Creating Multidim Charts

A multidim (multi-dimensional chart) is an interactive chart with dropdown selectors for different dimensions of the data. It can be created from scratch or by combining existing charts. For example, a single multidim with a Sex dropdown showing life expectancy for males or females.

Overview

A multidim requires three things:

1. A Python step file (minimal boilerplate)
2. A config YAML file (dimensions, views, chart settings)
3. A DAG entry in the appropriate dag/*.yml file

All files live in etl/steps/viz/chart/{namespace}/latest/.

Step-by-Step Process

Step 1: Identify the indicators

If the user provides chart URLs, fetch their metadata to discover the indicator names and catalog paths. If creating from scratch, find the relevant grapher dataset and its indicators.

# Get indicator shortNames and structure
https://ourworldindata.org/grapher/{chart-slug}.metadata.json

# Get the full catalogPath for each indicator (from fullMetadata URL in above response)
https://api.ourworldindata.org/v1/indicators/{id}.metadata.json

Reference indicators by the short {table}#{variable_name} form (e.g. child_labor#share_child_labor__sex_total__age_5_17). PathFinder resolves the namespace/version/dataset from the step's DAG dependency, so the config never hardcodes the version — when the dataset version bumps, only the DAG entry changes. See etl/steps/viz/chart/wid/latest/wealth_wid.config.yml for a real example.

The full form grapher/{namespace}/{version}/{dataset}/{table}#{variable_name} is valid too, but only reach for it to disambiguate when two DAG dependencies both contain a table of the same name. Never hardcode the version just to "be explicit" — it rots on the next update.

Look at the indicator shortNames to identify the dimensional structure. For example:

• life_expectancy__sex_female__age_0__type_period → dimensions: sex, age
• weekly_cases vs weekly_deaths → dimension: indicator (cases/deaths)

Step 2: Design the dimensions

Decide which aspects become dropdown dimensions vs. multi-line indicators on a single chart.

As separate views (dropdown dimension): When switching between them changes what the chart is about. Example: toggling between Males and Females.

As multiple y-indicators on one chart: When all values should be visible simultaneously for comparison. Example: life expectancy at different ages (birth, 10, 25, 65) shown as separate lines on one chart.

Step 3: Create the files

Directory structure

etl/steps/viz/chart/{namespace}/latest/
├── {short_name}.py
└── {short_name}.config.yml

Create the directory if it doesn't exist:

mkdir -p etl/steps/viz/chart/{namespace}/latest

Python file (always the same boilerplate)

from etl.helpers import PathFinder

paths = PathFinder(__file__)


def run() -> None:
    c = paths.create_chart(
        config=paths.load_config(),
        short_name="{short_name}",
    )
    c.save()

This is sufficient for config-driven multidims (explicit views in YAML). For more advanced patterns (programmatic view generation from table data, combining charts, grouping views), look at existing examples in etl/steps/viz/chart/ for reference.

Config YAML file

See below for the config structure and examples.

Step 4: Register in the DAG

Add to the appropriate dag/*.yml file (find it by searching for the grapher dataset dependency):

viz://chart/{namespace}/latest/{short_name}:
  - data://grapher/{namespace}/{version}/{dataset}

Place it right after the grapher step it depends on.

Step 5: Run and verify

Always run the step after creating it — schema validation only happens at runtime, so errors (like invalid fields in config) won't surface until the step is executed. CI will catch these, but it's better to fix them locally first.

# Chart steps write to the grapher DB, so they need the --grapher flag
.venv/bin/etl run {short_name} --grapher --only

This outputs a preview URL like:

PREVIEW: http://staging-site-{branch}/admin/grapher/{namespace}%2Flatest%2F{short_name}%23{short_name}/

The ETL has built-in change detection — if you modify the config, it will automatically re-run on the next invocation without needing --force.

Config YAML Structure

# REQUIRED — grapher chart-config schema the view configs below are written against, as a
# QUOTED string (a bare `011` is YAML octal). There is no fallback: ETL fails without it. Use the
# current DEFAULT_GRAPHER_SCHEMA version (etl/config.py) when authoring a new MDIM, then leave it
# alone: it is what lets Grapher migrate the config forward after a breaking schema change.
grapher_schema: "011"
# Never put `$schema` inside a view's `config` block: Grapher lets the view value override this
# chart-level pin, so the two silently disagree. ETL warns when that happens.

title:
  title: "Chart Title"
  title_variant: ""

# REQUIRED — one or more topic tags (see "Topic tags" section below)
topic_tags:
  - tag 1
  - tag 2

default_selection:
  - World

# Pre-select dimension values (use slug values)
default_dimensions:
  sex: female

# Shared config applied to all views
definitions:
  common_views:
    - config:
        originUrl: ourworldindata.org/topic-page
        hasMapTab: true        # or false for multi-indicator line charts
        tab: line              # or map
        chartTypes:
          - LineChart
        yAxis:
          min: 0
      metadata:
        description_key:
          - First key point about this data.
          - Second key point about methodology.

dimensions:
  - slug: sex
    name: Sex
    choices:
      - slug: female
        name: Females
      - slug: male
        name: Males

views:
  - dimensions:
      sex: female
    indicators:
      y:
        - catalogPath: table#variable_female
    config:
      title: "Title for females view"
      subtitle: "Subtitle for females view"

  - dimensions:
      sex: male
    indicators:
      y:
        - catalogPath: table#variable_male
    config:
      title: "Title for males view"
      subtitle: "Subtitle for males view"

Topic tags (required)

Every multidim must declare at least one topic_tags entry — it's a top-level key in the config (right after title).

topic_tags:
  - tag 1
  - tag 2

Rules:

• Each entry must exactly match one of the valid tag names below (case- and spelling-sensitive, e.g. War & Peace, not war and peace).
• The first tag is the primary topic — order it deliberately.
• Reuse the tags of the charts/topic the mdim is built from; a new mdim rarely needs a brand-new tag.

Valid topic tags (from topic_tags in schemas/dataset-schema.json):

The schema enum is a static snapshot; if a tag seems missing, the canonical live list is this Datasette query.

Access to Energy, Age Structure, Agricultural Production, Air Pollution, Alcohol Consumption, Animal Welfare, Antibiotics & Antibiotic Resistance, Artificial Intelligence, Biodiversity, Books, Burden of Disease, CO2 & Greenhouse Gas Emissions, COVID-19, Cancer, Cardiovascular Diseases, Causes of Death, Child & Infant Mortality, Child Labor, Clean Water, Clean Water & Sanitation, Climate Change, Corruption, Crop Yields, Democracy, Diarrheal Diseases, Diet Compositions, Economic Growth, Economic Inequality, Economic Inequality by Gender, Education Spending, Electricity Mix, Employment in Agriculture, Energy, Energy Mix, Environmental Impacts of Food Production, Eradication of Diseases, Famines, Farm Size, Fertility Rate, Fertilizers, Fish & Overfishing, Food Prices, Food Supply, Foreign Aid, Forests & Deforestation, Fossil Fuels, Gender Ratio, Global Education, Global Health, Government Spending, HIV/AIDS, Happiness & Life Satisfaction, Healthcare Spending, Homelessness, Homicides, Housing, Human Development Index (HDI), Human Height, Human Rights, Hunger & Undernourishment, Illicit Drug Use, Indoor Air Pollution, Influenza, Internet, LGBT+ Rights, Land Use, Lead Pollution, Life Expectancy, Light at Night, Literacy, Loneliness & Social Connections, Malaria, Marriages & Divorces, Maternal Mortality, Meat & Dairy Production, Medicine & Biotechnology, Mental Health, Metals & Minerals, Micronutrient Deficiency, Migration, Military Personnel & Spending, Mpox (monkeypox), Natural Disasters, Neglected Tropical Diseases, Nuclear Energy, Nuclear Weapons, Obesity, Oil Spills, Outdoor Air Pollution, Ozone Layer, Pandemics, Pesticides, Plastic Pollution, Pneumonia, Polio, Population Growth, Poverty, Religion, Renewable Energy, Research & Development, Sanitation, Smallpox, Smoking, Space Exploration & Satellites, State Capacity, Suicides, Taxation, Technological Change, Terrorism, Tetanus, Time Use, Tourism, Trade & Globalization, Transport, Trust, Tuberculosis, Uncategorized, Urbanization, Vaccination, Violence Against Children & Children's Rights, War & Peace, Waste Management, Water Use & Stress, Wildfires, Women's Employment, Women's Rights, Work & Employment, Working Hours

When a view should show several indicators as separate lines:

views:
  - dimensions:
      sex: female
    indicators:
      y:
        - catalogPath: tb#indicator_a
          display:
            name: "Label for line A"
        - catalogPath: tb#indicator_b
          display:
            name: "Label for line B"
    config:
      title: "Chart with multiple lines"
      subtitle: "Description"
      selectedFacetStrategy: entity   # Important for multi-indicator line charts
      hasMapTab: false                # Map doesn't work well with multiple indicators

Dimension-specific common_views overrides

Override settings for specific dimension combinations:

definitions:
  common_views:
    - config:
        # Base config for all views
        hasMapTab: true
        chartTypes: ["LineChart"]
    - dimensions:
        indicator: share
      config:
        # Override just for "share" indicator views
        note: "Share values sum to 100%"
        map:
          colorScale:
            binningStrategy: manual

Per-view FAUST: inherit from garden, don't re-type it

A view's chart config can omit title/subtitle/note — each view then inherits FAUST from the indicator's presentation.grapher_config in the garden .meta.yml (templated by dimension). Inheritance is from grapher_config only — there is no fallback to the indicator title/description_short/display.name. So to replicate an existing chart's FAUST across many views, set grapher_config.title/subtitle/note once in the garden metadata (e.g. age-aware via a Jinja <% if %> template), rebuild the grapher step, and leave the view configs thin. To verify what will actually render, read the resolved per-view config from multi_dim_x_chart_configs → chart_configs in the staging DB.

Common Dimension Patterns

Domain	Dimension	Typical choices
Demographics	sex	female, male, both_sexes
Demographics	age	at_birth, at_10, at_15, at_25, at_45, at_65, at_80
Economics	metric	absolute, per_capita, share_of_gdp
Time series	frequency	annual, monthly, weekly
Statistics	estimate	central, low, high

Chart Config Options

Key fields for config in views or common_views:

Field	Values	Notes
tab	line, map, table	Default tab shown
chartTypes	["LineChart"], ["DiscreteBar"], ["StackedBar"], ["ScatterPlot"]	Chart visualization type
hasMapTab	true/false	Show map tab (avoid with multi-indicator views)
selectedFacetStrategy	entity, metric, none	How to facet multi-indicator charts
yAxis.min	number	Y-axis minimum
originUrl	URL path	Links back to topic page
note	string	Footer note on chart

Troubleshooting

Chart built but not on staging: without --grapher, etlr viz://chart/... only writes the config under viz/chart/ and logs chart.not_upserted; pass --grapher to upsert.

Step not found in DAG: Check that the entry is under the steps: key in the correct dag/*.yml file, and that the file is included from dag/main.yml.

Preview URL shows errors: Verify that the catalogPaths in your config match actual indicators in the grapher dataset. Check by running the grapher step first: .venv/bin/etl run {dataset} --grapher.

config must not contain {'description_key'} or similar: View-level metadata like description_key, description_short, and presentation belong under metadata, not config. The config block is for chart settings only (title, subtitle, chartTypes, etc.).