1.1 Installing and loading data🔗ℹ

Use Racket 9.3 or later. Install the core package from the Racket catalog:

  raco pkg install --auto datasets-core

From a checkout of the repository, use raco pkg install ./datasets-core instead.

The repository contains two independently installable packages at its root: datasets-core/ and datasets/. Each has an info.rkt declaring a multi-collection package. They contribute different modules to the same datasets collection; package names and module paths need not match. The core package provides datasets/core.

Dataset identifiers are symbols, such as 'iris; column names are strings, such as "sepal-length". Loading uses bundled files and does not contact a server. There is no need to install R or run an import script.

1.2 Working with columns🔗ℹ

Use dataset-table-column to obtain a column, then ordinary vector operations to inspect its values. A vector index is zero-based.

A selection keeps the source rows and places columns in the order you request:

The column vectors and their strings are immutable. Keep derived results in new values rather than changing the bundled observations. Empty selections, duplicate names, and unknown names are errors; see Registry and loading for the selection rules.

1.3 Recognizing missing observations🔗ℹ

The airquality dataset contains missing ozone and solar-radiation measurements. A missing observation is represented by dataset-missing, a dedicated value recognized by dataset-missing?. It differs from #f, zero, and +nan.0.

Make any filtering decision explicit. For example, the following collects only observed ozone values; it does not modify the original table:

1.4 Reading metadata and category levels🔗ℹ

dataset-info returns an immutable hash whose keys are strings. Its "columns" entry is a list in source column order. Each entry describes one variable, including its normalized name, original spelling, semantic type, units, missing-value count, and category levels.

Labels remain strings in the table. The level list records their intended order; it does not assert that a categorical variable is ordinal. Metadata on a selected table still describes the complete source dataset. See Metadata fields for the field definitions.

1.5 Counting people and indexing months🔗ℹ

A row does not always represent one person. Titanic contains 32 contingency cells, each combining a class, sex, age group, and survival outcome. Its "frequency" column counts people, including zero for empty cells:

AirPassengers instead has one row per calendar month. Year and month are explicit columns, and passenger counts are in thousands:

The Dataset Catalog and Provenance records these conventions alongside source citations and transformation notes. Consult it before interpreting a variable or comparing a dataset with another library’s version.