This repository contains the data behind The Economist’s Big Mac index, and code that shows how we calculate it. To download the data, go to the latest release, where you can download the index data in a CSV or Excel, or the code behind it.

Our source data are from several places. Big Mac prices are from McDonald’s directly and from reporting around the world; exchange rates are from Thomson Reuters; GDP and population data used to calculate the euro area averages are from Eurostat and GDP per person data are from the IMF World Economic Outlook reports.

The script provides data in three files:

• big-mac-raw-index.csv contains values for the “raw” index
• big-mac-adjusted-index.csv contains values for the “adjusted” index
• big-mac-full-index.csv contains both

Each file also contains the source data used to calculate it.

The code to calculate the index is provided in a Jupyter Notebook. The code itself is written in R, a programming language designed for data manipulation and statistics. You can view the notebook on github.

If you want to run our code, you’ll need to set up a few things:

You can refer to the installation instructions at the Hitchhiker’s Guide to Python

On a Mac, you already have Python 2.7 installed, but it does not come with Python’s package manager. We recommend using Python 3. To install it, we recommend using Homebrew. In terminal, install Homebrew:

$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

Then, use Homebrew to install Python 3.x:

$ brew install python3

On Ubuntu Linux you can use aptitude:

$ sudo apt-get update
$ sudo apt-get install python3.6

On Windows, instructions coming.

On Mac or Linux, you should now also have pip installed. pip is a package manager for Python. You can install Jupyter with pip:

$ python3 -m pip install jupyter

You’re all set. (If you are using Python 2, run python -m pip install jupyter.)

On Windows, instructions coming.

On a Mac, use Homebrew again. At a terminal prompt, run:

$ brew install R

On Ubuntu Linux, you’re recommended to add a new source to your aptitude setup to install R. Run:

$ sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9

Once you have added the key, add R repository (called CRAN):

$ sudo add-apt-repository 'deb [arch=amd64,i386] https://cran.rstudio.com/bin/linux/ubuntu xenial/'

Now, you can install R:

$ sudo apt-get update
$ sudo apt-get install r-base

On Windows, instructions coming.

IRKernel lets you run R code in Jupyter notebooks. This is the best way to work with R code (this is a truth not yet universally acknowledged). Installation instructions for IRKernel are here. In short:

At a terminal prompt, start R:

$ R
> install.packages(c('repr', 'IRdisplay', 'evaluate', 'crayon', 'pbdZMQ', 'devtools', 'uuid', 'digest'))
> devtools::install_github('IRkernel/IRkernel')
> IRkernel::installspec()

Congratulations, you can run R in Jupyter.

Finally, our R script uses a few R packages you’ll need to install. The tidyverse is a collection of useful packages for data science work in R. Data.table is a complicated but extremely useful alternative to R’s standard data frames for storing and manipulating data. countrycode is a simple function for converting between different systems for encoding countries. At the R prompt from above, run:

> install.packages('tidyverse','data.table','countrycode')

You’re all set.

Navigate to the repository on the command line, and run:

jupyter notebook

You should see a browser window pop up on http://localhost:8888. Click on “Big Mac data generator” to launch the notebook.

To run the notebook, you can run the code cell by cell by clicking on the first cell and using shift+enter to run each cell in turn. Or you can run the whole thing by clicking on the “Cell” menu and selecting “Run All”.