps all the variables from the Census shapefile obtained by tigris. Defaults to FALSE. |
shift_geo |
(deprecated) if TRUE, returns geometry with Alaska and Hawaii shifted for thematic
mapping of the entire US. As of May 2021, we recommend using |
key |
Your Census API key.
Obtain one at https://api.census.gov/data/key_signup.html. Can be stored
in your .Renviron with |
show_call |
if TRUE, display call made to Census API. This can be very useful in debugging and determining if error messages returned are due to tidycensus or the Census API. Copy to the API call into a browser and see what is returned by the API directly. Defaults to FALSE. |
... |
other keyword arguments |
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.
A tibble, or sf tibble, of population estimates data
https://www.census.gov/programs-surveys/popest/about.html
Obtain data and feature geometry for American Community Survey Migration Flows
get_flows( geography, variables = NULL, breakdown = NULL, breakdown_labels = FALSE, year = 2018, output = "tidy", state = NULL, county = NULL, msa = NULL, geometry = FALSE, key = NULL, moe_level = 90, show_call = FALSE )get_flows( geography, variables = NULL, breakdown = NULL, breakdown_labels = FALSE, year = 2018, output = "tidy", state = NULL, county = NULL, msa = NULL, geometry = FALSE, key = NULL, moe_level = 90, show_call = FALSE )
geography |
The geography of your requested data. Possible values are
|
variables |
Character string or vector of character strings of variable
names. By default, |
breakdown |
A character vector of the population breakdown characteristics to be crossed with migration flows data. For datasets between 2006-2010 and 2011-2015, selected demographic characteristics such as age, race, employment status, etc. are available. Possible values are "AGE", "SEX", "RACE", "HSGP", "REL", "HHT", "TEN", "ENG", "POB", "YEARS", "ESR", "OCC", "WKS", "SCHL", "AHINC", "APINC", and "HISP_ORIGIN". For more information and to see which characteristics are available in each year, visit the Census Migration Flows documentation at https://www.census.gov/data/developers/data-sets/acs-migration-flows.html. Note: not all characteristics are available in all years. |
breakdown_labels |
Whether or not to add columns with labels for the
breakdown characteristic codes. Defaults to |
year |
The year, or endyear, of the ACS sample. The Migration Flows API is available for 5-year ACS samples from 2010 to 2018. Defaults to 2018. |
output |
One of "tidy" (the default) in which each row represents an enumeration unit-variable combination, or "wide" in which each row represents an enumeration unit and the variables are in the columns. |
state |
An optional vector of states for which you are requesting data. State names, postal codes, and FIPS codes are accepted. When requesting county subdivision data, you must specify at least one state. |
county |
The county for which you are requesting data. County names and FIPS codes are accepted. Must be combined with a value supplied to 'state'. |
msa |
The metropolitan statistical area for which you are requesting
data. Specify a single value or a vector of values to get data for more
than one MSA. Numeric or character MSA GEOIDs are accepted. When specifying
MSAs, geography must be set to |
geometry |
if FALSE (the default), return a tibble of ACS Migration
Flows data. If TRUE, return an sf object with the centroids of both origin
and destination as |
key |
Your Census API key. Obtain one at https://api.census.gov/data/key_signup.html |
moe_level |
The confidence level of the returned margin of error. One of 90 (the default), 95, or 99. |
show_call |
if TRUE, display call made to Census API. This can be very useful in debugging and determining if error messages returned are due to tidycensus or the Census API. Copy to the API call into a browser and see what is returned by the API directly. Defaults to FALSE. |
A tibble or sf tibble of ACS Migration Flows data
## Not run: get_flows( geography = "county", state = "VT", county = c("Washington", "Chittenden") ) get_flows( geography = "county subdivision", breakdown = "RACE", breakdown_labels = TRUE, state = "NY", county = "Westchester", output = "wide", year = 2015 ) get_flows( geography = "metropolitan statistical area", variables = c("POP1YR", "POP1YRAGO"), geometry = TRUE, output = "wide", show_call = TRUE ) ## End(Not run)## Not run: get_flows( geography = "county", state = "VT", county = c("Washington", "Chittenden") ) get_flows( geography = "county subdivision", breakdown = "RACE", breakdown_labels = TRUE, state = "NY", county = "Westchester", output = "wide", year = 2015 ) get_flows( geography = "metropolitan statistical area", variables = c("POP1YR", "POP1YRAGO"), geometry = TRUE, output = "wide", show_call = TRUE ) ## End(Not run)
Get available population groups for a given Decennial Census year and summary file
get_pop_groups(year, sumfile)get_pop_groups(year, sumfile)
year |
The decennial Census year; one of 2000, 2010, or 2020. |
sumfile |
The summary file. Available summary files are |
A tibble containing codes (to be used with the pop_group argument of get_decennial()) and descriptive names.
Load data from the American Community Survey Public Use Microdata Series API
get_pums( variables = NULL, state = NULL, puma = NULL, year = 2022, survey = "acs5", variables_filter = NULL, rep_weights = NULL, recode = FALSE, return_vacant = FALSE, show_call = FALSE, key = NULL )get_pums( variables = NULL, state = NULL, puma = NULL, year = 2022, survey = "acs5", variables_filter = NULL, rep_weights = NULL, recode = FALSE, return_vacant = FALSE, show_call = FALSE, key = NULL )
variables |
A vector of variables from the PUMS API.
Use |
state |
A state, or vector of states, for which you would like to
request data. The entire US can be requested with |
puma |
A vector of PUMAs from a single state, for which you would like
to request data. To get data from PUMAs in more than one state, specify a
named vector of state/PUMA pairs and set |
year |
The data year of the 1-year ACS sample or the endyear of the 5-year sample. Defaults to 2022. Please note that 1-year data for 2020 is not available in tidycensus, so users requesting 1-year data should supply a different year. |
survey |
The ACS survey; one of either |
variables_filter |
A named list of filters you'd like to return from the
PUMS API. For example, passing |
rep_weights |
Whether or not to return housing unit, person, or both
housing and person-level replicate weights for calculation of standard
errors; one of |
recode |
If TRUE, recodes variable values using Census data dictionary
and creates a new |
return_vacant |
If TRUE, makes a separate request to the Census API to retrieve microdata for vacant housing units, which are handled differently in the API as they do not have person-level characteristics. All person-level columns in the returned dataset will be populated with NA for vacant housing units. Defaults to FALSE. |
show_call |
If TRUE, display call made to Census API. This can be very useful in debugging and determining if error messages returned are due to tidycensus or the Census API. Copy to the API call into a browser and see what is returned by the API directly. Defaults to FALSE. |
key |
Your Census API key. Obtain one at https://api.census.gov/data/key_signup.html |
A tibble of microdata from the ACS PUMS API.
## Not run: get_pums(variables = "AGEP", state = "VT") get_pums(variables = "AGEP", state = "multiple", puma = c("UT" = 35008, "NV" = 00403)) get_pums(variables = c("AGEP", "ANC1P"), state = "VT", recode = TRUE) get_pums(variables = "AGEP", state = "VT", survey = "acs1", rep_weights = "person") ## End(Not run)## Not run: get_pums(variables = "AGEP", state = "VT") get_pums(variables = "AGEP", state = "multiple", puma = c("UT" = 35008, "NV" = 00403)) get_pums(variables = c("AGEP", "ANC1P"), state = "VT", recode = TRUE) get_pums(variables = "AGEP", state = "VT", survey = "acs1", rep_weights = "person") ## End(Not run)
A common use-case when working with time-series small-area Census data is to transfer data from one set of shapes (e.g. 2010 Census tracts) to another set of shapes (e.g. 2020 Census tracts). Population-weighted interpolation is one such solution to this problem that takes into account the distribution of the population within a Census unit to intelligently transfer data between incongruent units.
interpolate_pw( from, to, to_id = NULL, extensive, weights, weight_column = NULL, weight_placement = c("surface", "centroid"), crs = NULL )interpolate_pw( from, to, to_id = NULL, extensive, weights, weight_column = NULL, weight_placement = c("surface", "centroid"), crs = NULL )
from |
The spatial dataset from which numeric attributes will be interpolated to target zones. By default, all numeric columns in this dataset will be interpolated. |
to |
The target geometries (zones) to which numeric attributes will be interpolated. |
to_id |
(optional) An ID column in the target dataset to be retained in the output. For data obtained with tidycensus, this will be |
extensive |
if |
weights |
An input spatial dataset to be used as weights. If the dataset is not of geometry type |
weight_column |
(optional) a column in |
weight_placement |
(optional) One of |
crs |
(optional) The EPSG code of the output projected coordinate reference system (CRS). Useful as all input layers ( |
The approach implemented here is based on Esri's data apportionment algorithm, in which an "apportionment layer" of points (referred to here as the weights) is used to determine how to weight areas of overlap between origin and target zones. Users must supply a "from" dataset as an sf object (the dataset from which numeric columns will be interpolated) and a "to" dataset, also of class sf, that contains the target zones. A third sf object, the "weights", may be an object of geometry type POINT or polygons from which points will be derived using sf::st_point_on_surface().
An intersection is computed between from and to, and a spatial join is computed between the intersection layer and the weights layer, represented as points. A specified weight_column in weights will be used to determine the relative influence of each point on the allocation of values between from and to; if no weight column is specified, all points will be weighted equally.
The extensive parameter (logical) should reflect the values being interpolated correctly. If TRUE, the function returns a weighted sum for each zone. If FALSE, a weighted mean will be returned. For Census data, extensive = TRUE should be used for transferring counts / estimated counts between zones. Derived metrics (e.g. population density, percentages, etc.) should use extensive = FALSE. Margins of error in the ACS will not be transferred correctly with this function, so please use with caution.
A dataset of class sf with the geometries and an ID column from to (the target shapes) but with numeric attributes of from interpolated to those shapes.
## Not run: # Example: interpolating work-from-home from 2011-2015 ACS # to 2020 shapes library(tidycensus) library(tidyverse) library(tigris) options(tigris_use_cache = TRUE) wfh_15 <- get_acs( geography = "tract", variables = "B08006_017", year = 2015, state = "AZ", county = "Maricopa", geometry = TRUE ) %>% select(estimate) wfh_20 <- get_acs( geography = "tract", variables = "B08006_017", year = 2020, state = "AZ", county = "Maricopa", geometry = TRUE ) maricopa_blocks <- blocks( "AZ", "Maricopa", year = 2020 ) wfh_15_to_20 <- interpolate_pw( from = wfh_15, to = wfh_20, to_id = "GEOID", weights = maricopa_blocks, weight_column = "POP20", crs = 26949, extensive = TRUE ) ## End(Not run)## Not run: # Example: interpolating work-from-home from 2011-2015 ACS # to 2020 shapes library(tidycensus) library(tidyverse) library(tigris) options(tigris_use_cache = TRUE) wfh_15 <- get_acs( geography = "tract", variables = "B08006_017", year = 2015, state = "AZ", county = "Maricopa", geometry = TRUE ) %>% select(estimate) wfh_20 <- get_acs( geography = "tract", variables = "B08006_017", year = 2020, state = "AZ", county = "Maricopa", geometry = TRUE ) maricopa_blocks <- blocks( "AZ", "Maricopa", year = 2020 ) wfh_15_to_20 <- interpolate_pw( from = wfh_15, to = wfh_20, to_id = "GEOID", weights = maricopa_blocks, weight_column = "POP20", crs = 26949, extensive = TRUE ) ## End(Not run)
Finding the right variables to use with get_decennial() or get_acs() can be challenging; load_variables() attempts to make this easier for you. Choose a year and a dataset to search for variables; those variables will be loaded from the Census website as an R data frame. It is recommended that RStudio users use the View() function to interactively browse and filter these variables to find the right variables to use.
load_variables( year, dataset = c("sf1", "sf2", "sf3", "sf4", "pl", "dhc", "dp", "ddhca", "ddhcb", "sdhc", "as", "gu", "mp", "vi", "acsse", "dpas", "dpgu", "dpmp", "dpvi", "dhcvi", "dhcgu", "dhcvi", "dhcas", "acs1", "acs3", "acs5", "acs1/profile", "acs3/profile", "acs5/profile", "acs1/subject", "acs3/subject", "acs5/subject", "acs1/cprofile", "acs5/cprofile", "sf2profile", "sf3profile", "sf4profile", "aian", "aianprofile", "cd110h", "cd110s", "cd110hprofile", "cd110sprofile", "sldh", "slds", "sldhprofile", "sldsprofile", "cqr", "cd113", "cd113profile", "cd115", "cd115profile", "cd116", "plnat", "cd118"), cache = FALSE )load_variables( year, dataset = c("sf1", "sf2", "sf3", "sf4", "pl", "dhc", "dp", "ddhca", "ddhcb", "sdhc", "as", "gu", "mp", "vi", "acsse", "dpas", "dpgu", "dpmp", "dpvi", "dhcvi", "dhcgu", "dhcvi", "dhcas", "acs1", "acs3", "acs5", "acs1/profile", "acs3/profile", "acs5/profile", "acs1/subject", "acs3/subject", "acs5/subject", "acs1/cprofile", "acs5/cprofile", "sf2profile", "sf3profile", "sf4profile", "aian", "aianprofile", "cd110h", "cd110s", "cd110hprofile", "cd110sprofile", "sldh", "slds", "sldhprofile", "sldsprofile", "cqr", "cd113", "cd113profile", "cd115", "cd115profile", "cd116", "plnat", "cd118"), cache = FALSE )
year |
The year for which you are requesting variables. Either the year or endyear of the decennial Census or ACS sample. 5-year ACS data is available from 2009 through 2020. 1-year ACS data is available from 2005 through 2021, with the exception of 2020. |
dataset |
The dataset name as used on the Census website. See the Details in this documentation for a full list of dataset names. |
cache |
Whether you would like to cache the dataset for future access, or load the dataset from an existing cache. Defaults to FALSE. |
load_variables() returns three columns by default: name, which is the Census ID code to be supplied to the variables parameter in get_decennial() or get_acs(); label, which is a detailed description of the variable; and concept, which provides information about the table that a given variable belongs to. For 5-year ACS detailed tables datasets, a fourth column, geography, tells you the smallest geography at which a given variable is available.
Datasets available are as follows: "sf1", "sf2", "sf3", "sf4", "pl", "dhc", "dp", "dhca", "ddhca", "ddhcb", "sdhc", "as", "gu", "mp", "vi", "acsse", "dpas", "dpgu", "dpmp", "dpvi", "dhcvi", "dhcgu", "dhcvi", "dhcas", "acs1", "acs3", "acs5", "acs1/profile", "acs3/profile", "acs5/profile", "acs1/subject", "acs3/subject", "acs5/subject", "acs1/cprofile", "acs5/cprofile", "sf2profile", "sf3profile", "sf4profile", "aian", "aianprofile", "cd110h", "cd110s", "cd110hprofile", "cd110sprofile", "sldh", "slds", "sldhprofile", "sldsprofile", "cqr", "cd113", "cd113profile", "cd115", "cd115profile", "cd116", "cd118", and "plnat".
A tibble of variables from the requested dataset.
## Not run: v15 <- load_variables(2015, "acs5", cache = TRUE) View(v15) ## End(Not run)## Not run: v15 <- load_variables(2015, "acs5", cache = TRUE) View(v15) ## End(Not run)
Built-in dataset for Migration Flows code label lookup.
characteristic: Characteristic variable name
code: Characteristic calue code
desc: Characteristic calue label
ordered: Whether or not recoded value should be ordered factor
data(mig_recodes)data(mig_recodes)
An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 120 rows and 4 columns.
Dataset with Migration Flows characteristic recodes
Built-in dataset that is created from the
Migration
Flows API documentation. This dataset contains labels for the coded values
returned by the Census API and is used when breakdown_labels = TRUE in
get_flows.
Calculate the margin of error for a derived product
moe_product(est1, est2, moe1, moe2)moe_product(est1, est2, moe1, moe2)
est1 |
The first factor in the multiplication equation (an estimate) |
est2 |
The second factor in the multiplication equation (an estimate) |
moe1 |
The margin of error of the first factor |
moe2 |
The margin of error of the second factor |
A margin of error for a derived product
Calculate the margin of error for a derived proportion
moe_prop(num, denom, moe_num, moe_denom)moe_prop(num, denom, moe_num, moe_denom)
num |
The numerator involved in the proportion calculation (an estimate) |
denom |
The denominator involved in the proportion calculation (an estimate) |
moe_num |
The margin of error of the numerator |
moe_denom |
The margin of error of the denominator |
A margin of error for a derived proportion
Calculate the margin of error for a derived ratio
moe_ratio(num, denom, moe_num, moe_denom)moe_ratio(num, denom, moe_num, moe_denom)
num |
The numerator involved in the ratio calculation (an estimate) |
denom |
The denominator involved in the ratio calculation (an estimate) |
moe_num |
The margin of error of the numerator |
moe_denom |
The margin of error of the denominator |
A margin of error for a derived ratio
Generates a margin of error for a derived sum. The function requires a vector of margins of error involved in a sum calculation, and optionally a vector of estimates associated with the margins of error. If the associated estimates are not specified, the user risks inflating the derived margin of error in the event of multiple zero estimates. It is recommended to inspect your data for multiple zero estimates before using this function and setting the inputs accordingly.
moe_sum(moe, estimate = NULL, na.rm = FALSE)moe_sum(moe, estimate = NULL, na.rm = FALSE)
moe |
A vector of margins of error involved in the sum calculation |
estimate |
A vector of estimates, the same length as |
na.rm |
A logical value indicating whether missing values (including NaN) should be removed |
A margin of error for a derived sum
https://www2.census.gov/programs-surveys/acs/tech_docs/accuracy/MultiyearACSAccuracyofData2015.pdf
Built-in dataset for variable name and code label lookup.
To access the data directly, issue the command data(pums_variables).
survey: acs1 or acs5
year: Year of data. For 5-year data, last year in range.
var_code: Variable name
var_label: Variable label
data_type: chr or num
level: housing or person
val_min: For numeric variables, the minimum value
val_max: For numeric variables, the maximum value
val_label: Value label
recode: Use labels to recode values
val_length: Length of value returned
val_na: Value of NA value returned by API (if known)
data(pums_variables)data(pums_variables)
An object of class tbl_df (inherits from tbl, data.frame) with 63966 rows and 12 columns.
Dataset with PUMS variables and codes
Built-in dataset that is created from the
Census
PUMS data dictionaries. Use this dataset to lookup the names of variables to
use in get_pums. This dataset also contains labels for the
coded values returned by the Census API and is used when recode = TRUE
in get_pums.
Because variable names and codes change from year to year, you should filter this dataset for the survey and year of interest. NOTE: 2017 - 2019 and 2021 acs1 and 2017 - 2021 acs5 variables are available.
Evaluate whether the difference in two estimates is statistically significant.
significance(est1, est2, moe1, moe2, clevel = 0.9)significance(est1, est2, moe1, moe2, clevel = 0.9)
est1 |
The first estimate. |
est2 |
The second estimate |
moe1 |
The margin of error of the first estimate |
moe2 |
The margin of error of the second estimate |
clevel |
The confidence level. May by 0.9, 0.95, or 0.99 |
TRUE if the difference is statistically signifiant, FALSE otherwise.
https://www.census.gov/content/dam/Census/library/publications/2018/acs/acs_general_handbook_2018_ch07.pdf
Built-in dataset for use with shift_geo = TRUE
Dataset of US states with Alaska and Hawaii shifted and re-scaled
data(state_laea) data(state_laea)data(state_laea) data(state_laea)
An object of class sf (inherits from data.frame) with 51 rows and 2 columns.
Dataset with state geometry for use when shifting Alaska and Hawaii
Built-in dataset for use with the shift_geo parameter, with the continental United States in a
Lambert azimuthal equal area projection and Alaska and Hawaii shifted and re-scaled. The data were originally
obtained from the albersusa R package (https://github.com/hrbrmstr/albersusa).
Identify summary files for a given decennial Census year
summary_files(year)summary_files(year)
year |
The year of the decennial Census |
A vector of available summary files for a given decennial Census year. To access data for a given summary file, supply the desired value to the sumfile parameter in get_decennial().
This packages uses US Census Bureau data but is neither endorsed nor supported by the US Census Bureau.
Kyle Walker
Useful links:
This helper function takes a data frame returned by
get_pums and converts it to a tbl_svy from the srvyr
as_survey package or a svyrep.design object from the
svrepdesign package. You can then use functions from
the srvyr or survey to calculate weighted estimates with replicate weights
included to provide accurate standard errors.
to_survey( df, type = c("person", "housing"), class = c("srvyr", "survey"), design = "rep_weights" )to_survey( df, type = c("person", "housing"), class = c("srvyr", "survey"), design = "rep_weights" )
df |
A data frame with PUMS person or housing weight variables, most
likely returned by |
type |
Whether to use person or housing-level weights; either
|
class |
Whether to convert to a srvyr or survey object; either
|
design |
The survey design to use when creating a survey object.
Currently the only option is |
A tbl_svy or svyrep.design object.
## Not run: pums <- get_pums(variables = "AGEP", state = "VT", rep_weights = "person") pums_design <- to_survey(pums, type = "person", class = "srvyr") survey::svymean(~AGEP, pums_design) ## End(Not run)## Not run: pums <- get_pums(variables = "AGEP", state = "VT", rep_weights = "person") pums_design <- to_survey(pums, type = "person", class = "srvyr") survey::svymean(~AGEP, pums_design) ## End(Not run)