Sample Data Model

All interactive examples in the TPL documentation use a sample employment survey dataset. This page explains the dimensions and measures available in the playground.

Sample Dataset

The playground uses an employment survey dataset (samples.csv) containing 6,639 records with demographic, employment, and income information. This is the same data used in all documentation examples.

Available Dimensions

Dimensions are categorical variables used for grouping data. The sample dataset defines these dimensions in a Malloy source file that transforms raw column values into meaningful labels.

education

Education level (3 categories) derived from years of education:

education is
  pick '<HS' when educ < 12
  pick 'HS' when educ = 12
  pick 'College' when educ >= 13
  else null

education_detail

A more detailed breakdown of education levels:

education_detail is
  pick '<HS' when educ < 12
  pick 'HS graduate' when educ = 12
  pick 'Some College' when educ >= 13 and educ <= 15
  pick 'College Grad' when educ = 16
  pick 'Some Graduate' when educ >= 17
  else null

gender

Gender from a character column (simple alias):

gender is gendchar

occupation

Occupation categories derived from occupation codes:

occupation is
  pick 'Managerial' when occup = 1
  pick 'Professional' when occup = 2
  pick 'Technical' when occup = 3
  pick 'Sales' when occup = 4
  pick 'Clerical' when occup = 5
  pick 'Services' when occup >= 6 and occup <= 8
  pick 'Manufacturing' when occup = 9 or occup = 10
  pick 'Transport' when occup = 11 or occup = 12
  pick 'Farming' when occup = 13 or occup = 14
  else null

employment

Full-time vs part-time status:

employment is
  pick 'Full-time' when fulltime = 2
  pick 'Part-time' when fulltime >= 3
  else null

sector_label

Public vs private sector employment:

sector_label is
  pick 'Private' when sector = 1 or sector = 5 or sector = 6
  pick 'Public' when sector >= 2 and sector <= 4
  else null

marital_status

Marital status categories:

marital_status is
  pick 'Married' when marital >= 1 and marital <= 3
  pick 'Widowed' when marital = 4
  pick 'Divorced/Sep.' when marital = 5 or marital = 6
  pick 'Never Married' when marital = 7
  else null

country

Country of origin:

country is
  pick 'North America' when ctry = 1
  pick 'South America' when ctry = 2
  pick 'Other' when ctry >= 3
  else null

union_status

Union membership:

union_status is
  pick 'Non-Union' when `union` = 1
  pick 'Union' when `union` = 2
  else null

Available Measures

Measures are numeric columns that can be aggregated. The sample dataset includes these measure columns:

income

Annual income in dollars. Supports aggregations:

• income.sum - Total income
• income.mean - Average income
• income.min - Minimum income
• income.max - Maximum income
• income.p25, income.p50, income.p75 - Percentiles

hourly

Hourly wage rate. Supports aggregations:

• hourly.sum - Total of hourly rates
• hourly.avg - Average hourly rate
• hourly.min, hourly.max - Range

sat

Satisfaction score. Supports aggregations:

• sat.sum - Total satisfaction
• sat.avg - Average satisfaction

n (count)

Record count is available via the special n measure:

• n - Count of records

Using Dimensions in Your Queries

When working with the playground, you can use any of these dimensions and measures directly:

Try It

TABLE ROWS occupation COLS education * income.sum;

Dataset:samplesoccupation, education, gender, income

Defining Your Own Dimensions

For your own data, define dimensions using Malloy's pick syntax via .extend():

import { fromCSV } from 'tplm-lang'

const tpl = fromCSV('employees.csv').extend(`
  dimension:
    region is
      pick 'North' when region_code = 1
      pick 'South' when region_code = 2
      else 'Other'
    region_order is region_code  // for definition-order sorting
`)

const { html } = await tpl.query('TABLE ROWS region * sales.sum COLS quarter;')

Condition Syntax

Conditions can use various comparison operators:

Syntax	Meaning
= value	Equal to value
< value	Less than value
> value	Greater than value
<= value	Less than or equal
>= value	Greater than or equal
and	Combine conditions
or	Alternative conditions

Simple Aliases

If a column already contains the labels you want, use a simple alias:

const tpl = fromCSV('data.csv').extend(`
  dimension: department is dept_name
`)

Next Steps

• Quick Start - Get started with TPL
• Basic Crosstab - Your first table
• Playground - Experiment with the sample data