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tidygapminder

R-CMD-check test-coverage codecov CRAN status CRAN downloads Project Status: Active

Gapminder data, minus the mess.

Gapminder is a goldmine of global development data — life expectancy, income, CO₂ emissions, literacy rates, and hundreds more indicators spanning centuries. The catch? Every sheet looks like this:

life expectancy years | 1800 | 1801 | 1802 | ...
----------------------|------|------|------|----
Afghanistan           | 28.2 | 28.2 | 28.2 | ...
Albania               | 35.4 | 35.4 | 35.4 | ...
...

Countries as rows, years as columns, the indicator name hiding in cell A1. Great for a spreadsheet. Terrible for R.

tidygapminder fixes that in one function call.

Installation

```{r} # From CRAN install.packages(“tidygapminder”)

Development version

pak::pak(“ebedthan/tidygapminder”)


## Two functions. That's it.

### `tidy_index()`: one file at a time

Point it at a Gapminder `.csv`, `.xlsx`, or `.xls` file and get back a clean
tibble:

```{r}
library(tidygapminder)

csv_path <- system.file("extdata/life_expectancy_years.csv", package = "tidygapminder")

tidy_index(csv_path)

Three columns: country, year, and the indicator, ready to filter, plot, or model.

tidy_bunch(): a whole folder at once

Downloaded ten indicators? No problem. Point tidy_bunch() at the folder:

```{r} dir_path <- system.file(“extdata”, package = “tidygapminder”)

Returns a named list of tibbles, one per file

result <- tidy_bunch(dir_path) names(result)


Want everything in one data frame joined by `country` and `year`?

```{r}
tidy_bunch(dir_path, combine = TRUE)

Why tidygapminder?

  • Zero friction: no arguments to learn beyond a file path
  • Handles the quirks: indicator name in cell A1, non-numeric year columns, mixed file formats: all taken care of
  • Lightweight: only two dependencies (readxl and tibble)
  • Informative errors: tells you exactly what went wrong and where

Getting help