---
title: "NHGIS API Requests"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{NHGIS API Requests}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r, echo=FALSE, results="hide"}
library(vcr)
vcr_dir <- "fixtures"
have_api_access <- TRUE
if (!nzchar(Sys.getenv("IPUMS_API_KEY"))) {
if (dir.exists(vcr_dir) && length(dir(vcr_dir)) > 0) {
# Fake API token to fool ipumsr API functions
Sys.setenv("IPUMS_API_KEY" = "foobar")
} else {
# If there are no mock files nor API token, can't run API tests
have_api_access <- FALSE
}
}
vcr_configure(
filter_sensitive_data = list(
"<<<IPUMS_API_KEY>>>" = Sys.getenv("IPUMS_API_KEY")
),
write_disk_path = vcr_dir,
dir = vcr_dir
)
check_cassette_names()
# We do not expose detailed pagination options to users, but we do not want
# to save a full record of summary metadata in a .yml fixture for this
# vignette. This helper allows us to request just a few records, which
# we pretend is the full set of records for the purposes of the vignette.
get_truncated_metadata <- function(collection,
type,
page_size = 10,
max_pages = 1,
api_key = Sys.getenv("IPUMS_API_KEY")) {
url <- ipumsr:::api_request_url(
collection = collection,
path = ipumsr:::metadata_request_path(collection, type),
queries = list(pageNumber = 1, pageSize = page_size)
)
responses <- ipumsr:::ipums_api_paged_request(
url = url,
max_pages = max_pages,
delay = 0,
api_key = api_key
)
metadata <- purrr::map_dfr(
responses,
function(res) {
content <- jsonlite::fromJSON(
httr::content(res, "text"),
simplifyVector = TRUE
)
content$data
}
)
# Recursively convert all metadata data.frames to tibbles and all
# camelCase names to snake_case
ipumsr:::convert_metadata(metadata)
}
```
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](ipums-api.html). 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](ipums-api-micro.html).
Before getting started, we'll load ipumsr and some helpful packages for
this demo:
```{r, message=FALSE}
library(ipumsr)
library(dplyr)
library(purrr)
```
## Basic IPUMS NHGIS concepts
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.
## IPUMS NHGIS metadata
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.
### Summary metadata
```{r, echo=FALSE, results="hide", message=FALSE}
insert_cassette("nhgis-metadata-summary")
```
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.
```{r}
ds <- get_metadata_nhgis(type = "datasets")
head(ds)
```
We 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`:
```{r}
ds %>%
filter(
group == "1900 Census",
grepl("Agriculture", description)
)
```
The 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:
```{r, echo=FALSE, results="hide", message=FALSE}
# Secretly get truncated number of tst records because otherwise the .yml
# fixture becomes very large.
# Make sure that any code that uses this metadata is consistent with the output
# that would be obtained were the entire metadata set loaded!
tst <- get_truncated_metadata("nhgis", "time_series_tables")
```
```{r, eval=FALSE}
tst <- get_metadata_nhgis("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:
```{r}
head(tst)
```
Note that for time series tables, some metadata fields are stored in
list columns, where each entry is itself a data frame:
```{r}
tst$years[[1]]
tst$geog_levels[[1]]
```
To 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:
```{r}
# Iterate over each `years` entry, identifying whether that entry
# contains "1840" in its `name` column.
tst %>%
filter(map_lgl(years, ~ "1840" %in% .x$name))
```
For more details on working with nested data frames, see this
[tidyr article](https://tidyr.tidyverse.org/articles/nest.html).
```{r, echo=FALSE, results="hide", message=FALSE}
eject_cassette("nhgis-metadata-summary")
```
### Detailed metadata
```{r, echo=FALSE, results="hide", message=FALSE}
insert_cassette("nhgis-metadata-detailed")
```
Once we have identified a data source of interest, we can find out more
about its detailed options by providing its name to the corresponding
argument of `get_metadata_nhgis()`:
```{r}
cAg_meta <- get_metadata_nhgis(dataset = "1900_cAg")
```
This provides a comprehensive list of the possible specifications for
the input data source. For instance, for the `1900_cAg` dataset, we have
66 tables to choose from, and 3 possible geographic levels:
```{r}
cAg_meta$data_tables
cAg_meta$geog_levels
```
You can also get detailed metadata for an individual data table. Since
data tables belong to specific datasets, both need to be specified to
identify a data table:
```{r}
get_metadata_nhgis(dataset = "1900_cAg", data_table = "NT2")
```
Note that the `name` element is the one that contains the codes used for
interacting with the IPUMS API. The `nhgis_code` element refers to the
prefix attached to individual variables in the output data, and the API
will throw an error if you use it in an extract definition. For more
details on interpreting each of the provided metadata elements, see the
documentation for `get_metadata_nhgis()`.
Now that we have identified some of our options, we can go ahead and
define an extract request to submit to the IPUMS API.
```{r, echo=FALSE, results="hide", message=FALSE}
eject_cassette("nhgis-metadata-detailed")
```
## Defining an IPUMS NHGIS extract request
To create an extract definition containing the specifications for a
specific set of IPUMS NHGIS data, use `define_extract_nhgis()`.
When you define an extract request, you can specify the data to be
included in the extract and indicate the desired format and layout.
### Basic extract definitions
Let's say we're interested in getting state-level data on the number of
farms and their average size from the `1900_cAg` dataset that we
identified above. As we can see in the metadata, these data are
contained in tables `NT2` and `NT3`:
```{r}
cAg_meta$data_tables
```
#### Dataset specifications
To 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):
```{r}
dataset <- ds_spec(
"1900_cAg",
data_tables = c("NT1", "NT2"),
geog_levels = "state"
)
str(dataset)
```
This dataset specification can then be provided to the extract
definition:
```{r}
nhgis_ext <- define_extract_nhgis(
description = "Example farm data in 1900",
datasets = dataset
)
nhgis_ext
```
Dataset specifications can also include selections for `years` and
`breakdown_values`, but these are not available for all datasets.
#### Time series table specifications
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:
```{r}
define_extract_nhgis(
description = "Example time series table request",
time_series_tables = tst_spec(
"CW3",
geog_levels = c("county", "tract"),
years = c("1990", "2000")
)
)
```
#### Shapefile specifications
Shapefiles don't have any additional specification options, and
therefore can be requested simply by providing their names:
```{r}
define_extract_nhgis(
description = "Example shapefiles request",
shapefiles = c("us_county_2021_tl2021", "us_county_2020_tl2020")
)
```
#### Invalid specifications
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:
```{r, error=TRUE, purl=FALSE}
define_extract_nhgis(
description = "Invalid extract",
datasets = ds_spec("1900_STF1", data_tables = "NP1")
)
```
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.
### More complicated extract definitions
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()`:
```{r}
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")
)
```
For 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:
```{r}
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
```
This 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.)
### Data layout and file format
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.
## Next steps
Once you have defined an extract request, you can submit the extract for
processing:
```{r, eval=FALSE}
nhgis_ext_submitted <- submit_extract(nhgis_ext)
```
The workflow for submitting and monitoring an extract request and
downloading its files when complete is described in the [IPUMS API
introduction](ipums-api.html).