library(hockeyR)load_pbp()As mentioned on the home page, the main
function of the hockeyR package is to load raw NHL
play-by-play data without having to scrape it and clean it yourself. The
load_pbp() function will do that for you. The
season argument in load_pbp() is very
accepting. You may use any of the following syntax when loading
play-by-play data for the 2020-21 NHL season:
To load more than one season, wrap your desired years in
c(). That is, to get data for the last two years, one could
enter load_pbp(c(2020,2021)).
get_game_ids()If you want to load play-by-play data for a game that isn’t in the data repository,
or perhaps you just want a single game and don’t need to load a full
season, you’ll first need to find the numeric game ID. The
get_game_ids() function can find it for you as long as you
supply it with the date of the game in YYY-MM-DD format.
The function defaults to the current date as defined by your operating
system.
# get single day ids
get_game_ids(day = "2017-10-17")
#> # A tibble: 11 x 8
#> game_id season_full date home_name away_~1 home_~2 away_~3 game_~4
#> <int> <chr> <chr> <chr> <chr> <int> <int> <chr>
#> 1 2017020082 20172018 2017-10-17 New York R~ Pittsb~ 4 5 REG
#> 2 2017020083 20172018 2017-10-17 Philadelph~ Florid~ 5 1 REG
#> 3 2017020084 20172018 2017-10-17 Washington~ Toront~ 0 2 REG
#> 4 2017020081 20172018 2017-10-17 New Jersey~ Tampa ~ 5 4 REG
#> 5 2017020085 20172018 2017-10-17 Ottawa Sen~ Vancou~ 0 3 REG
#> 6 2017020086 20172018 2017-10-17 Nashville ~ Colora~ 4 1 REG
#> 7 2017020087 20172018 2017-10-17 Winnipeg J~ Columb~ 2 5 REG
#> 8 2017020088 20172018 2017-10-17 Dallas Sta~ Arizon~ 3 1 REG
#> 9 2017020089 20172018 2017-10-17 Edmonton O~ Caroli~ 3 5 REG
#> 10 2017020090 20172018 2017-10-17 Vegas Gold~ Buffal~ 5 4 REG
#> 11 2017020091 20172018 2017-10-17 San Jose S~ Montré~ 5 2 REG
#> # ... with abbreviated variable names 1: away_name, 2: home_final_score,
#> # 3: away_final_score, 4: game_typeYou can instead supply a season to get_game_ids() to
grab a full year’s worth of IDs as well as final scores, home and road
teams, and game dates for each game in the given season.
scrape_game()This function scrapes a single game with a supplied game ID, which
can be retrieved with get_game_ids(). Live game scraping
has yet to undergo testing.
scrape_game(game_id = 2020030175)
#> # A tibble: 718 x 104
#> event_~1 event secon~2 event~3 event~4 descr~5 period perio~6 perio~7 game_~8
#> <chr> <chr> <chr> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 GAME_SC~ Game~ <NA> <NA> <NA> Game S~ 1 0 1200 0
#> 2 CHANGE Chan~ <NA> Montré~ away ON: Sh~ 1 0 1200 0
#> 3 CHANGE Chan~ Line c~ Toront~ home ON: Wa~ 1 0 1200 0
#> 4 FACEOFF Face~ <NA> Toront~ home Auston~ 1 0 1200 0
#> 5 HIT Hit <NA> Toront~ home Zach H~ 1 13 1187 13
#> 6 CHANGE Chan~ On the~ Montré~ away ON: Je~ 1 24 1176 24
#> 7 CHANGE Chan~ On the~ Toront~ home ON: Al~ 1 27 1173 27
#> 8 CHANGE Chan~ On the~ Montré~ away ON: Co~ 1 29 1171 29
#> 9 SHOT Shot Wrist ~ Toront~ home Alex G~ 1 32 1168 32
#> 10 CHANGE Chan~ On the~ Toront~ home ON: Ja~ 1 32 1168 32
#> # ... with 708 more rows, 94 more variables: game_seconds_remaining <dbl>,
#> # home_score <dbl>, away_score <dbl>, event_player_1_name <chr>,
#> # event_player_1_type <chr>, event_player_2_name <chr>,
#> # event_player_2_type <chr>, event_player_3_name <chr>,
#> # event_player_3_type <chr>, event_goalie_name <chr>, strength_state <glue>,
#> # strength_code <chr>, strength <chr>, game_winning_goal <lgl>,
#> # empty_net <lgl>, penalty_severity <chr>, penalty_minutes <int>, ...scrape_day()This is the backbone function that keeps the hockeyR-data
repository up to date during the season. Supply a date
(YYY-MM-DD) and it will scrape play-by-play data for all
games on that day. Live game scraping is still awaiting testing.
scrape_day("2015-01-06")
#> # A tibble: 6,472 x 105
#> event_~1 event secon~2 event~3 event~4 descr~5 period perio~6 perio~7 game_~8
#> <chr> <chr> <chr> <chr> <chr> <chr> <int> <dbl> <dbl> <dbl>
#> 1 GAME_SC~ Game~ <NA> <NA> <NA> Game S~ 1 0 1200 0
#> 2 CHANGE Chan~ <NA> Buffal~ away ON: Jo~ 1 0 1200 0
#> 3 CHANGE Chan~ Line c~ New Je~ home ON: Pa~ 1 0 1200 0
#> 4 FACEOFF Face~ <NA> Buffal~ away Zemgus~ 1 0 1200 0
#> 5 BLOCKED~ Bloc~ <NA> Buffal~ away Andy G~ 1 10 1190 10
#> 6 CHANGE Chan~ On the~ Buffal~ away ON: Ch~ 1 36 1164 36
#> 7 GIVEAWAY Give~ <NA> New Je~ home Giveaw~ 1 38 1162 38
#> 8 TAKEAWAY Take~ <NA> New Je~ home Takeaw~ 1 41 1159 41
#> 9 CHANGE Chan~ On the~ New Je~ home ON: Ma~ 1 41 1159 41
#> 10 CHANGE Chan~ On the~ New Je~ home ON: Ja~ 1 48 1152 48
#> # ... with 6,462 more rows, 95 more variables: game_seconds_remaining <dbl>,
#> # home_score <dbl>, away_score <dbl>, event_player_1_name <chr>,
#> # event_player_1_type <chr>, event_player_2_name <chr>,
#> # event_player_2_type <chr>, event_player_3_name <chr>,
#> # event_player_3_type <chr>, event_goalie_name <chr>, strength_state <glue>,
#> # strength_code <chr>, strength <chr>, game_winning_goal <lgl>,
#> # empty_net <lgl>, penalty_severity <chr>, penalty_minutes <int>, ...If you can wait until the day after a game, the
load_pbp() function is the only one you’ll need. If you’d
like to scrape the data yourself immediately following a game, the other
functions discussed here will do the job for you.