Wine reviews from France, Switzerland, Austria and Germany.

Format

A data frame with 9627 observations on the following 9 variables.

country

country of origin

points

the number of points WineEnthusiast rated the wine on a scale of 1-100 (though they say they only post reviews for wines that score >=80)

price

the cost for a bottle of the wine

province

the province or state that the wine is from

taster_name

name of the person who tasted and reviewed the wine

taster_twitter_handle

Twitter handle for the person who tasted ane reviewed the wine

variety

the type of grapes used to make the wine (ie pinot noir)

winery

the winery that made the wine

variety_main

broader category as variety

Details

The data was scraped from WineEnthusiast during the week of Nov 22th, 2017. The code for the scraper can be found at https://github.com/zackthoutt/wine-deep-learning This data set is slightly modified, i.e. only four countries are selected and broader categories on the variety have been added.

Examples


data(wine)
str(wine)
#> spec_tbl_df [9,627 × 9] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
#>  $ country              : Factor w/ 3 levels "Austria","France",..: 3 2 2 3 3 3 3 3 3 2 ...
#>  $ points               : num [1:9627] 87 86 86 86 86 91 91 91 91 88 ...
#>  $ price                : num [1:9627] 24 24 15 9 10 16 14 30 22 27 ...
#>  $ province             : chr [1:9627] "Mosel" "Burgundy" "Burgundy" "Rheinhessen" ...
#>  $ taster_name          : Factor w/ 5 levels "Anna Lee C. Iijima",..: 1 5 5 1 1 1 1 1 1 5 ...
#>  $ taster_twitter_handle: chr [1:9627] NA "@vossroger" "@vossroger" NA ...
#>  $ variety              : Factor w/ 29 levels "Cabernet Franc-Cabernet Sauvignon",..: 19 5 5 19 19 19 19 19 19 5 ...
#>  $ winery               : chr [1:9627] "Richard Böcking" "Simonnet-Febvre" "Vignerons des Terres Secrètes" "Schmitt Söhne" ...
#>  $ variety_main         : Factor w/ 5 levels "Chardonnay","Pinot Noir",..: 3 1 1 3 3 3 3 3 3 1 ...
aggr(wine)