A Decade of Drinking Beer on Untappd

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

Terence Eden is on Mastodon

@edent

Is there any service which will let me "check in" to a beer? Because this Chocolate Tom I'm drinking is amazing.

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

Terence Eden is on Mastodon

@edent

I just earned the 'Newbie' badge on @untappd! http://untp.it/p3POA0

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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

Graphs

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

 Rlibrary(jsonlite)

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)

<img src="https://shkspr.mobi/blog/wp-content/uploads/2021/07/scatter.png" alt="A very busy scatter graph." width="627" height="614" class="aligncenter size-full wp-image-39661" />

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

 Rlibrary(hexbin)

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

plot(bin, main="Hexagonal Binning")

<img src="https://shkspr.mobi/blog/wp-content/uploads/2021/07/hex.png" alt="A hex graph with a strong centre." width="637" height="549" class="aligncenter size-full wp-image-39660" />

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:

 Rlibrary(data.table)

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

<img src="https://shkspr.mobi/blog/wp-content/uploads/2021/07/Cider-Scatter.png" alt="Scatter plot with weak positive correlation." width="650" height="637" class="aligncenter size-full wp-image-39662" />

<img src="https://shkspr.mobi/blog/wp-content/uploads/2021/07/Cider-Hex.png" alt="Hex plot." width="637" height="549" class="aligncenter size-full wp-image-39663" />

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

 Rjustbeer <- beer_data[!grepl("Cider", beer_data$beer$beer_name), ]

boxplot(cscr, ylab="Score", main="Cider Scores", ylim=c(0,5))

<img src="https://shkspr.mobi/blog/wp-content/uploads/2021/07/cider-vs-beer.png" alt="Box and whisker diagrams." width="568" height="585" class="aligncenter size-full wp-image-39669" />

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.

 Rlibrary(jsonlite)

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:

 Rlibrary(sf)

library(mapview)

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

mapview(locations_sf)

Let's zoom in on 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