OWID Chart Recreator

Purpose

Help recreate Our World in Data (OWID) charts (line, bar, bubble) as faithfully as possible in this repo’s Jupyter notebooks, using pandas + Matplotlib (and Plotly when requested), with correct data, filters, and OWID-style aesthetics.

When to Use

• The user shares an OWID grapher URL and asks to:
  • Recreate that chart in a notebook.
  • Add a new chart to the GS305 workshop.
  • Tweak styling or data filters of an existing OWID-based chart.
• The user wants to align a chart with OWID’s look and feel (titles, labels, units, colors, source line).

Relevant Repo Context

• This repo: dataviz_GS305
• Plans & status:
  • .cursor/plans/owid_workshop_implementation.md
• Notebooks (once created):
  • notebooks/01_line_chart_clean_fuels.ipynb
  • notebooks/03_line_chart_death_rate.ipynb
  • notebooks/05_bubble_chart_clean_fuels_vs_gdp.ipynb
  • notebooks/06_optional_join_bubble.ipynb

Behavior and Guidelines

1. Read the plan first

   • Open .cursor/plans/owid_workshop_implementation.md.
   • Use:
     • The chart list (URLs, country filters, years).
     • The notebook paths.
     • The styling guidance (OWID-like aesthetic).
   • Align any new chart work with that structure and style.

2. Understand the OWID chart

   • From the OWID grapher URL:
     • Identify:
       • Indicator name and unit (e.g. %, deaths per 100,000 people).
       • Time range.
       • Country/entity filters.
       • Chart type: line, discrete bar, bubble/scatter.
     • If the chart is a bubble or multi-metric chart, determine:
       • x, y, bubble size, bubble color.
   • Note the source text and provider (e.g. WHO GHO, IHME GBD) from the “Sources & processing”/“Reuse this work” sections on OWID.

3. Data loading strategy

   • Prefer local snapshot CSVs in notebooks/data/ for reproducibility in class and Colab.
     • Example: pd.read_csv("data/clean_fuels.csv").
   • Treat OWID grapher/table URLs as the source of truth for citation and spec matching, but do not rely on live URL reads during workshop execution.
   • If a required snapshot file is missing:
     • Ask the user to create/export it first from OWID and save it under notebooks/data/.
   • When a bubble chart requires multiple datasets:
     • Either use OWID’s already-combined CSV (if available).
     • Or plan a merge:
       • Example: clean fuels + GDP per capita (for clean_fuels_vs_gdp chart).
       • Example: clean fuels + death rate (for indoor_pollution_vs_clean_fuels chart).

4. Notebook structure

   • For a new chart notebook, follow this pattern:
     1. Markdown: chart title, question, and brief explanation.
     2. Imports: pandas, matplotlib.pyplot, and plotly.express if interactive.
   1. Data loading cell: pd.read_csv("data/<snapshot>.csv").
   2. Data inspection cell: head(), info(), basic summaries.
   3. Data preparation:
      • Filter by countries/entities and year range.
      • Handle missing values if necessary (document if you drop/keep).
      • Rename key columns to readable names if helpful.
   4. Plotting cell(s):
      • Matplotlib line/bar or scatter/bubble.
      • Optional Plotly for interactive versions (when requested).
   5. Markdown or print cell summarizing the main takeaway (optional but encouraged for teaching).

5. OWID-like aesthetic

   • Use consistent style across charts:
     • Font: sans-serif (e.g. default Matplotlib sans-serif is acceptable).
     • Colors: a small, readable palette (e.g. different hues for each country).
     • Background: white; light horizontal grid on y-axis.
     • Minimal chart junk: no heavy borders, avoid overwhelming tick labels.
   • Always include:
     • Title matching (or closely paraphrasing) OWID’s title.
     • Axis labels with units.
     • A source line similar to OWID’s short citation, e.g.:
       • Source: World Health Organization – Global Health Observatory (2025), processed by Our World in Data
       • Source: IHME, Global Burden of Disease (2025), with processing by Our World in Data
   • If there’s a mismatch between OWID and your reproduction (e.g. slightly different colors or tick marks), aim for clarity and explain in comments if relevant pedagogically.

6. Bubble and interactive charts

   • For bubble charts:
     • Decide on:
       • x and y metrics (e.g. GDP per capita vs. % access to clean fuels).
       • Bubble size (e.g. population).
       • Bubble color (e.g. region or another categorization).
     • Normalize sizes so bubbles are visible but not overwhelming.
   • For Plotly versions:
     • Use plotly.express.scatter(...).
     • Include hover info (country, year, and core metrics).
     • Keep styling close to the Matplotlib version when possible (titles, axis labels).

7. Teaching and Colab compatibility

   • Ensure:
     • No absolute local paths; use simple relative paths like data/....
     • Cells can be run top-to-bottom with a “Run all” in Colab.
   • For shared Google Drive usage in Colab, ensure the notebook includes or references a path-setup step (drive.mount(...) + os.chdir(...)) before data-loading cells.
   • Favor explicit, step-by-step code over heavy abstraction; students should see clearly:
     • Where the data comes from.
     • How it’s filtered/transformed.
     • How the plot is constructed.

8. Updating the plan and status

   • After adding or significantly updating a chart notebook:
     • Suggest updating .cursor/plans/owid_workshop_implementation.md:
       • Mark the relevant notebook as in progress or done in the Status Board.
       • Add any new OWID charts to the chart list if applicable.

Things to Avoid

• Don’t silently change the meaning of the chart (different indicator, time range, or country set) without clearly noting it.
• Don’t introduce non-reproducible steps (manual GUI exports, screenshots) into the notebook workflow.
• Don’t rely on hidden state between cells; each chart notebook should work from a fresh kernel with a simple “Run all”.