# Microdata API Requests

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. 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 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.

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

    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 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

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.

    cps_samps <- get_sample_info("cps")

    head(cps_samps)
    #> # A tibble: 6 × 2
    #>   name        description
    #>   <chr>       <chr>
    #> 1 cps1962_03s IPUMS-CPS, ASEC 1962
    #> 2 cps1963_03s IPUMS-CPS, ASEC 1963
    #> 3 cps1964_03s IPUMS-CPS, ASEC 1964
    #> 4 cps1965_03s IPUMS-CPS, ASEC 1965
    #> 5 cps1966_03s IPUMS-CPS, ASEC 1966
    #> 6 cps1967_03s IPUMS-CPS, ASEC 1967

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:

    ipumsi_samps <- get_sample_info("ipumsi")

    ipumsi_samps %>%
      filter(grepl("Mexico", description))
    #> # A tibble: 71 × 2
    #>    name    description
    #>    <chr>   <chr>
    #>  1 mx1960a Mexico 1960
    #>  2 mx1970a Mexico 1970
    #>  3 mx1990a Mexico 1990
    #>  4 mx1995a Mexico 1995
    #>  5 mx2000a Mexico 2000
    #>  6 mx2005a Mexico 2005
    #>  7 mx2010a Mexico 2010
    #>  8 mx2015a Mexico 2015
    #>  9 mx2005h Mexico 2005 Q1 LFS
    #> 10 mx2005i Mexico 2005 Q2 LFS
    #> # ℹ 61 more rows

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 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 for more details.

## 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:

    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
    #> Unsubmitted IPUMS CPS extract
    #> Description: Example CPS extract
    #>
    #> Samples: (2 total) cps2018_03s, cps2019_03s
    #> Variables: (4 total) AGE, SEX, RACE, STATEFIP

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.

### 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:

    var <- var_spec("SEX", case_selections = "2")

    str(var)
    #> List of 3
    #>  $ name               : chr "SEX"
    #>  $ case_selections    : chr "2"
    #>  $ case_selection_type: chr "general"
    #>  - attr(*, "class")= chr [1:3] "var_spec" "ipums_spec" "list"

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:

    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")
      )
    )
    #> Unsubmitted IPUMS CPS extract
    #> Description: Case selection example
    #>
    #> Samples: (2 total) cps2018_03s, cps2019_03s
    #> Variables: (2 total) SEX, AGE

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:

    str(cps_extract$variables)
    #> List of 4
    #>  $ AGE     :List of 1
    #>   ..$ name: chr "AGE"
    #>   ..- attr(*, "class")= chr [1:3] "var_spec" "ipums_spec" "list"
    #>  $ SEX     :List of 1
    #>   ..$ name: chr "SEX"
    #>   ..- attr(*, "class")= chr [1:3] "var_spec" "ipums_spec" "list"
    #>  $ RACE    :List of 1
    #>   ..$ name: chr "RACE"
    #>   ..- attr(*, "class")= chr [1:3] "var_spec" "ipums_spec" "list"
    #>  $ STATEFIP:List of 1
    #>   ..$ name: chr "STATEFIP"
    #>   ..- attr(*, "class")= chr [1:3] "var_spec" "ipums_spec" "list"

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

    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:

    define_extract_micro(
      "cps",
      description = "Case selection example",
      samples = c("cps2018_03s", "cps2019_03s"),
      variables = list(
        var_spec("SEX", case_selections = "2"),
        "AGE"
      )
    )
    #> Unsubmitted IPUMS CPS extract
    #> Description: Case selection example
    #>
    #> Samples: (2 total) cps2018_03s, cps2019_03s
    #> Variables: (2 total) SEX, 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:

    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:

    var$case_selection_type
    #> [1] "general"

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"`:

    # General case selection is the default
    var_spec("RACE", case_selections = "8")
    #> $name
    #> [1] "RACE"
    #>
    #> $case_selections
    #> [1] "8"
    #>
    #> $case_selection_type
    #> [1] "general"
    #>
    #> attr(,"class")
    #> [1] "var_spec"   "ipums_spec" "list"

    # For detailed case selection, change the `case_selection_type`
    var_spec(
      "RACE",
      case_selections = c("811", "812"),
      case_selection_type = "detailed"
    )
    #> $name
    #> [1] "RACE"
    #>
    #> $case_selections
    #> [1] "811" "812"
    #>
    #> $case_selection_type
    #> [1] "detailed"
    #>
    #> attr(,"class")
    #> [1] "var_spec"   "ipums_spec" "list"

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 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:

    define_extract_micro(
      "usa",
      description = "Household level case selection",
      samples = "us2021a",
      variables = var_spec("RACE", case_selections = "8"),
      case_select_who = "households"
    )
    #> Unsubmitted IPUMS USA extract
    #> Description: Household level case selection
    #>
    #> Samples: (1 total) us2021a
    #> Variables: (1 total) RACE

### 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:

    var_spec("SEX", attached_characteristics = "spouse")
    #> $name
    #> [1] "SEX"
    #>
    #> $attached_characteristics
    #> [1] "spouse"
    #>
    #> attr(,"class")
    #> [1] "var_spec"   "ipums_spec" "list"

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:

    var_spec("AGE", attached_characteristics = c("mother", "father"))
    #> $name
    #> [1] "AGE"
    #>
    #> $attached_characteristics
    #> [1] "mother" "father"
    #>
    #> attr(,"class")
    #> [1] "var_spec"   "ipums_spec" "list"

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()`:

    var_spec("RACE", data_quality_flags = TRUE)
    #> $name
    #> [1] "RACE"
    #>
    #> $data_quality_flags
    #> [1] TRUE
    #>
    #> attr(,"class")
    #> [1] "var_spec"   "ipums_spec" "list"

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:

    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:

    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 and IPUMS USA 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:

    define_extract_micro(
      "atus",
      description = "Time use variable demo",
      samples = "at2017",
      time_use_variables = "ACT_PCARE"
    )
    #> Unsubmitted IPUMS ATUS extract
    #> Description: Time use variable demo
    #>
    #> Samples: (1 total) at2017
    #> Time Use Variables: (1 total) 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:

    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:

    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

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.

    define_extract_micro(
      "nhis",
      description = "NHIS hierarchical",
      samples = "ih2002",
      variables = c("REGION", "AGE", "SEX", "BMI"),
      data_structure = "hierarchical"
    )

See the IPUMS data reading vignette 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).

    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.

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.

    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:

    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.