The Census APIs have over 200 endpoints, covering dozens of different datasets.
To see a current table of every available endpoint, run listCensusApis
:
apis <- listCensusApis()
View(apis)
Here is a list of examples for dozens of the Census’s API endpoints. Read more about discovering an API’s variable and geography options in Getting started with censusapi
Get median household income, with associated annotations and margin of error, for census tracts in Alaska.
acs_income <- getCensus(
name = "acs/acs5",
vintage = 2018,
vars = c("NAME", "B19013_001E", "B19013_001EA", "B19013_001M", "B19013_001MA"),
region = "tract:*",
regionin = "state:02")
head(acs_income)
state | county | tract | NAME | B19013_001E | B19013_001EA | B19013_001M | B19013_001MA |
---|---|---|---|---|---|---|---|
02 | 068 | 000100 | Census Tract 1, Denali Borough, Alaska | 84196 | NA | 7160 | NA |
02 | 261 | 000200 | Census Tract 2, Valdez-Cordova Census Area, Alaska | 85933 | NA | 5274 | NA |
02 | 261 | 000300 | Census Tract 3, Valdez-Cordova Census Area, Alaska | 92813 | NA | 19527 | NA |
02 | 261 | 000100 | Census Tract 1, Valdez-Cordova Census Area, Alaska | 52419 | NA | 11601 | NA |
02 | 122 | 000200 | Census Tract 2, Kenai Peninsula Borough, Alaska | 56838 | NA | 6437 | NA |
02 | 122 | 000800 | Census Tract 8, Kenai Peninsula Borough, Alaska | 53882 | NA | 2985 | NA |
Get the percent of people without an internet subscription by income for the five counties of New York City, with associated margins of error:
S2801_C02_019E
S2801_C02_023E
S2801_C02_027E
S2801_C02_031E
acs_subject <- getCensus(
name = "acs/acs1/subject",
vintage = 2018,
vars = c("NAME", "S2801_C02_019E", "S2801_C02_019M",
"S2801_C02_023E", "S2801_C02_023M",
"S2801_C02_027E", "S2801_C02_027M",
"S2801_C02_031E", "S2801_C02_031M"),
region = "county:005,047,061,081,085",
regionin = "state:36")
head(acs_subject)
state | county | NAME | S2801_C02_019E | S2801_C02_019M | S2801_C02_023E | S2801_C02_023M | S2801_C02_027E | S2801_C02_027M | S2801_C02_031E | S2801_C02_031M |
---|---|---|---|---|---|---|---|---|---|---|
36 | 081 | Queens County, New York | 13.6 | 0.6 | 35.7 | 2.3 | 15.6 | 1.1 | 5.5 | 0.6 |
36 | 005 | Bronx County, New York | 21.3 | 1.0 | 39.0 | 2.5 | 17.4 | 1.4 | 6.8 | 1.2 |
36 | 061 | New York County, New York | 12.6 | 0.8 | 36.6 | 2.7 | 15.9 | 1.8 | 3.3 | 0.6 |
36 | 047 | Kings County, New York | 17.2 | 0.6 | 39.7 | 1.8 | 18.5 | 1.0 | 5.6 | 0.5 |
36 | 085 | Richmond County, New York | 16.3 | 1.5 | 40.7 | 6.4 | 22.8 | 3.6 | 6.4 | 1.8 |
acs_profile <- getCensus(
name = "acs/acs1/profile",
vintage = 2018,
vars = "group(DP05)",
region = "region:*")
Get the mean travel time to work (in minutes) for the past five years.
acs_comparison <- getCensus(
name = "acs/acs1/cprofile",
vintage = 2017,
vars = c("NAME", "CP03_2013_025E", "CP03_2014_025E", "CP03_2015_025E", "CP03_2016_025E", "CP03_2017_025E"),
region = "metropolitan statistical area/micropolitan statistical area:*")
head(acs_comparison)
metropolitan_statistical_area_micropolitan_statistical_area | NAME | CP03_2013_025E | CP03_2014_025E | CP03_2015_025E | CP03_2016_025E | CP03_2017_025E |
---|---|---|---|---|---|---|
10140 | Aberdeen, WA Micro Area | 24.0 | 25.0 | 23.3 | 26.5 | 29.6 |
10180 | Abilene, TX Metro Area | 16.9 | 17.0 | 15.6 | 19.1 | 18.3 |
10300 | Adrian, MI Micro Area | 25.4 | 26.8 | 29.6 | 25.3 | 27.7 |
10380 | Aguadilla-Isabela, PR Metro Area | 26.0 | 24.8 | 25.3 | 23.9 | 25.2 |
10420 | Akron, OH Metro Area | 23.1 | 22.9 | 24.3 | 22.9 | 23.2 |
10460 | Alamogordo, NM Micro Area | 18.3 | 18.4 | 17.7 | 20.4 | 17.3 |
American Community Survey Migration Flows documentation
flows <- getCensus(
name = "acs/flows",
vintage = 2016,
vars = c("MOVEDIN", "MOVEDOUT", "FULL1_NAME", "FULL2_NAME", "GEOID2"),
region = "county:001",
regionin = "state:01")
head(flows)
state | county | MOVEDIN | MOVEDOUT | FULL1_NAME | FULL2_NAME | GEOID2 |
---|---|---|---|---|---|---|
01 | 001 | 70 | NA | Autauga County, Alabama | Asia | NA |
01 | 001 | 51 | NA | Autauga County, Alabama | Europe | NA |
01 | 001 | 36 | 126 | Autauga County, Alabama | Baldwin County, Alabama | 1003 |
01 | 001 | 4 | 0 | Autauga County, Alabama | Barbour County, Alabama | 1005 |
01 | 001 | 7 | 135 | Autauga County, Alabama | Bibb County, Alabama | 1007 |
01 | 001 | 4 | 0 | Autauga County, Alabama | Blount County, Alabama | 1009 |
American Community Survey Language Statistics documentation
Get the number of people in New York state who speak each language.
languages <- getCensus(
name = "language",
vintage = 2013,
vars = c("EST", "LAN", "LANLABEL"),
region = "state:36")
head(languages)
state | EST | LAN | LANLABEL |
---|---|---|---|
36 | 2705225 | 625 | Spanish |
36 | NA | 627 | Ladino |
36 | 133535 | 620 | French |
36 | 5645 | 622 | Patois |
36 | NA | 624 | Cajun |
36 | 44980 | 629 | Portuguese |
Annual Survey of Entrepreneurs documentation
ase_csa <- getCensus(
name = "ase/csa",
vintage = 2014,
vars = c("GEO_TTL", "NAICS2012", "NAICS2012_TTL", "EMPSZFI", "EMPSZFI_TTL", "FIRMPDEMP"),
region = "us:*")
head(ase_csa)
us | GEO_TTL | NAICS2012 | NAICS2012_TTL | EMPSZFI | EMPSZFI_TTL | FIRMPDEMP |
---|---|---|---|---|---|---|
00 | United States | 00 | Total for all sectors | 001 | All firms | 5437782 |
00 | United States | 00 | Total for all sectors | 611 | Firms with no employees | 547115 |
00 | United States | 00 | Total for all sectors | 612 | Firms with 1 to 4 employees | 2768756 |
00 | United States | 00 | Total for all sectors | 620 | Firms with 5 to 9 employees | 950224 |
00 | United States | 00 | Total for all sectors | 630 | Firms with 10 to 19 employees | 585516 |
00 | United States | 00 | Total for all sectors | 641 | Firms with 20 to 49 employees | 376051 |
ase_cscb <- getCensus(
name = "ase/cscb",
vintage = 2014,
vars = c("GEO_TTL", "NAICS2012_TTL", "ASECB", "ASECB_TTL", "SPOUSES", "SPOUSES_TTL", "YEAR",
"FIRMPDEMP", "FIRMPDEMP_PCT", "RCPPDEMP", "RCPPDEMP_F", "RCPPDEMP_PCT",
"EMP", "EMP_PCT", "PAYANN", "PAYANN_PCT", "FIRMPDEMP_S", "FIRMPDEMP_PCT_S",
"RCPPDEMP_S", "RCPPDEMP_PCT_S", "EMP_S", "EMP_PCT_S", "PAYANN_S", "PAYANN_PCT_S"),
region = "us:*")
head(ase_cscb)
us | GEO_TTL | NAICS2012_TTL | ASECB | ASECB_TTL | SPOUSES | SPOUSES_TTL | YEAR | FIRMPDEMP | FIRMPDEMP_PCT | RCPPDEMP | RCPPDEMP_F | RCPPDEMP_PCT | EMP | EMP_PCT | PAYANN | PAYANN_PCT | FIRMPDEMP_S | FIRMPDEMP_PCT_S | RCPPDEMP_S | RCPPDEMP_PCT_S | EMP_S | EMP_PCT_S | PAYANN_S | PAYANN_PCT_S |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
00 | United States | Total for all sectors | 0000 | All firms | LZ | Jointly owned and equally operated by spouses | 2014 | 335149 | 30.6 | 493143589 | NA | 15.4 | 3303608 | 23.0 | 104343482 | 19.2 | 0.6 | 0.3 | 3.7 | 5.5 | 1.7 | 6.2 | 1.8 | 5.5 |
00 | United States | Total for all sectors | 0000 | All firms | MA | Jointly owned but primarily operated by male spouse | 2014 | 336310 | 30.7 | 603733952 | NA | 18.8 | 3015332 | 21.0 | 109460428 | 20.2 | 0.8 | 0.3 | 3.2 | 1.8 | 1.8 | 3.5 | 3.0 | 3.5 |
00 | United States | Total for all sectors | 0000 | All firms | MB | Jointly owned but primarily operated by female spouse | 2014 | 96475 | 8.8 | 140228793 | NA | 4.4 | 850573 | 5.9 | 25984506 | 4.8 | 1.9 | 0.2 | 12.6 | 1.1 | 3.6 | 0.6 | 4.7 | 0.3 |
00 | United States | Total for all sectors | 0000 | All firms | MC | Not jointly owned by spouses | 2014 | 328625 | 30.0 | 1966858366 | NA | 61.4 | 7222018 | 50.2 | 302838280 | 55.8 | 0.6 | 0.5 | 2.5 | 8.1 | 1.6 | 10.3 | 1.9 | 9.0 |
00 | United States | Total for all sectors | 0000 | All firms | MD | Total reporting | 2014 | 1096559 | 100.0 | 3203964700 | NA | 100.0 | 14391531 | 100.0 | 542626696 | 100.0 | 0.2 | 0.0 | 1.8 | 0.0 | 0.9 | 0.0 | 1.3 | 0.0 |
00 | United States | Total for all sectors | 0000 | All firms | ME | Item not reported | 2014 | 2733098 | 0.0 | 4926927597 | NA | 0.0 | 25408994 | 0.0 | 1074761744 | 0.0 | 0.2 | 0.0 | 1.7 | 0.0 | 0.6 | 0.0 | 0.6 | 0.0 |
ase_cscbo <- getCensus(
name = "ase/cscbo",
vintage = 2014,
vars = c("GEO_TTL", "NAICS2012_TTL", "ASECBO", "ASECBO_TTL", "ACQBUS", "ACQBUS_TTL",
"YEAR", "OWNPDEMP", "OWNPDEMP_PCT", "OWNPDEMP_S", "OWNPDEMP_PCT_S"),
region = "us:*")
head(ase_cscbo)
us | GEO_TTL | NAICS2012_TTL | ASECBO | ASECBO_TTL | ACQBUS | ACQBUS_TTL | YEAR | OWNPDEMP | OWNPDEMP_PCT | OWNPDEMP_S | OWNPDEMP_PCT_S |
---|---|---|---|---|---|---|---|---|---|---|---|
00 | United States | Total for all sectors | 00 | All owners of respondent firms | CA | Founded or started | 2014 | 4063687 | 70.4 | 0.2 | 0.3 |
00 | United States | Total for all sectors | 00 | All owners of respondent firms | CB | Purchased | 2014 | 1211902 | 21 | 0.5 | 0.2 |
00 | United States | Total for all sectors | 00 | All owners of respondent firms | CC | Inherited | 2014 | 227408 | 3.9 | 1.7 | 0.1 |
00 | United States | Total for all sectors | 00 | All owners of respondent firms | CD | Transfer of ownership or gift | 2014 | 405356 | 7 | 0.6 | 0.1 |
00 | United States | Total for all sectors | 00 | All owners of respondent firms | CE | Total reporting | 2014 | 5768389 | 100 | 0.2 | 0 |
00 | United States | Total for all sectors | 00 | All owners of respondent firms | CF | Item not reported | 2014 | 14476 | 0 | 7.3 | 0 |
Annual Survey of Manufactures documentation
asm_state <- getCensus(
name = "timeseries/asm/state",
vars = c("GEO_TTL", "NAICS_TTL", "EMP"),
region = "state:*",
time = 2016,
naics = "31-33")
head(asm_state)
time | state | GEO_TTL | NAICS_TTL | EMP | NAICS |
---|---|---|---|---|---|
2016 | 01 | Alabama | Manufacturing | 234803 | 31-33 |
2016 | 02 | Alaska | Manufacturing | 12178 | 31-33 |
2016 | 56 | Wyoming | Manufacturing | 8377 | 31-33 |
2016 | 04 | Arizona | Manufacturing | 136946 | 31-33 |
2016 | 05 | Arkansas | Manufacturing | 145733 | 31-33 |
2016 | 06 | California | Manufacturing | 1119896 | 31-33 |
asm_product <- getCensus(
name = "timeseries/asm/product",
vars = c("PSCODE_TTL", "GEO_TTL", "PRODVAL"),
region = "us:*",
time = 2016,
pscode = 311111)
head(asm_product)
time | us | PSCODE_TTL | GEO_TTL | PRODVAL | PSCODE |
---|---|---|---|---|---|
2016 | 1 | Dog and cat food manufacturing | United States | 22933334 | 311111 |
County Business Patterns and Nonemployer Statistics documentation
County Business Patterns documentation
Get employment data for the construction industry.
cbp_2018 <- getCensus(
name = "cbp",
vintage = 2018,
vars = c("EMP", "ESTAB"),
region = "state:*",
NAICS2017 = 23)
head(cbp_2018)
state | EMP | ESTAB | NAICS2017 |
---|---|---|---|
31 | 49552 | 6527 | 23 |
28 | 43156 | 3890 | 23 |
29 | 129188 | 13540 | 23 |
30 | 26859 | 5656 | 23 |
32 | 80043 | 5146 | 23 |
33 | 29049 | 4245 | 23 |
Get employment data by state for companies with more than 1,000 employees.
cbp_2008 <- getCensus(
name = "cbp",
vintage = 2008,
vars = c("YEAR", "GEO_TTL", "EMPSZES_TTL", "EMP", "ESTAB", "PAYANN"),
region = "state:*",
EMPSZES = 260)
head(cbp_2008)
state | YEAR | GEO_TTL | EMPSZES_TTL | EMP | ESTAB | PAYANN | EMPSZES |
---|---|---|---|---|---|---|---|
01 | 2008 | Alabama | Establishments with 1,000 employees or more | 175438 | 96 | 8034522 | 260 |
02 | 2008 | Alaska | Establishments with 1,000 employees or more | 22598 | 16 | 1469718 | 260 |
78 | 2008 | Virgin Islands of the United States | Establishments with 1,000 employees or more | 0 | 1 | 0 | 260 |
04 | 2008 | Arizona | Establishments with 1,000 employees or more | 301091 | 124 | 17089056 | 260 |
05 | 2008 | Arkansas | Establishments with 1,000 employees or more | 124452 | 68 | 4796665 | 260 |
06 | 2008 | California | Establishments with 1,000 employees or more | 1872632 | 797 | 124024587 | 260 |
Zip Codes Business Patterns documentation
zbp_2018 <- getCensus(
name = "zbp",
vintage = 2018,
vars = c("EMP", "ESTAB", "EMPSZES"),
region = "zipcode:90210")
head(zbp_2018)
zip_code | EMP | ESTAB | EMPSZES |
---|---|---|---|
90210 | 35324 | 2496 | 001 |
90210 | 0 | 1758 | 210 |
90210 | 0 | 322 | 220 |
90210 | 0 | 199 | 230 |
90210 | 0 | 137 | 241 |
90210 | 0 | 50 | 242 |
Nonemployer statistics documentation
nonemp <- getCensus(
name = "nonemp",
vintage = 2016,
vars = c("GEO_TTL", "NRCPTOT", "NAICS2012_TTL"),
region = "state:*",
naics2012 = 54)
head(nonemp)
state | GEO_TTL | NRCPTOT | NAICS2012_TTL | NAICS2012 |
---|---|---|---|---|
01 | Alabama | 1284130 | Professional, scientific, and technical services | 54 |
02 | Alaska | 265996 | Professional, scientific, and technical services | 54 |
04 | Arizona | 2991782 | Professional, scientific, and technical services | 54 |
05 | Arkansas | 616936 | Professional, scientific, and technical services | 54 |
06 | California | 28746664 | Professional, scientific, and technical services | 54 |
08 | Colorado | 3709131 | Professional, scientific, and technical services | 54 |
Decennial Census documentation Total population and housing units for metropolitan/micropolitan statistical areas in 2010.
data2010 <- getCensus(
name = "dec/sf1",
vintage = 2010,
vars = c("NAME", "P001001", "H010001"),
region = "metropolitan statistical area/micropolitan statistical area:*")
head(data2010)
metropolitan_statistical_area_micropolitan_statistical_area | NAME | P001001 | H010001 |
---|---|---|---|
31540 | Madison, WI Metro Area | 568593 | 554078 |
31580 | Madisonville, KY Micro Area | 46920 | 45834 |
36820 | Oskaloosa, IA Micro Area | 22381 | 21722 |
36860 | Ottawa-Streator, IL Micro Area | 154908 | 151500 |
36900 | Ottumwa, IA Micro Area | 35625 | 34758 |
36940 | Owatonna, MN Micro Area | 36576 | 35982 |
Get the urban/rural status group of variables (P2
) by metropolitan/micropolitan statistical areas in 2010.
# Show variable metadata for the P2 group
group_p2 <- listCensusMetadata(
name = "dec/sf1",
vintage = 2010,
type = "variables",
group = "P2")
# Get the P2 variable group (URBAN AND RURAL)
data2010 <- getCensus(
name = "dec/sf1",
vintage = 2010,
vars = "group(P2)",
region = "metropolitan statistical area/micropolitan statistical area:*")
head(data2010)
metropolitan_statistical_area_micropolitan_statistical_area | GEO_ID | P002001 | P002002 | P002003 | P002004 | P002005 | P002006 | NAME | P002001ERR |
---|---|---|---|---|---|---|---|---|---|
31540 | 310M100US31540 | 568593 | 455002 | 401661 | 53341 | 113591 | 0 | Madison, WI Metro Area | NA |
31580 | 310M100US31580 | 46920 | 24809 | 0 | 24809 | 22111 | 0 | Madisonville, KY Micro Area | NA |
36820 | 310M100US36820 | 22381 | 12545 | 0 | 12545 | 9836 | 0 | Oskaloosa, IA Micro Area | NA |
36860 | 310M100US36860 | 154908 | 94406 | 0 | 94406 | 60502 | 0 | Ottawa-Streator, IL Micro Area | NA |
36900 | 310M100US36900 | 35625 | 24771 | 0 | 24771 | 10854 | 0 | Ottumwa, IA Micro Area | NA |
36940 | 310M100US36940 | 36576 | 25394 | 0 | 25394 | 11182 | 0 | Owatonna, MN Micro Area | NA |
Get 2010 population by block group within a specific tract.
tract_pop <- getCensus(
name = "dec/sf1",
vintage = 2010,
vars = "P001001",
region = "block:*",
regionin = "state:36+county:027+tract:010000")
head(tract_pop)
state | county | tract | block | P001001 |
---|---|---|---|---|
36 | 027 | 010000 | 1000 | 31 |
36 | 027 | 010000 | 1011 | 17 |
36 | 027 | 010000 | 1028 | 41 |
36 | 027 | 010000 | 1001 | 0 |
36 | 027 | 010000 | 1031 | 0 |
36 | 027 | 010000 | 1002 | 4 |
Decennial Census Self-Response Rates documentation Get self-response rates for the 2020 and 2010 Decennial Census by state, county, tract, place and more.
Get overall and internet reponse rates for all counties.
county_responses <- getCensus(
name = "dec/responserate",
vintage = 2020,
vars = c("NAME", "RESP_DATE", "CRRALL", "CRRINT"),
region = "county:*")
head(county_responses)
state | county | NAME | RESP_DATE | CRRALL | CRRINT |
---|---|---|---|---|---|
21 | 137 | Lincoln County, Kentucky | 2020-10-28 | 69.1 | 26.7 |
21 | 139 | Livingston County, Kentucky | 2020-10-28 | 62.6 | 32.7 |
21 | 143 | Lyon County, Kentucky | 2020-10-28 | 55.6 | 24.1 |
21 | 145 | McCracken County, Kentucky | 2020-10-28 | 71.9 | 56.7 |
21 | 149 | McLean County, Kentucky | 2020-10-28 | 63.8 | 26.2 |
21 | 151 | Madison County, Kentucky | 2020-10-28 | 72.2 | 60.3 |
Get response rates for places (cities, towns, etc) within New York state.
ny_place_responses <- getCensus(
name = "dec/responserate",
vintage = 2020,
vars = c("NAME", "RESP_DATE", "CRRALL", "CRRINT"),
region = "place:*",
regionin = "state:36")
head(ny_place_responses)
state | place | NAME | RESP_DATE | CRRALL | CRRINT |
---|---|---|---|---|---|
36 | 23745 | Elba village, New York | 2020-10-28 | 66.3 | 52.9 |
36 | 23965 | Ellenville village, New York | 2020-10-28 | 58.5 | 45.3 |
36 | 24075 | Ellisburg village, New York | 2020-10-28 | 52.6 | 18.1 |
36 | 24229 | Elmira city, New York | 2020-10-28 | 60.1 | 40.7 |
36 | 24295 | Elmsford village, New York | 2020-10-28 | 62.9 | 55.0 |
36 | 24515 | Endicott village, New York | 2020-10-28 | 58.3 | 45.0 |
Get final 2010 Decennial Census self-response rates.
county_responses_2010 <- getCensus(
name = "dec/responserate",
vintage = 2010,
vars = c("NAME", "FSRR2010"),
region = "county:*")
head(county_responses_2010)
state | county | NAME | FSRR2010 |
---|---|---|---|
01 | 001 | Autauga County, Alabama | 68.6 |
01 | 003 | Baldwin County, Alabama | 59.4 |
01 | 005 | Barbour County, Alabama | 55.2 |
01 | 007 | Bibb County, Alabama | 54.8 |
01 | 009 | Blount County, Alabama | 66.3 |
01 | 011 | Bullock County, Alabama | 34.0 |
Decennial Census Surname documentation
Get counts of the top 25 most popular surnames and share of each by race.
top_surnames <- getCensus(
name = "surname",
vintage = 2010,
vars = c("NAME", "COUNT", "PROP100K", "PCTWHITE", "PCTBLACK", "PCTAIAN", "PCTAPI", "PCTHISPANIC", "PCT2PRACE"),
RANK = "1:25")
head(top_surnames)
NAME | COUNT | PROP100K | PCTWHITE | PCTBLACK | PCTAIAN | PCTAPI | PCTHISPANIC | PCT2PRACE | RANK |
---|---|---|---|---|---|---|---|---|---|
ANDERSON | 784404 | 265.92 | 75.17 | 18.93 | 0.74 | 0.61 | 2.44 | 2.11 | 15 |
BROWN | 1437026 | 487.16 | 57.95 | 35.6 | 0.87 | 0.51 | 2.52 | 2.55 | 4 |
DAVIS | 1116357 | 378.45 | 62.2 | 31.6 | 0.82 | 0.49 | 2.44 | 2.45 | 8 |
GARCIA | 1166120 | 395.32 | 5.38 | 0.45 | 0.47 | 1.41 | 92.03 | 0.26 | 6 |
GONZALEZ | 841025 | 285.11 | 4.03 | 0.35 | 0.14 | 0.38 | 94.97 | 0.13 | 13 |
HARRIS | 624252 | 211.63 | 51.4 | 42.39 | 0.67 | 0.47 | 2.26 | 2.80 | 25 |
ewks_2012 <- getCensus(
name = "ewks",
vintage = 2012,
vars = c("EMP", "OPTAX", "GEOTYPE"),
region = "state:*",
naics2012 = 54)
head(ewks_2012)
state | EMP | OPTAX | GEOTYPE | NAICS2012 |
---|---|---|---|---|
01 | 89988 | A | 02 | 54 |
01 | 88566 | T | 02 | 54 |
01 | 1422 | Y | 02 | 54 |
02 | 17648 | A | 02 | 54 |
08 | 4616 | Y | 02 | 54 |
02 | 17328 | T | 02 | 54 |
ewks_2007 <- getCensus(
name = "ewks",
vintage = 2007,
vars = c("EMP", "OPTAX", "GEOTYPE"),
region = "state:*",
naics2007 = 54)
head(ewks_2007)
state | EMP | OPTAX | GEOTYPE | NAICS2007 |
---|---|---|---|---|
01 | 94051 | A | 2 | 54 |
01 | 92759 | T | 2 | 54 |
01 | 1292 | Y | 2 | 54 |
02 | 12843 | A | 2 | 54 |
02 | 12509 | T | 2 | 54 |
02 | 334 | Y | 2 | 54 |
Economic Indicators documentation
eits <- getCensus(
name = "timeseries/eits/resconst",
vars = c("cell_value", "data_type_code", "time_slot_id", "error_data", "category_code", "seasonally_adj"),
region = "us:*",
time = "from 2004-05 to 2012-12")
head(eits)
cell_value | data_type_code | time_slot_id | error_data | category_code | seasonally_adj | time | us |
---|---|---|---|---|---|---|---|
367 | MULTI | 653 | no | ACOMPLETIONS | yes | 2004-05 | 1 |
1893 | TOTAL | 653 | no | ACOMPLETIONS | yes | 2004-05 | 1 |
1505 | SINGLE | 653 | no | ACOMPLETIONS | yes | 2004-05 | 1 |
11 | E_MULTI | 653 | yes | ACOMPLETIONS | yes | 2004-05 | 1 |
4 | E_TOTAL | 653 | yes | ACOMPLETIONS | yes | 2004-05 | 1 |
4 | E_SINGLE | 653 | yes | ACOMPLETIONS | yes | 2004-05 | 1 |
Health Insurance Statistics documentation
Get the uninsured rate by income group in Alabama for a single year.
sahie <- getCensus(
name = "timeseries/healthins/sahie",
vars = c("NAME", "IPRCAT", "IPR_DESC", "PCTUI_PT"),
region = "state:01",
time = 2018)
head(sahie)
time | state | NAME | IPRCAT | IPR_DESC | PCTUI_PT |
---|---|---|---|---|---|
2018 | 01 | Alabama | 0 | All Incomes | 11.9 |
2018 | 01 | Alabama | 1 | <= 200% of Poverty | 19.6 |
2018 | 01 | Alabama | 2 | <= 250% of Poverty | 18.5 |
2018 | 01 | Alabama | 3 | <= 138% of Poverty | 20.6 |
2018 | 01 | Alabama | 4 | <= 400% of Poverty | 15.5 |
2018 | 01 | Alabama | 5 | 138% to 400% of Poverty | 12.5 |
Get the uninsured rate in Alabama for multiple years.
sahie_annual <- getCensus(
name = "timeseries/healthins/sahie",
vars = c("NAME", "PCTUI_PT"),
region = "state:01",
time = "from 2006 to 2018")
sahie_annual
time | state | NAME | PCTUI_PT |
---|---|---|---|
2006 | 01 | Alabama | 15.7 |
2007 | 01 | Alabama | 14.6 |
2008 | 01 | Alabama | 15.3 |
2009 | 01 | Alabama | 15.8 |
2010 | 01 | Alabama | 16.9 |
2011 | 01 | Alabama | 16.6 |
2012 | 01 | Alabama | 15.8 |
2013 | 01 | Alabama | 15.9 |
2014 | 01 | Alabama | 14.2 |
2015 | 01 | Alabama | 11.9 |
2016 | 01 | Alabama | 10.8 |
2017 | 01 | Alabama | 11.0 |
2018 | 01 | Alabama | 11.9 |
Get the uninsured rate for non-elderly adults with incomes of 138 to 400% of the poverty line, by race and state.
sahie_nonelderly <- getCensus(
name = "timeseries/healthins/sahie",
vars = c("NAME", "IPR_DESC", "PCTUI_PT", "AGE_DESC", "RACECAT", "RACE_DESC"),
region = "state:*",
time = 2018,
IPRCAT = 5,
AGECAT = 1)
head(sahie_nonelderly)
time | state | NAME | IPR_DESC | PCTUI_PT | AGE_DESC | RACECAT | RACE_DESC | IPRCAT | AGECAT |
---|---|---|---|---|---|---|---|---|---|
2018 | 01 | Alabama | 138% to 400% of Poverty | 16.0 | 18 to 64 years | 0 | All Races | 5 | 1 |
2018 | 01 | Alabama | 138% to 400% of Poverty | 14.4 | 18 to 64 years | 1 | White alone, not Hispanic | 5 | 1 |
2018 | 01 | Alabama | 138% to 400% of Poverty | 15.9 | 18 to 64 years | 2 | Black alone, not Hispanic | 5 | 1 |
2018 | 01 | Alabama | 138% to 400% of Poverty | 37.5 | 18 to 64 years | 3 | Hispanic (any race) | 5 | 1 |
2018 | 02 | Alaska | 138% to 400% of Poverty | 22.5 | 18 to 64 years | 0 | All Races | 5 | 1 |
2018 | 02 | Alaska | 138% to 400% of Poverty | 18.5 | 18 to 64 years | 1 | White alone, not Hispanic | 5 | 1 |
International Data Base documentation
Get Census Bureau projections of 2020 populations and life expectancy at birth by country.
intl_pop <- getCensus(
name = "timeseries/idb/5year",
vars = c("NAME", "FIPS", "POP", "E0"),
time = 2020)
head(intl_pop)
time | NAME | FIPS | POP | E0 |
---|---|---|---|---|
2020 | Aruba | AA | 119428 | 77.52 |
2020 | Antigua and Barbuda | AC | 98179 | 77.31 |
2020 | United Arab Emirates | AE | 9992083 | 78.99 |
2020 | Afghanistan | AF | 36643815 | 52.84 |
2020 | Algeria | AG | 42972878 | 77.54 |
2020 | Azerbaijan | AJ | 10205810 | 73.58 |
Get predictions of population by age in 2050 for Norway for ages 10-18. https://api.census.gov/data/timeseries/idb/1year?get=AREA_KM2,NAME,AGE,POP&FIPS=NO&time=2050
norway_pop <- getCensus(
name = "timeseries/idb/1year",
vars = c("NAME", "POP"),
time = 2050,
FIPS = "NO",
AGE = "10:18")
head(norway_pop)
time | NAME | POP | FIPS | AGE |
---|---|---|---|---|
2050 | Norway | 66971 | NO | 10 |
2050 | Norway | 67018 | NO | 11 |
2050 | Norway | 67097 | NO | 12 |
2050 | Norway | 67199 | NO | 13 |
2050 | Norway | 67352 | NO | 14 |
2050 | Norway | 67605 | NO | 15 |
International Trade documentation
Note: The international trade datasets are buggy and frequently give the general error message of “There was an error while running your query. We’ve logged the error and we’ll correct it ASAP. Sorry for the inconvenience.” This error message comes from the U.S. Census Bureau. If you run in to repeated issues or inconsistencies, contact the Census Bureau for help or consider using a bulk data download instead.
Get the general imports value and imports for consumption value for all end-use codes and all countries for January 2018.
imports <- getCensus(
name = "timeseries/intltrade/imports/enduse",
vars = c("CTY_CODE", "CTY_NAME", "I_ENDUSE", "I_ENDUSE_LDESC", "GEN_VAL_MO", "CON_VAL_MO"),
time = "2018-01")
head(imports)
time | CTY_CODE | CTY_NAME | I_ENDUSE | I_ENDUSE_LDESC | GEN_VAL_MO | CON_VAL_MO |
---|---|---|---|---|---|---|
2018-01 | - | TOTAL FOR ALL COUNTRIES | - | TOTAL IMPORTS FOR ALL END-USE CODES | 203186767799 | 201524734942 |
2018-01 | 0001 | OPEC | - | TOTAL IMPORTS FOR ALL END-USE CODES | 6337094420 | 5573376643 |
2018-01 | 0003 | EUROPEAN UNION | - | TOTAL IMPORTS FOR ALL END-USE CODES | 36862681499 | 36655928596 |
2018-01 | 0014 | PACIFIC RIM COUNTRIES | - | TOTAL IMPORTS FOR ALL END-USE CODES | 75727333696 | 74587615456 |
2018-01 | 0017 | CAFTA-DR | - | TOTAL IMPORTS FOR ALL END-USE CODES | 1751485326 | 1736737434 |
2018-01 | 0020 | NAFTA | - | TOTAL IMPORTS FOR ALL END-USE CODES | 52035360959 | 52050679748 |
Population Estimates and Projections documentation
Population Estimates documentation Note that variable names in the PEP APIs are not always consistent from year to year.
popest <- getCensus(
name = "pep/population",
vintage = 2019,
vars = c("POP", "DATE_DESC"),
region = "state:*")
head(popest)
state | POP | DATE_DESC |
---|---|---|
28 | 2976149 | 7/1/2019 population estimate |
29 | 6137428 | 7/1/2019 population estimate |
30 | 1068778 | 7/1/2019 population estimate |
31 | 1934408 | 7/1/2019 population estimate |
32 | 3080156 | 7/1/2019 population estimate |
33 | 1359711 | 7/1/2019 population estimate |
popest_housing <- getCensus(
name = "pep/housing",
vintage = 2018,
vars = c("DATE_CODE", "DATE_DESC", "HUEST"),
region = "county:195",
regionin = "state:02")
head(popest_housing)
state | county | DATE_CODE | DATE_DESC | HUEST |
---|---|---|---|---|
02 | 195 | 1 | 4/1/2010 Census housing unit count | 1994 |
02 | 195 | 2 | 4/1/2010 housing unit estimates base | 1644 |
02 | 195 | 3 | 7/1/2010 housing unit estimate | 1646 |
02 | 195 | 4 | 7/1/2011 housing unit estimate | 1647 |
02 | 195 | 5 | 7/1/2012 housing unit estimate | 1659 |
02 | 195 | 6 | 7/1/2013 housing unit estimate | 1662 |
Population Projections documentation
popproj <- getCensus(
name = "pep/projpop",
vintage = 2014,
vars = c("YEAR", "POP", "AGE"),
region = "us:1")
head(popproj)
us | YEAR | POP | AGE |
---|---|---|---|
1 | 2014 | 3971847 | 0 |
1 | 2014 | 3957864 | 1 |
1 | 2014 | 3972081 | 2 |
1 | 2014 | 4003272 | 3 |
1 | 2014 | 4001929 | 4 |
1 | 2014 | 4002977 | 5 |
Poverty Statistics documentation
Get national poverty rates by race for the past 50 years.
poverty <- getCensus(
name = "timeseries/poverty/histpov2",
vars = c("RACE", "PCTPOV"),
region = "us:*",
time = "from 1968 to 2018")
head(poverty)
time | us | RACE | PCTPOV |
---|---|---|---|
2018 | 1 | 1 | 11.8 |
2017 | 1 | 1 | 12.3 |
2017 | 1 | 1 | 12.3 |
2016 | 1 | 1 | 12.7 |
2015 | 1 | 1 | 13.5 |
2014 | 1 | 1 | 14.8 |
Get poverty rate for children and overall for a single year.
saipe <- getCensus(
name = "timeseries/poverty/saipe",
vars = c("NAME", "SAEPOVRT0_17_PT", "SAEPOVRTALL_PT"),
region = "state:*",
time = 2018)
head(saipe)
time | state | NAME | SAEPOVRT0_17_PT | SAEPOVRTALL_PT |
---|---|---|---|---|
2018 | 01 | Alabama | 23.9 | 16.8 |
2018 | 02 | Alaska | 14.5 | 11.1 |
2018 | 04 | Arizona | 20.4 | 14.1 |
2018 | 05 | Arkansas | 23.8 | 16.8 |
2018 | 06 | California | 17.4 | 12.8 |
2018 | 08 | Colorado | 12.1 | 9.7 |
Get the poverty rate for children and overall in a single county over time.
saipe_years <- getCensus(
name = "timeseries/poverty/saipe",
vars = c("NAME", "SAEPOVRT0_17_PT", "SAEPOVRTALL_PT"),
region = "county:001",
regionin = "state:12",
time = "from 2000 to 2018")
head(saipe_years)
time | state | county | NAME | SAEPOVRT0_17_PT | SAEPOVRTALL_PT |
---|---|---|---|---|---|
2000 | 12 | 001 | Alachua County | 17.4 | 14.7 |
2001 | 12 | 001 | Alachua County | 18.3 | 15.1 |
2002 | 12 | 001 | Alachua County | 17.6 | 15.1 |
2003 | 12 | 001 | Alachua County | 19.8 | 16.2 |
2004 | 12 | 001 | Alachua County | 16.9 | 14.5 |
2005 | 12 | 001 | Alachua County | 22.8 | 21.8 |
Get the number (SAEPOV5_17V_PT
) and rate (SAEPOVRAT5_17RV_PT
) of children ages 5-17 living in poverty for unified school districts in Massachusetts.
saipe_schools <- getCensus(
name = "timeseries/poverty/saipe/schdist",
vars = c("SD_NAME", "SAEPOV5_17V_PT", "SAEPOVRAT5_17RV_PT"),
region = "school district (unified):*",
regionin = "state:25",
time = 2018)
head(saipe_schools)
time | state | school_district_unified | SD_NAME | SAEPOV5_17V_PT | SAEPOVRAT5_17RV_PT |
---|---|---|---|---|---|
2018 | 25 | 00001 | Quabbin School District | 2871 | 4.7 |
2018 | 25 | 00002 | Spencer-East Brookfield School District | 2087 | 7.8 |
2018 | 25 | 00013 | Southwick-Tolland-Granville Regional School District | 1880 | 10.9 |
2018 | 25 | 00067 | Manchester Essex Regional School District | 1578 | 4.8 |
2018 | 25 | 00542 | Ayer-Shirley School District | 2191 | 10.2 |
2018 | 25 | 00544 | Monomoy Regional School District | 1785 | 8.3 |
Quarterly Workforce Indicators documentation
The allow both simple calls and very specfic ones. Make sure to read the documentation closely. Here’s a simple call that gets employment data by county.
qwi_counties <- getCensus(
name = "timeseries/qwi/sa",
vars = c("Emp", "EarnBeg"),
region = "county:*",
regionin = "state:01",
time = "2016-Q1")
head(qwi_counties)
Emp | EarnBeg | time | state | county |
---|---|---|---|---|
11389 | 2887 | 2016-Q1 | 01 | 001 |
65363 | 2648 | 2016-Q1 | 01 | 003 |
7592 | 2668 | 2016-Q1 | 01 | 005 |
4008 | 2853 | 2016-Q1 | 01 | 007 |
8024 | 2664 | 2016-Q1 | 01 | 009 |
2659 | 2543 | 2016-Q1 | 01 | 011 |
Employment data over time for a single state.
qwi_time <- getCensus(
name = "timeseries/qwi/sa",
vars = c("Emp", "EarnBeg"),
region = "state:01",
time = "from 2007 to 2017")
head(qwi_time)
Emp | EarnBeg | time | state |
---|---|---|---|
1873162 | 2833 | 2007-Q1 | 01 |
1897715 | 2786 | 2007-Q2 | 01 |
1889780 | 2773 | 2007-Q3 | 01 |
1907646 | 2978 | 2007-Q4 | 01 |
1878593 | 2894 | 2008-Q1 | 01 |
1901380 | 2880 | 2008-Q2 | 01 |
Here’s a much more specific call. Read the Census Bureau’s documentation closely to see all of the options allowed by the QWI APIs.
qwi <- getCensus(
name = "timeseries/qwi/sa",
region = "state:02",
vars = c("Emp", "sex"),
year = 2012,
quarter = 1,
agegrp = "A07",
ownercode = "A05",
firmsize = 1,
seasonadj = "U",
industry = 21)
qwi
Emp | sex | year | quarter | agegrp | ownercode | firmsize | seasonadj | industry | state |
---|---|---|---|---|---|---|---|---|---|
52 | 0 | 2012 | 1 | A07 | A05 | 1 | U | 21 | 02 |
46 | 1 | 2012 | 1 | A07 | A05 | 1 | U | 21 | 02 |
6 | 2 | 2012 | 1 | A07 | A05 | 1 | U | 21 | 02 |
Survey of Business Owners documentation
sbo <- getCensus(
name = "sbo",
vintage = 2012,
vars = c("GEO_TTL", "RCPSZFI", "RCPSZFI_TTL", "FIRMPDEMP"),
region = "state:*")
head(sbo)
state | GEO_TTL | RCPSZFI | RCPSZFI_TTL | FIRMPDEMP |
---|---|---|---|---|
01 | Alabama | 522 | Firms with sales/receipts of $50,000 to $99,999 | 5479 |
01 | Alabama | 523 | Firms with sales/receipts of $100,000 to $249,999 | 13205 |
01 | Alabama | 525 | Firms with sales/receipts of $250,000 to $499,999 | 12005 |
01 | Alabama | 531 | Firms with sales/receipts of $500,000 to $999,999 | 10522 |
01 | Alabama | 532 | Firms with sales/receipts of $1,000,000 or more | 21053 |
01 | Alabama | 001 | All firms | 67449 |
sbo_groups <- getCensus(
name = "sbo",
vintage = 2012,
vars = c("GEO_TTL", "RACE_GROUP", "RACE_GROUP_TTL", "FIRMPDEMP"),
region = "county:*",
regionin = "state:09")
head(sbo_groups)
state | county | GEO_TTL | RACE_GROUP | RACE_GROUP_TTL | FIRMPDEMP |
---|---|---|---|---|---|
09 | 005 | Litchfield County | 00 | All firms | 3975 |
09 | 005 | Litchfield County | 30 | White | 3407 |
09 | 005 | Litchfield County | 40 | Black or African American | 3 |
09 | 005 | Litchfield County | 50 | American Indian and Alaska Native | 3 |
09 | 005 | Litchfield County | 60 | Asian | 107 |
09 | 005 | Litchfield County | 61 | Asian Indian | 53 |
The Planning Database documentation Get population and 2010 Census mail return rates for block groups in Autauga County, AL.
pdb <- getCensus(
name = "pdb/blockgroup",
vintage = 2018,
vars = c("GIDBG", "County_name", "State_name", "Tot_Population_CEN_2010", "Mail_Return_Rate_CEN_2010"),
region = "block group:*",
regionin = "state:01+county:001")
head(pdb)
County_name | State_name | Tot_Population_CEN_2010 | Mail_Return_Rate_CEN_2010 | state | county | tract | block_group | GIDBG |
---|---|---|---|---|---|---|---|---|
Autauga County | Alabama | 570 | 79.7 | 01 | 001 | 020400 | 4 | 10010204004 |
Autauga County | Alabama | 1737 | 84.6 | 01 | 001 | 020500 | 1 | 10010205001 |
Autauga County | Alabama | 7023 | 72.8 | 01 | 001 | 020500 | 2 | 10010205002 |
Autauga County | Alabama | 2006 | 86.0 | 01 | 001 | 020500 | 3 | 10010205003 |
Autauga County | Alabama | 2423 | 87.8 | 01 | 001 | 020600 | 1 | 10010206001 |
Autauga County | Alabama | 1245 | 73.4 | 01 | 001 | 020600 | 2 | 10010206002 |