This vignette details the options available for requesting IPUMS NHGIS data and metadata via the IPUMS API.
If you haven’t yet learned the basics of the IPUMS API workflow, you may want to start with the IPUMS API introduction. The code below assumes you have registered and set up your API key as described there.
In addition to NHGIS, the IPUMS API also supports several microdata projects. For details about obtaining IPUMS microdata using ipumsr, see the microdata-specific vignette.
Before getting started, we’ll load ipumsr and some helpful packages for this demo:
IPUMS NHGIS supports 3 main types of data products: datasets, time series tables, and shapefiles.
A dataset contains a collection of data tables that each correspond to a particular tabulated summary statistic. A dataset is distinguished by the years, geographic levels, and topics that it covers. For instance, 2021 1-year data from the American Community Survey (ACS) is encapsulated in a single dataset. In other cases, a single census product will be split into multiple datasets.
A time series table is a longitudinal data source that links comparable statistics from multiple U.S. censuses in a single bundle. A table is comprised of one or more related time series, each of which describes a single summary statistic measured at multiple times for a given geographic level.
A shapefile (or GIS file) contains geographic data for a given geographic level and year. Typically, these files are composed of polygon geometries containing the boundaries of census reporting areas.
Of course, to make a request for any of these data sources, we have
to know the codes that the API uses to refer to them. Fortunately, we
can browse the metadata for all available IPUMS NHGIS data sources with
get_metadata_nhgis().
Users can view summary metadata for all available data sources of a given data type, or detailed metadata for a specific data source by name.
To see a summary of all available sources for a given data product
type, use the type argument. This returns a data frame
containing the available datasets, data tables, time series tables, or
shapefiles.
ds <- get_metadata_nhgis(type = "datasets")
head(ds)
#> # A tibble: 6 × 4
#> name group description sequence
#> <chr> <chr> <chr> <int>
#> 1 1790_cPop 1790 Census Population Data [US, States & Counties] 101
#> 2 1800_cPop 1800 Census Population Data [US, States & Counties] 201
#> 3 1810_cPop 1810 Census Population Data [US, States & Counties] 301
#> 4 1820_cPop 1820 Census Population Data [US, States & Counties] 401
#> 5 1830_cPop 1830 Census Population Data [US, States & Counties] 501
#> 6 1840_cAg 1840 Census Agriculture Data [US, States & Counties] 601We can use basic functions from {dplyr} to filter the
metadata to those records of interest. For instance, if we wanted to
find all the data sources related to agriculture from the 1900 Census,
we could filter on group and description:
ds %>%
filter(
group == "1900 Census",
grepl("Agriculture", description)
)
#> # A tibble: 2 × 4
#> name group description sequence
#> <chr> <chr> <chr> <int>
#> 1 1900_cAg 1900 Census Agriculture Data [US, States & Counties] 1401
#> 2 1900_cPHAM 1900 Census Population, Housing, Agriculture & Manufactur… 1403The values listed in the name column correspond to the
code that you would use to request that dataset when creating an extract
definition to be submitted to the IPUMS API.
Similarly, for time series tables:
While some of the metadata fields are consistent across different
data types, some, like geographic_integration, are specific
to time series tables:
head(tst)
#> # A tibble: 6 × 7
#> name description geographic_integration sequence time_series years
#> <chr> <chr> <chr> <dbl> <list> <list>
#> 1 A00 Total Population Nominal 100. <tibble> <tibble>
#> 2 AV0 Total Population Nominal 100. <tibble> <tibble>
#> 3 B78 Total Population Nominal 100. <tibble> <tibble>
#> 4 CL8 Total Population Standardized to 2010 100. <tibble> <tibble>
#> 5 A57 Persons by Urban/R… Nominal 101. <tibble> <tibble>
#> 6 A59 Persons by Urban/R… Nominal 101. <tibble> <tibble>
#> # ℹ 1 more variable: geog_levels <list>Note that for time series tables, some metadata fields are stored in list columns, where each entry is itself a data frame:
tst$years[[1]]
#> # A tibble: 24 × 3
#> name description sequence
#> <chr> <chr> <int>
#> 1 1790 1790 1
#> 2 1800 1800 2
#> 3 1810 1810 3
#> 4 1820 1820 4
#> 5 1830 1830 5
#> 6 1840 1840 6
#> 7 1850 1850 7
#> 8 1860 1860 8
#> 9 1870 1870 12
#> 10 1880 1880 22
#> # ℹ 14 more rows
tst$geog_levels[[1]]
#> # A tibble: 2 × 3
#> name description sequence
#> <chr> <chr> <int>
#> 1 state State 4
#> 2 county State--County 25To filter on these columns, we can use map_lgl() from
{purrr}. For instance, to find all time series tables that
include data from a particular year:
# Iterate over each `years` entry, identifying whether that entry
# contains "1840" in its `name` column.
tst %>%
filter(map_lgl(years, ~ "1840" %in% .x$name))
#> # A tibble: 2 × 7
#> name description geographic_integration sequence time_series years
#> <chr> <chr> <chr> <dbl> <list> <list>
#> 1 A00 Total Population Nominal 100. <tibble> <tibble>
#> 2 A08 Persons by Sex [2] Nominal 102. <tibble> <tibble>
#> # ℹ 1 more variable: geog_levels <list>For more details on working with nested data frames, see this tidyr article.
Once we have identified a data source of interest, we can find out
more about its detailed options by providing its name to the
corresponding argument of get_metadata_nhgis():
This provides a comprehensive list of the possible specifications for
the input data source. For instance, for the 1900_cAg
dataset, we have 66 tables to choose from, and 3 possible geographic
levels:
cAg_meta$data_tables
#> # A tibble: 66 × 4
#> name nhgis_code description sequence
#> <chr> <chr> <chr> <int>
#> 1 NT1 AWS Total Population 1
#> 2 NT2 AW3 Number of Farms 2
#> 3 NT3 AXE Average Farm Size 3
#> 4 NT4 AXP Farm Acreage 4
#> 5 NT5 AXZ Farm Management 5
#> 6 NT6 AYA Race of Farmer 6
#> 7 NT7 AYJ Race of Farmer by Detailed Management 7
#> 8 NT8 AYK Number of Farms 8
#> 9 NT9 AYL Farms with Buildings 9
#> 10 NT10 AWT Acres of Farmland 10
#> # ℹ 56 more rows
cAg_meta$geog_levels
#> # A tibble: 3 × 4
#> name description has_geog_extent_selection sequence
#> <chr> <chr> <lgl> <int>
#> 1 nation Nation FALSE 1
#> 2 state State FALSE 4
#> 3 county State--County FALSE 25You can also get detailed metadata for an individual data table. Since data tables belong to specific datasets, both need to be specified to identify a data table:
get_metadata_nhgis(dataset = "1900_cAg", data_table = "NT2")
#> $name
#> [1] "NT2"
#>
#> $description
#> [1] "Number of Farms"
#>
#> $universe
#> [1] "Farms"
#>
#> $nhgis_code
#> [1] "AW3"
#>
#> $sequence
#> [1] 2
#>
#> $dataset_name
#> [1] "1900_cAg"
#>
#> $variables
#> # A tibble: 1 × 2
#> description nhgis_code
#> <chr> <chr>
#> 1 Total AW3001Note that the name element is the one that contains the
codes used for interacting with the IPUMS API. The
nhgis_code element refers to the prefix attached to
individual variables in the output data, and the API will throw an error
if you use it in an extract definition. For more details on interpreting
each of the provided metadata elements, see the documentation for
get_metadata_nhgis().
Now that we have identified some of our options, we can go ahead and define an extract request to submit to the IPUMS API.
To create an extract definition containing the specifications for a
specific set of IPUMS NHGIS data, use
define_extract_nhgis().
When you define an extract request, you can specify the data to be included in the extract and indicate the desired format and layout.
Let’s say we’re interested in getting state-level data on the number
of farms and their average size from the 1900_cAg dataset
that we identified above. As we can see in the metadata, these data are
contained in tables NT2 and NT3:
cAg_meta$data_tables
#> # A tibble: 66 × 4
#> name nhgis_code description sequence
#> <chr> <chr> <chr> <int>
#> 1 NT1 AWS Total Population 1
#> 2 NT2 AW3 Number of Farms 2
#> 3 NT3 AXE Average Farm Size 3
#> 4 NT4 AXP Farm Acreage 4
#> 5 NT5 AXZ Farm Management 5
#> 6 NT6 AYA Race of Farmer 6
#> 7 NT7 AYJ Race of Farmer by Detailed Management 7
#> 8 NT8 AYK Number of Farms 8
#> 9 NT9 AYL Farms with Buildings 9
#> 10 NT10 AWT Acres of Farmland 10
#> # ℹ 56 more rowsTo request these data, we need to make an explicit dataset
specification. All datasets must be associated with a selection of
data tables and geographic levels. We can use the ds_spec()
helper function to specify our selections for these parameters.
ds_spec() bundles all the selections for a given dataset
together into a single object (in this case, a ds_spec
object):
dataset <- ds_spec(
"1900_cAg",
data_tables = c("NT1", "NT2"),
geog_levels = "state"
)
str(dataset)
#> List of 3
#> $ name : chr "1900_cAg"
#> $ data_tables: chr [1:2] "NT1" "NT2"
#> $ geog_levels: chr "state"
#> - attr(*, "class")= chr [1:3] "ds_spec" "ipums_spec" "list"This dataset specification can then be provided to the extract definition:
nhgis_ext <- define_extract_nhgis(
description = "Example farm data in 1900",
datasets = dataset
)
nhgis_ext
#> Unsubmitted IPUMS NHGIS extract
#> Description: Example farm data in 1900
#>
#> Dataset: 1900_cAg
#> Tables: NT1, NT2
#> Geog Levels: stateDataset specifications can also include selections for
years and breakdown_values, but these are not
available for all datasets.
Similarly, to make a request for time series tables, use the
tst_spec() helper. This makes a tst_spec
object containing a time series table specification.
Time series tables do not contain individual data tables, but do require a geographic level selection, and allow an optional selection of years:
define_extract_nhgis(
description = "Example time series table request",
time_series_tables = tst_spec(
"CW3",
geog_levels = c("county", "tract"),
years = c("1990", "2000")
)
)
#> Unsubmitted IPUMS NHGIS extract
#> Description: Example time series table request
#>
#> Time Series Table: CW3
#> Geog Levels: county, tract
#> Years: 1990, 2000Shapefiles don’t have any additional specification options, and therefore can be requested simply by providing their names:
An attempt to define an extract that does not have all the required specifications for a given dataset or time series table will throw an error:
define_extract_nhgis(
description = "Invalid extract",
datasets = ds_spec("1900_STF1", data_tables = "NP1")
)
#> Error in `validate_ipums_extract()`:
#> ! Invalid `ds_spec` specification:
#> ✖ `geog_levels` must not contain missing values.Note that it is still possible to make invalid extract requests (for instance, by requesting a dataset or data table that doesn’t exist). This kind of issue will be caught upon submission to the API, not upon the creation of the extract definition.
It’s possible to request data for multiple datasets (or time series
tables) in a single extract definition. To do so, pass a
list of ds_spec or tst_spec
objects in define_extract_nhgis():
define_extract_nhgis(
description = "Slightly more complicated extract request",
datasets = list(
ds_spec("2018_ACS1", "B01001", "state"),
ds_spec("2019_ACS1", "B01001", "state")
),
shapefiles = c("us_state_2018_tl2018", "us_state_2019_tl2019")
)
#> Unsubmitted IPUMS NHGIS extract
#> Description: Slightly more complicated extract request
#>
#> Dataset: 2018_ACS1
#> Tables: B01001
#> Geog Levels: state
#>
#> Dataset: 2019_ACS1
#> Tables: B01001
#> Geog Levels: state
#>
#> Shapefiles: us_state_2018_tl2018, us_state_2019_tl2019For extracts with multiple datasets or time series tables, it may be
easier to generate the specifications independently before creating your
extract request object. You can quickly create multiple
ds_spec objects by iterating across the specifications you
want to include. Here, we use {purrr} to do so, but you
could also use a for loop:
ds_names <- c("2019_ACS1", "2018_ACS1")
tables <- c("B01001", "B01002")
geogs <- c("county", "state")
# For each dataset to include, create a specification with the
# data tabels and geog levels indicated above
datasets <- purrr::map(
ds_names,
~ ds_spec(name = .x, data_tables = tables, geog_levels = geogs)
)
nhgis_ext <- define_extract_nhgis(
description = "Slightly more complicated extract request",
datasets = datasets
)
nhgis_ext
#> Unsubmitted IPUMS NHGIS extract
#> Description: Slightly more complicated extract request
#>
#> Dataset: 2019_ACS1
#> Tables: B01001, B01002
#> Geog Levels: county, state
#>
#> Dataset: 2018_ACS1
#> Tables: B01001, B01002
#> Geog Levels: county, stateThis workflow also makes it easy to quickly update the specifications
in the future. For instance, to add the 2017 ACS 1-year data to the
extract definition above, you’d only need to add
"2017_ACS1" to the ds_names variable. The
iteration would automatically add the selected tables and geog levels
for the new dataset. (This workflow works particularly well for ACS
datasets, which often have the same data table names across
datasets.)
IPUMS NHGIS extract definitions also support additional options to modify the layout and format of the extract’s resulting data files.
For extracts that contain time series tables, the
tst_layout argument indicates how the longitudinal data
should be organized.
For extracts that contain datasets with multiple breakdowns or data
types, use the breakdown_and_data_type_layout argument to
specify a layout . This is most common for data sources that contain
both estimates and margins of error, like the ACS.
File formats can be specified with the data_format
argument. IPUMS NHGIS currently distributes files in csv and fixed-width
format.
See the documentation for define_extract_nhgis() for
more details on these options.
Once you have defined an extract request, you can submit the extract for processing:
The workflow for submitting and monitoring an extract request and downloading its files when complete is described in the IPUMS API introduction.