Data

Below we show metadata for the entire IRW, an example dataset, and illustrations of how to access the data programmatically. You can also explore the data here.

Metadata

Individual dataset

summaries_map = new Map(Object.entries(summaries))
viewof dataset = Inputs.select(summaries_map, {label: 'Dataset'})

// Function to find the key that corresponds to the selected dataset
function findDatasetNameFromMap(map, selectedDataset) {
  for (let [key, value] of map.entries()) {
    if (value === selectedDataset) {
      return key; 
    }
  }
  return null;
}

// Look up the name of the dataset and set up the URL
dataset_name = findDatasetNameFromMap(summaries_map, dataset);
dataset_url = "https://redivis.com/embed/tables/datapages.item_response_warehouse:as2e:current." + dataset_name

html`<iframe id="myIframe" width="800" height="500" allowfullscreen style="border:0;" src = "${dataset_url}"></iframe>`

Programmatic access

You can also access IRW data programmatically using the Redivis API for R or Python (note that you will first need to generate and set an API token). For example:

R

# first install redivis package: devtools::install_github("redivis/redivis-r", ref="main")

# individual dataset
dataset <- redivis::user("datapages")$dataset("item_response_warehouse")
df <- dataset$table("4thgrade_math_sirt")$to_tibble()

# metadata
project <- redivis::user("mikabr")$project("irw")
metadata <- project$table("metadata_output")$to_tibble()

Python

import redivis

# individual dataset
dataset = redivis.user('datapages').dataset('item_response_warehouse')
df = dataset.table('4thgrade_math_sirt').to_pandas_dataframe()

# metadata
project = redivis.user('mikabr').project('irw')
metadata = project.table('metadata_output').to_pandas_dataframe()