BoardGameGeek.jl
Utilities to scrape BoardGameGeek.com, the IMDB of board games.
Installation
To install this package, open the Julia package manager by typing ] in the REPL and run
pkg> add BoardGameGeek
BoardGameGeek.jl is compatible with all Julia versions ⩾ 1.6.
Getting started
Start by importing this package and DataFrames.jl:
using BoardGameGeek
using DataFrames
We can query the reviews a specific user wrote via userreviews(username)
julia> user = "bggjulia"
julia> DataFrame(userreviews(user))
4×5 DataFrame
Row │ id name username rating comment
│ Int64 String String Float32 String
─────┼───────────────────────────────────────────────────────────────
1 │ 140934 Arboretum bggjulia 9.0
2 │ 225694 Decrypto bggjulia 8.5
3 │ 188 Go bggjulia 10.0
4 │ 256960 Pax Pamir: Second Edition bggjulia 9.0
or simply use collection get the game IDs of their collection
julia> collection(user)
4-element Vector{Int64}:
140934
225694
188
256960
Game reviews
Use gamereviews(id) to scrape all reviews that were written for a specific game
julia> DataFrame(gamereviews(188))
Scraping 15700 reviews for Go... 100%|█████████████████████| Time: 0:06:31
15700×5 DataFrame
Row │ id name username rating comment ⋯
│ Int64 String String Float32 String ⋯
───────┼──────────────────────────────────────────────────────────────────────────
1 │ 188 Go xenocles 10.0 My all time favourite 'classi ⋯
2 │ 188 Go guus 10.0 The mother of all strategy ga
3 │ 188 Go Varthlokkur 10.0
4 │ 188 Go Hiroshi Ishikawa 10.0 Simple rule yet extremely dee
5 │ 188 Go layotte 10.0 ⋯
⋮ │ ⋮ ⋮ ⋮ ⋮ ⋮ ⋱
15696 │ 188 Go aircastle 1.0
15697 │ 188 Go ashleybobal53 1.0
15698 │ 188 Go danperrault 1.0
15699 │ 188 Go vikings40 1.0 ⋯
15700 │ 188 Go akaiready 1.0
1 column and 15690 rows omitted
Note that this can take a while for games with many reviews, as we don't want to run into the BoardGameGeek API rate limit.
The default wait time of 2 seconds per 100 reviews can be changed via the keyword argument waittime.
Full game info
Use gameinfo(id) to obtain all sorts of information about a game.
A summary of all data is given in the reference below.
julia> DataFrame(gameinfo(collection(user)))
4×24 DataFrame
Row │ id name yearpublished minplayers maxplayers ⋯
│ Int64 String Int64 Int64 Int64 ⋯
─────┼────────────────────────────────────────────────────────────────────────────
1 │ 140934 Arboretum 2015 2 4 ⋯
2 │ 225694 Decrypto 2018 3 8
3 │ 188 Go -2200 2 2
4 │ 256960 Pax Pamir: Second Edition 2019 1 5
19 columns omitted
GeekBuddies
Finally, we can also take a look at "GeekBuddies" and user profiles via userinfo(name):
julia> buddies(user)
2-element Vector{String}:
"Aldie"
"dakarp"
julia> DataFrame(userinfo(buddies(user)))
2×5 DataFrame
Row │ id name country yearregistered lastlogin
│ Int64 String String Int64 Date
─────┼──────────────────────────────────────────────────────────
1 │ 688 Aldie United States 1999 2022-02-13
2 │ 792 dakarp United States 2002 2022-02-13
Reference
Exported functions
| Description | |
|---|---|
gamereviews(id) |
return all ratings & reviews written about a game |
gameinfo(id) |
return basic information about a game |
userreviews(name) |
return all reviews written by a user |
userinfo(name) |
return basic user information |
buddies(name) |
return usernames of a user's friends |
collection(name) |
return game IDs of a user's collection |
Data types
Fields of a BGGGameInfo object returned by gameinfo(id):
id :: Int64
name :: String
yearpublished :: Int64
minplayers :: Int64
maxplayers :: Int64
playingtime :: Int64
minplaytime :: Int64
maxplaytime :: Int64
minage :: Int64
usersrated :: Int64
average :: Float32
bayesaverage :: Float32
stddev :: Float32
median :: Float32
owned :: Int64
trading :: Int64
wanting :: Int64
wishing :: Int64
numcomments :: Int64
numweights :: Int64
averageweight :: Float32
mechanics :: Vector{String}
families :: Vector{String}
suggested_numplayers :: Dict{String, Tuple{Int64, Int64, Int64}}