Site icon R-bloggers

The Economist’s Big Mac Index is calculated with R

[This article was first published on Revolutions, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

The Economist's Big Mac Index (also described on Wikipedia if you're not a subscriber) was created (somewhat tongue-in-cheek) as a measure to compare the purchasing power of money in different countries. Since Big Macs are available just about everywhere in the world, the price of a Big Mac in Sweden — expressed in US dollars — gives an American traveler a sense of how much more expensive things will be in Stockholm. And comparing the price of a Big Mac in several countries converted to a single baseline currency is a measure of how over-valued (or undervalued) those other currencies are compared to that baseline.

Since its inception in 1986, the Big Mac Index has been compiled and calculated manually, twice a year. But starting with the most recent published index (July 2018, shown above), the index is now calculated with R. This is the first example of a new program at The Economist to publish the data and methods behind its journalism, and here the data and code behind the Big Mac Index have been published as a Github repository. The method behind the index is provided as a richly-commented Jupyter Notebook, where you can also find some additional charts and within-currency analyses not published in the main index.

The repository is published under an open MIT license, meaning you can remix the code to create a new index on prices of another international commodity, provided you can find the data. Find everything you need at the Github repository linked below.

Github (TheEconomist): Data and methodology for the Big Mac index

To leave a comment for the author, please follow the link and comment on their blog: Revolutions.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.