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)
#> spc_tbl_ [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)
