The Census APIs have over 200 endpoints, covering dozens of different datasets.

library(censusapi)

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

American Community Survey

ACS Detailed Tables

Get median household income, with associated annotations and margin of error, for census tracts in Alaska.

acs_income <- getCensus(name = "acs/acs5",
    vintage = 2017, 
    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
02 261 000300 Census Tract 3, Valdez-Cordova Census Area, Alaska 89000 NA 20435 NA
02 122 000600 Census Tract 6, Kenai Peninsula Borough, Alaska 58125 NA 5725 NA
02 122 001100 Census Tract 11, Kenai Peninsula Borough, Alaska 69028 NA 5941 NA
02 261 000100 Census Tract 1, Valdez-Cordova Census Area, Alaska 49076 NA 7165 NA
02 122 000200 Census Tract 2, Kenai Peninsula Borough, Alaska 57694 NA 6526 NA
02 122 000800 Census Tract 8, Kenai Peninsula Borough, Alaska 50904 NA 3723 NA

ACS Subject Tables

Get the percent of people without an internet subscription by income for the five counties of New York City, with associated margins of error:

  • overall: S2801_C02_019E
  • income less $20,000: S2801_C02_023E
  • income $20,000 to $74,999: S2801_C02_027E
  • income $75,000 or greater: S2801_C02_031E
acs_subject <- getCensus(name = "acs/acs1/subject",
    vintage = 2017, 
    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
36 005 Bronx County, New York 22.4 1.0 39.9 2.4 19.0 1.5 6.2 1.0
36 047 Kings County, New York 18.8 0.6 42.6 1.8 18.9 1.1 6.0 0.6
36 061 New York County, New York 14.1 0.8 40.4 2.9 16.1 1.7 4.2 0.7
36 081 Queens County, New York 15.6 0.7 37.0 3.0 18.4 1.3 6.0 0.6
36 085 Richmond County, New York 19.1 1.5 47.6 5.1 21.6 3.5 8.8 1.8

ACS Data Profile

acs_profile <- getCensus(name = "acs/acs1/profile",
    vintage = 2017, 
    vars = "group(DP05)", 
    region = "region:*")

ACS Comparison Profiles

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

ACS Migration Flows

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

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 4210 601 Jamaican Creole
36 470 602 Krio
36 55 604 Pidgin
36 NA 605 Gullah
36 NA 606 Saramacca
36 65165 607 German

Annual Survey of Entrepreneurs

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 A1 All firms 2014 5437782 0 33036935112 NA 0 115129295 0 5640982990 0 0 0 0.5 0 0.3 0 0.3 0
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 104343482 19.2 0.6 0.3 3.7 5.5 1.7 6.2 1.8 5.4
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 109460428 20.2 0.8 0.3 3.2 1.8 1.8 3.5 3 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 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
00 United States Total for all sectors 0000 All firms MD Total reporting 2014 1096559 100 3203964700 NA 100 14391531 100 542626696 100 0.2 0 1.8 0 0.9 0 1.3 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

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 04 Arizona Manufacturing 136946 31-33
2016 05 Arkansas Manufacturing 145733 31-33
2016 06 California Manufacturing 1119896 31-33
2016 08 Colorado Manufacturing 121069 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

Business Dynamics Statistics

Business Dynamics Statistics documentation

firms_states <- getCensus(name = "timeseries/bds/firms",
    vars = c("firms", "emp", "fage4"),
    region = "state:*",
    time = 2014)
head(firms_states)
firms emp fage4 time state
4224 31215 a 2014 01
956 3709 a 2014 02
7519 52198 a 2014 04
2984 19464 a 2014 05
55434 324873 a 2014 06
9648 49369 a 2014 08 
firms_years <- getCensus(name = "timeseries/bds/firms",
    vars = c("firms", "emp"),
    region = "state:01",
    time = "from 1977 to 2014")
head(firms_years)
firms emp time state
52371 957297 1977 01
54168 1032199 1978 01
54730 1083112 1979 01
54494 1080569 1980 01
52551 1039172 1981 01
51528 1032118 1982 01

County Business Patterns and Nonemployer Statistics

County Business Patterns and Nonemployer Statistics documentation

County Business Patterns

County Business Patterns documentation

Get employment data for the construction industry.

cbp_2016 <- getCensus(name = "cbp",
 vintage = 2016,
 vars = c("GEO_TTL", "EMP", "ESTAB", "NAICS2012_TTL"),
 region = "state:*",
 naics2012 = 23)
 head(cbp_2016)
state GEO_TTL EMP ESTAB NAICS2012_TTL NAICS2012
01 Alabama 82327 7424 Construction 23
02 Alaska 17022 2521 Construction 23
04 Arizona 141740 11921 Construction 23
05 Arkansas 45609 5293 Construction 23
06 California 723574 71981 Construction 23
08 Colorado 152325 17730 Construction 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
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
08 2008 Colorado Establishments with 1,000 employees or more 285608 120 14946331 260

Zip Codes Business Patterns

Zip Codes Business Patterns documentation

zbp_2016 <- getCensus(name = "zbp",
 vintage = 2016,
 vars = c("GEO_TTL", "EMP"),
 region = "zipcode:90210")
head(zbp_2016)
zipcode GEO_TTL EMP
90210 90210(BEVERLY HILLS,CA) 37602

Nonemployer statistics

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

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.

data2000 <- getCensus(name = "sf1",
    vintage = 2000,
    vars = "P001001", 
    region = "block:*",
    regionin = "state:36+county:027+tract:010000")
head(data2000)
state county tract block P001001
36 027 010000 1000 18
36 027 010000 1001 26
36 027 010000 1002 59
36 027 010000 1003 67
36 027 010000 1004 52
36 027 010000 1005 116

Decennial Census Surname Files

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
SMITH 2442977 828.19 70.9 23.11 0.89 0.5 2.4 2.19 1
JOHNSON 1932812 655.24 58.97 34.63 0.94 0.54 2.36 2.56 2
WILLIAMS 1625252 550.97 45.75 47.68 0.82 0.46 2.49 2.81 3
BROWN 1437026 487.16 57.95 35.6 0.87 0.51 2.52 2.55 4
JONES 1425470 483.24 55.19 38.48 1 0.44 2.29 2.61 5
GARCIA 1166120 395.32 5.38 0.45 0.47 1.41 92.03 0.26 6

Economic Census

Economic Census documentation

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
02 17328 T 02 54
02 320 Y 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
60 170 99 002 54
66 2217 99 002 54
69 404 99 002 54
72 32801 99 002 54
78 1370 99 002 54
01 94051 A 002 54

Economic Indicators

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

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 = 2017)
head(sahie)
time state NAME IPRCAT IPR_DESC PCTUI_PT
2017 01 Alabama 0 All Incomes 11.0
2017 01 Alabama 1 <= 200% of Poverty 18.3
2017 01 Alabama 2 <= 250% of Poverty 17.3
2017 01 Alabama 3 <= 138% of Poverty 19.4
2017 01 Alabama 4 <= 400% of Poverty 14.5
2017 01 Alabama 5 138% to 400% of Poverty 11.5

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 2017")
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

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 = 2017,
    IPRCAT = 5,
    AGECAT = 1)
head(sahie_nonelderly)
time state NAME IPR_DESC PCTUI_PT AGE_DESC RACECAT RACE_DESC IPRCAT AGECAT
2017 01 Alabama 138% to 400% of Poverty 14.6 18 to 64 years 0 All Races 5 1
2017 02 Alaska 138% to 400% of Poverty 24.3 18 to 64 years 0 All Races 5 1
2017 04 Arizona 138% to 400% of Poverty 16.6 18 to 64 years 0 All Races 5 1
2017 05 Arkansas 138% to 400% of Poverty 12.4 18 to 64 years 0 All Races 5 1
2017 06 California 138% to 400% of Poverty 13.6 18 to 64 years 0 All Races 5 1
2017 08 Colorado 138% to 400% of Poverty 14.6 18 to 64 years 0 All Races 5 1

International Data Base

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

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 203478379854 201775991054
2018-01 4623 UKRAINE - TOTAL IMPORTS FOR ALL END-USE CODES 94114125 94240024
2018-01 4631 ARMENIA - TOTAL IMPORTS FOR ALL END-USE CODES 8133328 8183048
2018-01 4632 AZERBAIJAN - TOTAL IMPORTS FOR ALL END-USE CODES 117941682 2491682
2018-01 4633 GEORGIA - TOTAL IMPORTS FOR ALL END-USE CODES 25082886 25085443
2018-01 4634 KAZAKHSTAN - TOTAL IMPORTS FOR ALL END-USE CODES 91374490 85589698

Population Estimates and Projections

Population Estimates and Projections documentation

Population Estimates

Population Estimates documentation

popest <- getCensus(name = "pep/population",
    vintage = 2018,
    vars = c("GEONAME", "POP", "DATE_DESC"),
    region = "state:*")
head(popest)
state GEONAME POP DATE_DESC
01 Alabama 4887871 7/1/2018 population estimate
02 Alaska 737438 7/1/2018 population estimate
04 Arizona 7171646 7/1/2018 population estimate
05 Arkansas 3013825 7/1/2018 population estimate
06 California 39557045 7/1/2018 population estimate
08 Colorado 5695564 7/1/2018 population estimate 
popest_housing <- getCensus(name = "pep/housing",
    vintage = 2017,
    vars = c("DATE", "DATE_DESC", "GEONAME", "HUEST"),
    region = "county:195",
    regionin = "state:2")
head(popest_housing)
state county DATE DATE_DESC GEONAME HUEST
02 195 1 4/1/2010 Census housing unit count Petersburg Borough, Alaska 1994
02 195 2 4/1/2010 housing unit estimates base Petersburg Borough, Alaska 1644
02 195 3 7/1/2010 housing unit estimate Petersburg Borough, Alaska 1646
02 195 4 7/1/2011 housing unit estimate Petersburg Borough, Alaska 1647
02 195 5 7/1/2012 housing unit estimate Petersburg Borough, Alaska 1659
02 195 6 7/1/2013 housing unit estimate Petersburg Borough, Alaska 1662

Population Projections

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

Poverty Statistics documentation

Current Population Survey Poverty Statistics

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 1967 to 2017")
head(poverty)
time us RACE PCTPOV
2017 1 1 12.3
2016 1 1 12.7
2015 1 1 13.5
2014 1 1 14.8
2013 1 1 14.8
2013 1 1 14.5

Small Area Income and Poverty Estimates

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 = 2017)
head(saipe)
time state NAME SAEPOVRT0_17_PT SAEPOVRTALL_PT
2017 04 Arizona 21.0 14.9
2017 05 Arkansas 22.5 16.3
2017 01 Alabama 24.4 16.9
2017 02 Alaska 14.5 11
2017 06 California 18.1 13.3
2017 08 Colorado 12.2 10.3

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 2017")
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

SAIPE School Districts

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 = 2017)
head(saipe_schools)
time state school_district_unified SD_NAME SAEPOV5_17V_PT SAEPOVRAT5_17RV_PT
2017 25 00001 Quabbin School District 2891 4.5
2017 25 00002 Spencer-East Brookfield School District 2100 9.3
2017 25 00013 Southwick-Tolland-Granville Regional School District 1892 10.4
2017 25 00067 Manchester Essex Regional School District 1579 4.6
2017 25 00542 Ayer-Shirley School District 2190 18.2
2017 25 00544 Monomoy Regional School District 1803 8.5

Quarterly Workforce Indicators

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
11366 2865 2016-Q1 01 001
64849 2627 2016-Q1 01 003
7634 2700 2016-Q1 01 005
3961 2821 2016-Q1 01 007
8002 2705 2016-Q1 01 009
2727 2587 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
1873208 2834 2007-Q1 01
1897768 2786 2007-Q2 01
1889844 2774 2007-Q3 01
1907629 2978 2007-Q4 01
1878590 2894 2008-Q1 01
1901453 2881 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
61 0 2012 1 A07 A05 1 U 21 02
55 1 2012 1 A07 A05 1 U 21 02
6 2 2012 1 A07 A05 1 U 21 02

Survey of Business Owners

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 001 All firms 67449
01 Alabama 511 Firms with sales/receipts of less than $5,000 356
01 Alabama 518 Firms with sales/receipts of $5,000 to $9,999 533
01 Alabama 519 Firms with sales/receipts of $10,000 to $24,999 1453
01 Alabama 521 Firms with sales/receipts of $25,000 to $49,999 2843
01 Alabama 522 Firms with sales/receipts of $50,000 to $99,999 5479 
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
09 001 Fairfield County 00 All firms 21782
09 001 Fairfield County 30 White 17600
09 001 Fairfield County 40 Black or African American 176
09 001 Fairfield County 50 American Indian and Alaska Native 15
09 001 Fairfield County 60 Asian 1243
09 001 Fairfield County 61 Asian Indian 400

The Planning Database

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 698 81.3 01 001 020100 1 010010201001
Autauga County Alabama 1214 84.8 01 001 020100 2 010010201002
Autauga County Alabama 1003 80.2 01 001 020200 1 010010202001
Autauga County Alabama 1167 82.3 01 001 020200 2 010010202002
Autauga County Alabama 2549 80.7 01 001 020300 1 010010203001
Autauga County Alabama 824 76.2 01 001 020300 2 010010203002

Disclaimer

This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.