Access and Analyze Data from the Item Response Warehouse

Find, download, and analyze datasets from the Item Response Warehouse, a harmonized collection of item response data from educational and psychological measurement described in Nadela et al. (2026) . Functions search tables by metadata, fetch them in a common long format, reshape between long and wide layouts, simulate item response data, and compare the predictive accuracy of fitted models with the InterModel Vigorish.

irw: R Package for the Item Response Warehouse

This repository hosts the R package irw, which provides programmatic access to the Item Response Warehouse (IRW), an open repository of harmonized item response data.

Documentation for all available functions can be found at: https://itemresponsewarehouse.github.io/Rpkg/reference/index.html.

Project map: ARCHITECTURE.md in ben-domingue/irw — which repo owns what, where the data lives, and which document is authoritative when two disagree.

Installation

# Install the development version of irw from GitHub
# install.packages("pak")  # if you don't already have pak
pak::pak("itemresponsewarehouse/Rpkg")

# Load the package
library(irw)

Downloading tables also needs redivis, the client for the warehouse itself. It is not on CRAN and pak does not install it for you; get it from r-universe:

install.packages("redivis", repos = c("https://redivis.r-universe.dev", getOption("repos")))

The functions that do not touch the warehouse --- irw_simdata(), irw_simdata_comp(), irw_simu_diff(), irw_imv(), irw_predict(), irw_long2resp(), irw_resp2long(), irw_check_resp() and irw_covariates() --- work without it.

IMPORTANT: Redivis Authentication

The IRW tables are hosted on Redivis, a data management platform. To access these datasets, you'll need to:

1. Have a Redivis account (create one at https://redivis.com/?createAccount if you don't have one).

2. Authenticate using the Redivis R Client:

   1. When you first use a function in irw that connects to Redivis (e.g. irw_info()), a browser window will open, prompting you to sign in to your Redivis account.

   2. After signing in, click Allow to grant access for the Redivis R Client.

   3. Once authentication is successful, close the browser window. You will see the message “Authentication was successful” in the R console.

Note: You only need to authenticate once per session. For detailed instructions, refer to the Redivis R Client documentation.

In a script, a scheduled job or another non-interactive session, irw does not open a browser. If no login is cached it stops and asks you to sign in once from an interactive R session (the login is then reused) or to set the REDIVIS_API_TOKEN environment variable. irw_has_credentials() reports whether a login is available.

Usage Examples

# Get information about the IRW
irw_info() 

# View list of available tables in IRW
irw_list_tables()

# Filter tables containing the variable `rt`
irw_filter(var = "rt")

# fetch an IRW table
x = irw_fetch("4thgrade_math_sirt") 

# summarize a table without downloading it: item and response value sets,
# and optionally per-item counts and response ranges
s = irw_table_sets("4thgrade_math_sirt", per_item = TRUE)
s$items
s$resp

# read the whole corpus as one IRW version held it: the reproducibility switch
irw_use_version(332)                                 # everything below is frozen
df <- irw_fetch("gilbert_meta_12")                    # v332's copy, today and next year

irw_use_version()                                    # freeze today; record the number
irw_reset_version()                                  # back to the current release

IRW is eleven Redivis datasets versioned independently, so no single Redivis version describes the corpus. The IRW version number does: it increments whenever any dataset is published and names one exact combination of the eleven, so a paper can cite it and a reader can reproduce the run.

Put irw_use_version() at the top of an analysis script and every irw_fetch() below it is pinned — including metadata and item text — so the script returns the same data after IRW is corrected or extended. A table added later is an error rather than a quiet fetch from the current release.

# look a version up without reading it
irw_version()                                        # newest IRW version + its pins
irw_version(version = 332)                           # exactly what v332 held
irw_version("2026-08-01")                            # what was live on a date

# or pin one dataset at a time
irw_get_version()                                    # versions currently in use
irw_set_version("item_response_warehouse", "v45.1")

Cite the number, not the date. Date lookups before 21 July 2026 are approximate and say so — Redivis overwrote its own release timestamps for the older warehouse shards during a platform migration, so those versions can only be bracketed, and a date can resolve to the wrong version. IRW version numbers themselves are always exact.

Troubleshooting

Export Quota

irw_fetch() downloads every row of a table. Redivis caps the bytes an account can export in a rolling 30-day window, and one pass over the whole IRW corpus comes close to that cap on its own. If you only need to know which items or response values a table contains, use irw_table_sets(): it answers with a server-side query, returns in seconds even for tables with tens of millions of rows, and does not count against the export quota.

If the quota is exhausted, irw_fetch() reports it as a quota error rather than a missing table.

Authentication Issues

• No Browser Pop-up: If the browser window doesn't open automatically, check your pop-up blocker settings.

• Authentication errors: If you see an authentication error, try clearing your browser cookies and attempting again.

Feedback and Contributions

If you encounter issues or have suggestions for improving irw, please submit them on the GitHub Issues page. Contributions are welcome!

Releasing

Releases are cut from the Actions tab, not by hand:

1. Run release-prepare.yaml and choose patch, minor or major. It bumps Version: in DESCRIPTION and writes the NEWS.md entry from the pull requests merged since the last release tag, then opens a Release irw X.Y.Z pull request.
2. Tidy the generated NEWS.md on that branch if the PR titles need editing, then merge it.
3. release-publish.yaml sees the new version land on main, tags vX.Y.Z, and publishes a GitHub release using that NEWS.md section. The pkgdown site rebuilds on the published release.

Nothing else needs a version number, so DESCRIPTION and NEWS.md cannot drift apart. A hand-edited version bump merged to main still gets tagged and released; only the changelog would then be your problem.