A Decade of Drinking Beer on Untappd

by @edent | , , , | Read ~108 times.

10 years ago, I asked an innocent question on Twitter.

The answers came in swiftly - Untappd was the app to use. So, a few minutes later:

In the last decade, how much beer and cider have I drunk?

I've written before about how to extract your data from Untappd using their API.

(A few notes. I don't check in to every drink - I only tend to do so if it's a new beer. Some of these are only tasters of a beer - not a full pint. This is mostly an exercise in playing with R. Visit DrinkAware if you'd like to help manage your alcohol consumption.)

Quick Stats

  • 985 check ins.
  • 801 unique drinks
  • 3.68 average rating
  • 4.93 average ABV


Is there a correlation between how strong a drink is, and how much I like it?

beer_data <- read_json("untappd_data.json", simplifyVector = TRUE)

abv <- beer_data$beer$beer_abv
scr <- beer_data$rating_score

plot(abv, scr, main="ABV vs Score", xlab="ABV", ylab="Score")
abline(lm(scr~abv), col="red") # regression line (y~x)

A very busy scatter graph.

Hmmm... There's some week positive correlation there. But it's a bit muddled. Let's turn that into a hexmap:

bin<-hexbin(abv, scr, xbins=10, xlab="ABV", ylab="Score")
plot(bin, main="Hexagonal Binning")

A hex graph with a strong centre.

Aha! A bit easier to see. Most of the beers I drink are in the 4-5% ABV. And there is some correlation. But, mostly, I just like beer and cider. Hmmm... Which do I prefer?

Let's take a look at Cider first:

beer_data <- read_json("untappd_data.json", simplifyVector = TRUE)
cider <- beer_data[grepl("Cider", beer_data$beer$beer_name), ]
cabv <- cider$beer$beer_abv
cscr <- cider$rating_score

Scatter plot with weak positive correlation.

Hex plot.

How much do I like Cider vs Beer?
Just beer (OK, also includes Mead and a few other not Ciders)

justbeer <- beer_data[!grepl("Cider", beer_data$beer$beer_name), ]
boxplot(cscr, ylab="Score", main="Cider Scores", ylim=c(0,5))

Box and whisker diagrams.

I like Cider a bit more than beer. Yup!

Let's plot that data on a map! It's a bit more complicated because the JSON is nested.

beer_data <- read_json("untappd_data.json", simplifyVector = TRUE, flatten = TRUE)

venues_list <- beer_data$venue
venues <- as.data.frame(do.call(rbind, venues_list))
locations_list <- venues$location
locations <- as.data.frame(do.call(rbind, locations_list))
locations <-subset(locations, venue_state!="Everywhere")

Display them on an interactive map:

locations_sf <- st_as_sf(locations, coords = c("lng", "lat"), crs = 4326)

Map of the world with dots all over it.

Let's zoom in on London:
Points dotted all over Central London.

Yup! Looks about right.

Well, that was a fun afternoon of noodling with R. If you'd like to play with the data, you can download a decade of my Untappd data in JSON format

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