Retrieve Census data from a given API
Usage
getCensus(
name,
vintage = NULL,
key = Sys.getenv("CENSUS_KEY"),
vars,
region = NULL,
regionin = NULL,
time = NULL,
year = NULL,
date = NULL,
period = NULL,
monthly = NULL,
show_call = FALSE,
convert_variables = TRUE,
category_code = NULL,
data_type_code = NULL,
naics = NULL,
pscode = NULL,
naics2012 = NULL,
naics2007 = NULL,
naics2002 = NULL,
naics1997 = NULL,
sic = NULL,
...
)Arguments
- name
The programmatic name of your dataset, e.g. `timeseries/poverty/saipe` or `acs/acs5`. See `listCensusApis()` for options.
- vintage
Vintage (year) of dataset, e.g. 2014. Not required for timeseries APIs.
- key
Your Census API key, obtained at https://api.census.gov/data/key_signup.html. This function will default to a `CENSUS_KEY` stored in your .Renviron if available.
- vars
List of variables to get. Required.
- region
Geography to get.
- regionin
Optional hierarchical geography to limit region.
- time, year, date, period, monthly
Optional arguments used for some time series APIs.
- show_call
List the underlying API call that was sent to the Census Bureau.
- convert_variables
Convert likely numeric variables into numeric data. Default is true. If false, results will be characters, which is the type returned by the Census Bureau.
- category_code, data_type_code, naics, pscode, naics2012, naics2007, naics2002, naics1997, sic
Optional arguments used in economic data APIs.
- ...
Other valid arguments to pass to the Census API. Note: the APIs are case sensitive.
Examples
# \dontrun{
# Get total population and median household income for places (cities, towns, villages)
# in one state from the 5-year ACS.
acs_simple <- getCensus(
name = "acs/acs5",
vintage = 2020,
vars = c("NAME", "B01001_001E", "B19013_001E"),
region = "place:*",
regionin = "state:01")
head(acs_simple)
#> state place NAME B01001_001E B19013_001E
#> 1 01 44536 Lowndesboro town, Alabama 75 41667
#> 2 01 44608 Loxley town, Alabama 2767 62292
#> 3 01 44728 Luverne city, Alabama 2742 48468
#> 4 01 44800 Lynn town, Alabama 832 36115
#> 5 01 45040 McCalla CDP, Alabama 12077 71061
#> 6 01 45208 McDonald Chapel CDP, Alabama 662 49038
# Get all data from the B19013 variable group.
# This returns estimates as well as margins of error and annotation flags.
acs_group <- getCensus(
name = "acs/acs5",
vintage = 2020,
vars = c("B01001_001E", "group(B19013)"),
region = "place:*",
regionin = "state:01")
head(acs_group)
#> state place B01001_001E B19013_001E B19013_001EA B19013_001M B19013_001MA
#> 1 01 44536 75 41667 <NA> 27339 <NA>
#> 2 01 44608 2767 62292 <NA> 17233 <NA>
#> 3 01 44728 2742 48468 <NA> 10129 <NA>
#> 4 01 44800 832 36115 <NA> 8289 <NA>
#> 5 01 45040 12077 71061 <NA> 3184 <NA>
#> 6 01 45208 662 49038 <NA> 11453 <NA>
#> GEO_ID NAME
#> 1 1600000US0144536 Lowndesboro town, Alabama
#> 2 1600000US0144608 Loxley town, Alabama
#> 3 1600000US0144728 Luverne city, Alabama
#> 4 1600000US0144800 Lynn town, Alabama
#> 5 1600000US0145040 McCalla CDP, Alabama
#> 6 1600000US0145208 McDonald Chapel CDP, Alabama
# Retreive 2010 Decennial Census block-level data within a specific tract,
# using the regionin argument to precisely specify the Census tract.
decennial_2010 <- getCensus(
name = "dec/sf1",
vintage = 2010,
vars = c("NAME","P001001"),
region = "block:*",
regionin = "state:36+county:027+tract:010000")
head(decennial_2010)
#> state county tract block
#> 1 36 027 010000 1000
#> 2 36 027 010000 1011
#> 3 36 027 010000 1028
#> 4 36 027 010000 1001
#> 5 36 027 010000 1031
#> 6 36 027 010000 1002
#> NAME
#> 1 Block 1000, Block Group 1, Census Tract 100, Dutchess County, New York
#> 2 Block 1011, Block Group 1, Census Tract 100, Dutchess County, New York
#> 3 Block 1028, Block Group 1, Census Tract 100, Dutchess County, New York
#> 4 Block 1001, Block Group 1, Census Tract 100, Dutchess County, New York
#> 5 Block 1031, Block Group 1, Census Tract 100, Dutchess County, New York
#> 6 Block 1002, Block Group 1, Census Tract 100, Dutchess County, New York
#> P001001
#> 1 31
#> 2 17
#> 3 41
#> 4 0
#> 5 0
#> 6 4
# Get poverty rates for children and for people of all ages over time using the
# Small Area Income and Poverty Estimates API
saipe <- getCensus(
name = "timeseries/poverty/saipe",
vars = c("NAME", "SAEPOVRT0_17_PT", "SAEPOVRTALL_PT"),
region = "state:01",
year = "2000:2019")
head(saipe)
#> state NAME SAEPOVRT0_17_PT SAEPOVRTALL_PT YEAR
#> 1 01 Alabama 20.5 14.6 2000
#> 2 01 Alabama 22.1 15.7 2001
#> 3 01 Alabama 21.6 15.4 2002
#> 4 01 Alabama 22.3 15.3 2003
#> 5 01 Alabama 22.6 16.1 2004
#> 6 01 Alabama 24.3 16.9 2005
# Get County Business Patterns data for a specific NAICS sector.
cbp_2016 <- getCensus(
name = "cbp",
vintage = "2016",
vars = c("EMP", "ESTAB", "NAICS2012_TTL", "GEO_TTL"),
region = "state:*",
naics2012 = "23")
head(cbp_2016)
#> state EMP ESTAB NAICS2012_TTL GEO_TTL NAICS2012
#> 1 27 118033 16360 Construction Minnesota 23
#> 2 28 44551 3796 Construction Mississippi 23
#> 3 29 121494 13263 Construction Missouri 23
#> 4 25 131693 18618 Construction Massachusetts 23
#> 5 26 138920 18803 Construction Michigan 23
#> 6 30 25036 5326 Construction Montana 23
# }