Gilgamath

We have been invited to a champagne tasting party and competition. The rules of the contest are as follows: partygoers bring a bottle of champagne to share. They taste, then rate the different champagnes on offer, with ratings on a scale of 1 through 10. The average rating is computed for each bottle, then divided by the price (plus some offset) to arrive at an adjusted quality score. The champagne with the highest score nets a prize, and considerable bragging rights, for its owner. Presumably the offset is introduced to prevent small denominators from dominating the rating, and is advertised to have a value of around $25. The 'price' is, one infers, for a standard 750 ml bottle.

I decided to do my homework for a change, rather than SWAG it. I have been doing a lot of web scraping lately, so it was pretty simple to gather some data on champagnes from wine dot com. This file includes the advertised and sale prices, as well as advertised ratings from Wine Spectator (WS), Wine Enthusiast (WE), and so on. Some of the bottles are odd sizes, so I compute the cost per liter as well. (By the way, many people would consider the data collection the hard part of the problem. rvest made it pretty easy, though.) Here's a taste:

library(dplyr)
library(magrittr)
champ <- read.csv('../data/champagne.csv')
champ %>% arrange(price_per_liter) %>% head(10) %>% kable(format='markdown')