---
title: "Microdata API Requests"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Microdata 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
)
```

This vignette details the options available for requesting data from IPUMS
microdata projects 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.

## Supported microdata collections

IPUMS provides several data collections that are classified as microdata. 
Currently, the following microdata collections are supported by the IPUMS 
API (shown with the codes used to refer to them in ipumsr):

* IPUMS USA (`"usa"`)
* IPUMS CPS (`"cps"`)
* IPUMS International (`"ipumsi"`)
* IPUMS Time Use
    * IPUMS ATUS (`"atus"`)
    * IPUMS AHTUS (`"ahtus"`)
    * IPUMS MTUS (`"mtus"`)
* IPUMS Health Surveys
    * IPUMS NHIS (`"nhis"`)
    * IPUMS MEPS (`"meps"`)

API support will continue to be added for more collections in the future. 
See the [API documentation](https://developer.ipums.org/docs/apiprogram/)
for more information on upcoming additions to the API.

In addition to microdata projects, the IPUMS API also supports IPUMS aggregate
data projects. For details about obtaining IPUMS aggregate data using ipumsr, 
see the [aggregate data vignette](ipums-api-agg.html).

Before getting started, we'll load ipumsr and `{dplyr}`, which will be 
helpful for this demo:

```{r, message=FALSE}
library(ipumsr)
library(dplyr)
```

## Basic IPUMS microdata concepts

Every microdata extract definition must contain a set of requested _samples_
and _variables_.

In an IPUMS microdata collection, the term _sample_ is used to refer to a 
distinct dataset derived from a census or survey (or in some cases, such as the 
American Community Survey 5-year samples, multiple surveys). Each sample can be
thought of as a combination of records and variables. A record is a set of 
values that describe the characteristics of a single unit of observation (e.g. a
person or a household), and _variables_ define the characteristics 
that were observed.

A sample can contain multiple record types (e.g. person records, 
household records, or activity records). See the 
[section on data structure](#data-structure) below for options regarding how to 
combine multiple record types in your extract data file.

Note that our usage of 
the term "sample" does not correspond perfectly to the statistical sense of a 
subset of individuals from a population. Many IPUMS samples are samples in 
the statistical sense, but some are "full-count" samples, meaning they contain 
all individuals in a population.

## IPUMS microdata metadata

```{r, echo=FALSE, results="hide", message=FALSE}
insert_cassette("micro-sample-ids")
```

Of course, to request samples and variables, we have to know 
the codes that the API uses to refer to them. For samples, the IPUMS API uses 
special codes that don't appear in the web-based extract builder. For variables,
the API uses the same variable names that appear on the web.

While the IPUMS API does not yet provide a comprehensive set of metadata 
endpoints for IPUMS microdata collections, users can use the `get_sample_info()`
function to identify the codes used to refer to specific samples when 
communicating with the API.

```{r}
cps_samps <- get_sample_info("cps")

head(cps_samps)
```

The values listed in the `name` column correspond to the code that you would 
use to request that sample when creating an extract definition to be submitted 
to the IPUMS API.

We can use basic functions from dplyr to 
filter the metadata to samples of
interest. For instance, to find all IPUMS International samples for Mexico, we
could do the following:

```{r}
ipumsi_samps <- get_sample_info("ipumsi")

ipumsi_samps %>%
  filter(grepl("Mexico", description))
```

IPUMS intends to add support for accessing variable metadata via API 
in the future. Until then, use the web-based extract builder for a given 
collection to find variable names and availability by sample. 
See the 
[IPUMS API documentation](https://developer.ipums.org/docs/v2/workflows/explore_metadata/microdata/)
for links to the extract builder for each microdata collection with API support.

Alternatively, if you have made an extract previously through the web 
interface, you can use `get_extract_info()` to identify the variable names it 
includes. See the [IPUMS API introduction](ipums-api.html) for more 
details.

```{r, echo=FALSE, results="hide", message=FALSE}
eject_cassette()
```

## Defining an IPUMS microdata extract request

Define an extract for an IPUMS microdata collection with
`define_extract_micro()`. When you define an extract request, you 
specify the collection for the extract, the data to be included, and
the desired format and layout.

A simple extract definition needs only to contain the IPUMS collection
along with the names of the samples and variables to include in the request:

```{r}
cps_extract <- define_extract_micro(
  collection = "cps",
  description = "Example CPS extract",
  samples = c("cps2018_03s", "cps2019_03s"),
  variables = c("AGE", "SEX", "RACE", "STATEFIP")
)

cps_extract
```

This produces an `ipums_extract` object containing the extract request
specifications that is ready to be submitted to the IPUMS API.

When you request a variable in your extract definition, the resulting data 
extract will include that variable for all requested samples where it is 
available. If you request a variable that is not available for any requested 
samples, the IPUMS API will throw an informative error when you try to submit 
your request.

## Detailed variable specifications

You can refine your extract request by providing detailed 
specification options for individual variables included in the request, like
case selections, attached characteristics, and data quality flags. Note that not 
all variable-level options are available across all IPUMS data collections. For 
a summary of supported features by collection, see the [IPUMS API documentation](https://developer.ipums.org/docs/v2/apiprogram/apis/microdata/).

### Syntax

To add any of these options to a variable, we need to 
introduce the `var_spec()` helper function.

`var_spec()` bundles all the selections for a given variable together into a 
single `var_spec` object:

```{r}
var <- var_spec("SEX", case_selections = "2")

str(var)
```

To include this specification in our extract, we simply provide it to the
`variables` argument of our extract definition. When multiple variables are
included, pass a `list` of `var_spec` objects:

```{r}
define_extract_micro(
  "cps",
  description = "Case selection example",
  samples = c("cps2018_03s", "cps2019_03s"),
  variables = list(
    var_spec("SEX", case_selections = "2"),
    var_spec("AGE", attached_characteristics = "head")
  )
)
```

In fact, if you inspect our original extract object from above, you'll 
notice that the variables have automatically been converted to 
`var_spec` objects, even though they were provided as character vectors:

```{r}
str(cps_extract$variables)
```

So, a `var_spec` object with no additional specifications will produce
the default data for a given variable. That is, the following are equivalent:

```{r, eval=FALSE}
define_extract_micro(
  "cps",
  description = "Example CPS extract",
  samples = "cps2018_03s",
  variables = "AGE"
)

define_extract_micro(
  "cps",
  description = "Example CPS extract",
  samples = "cps2018_03s",
  variables = var_spec("AGE")
)
```

Because all specified variables are converted to `var_spec` objects, you can 
also pass a list where some elements are `var_spec` objects and some are just 
variable names. This is convenient when you only have detailed specifications 
for a subset of variables:

```{r}
define_extract_micro(
  "cps",
  description = "Case selection example",
  samples = c("cps2018_03s", "cps2019_03s"),
  variables = list(
    var_spec("SEX", case_selections = "2"),
    "AGE"
  )
)
```

Now that we've covered the basic syntax for including detailed variable
specifications, we can describe the available options in more depth.

### Case selections

Case selections allow us to limit the data to those
records that match a particular value on the specified variable.

For instance, the following specification would indicate that
only records with a value of `"27"` (Minnesota) or 
`"19"` (Iowa) for the variable `"STATEFIP"` should be included:

```{r}
var <- var_spec("STATEFIP", case_selections = c("27", "19"))
```

Some variables have versions with both general and detailed coding schemes.
By default, case selections are assumed to refer to the general codes:

```{r}
var$case_selection_type
```

For variables with detailed versions, you can also select on the detailed codes. 

For instance, the IPUMS USA variable RACE is available in both general and detailed 
versions. If you wanted to limit your extract to persons identifying as 
"Two major races", you could do so by specifying a case selection of `"8"`.
However, if you wanted to limit your extract to only persons identifying as 
"White and Chinese" or "White and Japanese", you would need to specify 
_detailed_ codes `"811"` and `"812"`.

To include case selections for detailed codes, set 
`case_selection_type = "detailed"`:

```{r}
# General case selection is the default
var_spec("RACE", case_selections = "8")
```

```{r}
# For detailed case selection, change the `case_selection_type`
var_spec(
  "RACE",
  case_selections = c("811", "812"),
  case_selection_type = "detailed"
)
```


As noted above, IPUMS intends to add support for accessing variable metadata via API 
in the future, such that users will be able to query variable coding schemes 
right from their R sessions.
Until then, use the IPUMS web interface for a given collection to 
find general and detailed variable codes for the purposes of case selection. 
See the 
[IPUMS API documentation](https://developer.ipums.org/docs/v2/workflows/explore_metadata/microdata/) 
for relevant links.

By default, case selection on person-level variables produces a data file that 
includes only those individuals who match the specified 
values for the specified variables. It's also possible to use case selection to 
include matching individuals _and_ all other members of their households, using 
the `case_select_who` parameter.

The `case_select_who` parameter must be the same for all case selections in an 
extract, and thus is set at the extract level rather than the `var_spec` level. 
To include all household members of matching individuals,
set `case_select_who = "households"` in the extract definition:

```{r}
define_extract_micro(
  "usa",
  description = "Household level case selection",
  samples = "us2021a",
  variables = var_spec("RACE", case_selections = "8"),
  case_select_who = "households"
)
```

### Attached characteristics

IPUMS allows users to create variables that reflect the characteristics of other 
household members. To do so, use the `attached_characteristics` argument of
`var_spec()`.

For instance, to attach the spouse's `SEX` value to a record:

```{r}
var_spec("SEX", attached_characteristics = "spouse")
```

This will add a new variable called `SEX_SP` to the output data that 
will contain the sex of a person's spouse. Person records without a spouse in 
the household will have a missing value for variable `SEX_SP`.

Multiple attached characteristics can be attached for a single variable:

```{r}
var_spec("AGE", attached_characteristics = c("mother", "father"))
```

Acceptable values are `"spouse"`, `"mother"`, `"father"`, and `"head"`. For data
collections with information on same-sex couples, specifying `"mother"` or
`"father"` will attach the characteristics of both mothers or both fathers for 
children with same-sex parents.

### Data quality flags

Some IPUMS variables have been edited for missing, illegible, and 
inconsistent values. Data quality flags indicate which values are edited
or allocated.

To include data quality flags for an individual variable, use the 
`data_quality_flags` argument to `var_spec()`:

```{r}
var_spec("RACE", data_quality_flags = TRUE)
```

This will produce a new variable (`QRACE`) containing the data quality flag for 
the given variable.

To add data quality flags for all variables that have them, set
`data_quality_flags = TRUE` in your extract definition directly:

```{r}
usa_extract <- define_extract_micro(
  "usa",
  description = "Data quality flags",
  samples = "us2021a",
  variables = list(
    var_spec("RACE", case_selections = "8"),
    var_spec("AGE")
  ),
  data_quality_flags = TRUE
)
```

Each data quality flag corresponds to one or more variables, and the codes for 
each flag vary based on the sample. See the documentation for the IPUMS
collection of interest for more information about data quality flag codes.

### Monetary value adjustment

IPUMS CPS and IPUMS USA offer the option to standardize income or other 
dollar values to 2010 dollars. This option both retains the original IPUMS 
variable in your extract and adds a new, adjusted variable called 
`{VARIABLE NAME}_cpiu_2010` that contains the inflation-adjusted values. 
Inflation adjusted values are only available for continuous variables that 
represent dollar amounts.

To request an adjusted variable in an extract, use the `adjust_monetary_values`
argument:

```{r}
#| eval: false
define_extract_micro(
  "cps",
  description = "monetary value adjustment example",
  samples = "cps2012_03s",
  variables = var_spec("HOURWAGE", adjust_monetary_values = TRUE)
)
```

Further information on monetary value adjustment can be found on 
the [IPUMS CPS](https://cps.ipums.org/cps/adjusted_monetary_values.shtml) 
and [IPUMS USA](https://usa.ipums.org/usa/adjusted_monetary_values.shtml) 
websites.

## Time use variables

For IPUMS Time Use collections (ATUS, AHTUS, and MTUS), users can request
time use variables using the `time_use_variables` argument.

For IPUMS-defined time use variables, simply provide the name:

```{r}
define_extract_micro(
  "atus",
  description = "Time use variable demo",
  samples = "at2017",
  time_use_variables = "ACT_PCARE"
)
```

You can also request time use variables that you have defined yourself using the
online extract builder. In this case, use the `tu_var_spec()` helper to provide
the time use variable name and your user email to identify the account the
variable was created under:

```{r, eval=FALSE}
define_extract_micro(
  "atus",
  description = "Time use variable demo",
  samples = "at2017",
  time_use_variables = tu_var_spec("MYTUVAR", owner = "user@example.com")
)
```

To request multiple user-defined time use variables or a combination of 
IPUMS-defined and user-defined time use variables, pass a `list` to the 
`time_use_variables` argument:

```{r, eval=FALSE}
define_extract_micro(
  "atus",
  description = "Time use variable demo",
  samples = "at2017",
  time_use_variables = list(
    "ACT_PCARE",
    tu_var_spec("MYTUVAR", owner = "user@example.com")
  )
)
```


## Data structure {#data-structure}

By default, microdata extract definitions will request data in a 
rectangular-on-persons data structure and a fixed-width file format. 

Rectangular-on-persons data are data where only person records are included, and 
household-level variables are converted to person-level variables by copying the 
values from the associated household record onto all household members.

To instead create a hierarchical extract, which includes separate records for 
each record type present in the data, set `data_structure = "hierarchical"` in 
your extract definition. 

```{r eval=FALSE}
define_extract_micro(
  "nhis",
  description = "NHIS hierarchical",
  samples = "ih2002",
  variables = c("REGION", "AGE", "SEX", "BMI"),
  data_structure = "hierarchical"
)
```


See the 
[IPUMS data reading vignette](ipums-read.html#hierarchical-extracts) 
for more information about loading hierarchical data into R.

While all microdata collections provide data in rectangular-on-persons and
hierarchical data structures, some collections provide data in other rectangular
structures. To request data in a different rectangular structure, set the 
`rectangular_on` argument in your extract definition to `"A"` 
(rectangular-on-activity), `"I"` (rectangular-on-injury), or `"R"` 
(rectangular-on-round). 

```{r eval=FALSE}
define_extract_micro(
  "meps",
  description = "MEPS rectangular-on-round",
  samples = "mp2021",
  variables = c("INCCHLD", "AGERD", "MARSTATRD"),
  rectangular_on = "R"
)
```

For a summary of rectangular structures available by 
collection, see the 
[IPUMS API documentation](https://developer.ipums.org/docs/v2/apiprogram/apis/microdata/).

Finally, for extracts containing only household-level variables, IPUMS USA can 
provide data containing only household records. To request a household-only 
data file, set `data_structure = "household_only"` in your IPUMS USA extract 
definition.

```{r eval=FALSE}
define_extract_micro(
  "usa",
  description = "USA household only",
  samples = "us2022a",
  variables = "STATEFIP",
  data_structure = "household_only"
)
```


## Data file format

By default, microdata extract definitions will request data in a fixed-width 
file format. 

To request a file format other than fixed-width, adjust the `data_format` 
argument in your call to `define_extract_micro()`. Note that
while you can request data in a variety of formats (Stata, SPSS, etc.), 
ipumsr's `read_ipums_micro()` function only supports fixed-width and csv files.

## Next steps

Once you have defined an extract request, you can submit the extract for
processing:

```{r, eval=FALSE}
usa_extract_submitted <- submit_extract(usa_extract)
```

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).
