Use log income in a scatter plot without logging zero

The direct answer

Precompute log10 of positive GDP per capita to spread out countries clustered at the lower end of the income scale. Map the transformed column explicitly, so the method remains reproducible even without a logarithmic-axis setting.

Prepare the observation table

Check that every GDP value is positive. Calculate log10_gdp_ppp in a spreadsheet and keep raw GDP beside it. Use log10_gdp_ppp as X and life_expectancy_years as Y. Label X “log10 GDP per capita, constant 2021 international $”.

Public dataset and a reproducible example

The download is a prepared extract, not a live data connection. The source links below include the original query, observation period and reuse policy. Keep an original copy when you update the data; revised source values can change the result.

World Bank WDI values retain the indicator units and country-year definitions. Derived counts and indexes are Datamon tutorial calculations, not new official World Bank statistics.

Snapshot retrieved: 2026-10-26. Prepared rows: 10.

Download the prepared CSVDownload the reference SVG

Build it in Datamon

Sign in with Google to create an editable visualization. Public articles and examples can be read without an account. The prepared CSV already contains the joins, filters or aggregations described above; do those steps in a spreadsheet first when using your own data.

1. Open Data and choose a file, or paste the table with its header row. Check the detected numeric columns, decimal separator and dates. The current importer accepts files below 20 MB; these examples are smaller. Keep blanks distinct from recorded zeros.
2. In Visualize, select Scatter plot. Connect the roles exactly as listed below. Review the preview against at least two rows in the example table rather than relying only on automatic mapping.
3. In Refine, add a title that states the question, the unit, observation period and source. Check labels and contrast at the size your reader will see. Keep a table when exact values matter.
4. Use the export controls for SVG, PNG, PDF or PowerPoint, then inspect the actual file. Save an editable project backup. Publish only after checking the underlying values: a public link makes the published data available to anyone possessing the URL.

Calculation and column mapping

The rule below is pseudocode. The downloadable CSV already contains the prepared values; spreadsheet functions and cell references depend on your sheet layout.

log10_gdp_ppp = LOG10(gdp_ppp_per_capita), only for positive GDP

X

log10_gdp_ppp

Y

life_expectancy_years

Point label

country

Read the result

In this extract, life_expectancy_years ranges from 69.83 (PHL) to 84.04 (JPN).

Use the preview to check the structure and the CSV to check exact values. Its styling is a reference; your Datamon rendering can look different. Follow the mapping and preparation above to reproduce the analytical meaning.

Mistakes that change the meaning

A one-unit difference on this transformed axis represents a tenfold income ratio. Do not label ticks as raw dollars or add an arbitrary constant to include zero. Zero or negative values need a different analysis and an explicit explanation.

Questions before sharing

What is free and what needs an account?

Reading this guide and downloading its CSV and chart images are free. Datamon currently has no paid checkout or subscription plans in this deployment. Creating and saving an editable visualization requires Google sign-in. Provider-backed AI, cloud analysis, publishing and video depend on configured services; this guide does not promise unlimited free connected services.

Sources and further reading

• World Bank WDI · gdp_ppp_per_capita
• World Bank WDI · gdp_ppp_per_capita · API / CSV
• World Bank WDI · life_expectancy_years · License / policy
• World Bank WDI · life_expectancy_years
• World Bank WDI · life_expectancy_years · API / CSV
• Data sources, queries and reuse notes
• Datamon · Blog
• Visualize scatter plot: easy guide, free example