# Getting started with censusapi

`censusapi` is a wrapper for the United States Census Bureau’s APIs. As of 2017 over 200 Census API endpoints are available, including Decennial Census, American Community Survey, Poverty Statistics, and Population Estimates APIs. This package is designed to let you get data from all of those APIs using the same main function—`getCensus`—and the same syntax for each dataset.

`censusapi` generally uses the APIs’ original parameter names so that users can easily transition between Census’s documentation and examples and this package. It also includes metadata functions to return data frames of available APIs, variables, and geographies.

## API key setup

To use the Census APIs, sign up for an API key. Then, if you’re on a non-shared computer, add your Census API key to your .Renviron profile and call it CENSUS_KEY. `censusapi` will use it by default without any extra work on your part. Within R, run:

    # Add key to .Renviron
    Sys.setenv(CENSUS_KEY=YOURKEYHERE)
    # 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 specify your key within `getCensus` instead.

## Finding your API

To get started, load the `censusapi` library.

    library(censusapi)

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)

This returns useful information about each endpoint, including `name`, which you’ll need to make your API call.

## Using `getCensus`

The main function in `censusapi` is `getCensus`, which makes an API call to a given Census API and returns a data frame of results. Each API has slightly different parameters, but there are always a few required arguments:

- `name`: the name of the API as defined by the Census, like “acs5” or “timeseries/bds/firms”
- `vintage`: the dataset year, generally required for non-timeseries APIs
- `vars`: the list of variable names to get
- `region`: the geography level to return, like state or county

Some APIs have additional required or optional arguments, like `time`, `monthly`, or `period`. Check the specific documentation for your API to see what options are allowed.

Let’s walk through an example getting uninsured rates by income group using the Small Area Health Insurance Estimates API, which provides detailed annual state-level and county-level estimates of health insurance rates.

### Choosing variables

`censusapi` includes a metadata function called `listCensusMetadata` to get information about an API’s variable options and geography options. Let’s see what variables are available in the SAHIE API:

    sahie_vars <- listCensusMetadata(name = "timeseries/healthins/sahie",
        type = "variables")
    head(sahie_vars)

name | label | concept | predicateType | group | limit | required
--- | --- | --- | --- | --- | --- | ---
AGE_DESC | Age Category Description | Demographic ID | int | N/A | 0 | NA
NUI_LB90 | Number Uninsured, Lower Bound for 90% Confidence Interval | Uncertainty Measure | int | N/A | 0 | NA
STATE | State FIPS Code | Geographic ID | int | N/A | 0 | NA
NIC_MOE | Number Insured, Margin of Error | Uncertainty Measure | int | N/A | 0 | NA
NIPR_PT | Number in Demographic Group for Selected Income Range, Estimate | Estimate | int | N/A | 0 | NA
RACECAT | Race Category | Demographic ID | int | N/A | 4 | default displayed

We’ll use a few of these variables to get uninsured rates by income group:

- `IPRCAT`: Income Poverty Ratio Category
- `IPR_DESC`: Income Poverty Ratio Category Description
- `PCTUI_PT`: Percent Uninsured in Demographic Group for Selected Income Range, Estimate
- `NAME`: Name of the geography returned (e.g. state or county name)

### Choosing regions

We can also use `listCensusMetadata` to see which geographic levels we can get data for using the SAHIE API.

    listCensusMetadata(name = "timeseries/healthins/sahie",
        type = "geography")

name | geoLevelId | limit | referenceDate | requires | wildcard | optionalWithWCFor
--- | --- | --- | --- | --- | --- | ---
us | 010 | 1 | 2015-01-01 | NULL | NULL | NA
county | 050 | 3142 | 2015-01-01 | state | state | state
state | 040 | 52 | 2015-01-01 | NULL | NULL | NA

This API has three geographic levels: `us`, `county` within states, and `state`.

First, using `getCensus`, let’s get uninsured rate by income group at the national level for 2017.

    getCensus(name = "timeseries/healthins/sahie",
        vars = c("NAME", "IPRCAT", "IPR_DESC", "PCTUI_PT"),
        region = "us:*",
        time = 2017)

time | us | NAME | IPRCAT | IPR_DESC | PCTUI_PT
--- | --- | --- | --- | --- | ---
2017 | 1 | United States | 0 | All Incomes | 10.2
2017 | 1 | United States | 1 | <= 200% of Poverty | 17.2
2017 | 1 | United States | 2 | <= 250% of Poverty | 16.5
2017 | 1 | United States | 3 | <= 138% of Poverty | 17.4
2017 | 1 | United States | 4 | <= 400% of Poverty | 14.2
2017 | 1 | United States | 5 | 138% to 400% of Poverty | 12.6

We can also get this data at the state level for every state by changing `region` to `"state:*"`:

    sahie_states <- getCensus(name = "timeseries/healthins/sahie",
        vars = c("NAME", "IPRCAT", "IPR_DESC", "PCTUI_PT"),
        region = "state:*",
        time = 2017)
    head(sahie_states)

time | state | NAME | IPRCAT | IPR_DESC | PCTUI_PT
--- | --- | --- | --- | --- | ---
2017 | 01 | Alabama | 0 | All Incomes | 11.0
2017 | 02 | Alaska | 0 | All Incomes | 14.8
2017 | 04 | Arizona | 0 | All Incomes | 12.1
2017 | 05 | Arkansas | 0 | All Incomes | 9.3
2017 | 06 | California | 0 | All Incomes | 8.2
2017 | 08 | Colorado | 0 | All Incomes | 8.7

Finally, we can get county-level data. The geography metadata showed that we can choose to get county-level data within states. We’ll use `region` to specify county-level results and `regionin` to request data for Alabama and Alaska.

    sahie_counties <- getCensus(name = "timeseries/healthins/sahie",
        vars = c("NAME", "IPRCAT", "IPR_DESC", "PCTUI_PT"),
        region = "county:*",
        regionin = "state:01,02",
        time = 2017)
    head(sahie_counties, n=12L)

time | state | county | NAME | IPRCAT | IPR_DESC | PCTUI_PT
--- | --- | --- | --- | --- | --- | ---
2017 | 01 | 003 | Baldwin County, AL | 0 | All Incomes | 11.3
2017 | 01 | 001 | Autauga County, AL | 0 | All Incomes | 8.7
2017 | 01 | 015 | Calhoun County, AL | 0 | All Incomes | 11.9
2017 | 01 | 005 | Barbour County, AL | 0 | All Incomes | 12.2
2017 | 01 | 007 | Bibb County, AL | 0 | All Incomes | 10.2
2017 | 01 | 009 | Blount County, AL | 0 | All Incomes | 13.4
2017 | 01 | 011 | Bullock County, AL | 0 | All Incomes | 11.4
2017 | 01 | 013 | Butler County, AL | 0 | All Incomes | 11.2
2017 | 01 | 027 | Clay County, AL | 0 | All Incomes | 13.9
2017 | 01 | 017 | Chambers County, AL | 0 | All Incomes | 11.9
2017 | 01 | 019 | Cherokee County, AL | 0 | All Incomes | 11.2
2017 | 01 | 021 | Chilton County, AL | 0 | All Incomes | 13.8

Because the SAHIE API is a timeseries (as indicated in its name), we can get multiple years of data at once using the `time` argument.

    sahie_years <- getCensus(name = "timeseries/healthins/sahie",
        vars = c("NAME", "PCTUI_PT"),
        region = "state:01",
        time = "from 2006 to 2017")
    head(sahie_years)

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

## Advanced topics

This package allows access to the full range of the U.S. Census Bureau’s APIs. Where the API allows it, you can specify complicated geographies or filter based on a range of parameters. Each API is a little different, so be sure to read the documentation for the specific API that you’re using. Also see more examples in the example masterlist.

### Miscellaneous parameters

Some of the APIs allow complex calls, including specifying a country FIPS code or age. The most commonly used parameters, including `time`, `date`, and `sic` are included as built-in options in `getCensus`, but you can also specify other parameters yourself. (Note: this generally does not apply to the popular American Community Survey and Decennial Census APIs.)

In the SAHIE API, we can filter data by the categorical variables `AGECAT` (age group), `IPRCAT` (income group), `RACECAT` (race) and `SEXCAT` (sex), in addition to geography and time. More information on those variables is available in the online documentation.

Here’s how to get the uninsured rate (`PCTUI_PT`) for non-elderly adults (`AGECAT = 1`) with incomes of 138 to 400% of the poverty line (`IPRCAT = 5`), by race (`RACECAT`) and state.

    sahie_nonelderly <- getCensus(name = "timeseries/healthins/sahie",
        vars = c("NAME", "PCTUI_PT", "IPR_DESC", "AGE_DESC", "RACECAT", "RACE_DESC"),
        region = "state:*",
        time = 2017,
        IPRCAT = 5,
        AGECAT = 1)
    head(sahie_nonelderly)

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

Note: data by race is only returned where the population is large enough, so some states will not have rows for some race groups. Here’s another example, getting national data from percent uninsured (`PCTUI_PT`) and number uninsured (`NUI_PT`), along with the associated margins of error, by race group and income group for all years.

    sahie_nonelderly_annual <- getCensus(name = "timeseries/healthins/sahie",
        vars = c("NAME", "PCTUI_PT", "PCTUI_MOE", "NUI_PT", "NUI_MOE", "IPRCAT", "IPR_DESC", "AGE_DESC", "RACECAT", "RACE_DESC"),
        region = "us:*",
        time = "from 2006 to 2017",
        AGECAT = 1)
    head(sahie_nonelderly_annual)

time | us | NAME | PCTUI_PT | PCTUI_MOE | NUI_PT | NUI_MOE | IPRCAT | IPR_DESC | AGE_DESC | RACECAT | RACE_DESC | AGECAT
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
2006 | 1 | United States | 19.5 | 0.3 | 36363986 | 549708 | 0 | All Incomes | 18 to 64 years | 0 | All Races | 1
2006 | 1 | United States | 39.5 | 0.7 | 19368368 | 440544 | 1 | <= 200% of Poverty | 18 to 64 years | 0 | All Races | 1
2006 | 1 | United States | 36.5 | 0.6 | 23595529 | 455801 | 2 | <= 250% of Poverty | 18 to 64 years | 0 | All Races | 1
2006 | 1 | United States | 13.7 | 0.3 | 17094552 | 364661 | 0 | All Incomes | 18 to 64 years | 1 | White alone, not Hispanic | 1
2006 | 1 | United States | 32.0 | 0.8 | 7846458 | 277188 | 1 | <= 200% of Poverty | 18 to 64 years | 1 | White alone, not Hispanic | 1
2006 | 1 | United States | 28.5 | 0.7 | 9614431 | 291480 | 2 | <= 250% of Poverty | 18 to 64 years | 1 | White alone, not Hispanic | 1

Other APIs can be filtered too. For example, the International Data Base population projections APIs allow you to get data by age and country.

See what variables the IDB 1 year API allows:

    listCensusMetadata(name = "timeseries/idb/1year",
        type = "variables")

name | label | concept | predicateType | group | limit | required
--- | --- | --- | --- | --- | --- | ---
AREA_KM2 | Area in square kilometers | Geographic Characteristics | int | N/A | 0 | NA
FIPS | FIPS country/area code | Geographic Characteristics | string | N/A | 0 | NA
NAME | Country or area name | Geographic Characteristics | string | N/A | 0 | NA
AGE | Single year of age from 0-100+ | Age and Sex | int | N/A | 0 | true
SEX | Sex | Age and Sex | int | N/A | 0 | default displayed
POP | Total mid-year population | Total Midyear Population | int | N/A | 0 | NA
YR | Year | Required variable | int | N/A | 0 | NA

Here’s a simple call getting projected population by age for all countries in 2050.

    pop_2050 <- getCensus(name = "timeseries/idb/1year",
        vars = c("FIPS", "NAME", "AGE", "POP"),
        time = 2050)
    head(pop_2050)

time | FIPS | NAME | AGE | POP
--- | --- | --- | --- | ---
2050 | AA | Aruba | 0 | 1554
2050 | AA | Aruba | 1 | 1554
2050 | AA | Aruba | 2 | 1551
2050 | AA | Aruba | 3 | 1554
2050 | AA | Aruba | 4 | 1550
2050 | AA | Aruba | 5 | 1553

But we can make a much more specific call by specifying `FIPS` and `AGE` to get just the population projections for teenagers in Portugal.

    pop_portugal <- getCensus(name = "timeseries/idb/1year",
        vars = c("NAME", "POP"),
        time = 2050,
        FIPS = "PO",
        AGE = "13:19")
    pop_portugal

time | NAME | POP | FIPS | AGE
--- | --- | --- | --- | ---
2050 | Portugal | 82014 | PO | 13
2050 | Portugal | 82573 | PO | 14
2050 | Portugal | 83083 | PO | 15
2050 | Portugal | 83540 | PO | 16
2050 | Portugal | 83812 | PO | 17
2050 | Portugal | 83919 | PO | 18
2050 | Portugal | 83880 | PO | 19

The Quarterly Workforce Indicators APIs allow even more specific calls. Here’s one example:

    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

### Variable groups

For some surveys, particularly the American Community Survey and Decennial Census, you can get many related variables at once using a `group`, defined by the Census Bureau. In some other data tools, like American FactFinder, this idea is referred to as a `table`.

The American Community Survey (ACS) APIs include estimates (variable names ending in “E”), annotations, margins of error, and statistical significance, depending on the data set. Read more on ACS variable types and annotation symbol meanings on the Census website.

You can retrieve these annotation variables manually, by specifying a list of variables. We’ll get the estimate, margin of error and annotations for median household income in the past 12 months 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

You can also retrieve also estimates and annotations for a group of variables in one command. Here’s the `group` call for that same table, B19013.

    # See descriptions of the variables in group B19013
    group_B19013 <- listCensusMetadata(name = "acs/acs5",
        vintage = 2017,
        type = "variables",
        group = "B19013")
    group_B19013

name | label | concept | predicateType | group | limit | predicateOnly
--- | --- | --- | --- | --- | --- | ---
B19013_001E | Estimate!!Median household income in the past 12 months (in 2017 inflation-adjusted dollars) | MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2017 INFLATION-ADJUSTED DOLLARS) | int | B19013 | 0 | TRUE
B19013_001M | Margin of Error!!Median household income in the past 12 months (in 2017 inflation-adjusted dollars) | MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2017 INFLATION-ADJUSTED DOLLARS) | int | B19013 | 0 | TRUE
B19013_001EA | Annotation of Estimate!!Median household income in the past 12 months (in 2017 inflation-adjusted dollars) | NA | string | B19013 | 0 | TRUE
B19013_001MA | Annotation of Margin of Error!!Median household income in the past 12 months (in 2017 inflation-adjusted dollars) | NA | string | B19013 | 0 | TRUE

    acs_income_group <- getCensus(name = "acs/acs5",
        vintage = 2017,
        vars = c("NAME", "group(B19013)"),
        region = "tract:*",
        regionin = "state:02")

    #> Warning in responseFormat(raw): NAs introduced by coercion

    head(acs_income_group)

state | county | tract | NAME | GEO_ID | B19013_001E | B19013_001M | NAME_1 | B19013_001EA | B19013_001MA
--- | --- | --- | --- | --- | --- | --- | --- | --- | ---
02 | 261 | 000300 | Census Tract 3, Valdez-Cordova Census Area, Alaska | 1400000US02261000300 | 89000 | 20435 | NA | NA | NA
02 | 122 | 000600 | Census Tract 6, Kenai Peninsula Borough, Alaska | 1400000US02122000600 | 58125 | 5725 | NA | NA | NA
02 | 122 | 001100 | Census Tract 11, Kenai Peninsula Borough, Alaska | 1400000US02122001100 | 69028 | 5941 | NA | NA | NA
02 | 261 | 000100 | Census Tract 1, Valdez-Cordova Census Area, Alaska | 1400000US02261000100 | 49076 | 7165 | NA | NA | NA
02 | 122 | 000200 | Census Tract 2, Kenai Peninsula Borough, Alaska | 1400000US02122000200 | 57694 | 6526 | NA | NA | NA
02 | 122 | 000800 | Census Tract 8, Kenai Peninsula Borough, Alaska | 1400000US02122000800 | 50904 | 3723 | NA | NA | NA

Some variable groups contain many related variables and their associated annotations. As an example, we’ll get the list of variables included in group B17020, poverty status by age.

    group_B17020 <- listCensusMetadata(name = "acs/acs5",
        vintage = 2017,
        type = "variables",
        group = "B17020")
    head(group_B17020)

name | label | concept | predicateType | group | limit | predicateOnly
--- | --- | --- | --- | --- | --- | ---
B17020_002M | Margin of Error!!Total!!Income in the past 12 months below poverty level | POVERTY STATUS IN THE PAST 12 MONTHS BY AGE | int | B17020 | 0 | TRUE
B17020_002E | Estimate!!Total!!Income in the past 12 months below poverty level | POVERTY STATUS IN THE PAST 12 MONTHS BY AGE | int | B17020 | 0 | TRUE
B17020_001M | Margin of Error!!Total | POVERTY STATUS IN THE PAST 12 MONTHS BY AGE | int | B17020 | 0 | TRUE
B17020_001E | Estimate!!Total | POVERTY STATUS IN THE PAST 12 MONTHS BY AGE | int | B17020 | 0 | TRUE
B17020_004M | Margin of Error!!Total!!Income in the past 12 months below poverty level!!6 to 11 years | POVERTY STATUS IN THE PAST 12 MONTHS BY AGE | int | B17020 | 0 | TRUE
B17020_004E | Estimate!!Total!!Income in the past 12 months below poverty level!!6 to 11 years | POVERTY STATUS IN THE PAST 12 MONTHS BY AGE | int | B17020 | 0 | TRUE

### Advanced geographies

Some geographies, particularly Census tracts and blocks, need to be specified within larger geographies like states and counties. This varies by API endpoint, so make sure to read the documentation for your specific API and run `listCensusMetadata` to see the available geographies.

You may want to get get data for many geographies that require a parent geography. For example, tract-level data from the 1990 Decennial Census can only be requested from one state at a time.

In this example, we use the built in `fips` list of state FIPS codes to request tract-level data from each state and join into a single data frame.

    fips

    #>  [1] "01" "02" "04" "05" "06" "08" "09" "10" "11" "12" "13" "15" "16" "17"
    #> [15] "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" "31"
    #> [29] "32" "33" "34" "35" "36" "37" "38" "39" "40" "41" "42" "44" "45" "46"
    #> [43] "47" "48" "49" "50" "51" "53" "54" "55" "56"

    tracts <- NULL
    for (f in fips) {
        stateget <- paste("state:", f, sep="")
        temp <- getCensus(name = "sf3",
            vintage = 1990,
            vars = c("P0070001", "P0070002", "P114A001"),
            region = "tract:*",
            regionin = stateget)
        tracts <- rbind(tracts, temp)
    }
    head(tracts)

state | county | tract | P0070001 | P0070002 | P114A001
--- | --- | --- | --- | --- | ---
01 | 001 | 020100 | 944 | 917 | 11663
01 | 001 | 020200 | 917 | 1060 | 8555
01 | 001 | 020300 | 1451 | 1518 | 11782
01 | 001 | 020400 | 2166 | 2223 | 15323
01 | 001 | 020500 | 1604 | 1582 | 14522
01 | 001 | 020600 | 1784 | 1661 | 10630

The `regionin` argument of `getCensus` can also be used with a string of nested geographies, as shown below.

The 2010 Decennial Census summary file 1 requires you to specify a state and county to retrieve block-level data. Use `region` to request block level data, and `regionin` to specify the desired state and county.

    data2010 <- getCensus(name = "dec/sf1",
        vintage = 2010,
        vars = "P001001",
        region = "block:*",
        regionin = "state:36+county:027+tract:010000")
    head(data2010)

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

## Troubleshooting

The APIs contain hundreds of API endpoints and dozens of datasets, each of which work a little differently. The Census Bureau also makes frequent updates, which unfortunately are not always announced in advance. If you’re getting an error message or unexpected results, here are some things to check.

### Variables

Use `listCensusMetadata(type = "variables")` on your API to see the table of available variables. * Occasionally the variable names will change with data updates or API updates. The names may be different from year to year. * The Census APIs are case-sensitive, which means that if the variable name you want is uppercase you’ll need to write it uppercase in your request. Most of the APIs use uppercase variable names, but some use lowercase and some even use sentence case.

### Geographies

Use `listCensusMetadata(type = "geographies")` on your dataset to check which geographies you can use. * Each API has its own list of valid geographies and they occasionally change as the Census Bureau makes updates. If a previously available geography isn’t available anymore, email cnmp.developers.list@census.gov detailing the issue. * If you’re specifying a region by FIPS code, for example `state:01`, make sure to use the full code, padded with 0s if necessary. The APIs did not always enforce this (previously, `state:1` usually worked), but now they do. See the Census reference files for valid FIPS codes.

### Unexpected errors

Occasionally you might get the general error message `"There was an error while running your query. We've logged the error and we'll correct it ASAP. Sorry for the inconvenience."` This comes from the Census Bureau and could be caused by any number of problems, including server issues. Try rerunning your API call. If that doesn’t work and you are requesting a large amount of data, try reducing the amount that you’re requesting, for example getting only one state at a time. If you’re still having trouble, email cnmp.developers.list@census.gov. Include in your email the raw API call that’s provided in your `getCensus` error message (not your R code) so that they can try to help.

### Other ways to get help

- Open a Github issue for bugs or issues with this R package.
- Join the public Census Bureau Slack channel and ask your question in the R or API rooms.
- Email the Census Bureau API team at cnmp.developers.list@census.gov for questions relating to the underlying data and APIs.

## Additional resources

- Census Data API User Guide

## Disclaimer

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