tidytuesday VS big-mac-data

TidyTuesday is a weekly data cleaning project where a new, interesting data source is linked to each week: https://github.com/rfordatascience/tidytuesday

For R, I don't have specific resources, but I remember I started out with doing tidytuesdays challenge (https://github.com/rfordatascience/tidytuesday).

Tidy Tuesday has data and links to more data. The nice thing about those data sets is that you can search for what other people did with the data on social media (e.g. Twitter).

Dataset: https://github.com/rfordatascience/tidytuesday/tree/master/data/2022/2022-11-01

What you're describing is contributing to open source. Some things I'd suggest doing: - learn some git first - create GitHub account and create at least a practice repo - look at learning community-related repos, like Tidy Tuesday - follow R "power" users, people associated with RStudio, and similar folks on social media. Those folks will sometimes mention projects aimed at beginners.

Data: TidyTuesday April 4

The Tidy Tuesday git repo has a lot of example datasets to work with.

I mostly use ggplot2 in R for visualisations which means that The R Graph Gallery is my starting point for inspiration. The best thing to do is start with a simple idea that tells a story, and one of the best guys out there that does this is Cedric Scherer. He is involved a bit with the TidyTuesday project which I wish I had more time to play around with, and is a great starting point for developing a library of vis techniques.

>A McDonald’s in France costs twice as much where a coffee costs less than half as much.

Source? According to The Economist's big mac index data[1] the price of a big mac in the euro area and the US is within the same ballpark (~10% difference max) regardless of how you measure it. If anything big macs are cheaper in the euro area than in the US.

[1] https://github.com/TheEconomist/big-mac-data/blob/master/out...

If you read the article that you posted and the methodology, they document exactly how they calculate the big mac index and how they choose the price.

https://github.com/TheEconomist/big-mac-data#methodology-cha...

- The minimum wage in 1986 was $3.35[1] according to the US Department of Labor.

You could afford at most two Big Macs in 1986, not seven. Also you can definitely afford one big mac with an hour of minimum wage today (minimum wage: $7.25, big mac: $5.36).

0 - https://github.com/TheEconomist/big-mac-data/blob/master/sou...

1 - https://www.dol.gov/agencies/whd/minimum-wage/history/chart

>CPI is also a broken measure due to its inability to measure wealth inequality (sampling median purchasers only)

And that's fine, because the CPI's job is to measure how prices have changed (ie. inflation), not wealth inequality. Just because a given metric doesn't support your pet cause doesn't mean it's "broken". It's like complaining GDP is a broken metric because it doesn't measure how oppressed minorities are.

> Real inflation has likely been ~33% higher than reported for decades (the Big Mac index is probably the true gold standard).

I tried to confirm this and the results were far from conclusive. The big mac index data can be obtained from github[1] and gives a 129.9% increase between april 2000 (earliest data available) and july 2022 (latest data available). To compare this against the CPI data, I checked on FRED and the "Consumer Price Index for All Urban Consumers: Food Away from Home in U.S. City Average"[2] component of the CPI gives a 87.0% for the same time period. If you take those numbers and convert them to annualized rates, you will indeed find a 33.3% difference between them. Looks like your claim is confirmed, right? But not so fast. The CPI category listed above actually breaks down into more detailed categories, including "Limited service meals and snacks", which presumably the big mac falls into. That data isn't available on FRED but can be found on BLS's data viewer[3] and gives you a 105.8% increase for the same time period. If you convert that to an annualized rate the difference between that and the big mac index drops to 15%. I suspect if we try harder we can eliminate more of the discrepancy. For instance, the "Limited service meals and snacks" index was probably computed from a basket of items, not just big macs. Items that have fatter margins (eg. fries or drinks) might have inflated slower than big macs, thereby dragging the growth of the basket down and explaining the difference.

[1] https://github.com/TheEconomist/big-mac-data/

[2] https://fred.stlouisfed.org/series/CUSR0000SEFV

[3] https://beta.bls.gov/dataViewer/view

Read more about the Big Mac index in “Dollar-euro parity may be justified. But the yen looks cheap as chips”. You can also download the data or read the methodology behind the Big Mac index here.

Source: https://github.com/theeconomist/big-mac-data/releases/tag/2022-01

> States and is famously used in the Economist's Big Mac Index to measure inflation.

And a link to the Economist article (https://www.economist.com/big-mac-index) where they further link to their methodology: https://github.com/TheEconomist/big-mac-data

So this is the price of a big mac average for the world, I think. The USA average (I found this resource: https://www.zippia.com/advice/how-much-big-mac-costs-states/ - Nov 2021) is $4.40.