Example dataset to get you started. This page documents a giberish dataset of five non-existant people and their preference for dogs and cake. You should delete this.

This page provides documentation for some nonsense data. A few things to note in the docs/example.yaml file which defines the dataset features on this page:

  1. Datatype guesses - Datadocs attempts to guess the datatype of each filed by reading the provided .csv file. If you want to ensure the proper datatypes is included, you can use the 'type' key when defining a field.
  2. Private fields - The field sex is set to private. A private field is noted with a lock icon, and signals to the reader that while the field exists, it may not be available to be shared with others.
  3. Transformed fields - The is_male_likes_cake is set to transformed. Transformation means the datapoint has some how been constructed. For example, in this case is_male_likes_cake is determined based on answers to the Sex and Likes cake fields. Transformed fields are noted by an "edit" icon.
Identifiers and demographics

Unique ID's and basic demographics for each individual in the dataset.

Field Description Type
ID Unique identifier Numeric
First name First name Categorical
Last name Last name Categorical
Sex Sex. Note this field is set to "private" Categorical
Date of birth Date the person was borned in form YYYY-MM-DD Date

Things this person likes.

Field Description Type
Likes cake Whether this person likes cake or not Boolean
Likes dogs Whether this person likes dogs or not Boolean
is_male_likes_cake Indicates whether this individual is both a male and likes cake. This is an example of a "tranformed" field. Boolean