| state | @@ -1785,23 +1785,23 @@|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 24 | -Maryland | -17464 | +05 | +Arkansas | +3825 | Japanese alone or in any combination | 3824 | ||||
| 37 | -North Carolina | -20484 | +06 | +California | +469915 | Japanese alone or in any combination | 3824 | ||||
| 19 | -Iowa | -4179 | +08 | +Colorado | +31916 | Japanese alone or in any combination | 3824 | ||||
| 02 | -17328 | -T | +08 | +4616 | +Y | 02 | 54 | ||||
| 02 | -320 | -Y | +17328 | +T | 02 | 54 | 999 | -SINGLE | +966 | +MULTI | 0 | no | -APERMITS | +UNDERCONST | yes | -2023-12 | +2023-04 | 1 |
| 1354 | -TOTAL | +978 | +MULTI | 0 | no | -APERMITS | +UNDERCONST | yes | -2023-01 | +2023-05 | 1 |
| 1482 | -TOTAL | +991 | +MULTI | 0 | no | -APERMITS | +UNDERCONST | yes | -2023-02 | +2023-06 | 1 |
| 1437 | -TOTAL | +1001 | +MULTI | 0 | no | -APERMITS | +UNDERCONST | yes | -2023-03 | +2023-07 | 1 |
| 1417 | -TOTAL | +1000 | +MULTI | 0 | no | -APERMITS | +UNDERCONST | yes | -2023-04 | +2023-08 | 1 |
| 1496 | -TOTAL | +991 | +MULTI | 0 | no | -APERMITS | +UNDERCONST | yes | -2023-05 | +2023-09 | 1 |
| 2006 | +2015 | 06 | 037 | Los Angeles County, CA | -23.8 | +12.5 | |||||
| 2007 | +2016 | 06 | 037 | Los Angeles County, CA | -23.1 | +10.7 | |||||
| 2008 | +2017 | 06 | 037 | Los Angeles County, CA | -23.8 | +10.1 | |||||
| 2009 | +2018 | 06 | 037 | Los Angeles County, CA | -24.9 | +10.2 | |||||
| 2010 | +2019 | 06 | 037 | Los Angeles County, CA | -25.9 | +11.1 | |||||
| 2011 | +2020 | 06 | 037 | Los Angeles County, CA | -24.8 | +10.2 | |||||
| 2012 | +2021 | 06 | 037 | Los Angeles County, CA | -24.5 | +10.1 | |||||
| 2013 | +2006 | 06 | 037 | Los Angeles County, CA | -23.7 | +23.8 | |||||
| 2014 | +2007 | 06 | 037 | Los Angeles County, CA | -17.4 | +23.1 | |||||
| 2015 | +2008 | 06 | 037 | Los Angeles County, CA | -12.5 | +23.8 | |||||
| 2016 | +2009 | 06 | 037 | Los Angeles County, CA | -10.7 | +24.9 | |||||
| 2017 | +2010 | 06 | 037 | Los Angeles County, CA | -10.1 | +25.9 | |||||
| 2018 | +2011 | 06 | 037 | Los Angeles County, CA | -10.2 | +24.8 | |||||
| 2019 | +2012 | 06 | 037 | Los Angeles County, CA | -11.1 | +24.5 | |||||
| 2020 | +2013 | 06 | 037 | Los Angeles County, CA | -10.2 | +23.7 | |||||
| 2021 | +2014 | 06 | 037 | Los Angeles County, CA | -10.1 | +17.4 |
censusapi is a lightweight package that retrieves data
-from the U.S. Census Bureau’s APIs. More than
-1,000 Census API
-endpoints are available, including the Decennial Census, American
-Community Survey, Poverty Statistics, Population Estimates, and Census
-microdata. This package is designed to let you get data from all of
-those APIs using the same main functions and syntax for every
-dataset.
censusapi is a lightweight package that helps you
+retrieve data from the U.S. Census Bureau’s 1,600 API
+endpoints using one simple function, getCensus().
+Additional functions provide information about what datasets are
+available and how to use them.
This package returns the data as-is with the original variable names created by the Census Bureau and any quirks inherent in the data. Each dataset is a little different. Some are documented thoroughly, others @@ -108,94 +105,35 @@
If you save the key with the name CENSUS_KEY or
-CENSUS_API_KEY in your .Renviron file,
+CENSUS_API_KEY in your Renviron file,
censusapi will use it by default without any extra work on
your part.
To save your API key, within R, run:
-# Add key to .Renviron
+# Check to see if you already have a CENSUS_KEY or CENSUS_API_KEY saved
+# If so, no further action is needed
+get_api_key()
+
+# If not, add your key to your Renviron file
Sys.setenv(CENSUS_KEY=PASTEYOURKEYHERE)
+
# Reload .Renviron
readRenviron("~/.Renviron")
+
# Check to see that the expected key is output in your R console
-Sys.getenv("CENSUS_KEY")In some instances you might not want to put your key in your
.Renviron - for example, if you’re on a shared school computer. You can
always choose to manually set key = "PASTEYOURKEYHERE" as
an argument in getCensus() if you prefer.
To get started, load the censusapi library.
To see a current table of every available endpoint,
-uselistCensusApis(). This data frame includes useful
-information for making your API call, including the dataset’s name,
-description and title, as well as a contact email for questions about
-the underlying data.
-apis <- listCensusApis()
-colnames(apis)#> [1] "title" "name" "vintage" "type" "temporal"
-#> [6] "spatial" "url" "modified" "description" "contact"This returns useful information about each endpoint.
-censusapi functionsThere are three types of datasets included in the Census Bureau API
-universe: aggregate, microdata, and timeseries. These type names were
-defined by the Census Bureau and are included as a column in
-listCensusApis().
-table(apis$type)#>
-#> Aggregate Microdata Timeseries
-#> 624 894 81Most users will work with summary data, either aggregate or -timeseries. Summary data contains pre-calculated numbers or percentages -for a given statistic — like the number of children in a state or the -median household income. The examples below and in the broader list -of censusapi examples use summary data.
-Aggregate datasets, like the American Community Survey or Decennial
-Census, include data for only one time period (a vintage),
-usually one year. Datasets like the American Community Survey contain
-thousands of these pre-computed variables.
Timeseries datasets, including the Small Area Income and Poverty -Estimates, the Quarterly Workforce Estimates, and International Trade -statistics, allow users to query data for more than one time period in a -single API call.
-Microdata contains the individual-level responses for a survey for
-use in custom analysis. One row represents one person. Only advanced
-analysts will want to use microdata. Learn more about what microdata is
-and how to use it with censusapi in Accessing
-microdata.
getCensus
+The main function in censusapi is
getCensus(), which makes an API call to a given endpoint
@@ -212,11 +150,10 @@
getCensusvars: a list of variables to retrieve
region: the geography level to retrieve, such as state
-or county, required for most endpointsSome APIs have additional required or optional arguments, like
-time or monthly for some timeseries datasets.
-Check the specific documentation
+time for some timeseries datasets. Check the specific documentation
for your API and explore its metadata with
listCensusMetadata() to see what options are allowed.
Let’s walk through an example getting uninsured rates using the Small
@@ -230,8 +167,10 @@ Choosing variableslistCensusMetadata() to get information about an API’s
variable and geography options. Let’s see what variables are available
in the SAHIE API:
-sahie_vars <- listCensusMetadata(
+
+library(censusapi)
+
+sahie_vars <- listCensusMetadata(
name = "timeseries/healthins/sahie",
type = "variables")
@@ -245,7 +184,7 @@ Choosing variables#> [26] "PCTUI_UB90" "NUI_PT" "STABREV" "AGE_DESC" "NAME"
#> [31] "NIC_LB90" "PCTIC_PT" "PCTIC_MOE" "IPR_DESC" "NUI_LB90"
#> [36] "NIPR_UB90" "GEOCAT" "SEX_DESC" "RACECAT"
-
+
# Full info on the first several variables
head(sahie_vars)
@@ -342,7 +281,7 @@ Choosing regions
We can also use listCensusMetadata to see which
geographic levels are available.
-
+
listCensusMetadata(
name = "timeseries/healthins/sahie",
type = "geography")
@@ -401,15 +340,14 @@ Choosing regionscounty, and state. County data can be queried
for all counties nationally or within a specific state.
-
-
-Making a censusapi call
-
+
+Making a censusapi call
+
First, using getCensus(), let’s get the percent
-(PCTUI_PT) and number (NUI_PT) of people
-uninsured, using the wildcard star (*) to retrieve data for all
+(PCTUI_PT) and number (NUI_PT) of people who
+are uninsured, using the wildcard star (*) to retrieve data for all
counties.
-
+
sahie_counties <- getCensus(
name = "timeseries/healthins/sahie",
vars = c("NAME", "PCTUI_PT", "NUI_PT"),
@@ -482,14 +420,14 @@ Making a censusapi callregion
to specify county-level results
and regionin to filter to Virginia, state code 51. We’ll
get uninsured rates by income group, IPRCAT.
-
+
sahie_virginia <- getCensus(
name = "timeseries/healthins/sahie",
vars = c("NAME", "IPRCAT", "IPR_DESC", "PCTUI_PT"),
region = "county:*",
regionin = "state:51",
time = 2021)
-head(sahie_virginia)
+head(sahie_virginia, head = 12L)
We can also filter the data by income group using the
-IPRCAT variable again. IPRCAT = 3 represents
-<=138% of the federal poverty line. That is the threshold for Medicaid
+IPRCAT variable. See the possible values of
+IPRCAT using listCensusMetadata().
+IPRCAT = 3 represents <=138% of the federal poverty
+line. That is the threshold for Medicaid
eligibility in states that have expanded it under the Affordable
Care Act.
+
+listCensusMetadata(
+ name = "timeseries/healthins/sahie",
+ type = "values",
+ variable = "IPRCAT")
+
+
+
+code
+label
+
+
+
+0
+All Incomes
+
+
+1
+Less than or Equal to 200% of Poverty
+
+
+2
+Less than or Equal to 250% of Poverty
+
+
+3
+Less than or Equal to 138% of Poverty
+
+
+4
+Less than or Equal to 400% of Poverty
+
+
+5
+138% to 400% Poverty
+
+
+
+
Getting this data for Los Angeles county (fips code 06037) we can see
the dramatic decrease in the uninsured rate in this income group after
California expanded Medicaid.
-
+
sahie_138 <- getCensus(
name = "timeseries/healthins/sahie",
vars = c("NAME", "PCTUI_PT", "NUI_PT"),
@@ -739,225 +718,391 @@ Making a censusapi call
-2010
+2015
06
037
Los Angeles County, CA
-37.4
-894385
+17.8
+402977
3
-2011
+2016
06
037
Los Angeles County, CA
-35.1
-867577
+15.4
+329251
3
-2012
+2017
06
037
Los Angeles County, CA
-34.4
-865516
+14.3
+281842
3
-2013
+2018
06
037
Los Angeles County, CA
-33.0
-818978
+13.9
+255520
3
-2014
+2019
06
037
Los Angeles County, CA
-24.9
-607542
+15.1
+254740
3
-2015
+2020
06
037
Los Angeles County, CA
-17.8
-402977
+14.4
+230380
3
-2016
+2021
06
037
Los Angeles County, CA
-15.4
-329251
+15.1
+249186
3
-2017
+2010
06
037
Los Angeles County, CA
-14.3
-281842
+37.4
+894385
3
-2018
+2011
06
037
Los Angeles County, CA
-13.9
-255520
+35.1
+867577
3
-2019
+2012
06
037
Los Angeles County, CA
-15.1
-254740
+34.4
+865516
3
-2020
+2013
06
037
Los Angeles County, CA
-14.4
-230380
+33.0
+818978
3
-2021
+2014
06
037
Los Angeles County, CA
-15.1
-249186
+24.9
+607542
3
-We can also get data for other useful demographics such as age
-group.
-
+
+
+
+Finding your API
+
+What if you don’t already know your dataset’s name? To
+see a current table of every available endpoint, use
+listCensusApis(). This data frame includes useful
+information for making your API call, including the dataset’s name,
+vintage if applicable, description, and title.
+
+apis <- listCensusApis()
+colnames(apis)
+#> [1] "title" "name" "vintage" "type" "temporal"
+#> [6] "spatial" "url" "modified" "description" "contact"
+The columns included are:
+
+- title: Short written description of the dataset.
+- name: Programmatic name of the dataset, to be used with
+
censusapi functions.
+- vintage: Year of the survey, for use with microdata and aggregate
+datasets.
+- type: Dataset type, which is either “Aggregate”, “Microdata”, or
+“Timeseries”.
+- temporal: Time period of the dataset. Warning: not always
+documented.
+- spatial: Spatial region of the dataset. Warning: not always
+documented.
+- url: Base URL of the dataset endpoint.
+- modified: Date last modified. Warning: sometimes out of date.
+- description: Long written description of the dataset.
+- contact: Email address for specific questions about the Census
+Bureau survey.
+
+You can also get information on a subset of datasets using the
+optional name and/or vintage parameters. For
+example, get information about 2020 Decennial Census datasets.
+
+dec_apis <- listCensusApis(name = "dec", vintage = 2020)
+dec_apis[, 1:6]
-
-time
-state
-county
-NAME
-PCTUI_PT
-NUI_PT
-AGECAT
-AGE_DESC
+title
+name
+vintage
+type
+temporal
+spatial
-2021
-06
-037
-Los Angeles County, CA
-10.1
-834424
-0
-Under 65 years
+Decennial Census: 118th Congressional District Summary
+File
+dec/cd118
+2020
+Aggregate
+2020/2020
+US
-2021
-06
-037
-Los Angeles County, CA
-12.3
-768094
-1
-18 to 64 years
+Decennial Census of Island Areas: American Samoa
+Detailed Crosstabulations
+dec/crosstabas
+2020
+Aggregate
+2020/2020
+American Samoa
-2021
-06
-037
-Los Angeles County, CA
-11.7
-370339
-2
-40 to 64 years
+Decennial Census of Island Areas: Guam Detailed
+Crosstabulations
+dec/crosstabgu
+2020
+Aggregate
+2020/2020
+Guam
-2021
-06
-037
-Los Angeles County, CA
-10.2
-190193
-3
-50 to 64 years
+Decennial Census of Island Areas: Commonwealth of the
+Northern Mariana Islands Detailed Crosstabulations
+dec/crosstabmp
+2020
+Aggregate
+2020/2020
+Northern Mariana Islands
-2021
-06
-037
-Los Angeles County, CA
-3.5
-75771
-4
-Under 19 years
+Decennial Census of Island Areas: U.S. Virgin Islands
+Detailed Crosstabulations
+dec/crosstabvi
+2020
+Aggregate
+2020/2020
+U.S. Virgin Islands
-2021
-06
-037
-Los Angeles County, CA
-12.5
-737126
-5
-21 to 64 years
+Decennial Census: Detailed Demographic and Housing
+Characteristics File A
+dec/ddhca
+2020
+Aggregate
+2020/2020
+United States
+
+
+Decennial Census: Demographic and Housing
+Characteristics
+dec/dhc
+2020
+Aggregate
+2020/2020
+United States
+
+
+Decennial Census of Island Areas: American Samoa
+Demographic and Housing Characteristics
+dec/dhcas
+2020
+Aggregate
+2020/2020
+American Samoa
+
+
+Decennial Census of Island Areas: Guam Demographic and
+Housing Characteristics
+dec/dhcgu
+2020
+Aggregate
+2020/2020
+Guam
+
+
+Decennial Census of Island Areas: Commonwealth of the
+Northern Mariana Islands Demographic and Housing Characteristics
+dec/dhcmp
+2020
+Aggregate
+2020/2020
+Commonwealth of the Northern Mariana Islands
+
+
+Decennial Census of Island Areas: U.S. Virgin Islands
+Demographic and Housing Characteristics
+dec/dhcvi
+2020
+Aggregate
+2020/2020
+U.S. Virgin Islands
+
+
+Decennial Census: Demographic Profile
+dec/dp
+2020
+Aggregate
+2020/2020
+United States
+
+
+Decennial Census of Island Areas: American Samoa
+Demographic Profile
+dec/dpas
+2020
+Aggregate
+2020/2020
+United States
+
+
+Decennial Census of Island Areas: Guam Demographic
+Profile
+dec/dpgu
+2020
+Aggregate
+2020/2020
+United States
+
+
+2020 Commonwealth of the Northern Mariana Islands
+Demographic Profile
+dec/dpmp
+2020
+Aggregate
+2020/2020
+United States
+
+
+Decennial Census of Island Areas: U.S. Virgin Islands
+Demographic Profile
+dec/dpvi
+2020
+Aggregate
+2020/2020
+United States
+
+
+Decennial Census: Decennial Post-Enumeration
+Survey
+dec/pes
+2020
+Aggregate
+2020/2020
+US
+
+
+Decennial Census: Redistricting Data (PL 94-171)
+dec/pl
+2020
+Aggregate
+2020/2020
+United States
+
+
+Decennial Census: Decennial Self-Response Rate
+dec/responserate
+2020
+Aggregate
+NA
+NA
+
+Dataset types
+
+There are three types of datasets included in the Census Bureau API
+universe: aggregate, microdata, and timeseries. These type names were
+defined by the Census Bureau and are included as a column in
+listCensusApis().
+
+table(apis$type)
+#>
+#> Aggregate Microdata Timeseries
+#> 624 895 81
+Most users will work with summary data, either aggregate or
+timeseries. Summary data contains pre-calculated numbers or percentages
+for a given statistic — like the number of children in a state or the
+median household income. The examples below and in the broader list
+of censusapi examples use summary data.
+Aggregate datasets, like the American Community Survey or Decennial
+Census, include data for only one time period (a vintage),
+usually one year. Datasets like the American Community Survey contain
+thousands of these pre-computed variables.
+Timeseries datasets, including the Small Area Income and Poverty
+Estimates, the Quarterly Workforce Estimates, and International Trade
+statistics, allow users to query data over time in a single API
+call.
+Microdata contains the individual-level responses for a survey for
+use in custom analysis. One row represents one person. Only advanced
+analysts will want to use microdata. Learn more about what microdata is
+and how to use it with censusapi in Accessing
+microdata.
+
Variable groups
-For some surveys, particularly the American Community Survey and
+
For some surveys, including the American Community Survey and
Decennial Census, you can get many related variables at once using a
variable group. These groups are defined by the Census
Bureau. In some other data tools, like data.census.gov, this concept
is referred to as a table.
Some groups have several dozen variables, others just have a few. As
-an example, we’ll get the estimate, margin of error and annotations for
-median household income in the past 12 months for counties in Alabama
-using group B19013.
+an example, we’ll use the American Community Survey to get the estimate,
+margin of error and annotations for median household income in the past
+12 months for Census places (cities, towns, etc) in Alabama using group
+B19013.
First, see descriptions of the variables in group B19013:
group_B19013 <- listCensusMetadata(
@@ -1049,24 +1194,24 @@ Variable groups name = "acs/acs5",
vintage = 2022,
vars = "group(B19013)",
- region = "county:*",
+ region = "place:*",
regionin = "state:01")
head(acs_income_group)
Breaking changes
New features
getCensus() no longer requires key, the use of a Census Bureau API key. Users are still encouraged to register for and use an API key because the Census Bureau may rate limit IP addresses, but it is not required. (#87)
New get_api_key() helper function retrieves the value of a user’s stored Census Bureau API key from a saved environment variable or provides a warning message if none is found.
listCensusApis() now has optional name and vintage parameters to get metadata for a subset of datasets or a single dataset. (#103)
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 1dcdf2b..49bc0f2 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -7,7 +7,7 @@ articles:
frequently-asked-questions: frequently-asked-questions.html
getting-started: getting-started.html
censusapi: censusapi.html
-last_built: 2024-03-27T21:01Z
+last_built: 2024-04-01T14:24Z
urls:
reference: https://www.hrecht.com/censusapi/reference
article: https://www.hrecht.com/censusapi/articles
diff --git a/docs/reference/getCensus.html b/docs/reference/getCensus.html
index 5de623a..5d316e1 100644
--- a/docs/reference/getCensus.html
+++ b/docs/reference/getCensus.html
@@ -149,7 +149,7 @@ Arguments
Examples
- # \dontrun{
+ if (FALSE) {
# Get total population and median household income for Census places
# (cities, towns, villages) in a single state from the 5-year American Community Survey.
acs_simple <- getCensus(
@@ -159,13 +159,6 @@ Examples region = "place:*",
regionin = "state:01")
head(acs_simple)
-#> state place NAME B01001_001E B19013_001E
-#> 1 01 00100 Abanda CDP, Alabama 335 29263
-#> 2 01 00124 Abbeville city, Alabama 2309 35147
-#> 3 01 00460 Adamsville city, Alabama 4325 58631
-#> 4 01 00484 Addison town, Alabama 665 47188
-#> 5 01 00676 Akron town, Alabama 310 53929
-#> 6 01 00820 Alabaster city, Alabama 33417 89423
# Get all data from the B08301 variable group, "Means of Transportation to Work."
# This returns estimates as well as margins of error and annotation flags.
@@ -175,111 +168,6 @@ Examples vars = "group(B08301)",
region = "state:*")
head(acs_group)
-#> state B08301_001E B08301_001EA B08301_001M B08301_001MA B08301_002E
-#> 1 01 2183677 <NA> 9550 <NA> 1985736
-#> 2 02 351067 <NA> 2187 <NA> 274263
-#> 3 04 3244419 <NA> 9451 <NA> 2610395
-#> 4 05 1304084 <NA> 6929 <NA> 1177402
-#> 5 06 18353469 <NA> 22043 <NA> 14313290
-#> 6 08 3006848 <NA> 8149 <NA> 2304559
-#> B08301_002EA B08301_002M B08301_002MA B08301_003E B08301_003EA B08301_003M
-#> 1 <NA> 10017 <NA> 1808631 <NA> 9646
-#> 2 <NA> 2820 <NA> 231484 <NA> 3143
-#> 3 <NA> 12006 <NA> 2285920 <NA> 12162
-#> 4 <NA> 6616 <NA> 1051964 <NA> 6428
-#> 5 <NA> 23546 <NA> 12561068 <NA> 25223
-#> 6 <NA> 9026 <NA> 2062480 <NA> 8986
-#> B08301_003MA B08301_004E B08301_004EA B08301_004M B08301_004MA B08301_005E
-#> 1 <NA> 177105 <NA> 4751 <NA> 137578
-#> 2 <NA> 42779 <NA> 2105 <NA> 32397
-#> 3 <NA> 324475 <NA> 5200 <NA> 242556
-#> 4 <NA> 125438 <NA> 3531 <NA> 94684
-#> 5 <NA> 1752222 <NA> 14058 <NA> 1269237
-#> 6 <NA> 242079 <NA> 5338 <NA> 180493
-#> B08301_005EA B08301_005M B08301_005MA B08301_006E B08301_006EA B08301_006M
-#> 1 <NA> 4204 <NA> 24358 <NA> 1675
-#> 2 <NA> 1886 <NA> 5712 <NA> 684
-#> 3 <NA> 4612 <NA> 48889 <NA> 1844
-#> 4 <NA> 2975 <NA> 18415 <NA> 1284
-#> 5 <NA> 10568 <NA> 281935 <NA> 5811
-#> 6 <NA> 4522 <NA> 37353 <NA> 1984
-#> B08301_006MA B08301_007E B08301_007EA B08301_007M B08301_007MA B08301_008E
-#> 1 <NA> 8791 <NA> 1005 <NA> 2998
-#> 2 <NA> 2977 <NA> 646 <NA> 1039
-#> 3 <NA> 18286 <NA> 1327 <NA> 10171
-#> 4 <NA> 7226 <NA> 913 <NA> 2976
-#> 5 <NA> 109445 <NA> 3668 <NA> 61640
-#> 6 <NA> 14757 <NA> 1317 <NA> 6621
-#> B08301_008EA B08301_008M B08301_008MA B08301_009E B08301_009EA B08301_009M
-#> 1 <NA> 551 <NA> 3380 <NA> 602
-#> 2 <NA> 255 <NA> 654 <NA> 256
-#> 3 <NA> 1062 <NA> 4573 <NA> 689
-#> 4 <NA> 636 <NA> 2137 <NA> 354
-#> 5 <NA> 2554 <NA> 29965 <NA> 1345
-#> 6 <NA> 758 <NA> 2855 <NA> 496
-#> B08301_009MA B08301_010E B08301_010EA B08301_010M B08301_010MA B08301_011E
-#> 1 <NA> 6982 <NA> 910 <NA> 6523
-#> 2 <NA> 3805 <NA> 603 <NA> 3415
-#> 3 <NA> 40113 <NA> 2078 <NA> 35607
-#> 4 <NA> 3744 <NA> 551 <NA> 3387
-#> 5 <NA> 662772 <NA> 7190 <NA> 423288
-#> 6 <NA> 63953 <NA> 2586 <NA> 45745
-#> B08301_011EA B08301_011M B08301_011MA B08301_012E B08301_012EA B08301_012M
-#> 1 <NA> 928 <NA> 122 <NA> 117
-#> 2 <NA> 577 <NA> 1 <NA> 3
-#> 3 <NA> 1989 <NA> 960 <NA> 238
-#> 4 <NA> 494 <NA> 114 <NA> 97
-#> 5 <NA> 5639 <NA> 140862 <NA> 3435
-#> 6 <NA> 2229 <NA> 4035 <NA> 418
-#> B08301_012MA B08301_013E B08301_013EA B08301_013M B08301_013MA B08301_014E
-#> 1 <NA> 77 <NA> 55 <NA> 73
-#> 2 <NA> 15 <NA> 23 <NA> 14
-#> 3 <NA> 471 <NA> 198 <NA> 2692
-#> 4 <NA> 2 <NA> 5 <NA> 59
-#> 5 <NA> 58487 <NA> 2233 <NA> 28260
-#> 6 <NA> 2546 <NA> 416 <NA> 11234
-#> B08301_014EA B08301_014M B08301_014MA B08301_015E B08301_015EA B08301_015M
-#> 1 <NA> 95 <NA> 187 <NA> 100
-#> 2 <NA> 15 <NA> 360 <NA> 176
-#> 3 <NA> 455 <NA> 383 <NA> 143
-#> 4 <NA> 51 <NA> 182 <NA> 170
-#> 5 <NA> 1506 <NA> 11875 <NA> 985
-#> 6 <NA> 864 <NA> 393 <NA> 164
-#> B08301_015MA B08301_016E B08301_016EA B08301_016M B08301_016MA B08301_017E
-#> 1 <NA> 1253 <NA> 321 <NA> 1878
-#> 2 <NA> 1599 <NA> 368 <NA> 474
-#> 3 <NA> 6508 <NA> 836 <NA> 10036
-#> 4 <NA> 603 <NA> 202 <NA> 1278
-#> 5 <NA> 37588 <NA> 1610 <NA> 46193
-#> 6 <NA> 2955 <NA> 510 <NA> 4587
-#> B08301_017EA B08301_017M B08301_017MA B08301_018E B08301_018EA B08301_018M
-#> 1 <NA> 307 <NA> 1805 <NA> 302
-#> 2 <NA> 127 <NA> 2447 <NA> 414
-#> 3 <NA> 824 <NA> 19629 <NA> 1240
-#> 4 <NA> 269 <NA> 2052 <NA> 380
-#> 5 <NA> 1803 <NA> 135240 <NA> 3079
-#> 6 <NA> 503 <NA> 30681 <NA> 1435
-#> B08301_018MA B08301_019E B08301_019EA B08301_019M B08301_019MA B08301_020E
-#> 1 <NA> 24058 <NA> 1737 <NA> 16559
-#> 2 <NA> 27039 <NA> 1177 <NA> 13159
-#> 3 <NA> 54751 <NA> 2254 <NA> 40263
-#> 4 <NA> 19571 <NA> 1110 <NA> 11443
-#> 5 <NA> 437430 <NA> 5563 <NA> 220114
-#> 6 <NA> 79574 <NA> 2429 <NA> 24630
-#> B08301_020EA B08301_020M B08301_020MA B08301_021E B08301_021EA B08301_021M
-#> 1 <NA> 1341 <NA> 145406 <NA> 4523
-#> 2 <NA> 672 <NA> 28281 <NA> 1635
-#> 3 <NA> 2081 <NA> 462724 <NA> 7442
-#> 4 <NA> 1050 <NA> 87991 <NA> 2781
-#> 5 <NA> 4380 <NA> 2500842 <NA> 15830
-#> 6 <NA> 1187 <NA> 495909 <NA> 6033
-#> B08301_021MA GEO_ID NAME
-#> 1 <NA> 0400000US01 Alabama
-#> 2 <NA> 0400000US02 Alaska
-#> 3 <NA> 0400000US04 Arizona
-#> 4 <NA> 0400000US05 Arkansas
-#> 5 <NA> 0400000US06 California
-#> 6 <NA> 0400000US08 Colorado
# Retreive 2020 Decennial Census block group data within a specific Census tract,
# using the regionin argument to precisely specify the Census tract, county,
@@ -291,16 +179,6 @@ Examples region = "block group:*",
regionin = "state:36+county:027+tract:220300")
head(decennial_block_group)
-#> state county tract block_group
-#> 1 36 027 220300 1
-#> 2 36 027 220300 2
-#> 3 36 027 220300 3
-#> 4 36 027 220300 4
-#> NAME P1_001N
-#> 1 Block Group 1; Census Tract 2203; Dutchess County; New York 1467
-#> 2 Block Group 2; Census Tract 2203; Dutchess County; New York 1394
-#> 3 Block Group 3; Census Tract 2203; Dutchess County; New York 1192
-#> 4 Block Group 4; Census Tract 2203; Dutchess County; New York 885
# Get poverty rates for children and for people of all ages beginning in 2000 using the
# Small Area Income and Poverty Estimates API
@@ -310,13 +188,6 @@ Examples region = "state:01",
time = "from 2000")
head(saipe)
-#> time state NAME SAEPOVRT0_17_PT SAEPOVRTALL_PT
-#> 1 2000 01 Alabama 20.5 14.6
-#> 2 2001 01 Alabama 22.1 15.7
-#> 3 2002 01 Alabama 21.6 15.4
-#> 4 2003 01 Alabama 22.3 15.3
-#> 5 2004 01 Alabama 22.6 16.1
-#> 6 2005 01 Alabama 24.3 16.9
# Get the number of employees and number of establishments in the construction sector,
# NAICS2017 code 23, using the County Business Patterns API
@@ -327,14 +198,7 @@ Examples region = "county:*",
NAICS2017 = 23)
head(cbp)
-#> state county EMP ESTAB NAICS2017_LABEL NAICS2017
-#> 1 01 001 507 90 Construction 23
-#> 2 01 003 4322 705 Construction 23
-#> 3 01 005 75 20 Construction 23
-#> 4 01 007 654 25 Construction 23
-#> 5 01 009 592 118 Construction 23
-#> 6 01 011 50 8 Construction 23
-# }
+}
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 0353e36..aa68c16 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -111,6 +111,11 @@ Helpers
+ get_api_key()
+
+ Retrieve a Census API key stored the .Renivron file
+ -
+
fips
- List of state fips codes - 50 states plus DC
diff --git a/docs/reference/listCensusApis.html b/docs/reference/listCensusApis.html
index 5c5d0d9..f229a68 100644
--- a/docs/reference/listCensusApis.html
+++ b/docs/reference/listCensusApis.html
@@ -88,177 +88,27 @@ Arguments
Examples
- # \dontrun{
+ if (FALSE) {
# Get information about every dataset available in the APIs
apis <- listCensusApis()
head(apis)
-#> title name
-#> 1 Current Population Survey: Basic Monthly cps/basic/feb
-#> 2 Current Population Survey: Basic Monthly cps/basic/jan
-#> 3 Current Population Survey Annual Social and Economic Supplement cps/asec/mar
-#> 4 Current Population Survey: Basic Monthly cps/basic/apr
-#> 5 Current Population Survey: Basic Monthly cps/basic/aug
-#> 6 Current Population Survey: Basic Monthly cps/basic/dec
-#> vintage type temporal spatial
-#> 1 2024 Microdata 2024-02/2024-02 United States
-#> 2 2024 Microdata 2024-01/2024-01 United States
-#> 3 2023 Microdata 2023-03/2023-03 United States
-#> 4 2023 Microdata 2023-04/2023-04 United States
-#> 5 2023 Microdata 2023-08/2023-08 United States
-#> 6 2023 Microdata 2023-12/2023-12 United States
-#> url modified
-#> 1 http://api.census.gov/data/2024/cps/basic/feb 2024-01-11 15:35:41.0
-#> 2 http://api.census.gov/data/2024/cps/basic/jan 2024-01-11 15:35:41.0
-#> 3 http://api.census.gov/data/2023/cps/asec/mar 2023-08-14 09:09:01.0
-#> 4 http://api.census.gov/data/2023/cps/basic/apr 2023-01-10 15:11:40.0
-#> 5 http://api.census.gov/data/2023/cps/basic/aug 2023-01-10 15:11:40.0
-#> 6 http://api.census.gov/data/2023/cps/basic/dec 2023-01-10 15:11:40.0
-#> description
-#> 1 To provide estimates of employment, unemployment, and other characteristics of the general labor force, of the population as a whole, and of various subgroups of the population. Monthly labor force data for the country are used by the Bureau of Labor Statistics (BLS) to determine the distribution of funds under the Job Training Partnership Act. These data are collected through combined computer-assisted personal interviewing (CAPI) and computer-assisted telephone interviewing (CATI). In addition to the labor force data, the CPS basic funding provides annual data on work experience, income, health insurance, and migration data from the Annual Social and Economic Supplement (ASEC), and on school enrollment of the population from the October Supplement. Other supplements, some of which are sponsored by other agencies, are conducted biennially or intermittently.
-#> 2 To provide estimates of employment, unemployment, and other characteristics of the general labor force, of the population as a whole, and of various subgroups of the population. Monthly labor force data for the country are used by the Bureau of Labor Statistics (BLS) to determine the distribution of funds under the Job Training Partnership Act. These data are collected through combined computer-assisted personal interviewing (CAPI) and computer-assisted telephone interviewing (CATI). In addition to the labor force data, the CPS basic funding provides annual data on work experience, income, health insurance, and migration data from the Annual Social and Economic Supplement (ASEC), and on school enrollment of the population from the October Supplement. Other supplements, some of which are sponsored by other agencies, are conducted biennially or intermittently.
-#> 3 The Annual Social and Economic Supplement or March CPS supplement is the primary source of detailed information on income and work experience in the United States. Numerous publications based on this survey are issued each year by the Bureaus of Labor Statistics and Census. A public-use microdata file is available for private researchers, who also produce many academic and policy-related documents based on these data. The Annual Social and Economic Supplement is used to generate the annual Population Profile of the United States, reports on geographical mobility and educational attainment, and detailed analysis of money income and poverty status. The labor force and work experience data from this survey are used to profile the U.S. labor market and to make employment projections. To allow for the same type of in-depth analysis of hispanics, additional hispanic sample units are added to the basic CPS sample in March each year. Additional weighting is also performed so that estimates can be made for households and families, in addition to persons.
-#> 4 To provide estimates of employment, unemployment, and other characteristics of the general labor force, of the population as a whole, and of various subgroups of the population. Monthly labor force data for the country are used by the Bureau of Labor Statistics (BLS to determine the distribution of funds under the Job Training Partnership Act. These data are collected through combined computer-assisted personal interviewing (CAPI) and computer-assisted telephone interviewing (CATI). In addition to the labor force data, the CPS basic funding provides annual data on work experience, income, health insurance, and migration data from the Annual Social and Economic Supplement (ASEC), and on school enrollment of the population from the October Supplement. Other supplements, some of which are sponsored by other agencies, are conducted biennially or intermittently.
-#> 5 To provide estimates of employment, unemployment, and other characteristics of the general labor force, of the population as a whole, and of various subgroups of the population. Monthly labor force data for the country are used by the Bureau of Labor Statistics (BLS to determine the distribution of funds under the Job Training Partnership Act. These data are collected through combined computer-assisted personal interviewing (CAPI) and computer-assisted telephone interviewing (CATI). In addition to the labor force data, the CPS basic funding provides annual data on work experience, income, health insurance, and migration data from the Annual Social and Economic Supplement (ASEC), and on school enrollment of the population from the October Supplement. Other supplements, some of which are sponsored by other agencies, are conducted biennially or intermittently.
-#> 6 To provide estimates of employment, unemployment, and other characteristics of the general labor force, of the population as a whole, and of various subgroups of the population. Monthly labor force data for the country are used by the Bureau of Labor Statistics (BLS) to determine the distribution of funds under the Job Training Partnership Act. These data are collected through combined computer-assisted personal interviewing (CAPI) and computer-assisted telephone interviewing (CATI). In addition to the labor force data, the CPS basic funding provides annual data on work experience, income, health insurance, and migration data from the Annual Social and Economic Supplement (ASEC), and on school enrollment of the population from the October Supplement. Other supplements, some of which are sponsored by other agencies, are conducted biennially or intermittently.
-#> contact
-#> 1 dsd.cps@census.gov
-#> 2 dsd.cps@census.gov
-#> 3 dsd.cps@census.gov
-#> 4 dsd.cps@census.gov
-#> 5 dsd.cps@census.gov
-#> 6 dsd.cps@census.gov
# Get information about all vintage 2022 datasets
apis_2022 <- listCensusApis(vintage = 2022)
head(apis_2022)
-#> title
-#> 1 American Community Survey: 1-Year Estimates: Detailed Tables 1-Year
-#> 2 American Community Survey: 1-Year Estimates: Comparison Profiles 1-Year
-#> 3 American Community Survey: 1-Year Estimates: Data Profiles 1-Year
-#> 4 2022 American Community Survey: 1-Year Estimates - Public Use Microdata Sample
-#> 5 2022 American Community Survey: 1-Year Estimates - Public Use Microdata Sample Puerto Rico
-#> 6 American Community Survey: 1-Year Estimates: Selected Population Profiles 1-Year
-#> name vintage type temporal spatial
-#> 1 acs/acs1 2022 Aggregate 2022/2022 US
-#> 2 acs/acs1/cprofile 2022 Aggregate 2022/2022 US
-#> 3 acs/acs1/profile 2022 Aggregate 2022/2022 US
-#> 4 acs/acs1/pums 2022 Microdata 2022/2022 US
-#> 5 acs/acs1/pumspr 2022 Microdata 2022/2022 Puerto Rico
-#> 6 acs/acs1/spp 2022 Aggregate 2022/2022 US
-#> url modified
-#> 1 http://api.census.gov/data/2022/acs/acs1 2023-04-24 14:51:53.0
-#> 2 http://api.census.gov/data/2022/acs/acs1/cprofile 2023-04-24 14:49:08.0
-#> 3 http://api.census.gov/data/2022/acs/acs1/profile 2023-04-24 14:49:41.0
-#> 4 http://api.census.gov/data/2022/acs/acs1/pums 2023-06-08 10:03:42.0
-#> 5 http://api.census.gov/data/2022/acs/acs1/pumspr 2023-06-08 10:03:09.0
-#> 6 http://api.census.gov/data/2022/acs/acs1/spp 2023-04-24 14:51:20.0
-#> description
-#> 1 The American Community Survey (ACS) is an ongoing survey that provides data every year -- giving communities the current information they need to plan investments and services. The ACS covers a broad range of topics about social, economic, demographic, and housing characteristics of the U.S. population. Much of the ACS data provided on the Census Bureau's Web site are available separately by age group, race, Hispanic origin, and sex. Summary files, Subject tables, Data profiles, and Comparison profiles are available for the nation, all 50 states, the District of Columbia, Puerto Rico, every congressional district, every metropolitan area, and all counties and places with populations of 65,000 or more. Detailed Tables contain the most detailed cross-tabulations published for areas 65k and more. The data are population counts. There are over 31,000 variables in this dataset.
-#> 2 The American Community Survey (ACS) is an ongoing survey that provides data every year -- giving communities the current information they need to plan investments and services. The ACS covers a broad range of topics about social, economic, demographic, and housing characteristics of the U.S. population. Much of the ACS data provided on the Census Bureau's Web site are available separately by age group, race, Hispanic origin, and sex. Summary files, Subject tables, Data profiles, and Comparison profiles are available for the nation, all 50 states, the District of Columbia, Puerto Rico, every congressional district, every metropolitan area, and all counties and places with populations of 65,000 or more. Comparison profiles are similar to Data profiles but also include comparisons with past-year data. The current year data are compared with each of the last four years of data and include statistical significance testing. There are over 1,000 variables in this dataset.
-#> 3 The American Community Survey (ACS) is a US-wide survey designed to provide communities a fresh look at how they are changing. The ACS replaced the decennial census long form in 2010 and thereafter by collecting long form type information throughout the decade rather than only once every 10 years. Questionnaires are mailed to a sample of addresses to obtain information about households -- that is, about each person and the housing unit itself. The American Community Survey produces demographic, social, housing and economic estimates in the form of 1 and 5-year estimates based on population thresholds. The strength of the ACS is in estimating population and housing characteristics. The data profiles provide key estimates for each of the topic areas covered by the ACS for the us, all 50 states, the District of Columbia, Puerto Rico, every congressional district, every metropolitan area, and all counties and places with populations of 65,000 or more. Although the ACS produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the US, states, counties, cities and towns, and estimates of housing units for states and counties. For 2010 and other decennial census years, the Decennial Census provides the official counts of population and housing units.
-#> 4 The American Community Survey (ACS) Public Use Microdata Sample (PUMS) contains a sample of responses to the ACS. The ACS PUMS dataset includes variables for nearly every question on the survey, as well as many new variables that were derived after the fact from multiple survey responses (such as poverty status). Each record in the file represents a single person, or, in the household-level dataset, a single housing unit. In the person-level file, individuals are organized into households, making possible the study of people within the contexts of their families and other household members. Individuals living in Group Quarters, such as nursing facilities or college facilities, are also included on the person file. ACS PUMS data are available at the nation, state, and Public Use Microdata Area (PUMA) levels. PUMAs are special non-overlapping areas that partition each state into contiguous geographic units containing roughly 100,000 people each. ACS PUMS files for an individual year, such as 2022, contain data on approximately one percent of the United States population.
-#> 5 The Public Use Microdata Sample (PUMS) for Puerto Rico (PR) contains a sample of responses to the Puerto Rico Community Survey (PRCS). The PRCS is similar to, but separate from, the American Community Survey (ACS). The PRCS collects data about the population and housing units in Puerto Rico. Puerto Rico data is not included in the national PUMS files. It is published as a state equivalent file and has a State FIPS code of "72". The file includes variables for nearly every question on the survey, as well as many new variables that were derived after the fact from multiple survey responses (such as poverty status). Each record in the file represents a single person, or, in the household-level dataset, a single housing unit. In the person-level file, individuals are organized into households, making possible the study of people within the contexts of their families and other household members. Individuals living in Group Quarters, such as nursing facilities or college facilities, are also included on the person file. Data are available at the state and Public Use Microdata Area (PUMA) levels. PUMAs are special non-overlapping areas that partition Puerto Rico into contiguous geographic units containing roughly 100,000 people each. The Puerto Rico PUMS file for an individual year, such as 2022, contain data on approximately one percent of the Puerto Rico population.
-#> 6 Selected Population Profiles provide broad social, economic, and housing profiles for a large number of race, ethnic, ancestry, and country/region of birth groups. The data are presented as population counts for the total population and various subgroups and percentages.
-#> contact
-#> 1 acso.users.support@census.gov
-#> 2 acso.users.support@census.gov
-#> 3 acso.users.support@census.gov
-#> 4 acso.users.support@census.gov
-#> 5 acso.users.support@census.gov
-#> 6 acso.users.support@census.gov
# Get information about all timeseries datasets
apis_timeseries <- listCensusApis(name = "timeseries")
head(apis_timeseries)
-#> title
-#> 1 Annual Economic Surveys: Annual Survey of Manufactures Area
-#> 2 Economic Surveys: Annual Survey of Manufactures: Annual Survey of Manufactures Area
-#> 3 Annual Economic Surveys: Annual Survey of Manufactures Benchmark 2017
-#> 4 Time Series Annual Survey of Manufactures: Statistics for Industry Groups and Industries
-#> 5 Time Series Annual Survey of Manufactures: Value of Shipments for Product Classes
-#> 6 Time Series Annual Survey of Manufactures: Statistics for All Manufacturing by State
-#> name type temporal spatial
-#> 1 timeseries/asm/area2012 Timeseries <NA> <NA>
-#> 2 timeseries/asm/area2017 Timeseries 2018/2021 United States
-#> 3 timeseries/asm/benchmark2017 Timeseries <NA> <NA>
-#> 4 timeseries/asm/industry Timeseries <NA> United States
-#> 5 timeseries/asm/product Timeseries <NA> United States
-#> 6 timeseries/asm/state Timeseries <NA> United States
-#> url modified
-#> 1 http://api.census.gov/data/timeseries/asm/area2012 2018-12-13 00:00:00.0
-#> 2 http://api.census.gov/data/timeseries/asm/area2017 2020-03-17 00:00:00.0
-#> 3 http://api.census.gov/data/timeseries/asm/benchmark2017 2021-03-17 00:00:00.0
-#> 4 http://api.census.gov/data/timeseries/asm/industry 2018-06-29
-#> 5 http://api.census.gov/data/timeseries/asm/product 2017-12-15
-#> 6 http://api.census.gov/data/timeseries/asm/state 2017-12-15
-#> description
-#> 1 The Annual Survey of Manufactures (ASM) provides key intercensal measures of manufacturing activity, products, and location for the public and private sectors. The ASM provides the best current measure of current U.S. manufacturing industry outputs, inputs, and operating status, and is the primary basis for updates of the Longitudinal Research Database (LRD). Census Bureau staff and academic researchers with sworn agent status use the LRD for micro data analysis.
-#> 2 The Annual Survey of Manufactures (ASM) provides key intercensal measures of manufacturing activity, products, and location for the public and private sectors. The ASM provides the best current measure of current U.S. manufacturing industry outputs, inputs, and operating status, and is the primary basis for updates of the Longitudinal Research Database (LRD). Census Bureau staff and academic researchers with sworn agent status use the LRD for micro data analysis.
-#> 3 The Annual Survey of Manufactures (ASM) Benchmark provides key intercensal measures of manufacturing activity and products for the public and private sectors for four years following the Economic Census of Manufacturing. These benchmark tables present manufacturing establishment statistics from the 2013-2016 Annual Survey of Manufactures (ASM). The ASM Benchmark provides statistics on employment, payroll, worker hours, cost of materials, value added by manufacturing, inventories, and estimates for value of shipments for product classes of products manufactured as defined by the North American Industry Classification System. The ASM provides the best current measure of current U.S. manufacturing industry outputs, inputs, and operating status, and is the primary basis for updates of the Longitudinal Research Database (LRD). Census Bureau staff and academic researchers with sworn agent status use the LRD for micro data analysis.
-#> 4 The Annual Survey of Manufactures (ASM) provides key intercensal measures of manufacturing activity, products, and location for the public and private sectors. The ASM provides the best current measure of current U.S. manufacturing industry outputs, inputs, and operating status, and is the primary basis for updates of the Longitudinal Research Database (LRD). Census Bureau staff and academic researchers with sworn agent status use the LRD for micro data analysis.
-#> 5 The Annual Survey of Manufactures (ASM) provides key intercensal measures of manufacturing activity, products, and location for the public and private sectors. The ASM provides the best current measure of current U.S. manufacturing industry outputs, inputs, and operating status, and is the primary basis for updates of the Longitudinal Research Database (LRD). Census Bureau staff and academic researchers with sworn agent status use the LRD for micro data analysis.
-#> 6 The Annual Survey of Manufactures (ASM) provides key intercensal measures of manufacturing activity, products, and location for the public and private sectors. The ASM provides the best current measure of current U.S. manufacturing industry outputs, inputs, and operating status, and is the primary basis for updates of the Longitudinal Research Database (LRD). Census Bureau staff and academic researchers with sworn agent status use the LRD for micro data analysis.
-#> contact
-#> 1 Ewd.outreach@census.gov
-#> 2 ewd.outreach@census.gov
-#> 3 Ewd.outreach@census.gov
-#> 4 econ.dissemination@census.gov
-#> 5 stephen.c.mangum@census.gov
-#> 6 stephen.c.mangum@census.gov
# Get information about 2020 Decennial Census datasets
apis_decennial_2020 <- listCensusApis(name = "dec", vintage = 2020)
head(apis_decennial_2020)
-#> title
-#> 1 Decennial Census: 118th Congressional District Summary File
-#> 2 Decennial Census of Island Areas: American Samoa Detailed Crosstabulations
-#> 3 Decennial Census of Island Areas: Guam Detailed Crosstabulations
-#> 4 Decennial Census of Island Areas: Commonwealth of the Northern Mariana Islands Detailed Crosstabulations
-#> 5 Decennial Census of Island Areas: U.S. Virgin Islands Detailed Crosstabulations
-#> 6 Decennial Census: Detailed Demographic and Housing Characteristics File A
-#> name vintage type temporal spatial
-#> 1 dec/cd118 2020 Aggregate 2020/2020 US
-#> 2 dec/crosstabas 2020 Aggregate 2020/2020 American Samoa
-#> 3 dec/crosstabgu 2020 Aggregate 2020/2020 Guam
-#> 4 dec/crosstabmp 2020 Aggregate 2020/2020 Northern Mariana Islands
-#> 5 dec/crosstabvi 2020 Aggregate 2020/2020 U.S. Virgin Islands
-#> 6 dec/ddhca 2020 Aggregate 2020/2020 United States
-#> url modified
-#> 1 http://api.census.gov/data/2020/dec/cd118 2022-10-25 00:00:00.0
-#> 2 http://api.census.gov/data/2020/dec/crosstabas 2023-05-09 10:44:24.0
-#> 3 http://api.census.gov/data/2020/dec/crosstabgu 2023-05-09 10:46:02.0
-#> 4 http://api.census.gov/data/2020/dec/crosstabmp 2023-05-09 10:45:29.0
-#> 5 http://api.census.gov/data/2020/dec/crosstabvi 2023-05-09 10:44:57.0
-#> 6 http://api.census.gov/data/2020/dec/ddhca 2022-09-09 00:00:00.0
-#> description
-#> 1 The Congressional District Summary File (118th Congress) (CD118) contains the data compiled from the questions asked of all people and about every housing unit in the 2020 Census. This product retabulates selected summary levels from the Demographic and Housing Characteristics File (DHC) for the 118th Congress and 2022 state legislative districts. Population items include age, sex, race, Hispanic or Latino origin, household type, family type, relationship to householder, group quarters population, housing occupancy and housing tenure (whether a housing unit is owner-occupied or renter-occupied).
-#> 2 This product will include key socio-demographic and economic variables presented in cross-tabulations that present the complex data together.
-#> 3 This product will include….
-#> 4 This product will include key socio-demographic and economic variables presented in cross-tabulations that present the complex data together.
-#> 5 This product will include key socio-demographic and economic variables presented in cross-tabulations that present the complex data together.
-#> 6 This product provides the population counts and sex and age statistics for detailed racial and ethnic groups and American Indian and Alaska Native tribes and villages.
-#> contact
-#> 1 pio@census.gov
-#> 2 pio@census.gov
-#> 3 pio@census.gov
-#> 4 pio@census.gov
-#> 5 pio@census.gov
-#> 6 pio@census.gov
# Get information about one particular dataset
api_sahie <- listCensusApis(name = "timeseries/healthins/sahie")
head(api_sahie)
-#> title
-#> 1 Small Area Health Insurance Estimates: Small Area Health Insurance Estimates
-#> name type temporal spatial
-#> 1 timeseries/healthins/sahie Timeseries 2006/2020 US
-#> url modified
-#> 1 http://api.census.gov/data/timeseries/healthins/sahie 2021-04-12 00:00:00.0
-#> description
-#> 1 The U.S. Census Bureau's Small Area Health Insurance Estimates program produces the only source of data for single-year estimates of health insurance coverage status for all counties in the U.S. by selected economic and demographic characteristics. This program is partially funded by the Centers for Disease Control and Prevention's (CDC) Division of Cancer Prevention and Control (DCPC). The CDC have a congressional mandate to provide screening services for breast and cervical cancer to low-income, uninsured, and underserved women through the National Breast and Cervical Cancer Early Detection Program (NBCCEDP). For estimation, SAHIE uses statistical models that combine survey data from the American Community Survey (ACS) with administrative records data and Census 2010 data.
-#> contact
-#> 1 Sehsd.sahie@census.gov
-# }
+}
diff --git a/docs/reference/listCensusMetadata.html b/docs/reference/listCensusMetadata.html
index 09ae5c3..af51948 100644
--- a/docs/reference/listCensusMetadata.html
+++ b/docs/reference/listCensusMetadata.html
@@ -91,12 +91,14 @@ Arguments
+Type of metadata to return. Options are:
+* "variables" (default) - list of variable names and descriptions
+ for the dataset.
+ * "geographies" - available geographies.
+ * "groups" - available variable groups. Only available
+ for some datasets.
+ * "values" - encoded value labels for a given variable. Pair with
+ "variable_name". Only available for some datasets.
- group
@@ -119,41 +121,13 @@ Arguments
Examples
- # \dontrun{
+ if (FALSE) {
# type: variables
# List the variables available in the Small Area Health Insurance Estimates.
variables <- listCensusMetadata(
name = "timeseries/healthins/sahie",
type = "variables")
head(variables)
-#> name
-#> 1 for
-#> 2 in
-#> 3 time
-#> 4 NIPR_LB90
-#> 5 NIPR_PT
-#> 6 AGECAT
-#> label
-#> 1 Census API FIPS 'for' clause
-#> 2 Census API FIPS 'in' clause
-#> 3 ISO-8601 Date/Time value
-#> 4 Number in Demographic Group for Selected Income Range, Upper Bound for 90% Confidence Interval
-#> 5 Number in Demographic Group for Selected Income Range, Estimate
-#> 6 Age Category
-#> concept predicateType group limit predicateOnly
-#> 1 Census API Geography Specification fips-for N/A 0 TRUE
-#> 2 Census API Geography Specification fips-in N/A 0 TRUE
-#> 3 Census API Date/Time Specification datetime N/A 0 TRUE
-#> 4 <NA> int N/A 0 <NA>
-#> 5 <NA> int N/A 0 <NA>
-#> 6 <NA> string N/A 0 <NA>
-#> required
-#> 1 <NA>
-#> 2 <NA>
-#> 3 true
-#> 4 <NA>
-#> 5 <NA>
-#> 6 default displayed
# type: variables for a single variable group
# List the variables that are included in the B17020 group in the
@@ -164,34 +138,6 @@ Examples type = "variables",
group = "B17020")
head(variable_group)
-#> name
-#> 1 B17020_017EA
-#> 2 B17020_016MA
-#> 3 B17020_016EA
-#> 4 B17020_015MA
-#> 5 B17020_015EA
-#> 6 B17020_014EA
-#> label
-#> 1 Annotation of Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!85 years and over
-#> 2 Annotation of Margin of Error!!Total:!!Income in the past 12 months at or above poverty level:!!75 to 84 years
-#> 3 Annotation of Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!75 to 84 years
-#> 4 Annotation of Margin of Error!!Total:!!Income in the past 12 months at or above poverty level:!!60 to 74 years
-#> 5 Annotation of Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!60 to 74 years
-#> 6 Annotation of Estimate!!Total:!!Income in the past 12 months at or above poverty level:!!18 to 59 years
-#> concept predicateType group limit
-#> 1 Poverty Status in the Past 12 Months by Age string B17020 0
-#> 2 Poverty Status in the Past 12 Months by Age string B17020 0
-#> 3 Poverty Status in the Past 12 Months by Age string B17020 0
-#> 4 Poverty Status in the Past 12 Months by Age string B17020 0
-#> 5 Poverty Status in the Past 12 Months by Age string B17020 0
-#> 6 Poverty Status in the Past 12 Months by Age string B17020 0
-#> predicateOnly universe
-#> 1 TRUE Population for whom poverty status is determined
-#> 2 TRUE Population for whom poverty status is determined
-#> 3 TRUE Population for whom poverty status is determined
-#> 4 TRUE Population for whom poverty status is determined
-#> 5 TRUE Population for whom poverty status is determined
-#> 6 TRUE Population for whom poverty status is determined
# type: variables, with value labels
# Create a data dictionary with all variable names and encoded values for
@@ -202,27 +148,6 @@ Examples type = "variables",
include_values = TRUE)
head(variable_values)
-#> name label
-#> 1 for Census API FIPS 'for' clause
-#> 2 in Census API FIPS 'in' clause
-#> 3 ucgid Uniform Census Geography Identifier clause
-#> 4 PEEDUCA Demographics-highest level of school completed
-#> 5 PEEDUCA Demographics-highest level of school completed
-#> 6 PEEDUCA Demographics-highest level of school completed
-#> concept predicateType group limit predicateOnly
-#> 1 Census API Geography Specification fips-for N/A 0 TRUE
-#> 2 Census API Geography Specification fips-in N/A 0 TRUE
-#> 3 Census API Geography Specification ucgid N/A 0 TRUE
-#> 4 <NA> int N/A 0 <NA>
-#> 5 <NA> int N/A 0 <NA>
-#> 6 <NA> int N/A 0 <NA>
-#> suggested_weight is_weight values_code values_label
-#> 1 <NA> <NA> <NA> <NA>
-#> 2 <NA> <NA> <NA> <NA>
-#> 3 <NA> <NA> <NA> <NA>
-#> 4 PWSSWGT <NA> 46 DOCTORATE DEGREE(EX:PhD,EdD)
-#> 5 PWSSWGT <NA> 33 5th Or 6th Grade
-#> 6 PWSSWGT <NA> 44 MASTER'S DEGREE(EX:MA,MS,MEng,MEd,MSW)
# type: geographies
# List the geographies available in the 5-year American Community Survey.
@@ -231,20 +156,6 @@ Examples vintage = 2022,
type = "geographies")
head(geographies)
-#> name geoLevelDisplay referenceDate requires wildcard
-#> 1 us 010 2022-01-01 NULL NULL
-#> 2 region 020 2022-01-01 NULL NULL
-#> 3 division 030 2022-01-01 NULL NULL
-#> 4 state 040 2022-01-01 NULL NULL
-#> 5 county 050 2022-01-01 state state
-#> 6 county subdivision 060 2022-01-01 state, county county
-#> optionalWithWCFor
-#> 1 <NA>
-#> 2 <NA>
-#> 3 <NA>
-#> 4 <NA>
-#> 5 state
-#> 6 county
# type: groups
# List the variable groups available in the 5-year American Community Survey.
@@ -253,34 +164,6 @@ Examples vintage = 2022,
type = "groups")
head(groups)
-#> name
-#> 1 B17015
-#> 2 B18104
-#> 3 B17016
-#> 4 B18105
-#> 5 B17017
-#> 6 B18106
-#> description
-#> 1 Poverty Status in the Past 12 Months of Families by Family Type by Social Security Income by Supplemental Security Income (SSI) and Cash Public Assistance Income
-#> 2 Sex by Age by Cognitive Difficulty
-#> 3 Poverty Status in the Past 12 Months of Families by Family Type by Work Experience of Householder and Spouse
-#> 4 Sex by Age by Ambulatory Difficulty
-#> 5 Poverty Status in the Past 12 Months by Household Type by Age of Householder
-#> 6 Sex by Age by Self-Care Difficulty
-#> variables
-#> 1 http://api.census.gov/data/2022/acs/acs5/groups/B17015.json
-#> 2 http://api.census.gov/data/2022/acs/acs5/groups/B18104.json
-#> 3 http://api.census.gov/data/2022/acs/acs5/groups/B17016.json
-#> 4 http://api.census.gov/data/2022/acs/acs5/groups/B18105.json
-#> 5 http://api.census.gov/data/2022/acs/acs5/groups/B17017.json
-#> 6 http://api.census.gov/data/2022/acs/acs5/groups/B18106.json
-#> universe
-#> 1 Families
-#> 2 Civilian noninstitutionalized population 5 years and over
-#> 3 Families
-#> 4 Civilian noninstitutionalized population 5 years and over
-#> 5 Households
-#> 6 Civilian noninstitutionalized population 5 years and over
# type: values for a single variable
# List the value labels of the NAICS2017 variable in the County Business Patterns dataset.
@@ -290,14 +173,7 @@ Examples type = "values",
variable = "NAICS2017")
head(naics_values)
-#> code label
-#> 1 00 Total for all sectors
-#> 2 000000 Industry total
-#> 3 11 Agriculture, forestry, fishing and hunting
-#> 4 111 Crop production
-#> 5 1111 Oilseed and grain farming
-#> 6 11111 Soybean farming
-# }
+}