Get Schedule
Source:R/0_generics.R, R/espn_schedule.R, R/flea_schedule.R, and 2 more
ff_schedule.RdThis function returns a tidy dataframe with one row for every team for every weekly matchup
Usage
ff_schedule(conn, ...)
# S3 method for espn_conn
ff_schedule(conn, ...)
# S3 method for flea_conn
ff_schedule(conn, week = 1:17, ...)
# S3 method for mfl_conn
ff_schedule(conn, ...)
# S3 method for sleeper_conn
ff_schedule(conn, ...)Arguments
- conn
a conn object created by
ff_connect()- ...
for other platforms
- week
a numeric or numeric vector specifying which weeks to pull
Methods (by class)
ff_schedule(espn_conn): ESPN: returns schedule data, one row for every franchise for every week. Completed games have result data.ff_schedule(flea_conn): Flea: returns schedule data, one row for every franchise for every week. Completed games have result data.ff_schedule(mfl_conn): MFL: returns schedule data, one row for every franchise for every week. Completed games have result data.ff_schedule(sleeper_conn): Sleeper: returns all schedule data
Examples
# \donttest{
try({ # try only shown here because sometimes CRAN checks are weird
espn_conn <- espn_connect(season = 2020, league_id = 899513)
ff_schedule(espn_conn)
}) # end try
#> # A tibble: 140 × 6
#> week franchise_id franchise_score result opponent_id opponent_score
#> <int> <int> <dbl> <chr> <int> <dbl>
#> 1 1 1 102. L 4 130.
#> 2 1 2 156. W 3 135.
#> 3 1 3 135. L 2 156.
#> 4 1 4 130. W 1 102.
#> 5 1 5 133. W 7 131.
#> 6 1 6 119. L 9 124.
#> 7 1 7 131. L 5 133.
#> 8 1 8 120. L 10 122.
#> 9 1 9 124. W 6 119.
#> 10 1 10 122. W 8 120.
#> # … with 130 more rows
# }
# \donttest{
try({ # try only shown here because sometimes CRAN checks are weird
conn <- fleaflicker_connect(season = 2019, league_id = 206154)
ff_schedule(conn, week = 2:4)
}) # end try
#> # A tibble: 48 × 14
#> week franch…¹ franc…² franc…³ result oppon…⁴ oppon…⁵ oppon…⁶ game_id isFin…⁷
#> <int> <int> <chr> <dbl> <chr> <int> <chr> <dbl> <chr> <lgl>
#> 1 2 1373991 Top Ci… 226. WIN 1373393 Philad… 138. 425065… TRUE
#> 2 2 1373480 Golden… 161. LOSE 1373475 Winter… 300. 422451… TRUE
#> 3 2 1371776 Winter… 178. WIN 1373501 Midgar… 172. 425065… TRUE
#> 4 2 1373993 Boomto… 167. WIN 1373970 Bamend… 159. 422451… TRUE
#> 5 2 1373883 Manito… 201. WIN 1373973 Red Ri… 103. 425065… TRUE
#> 6 2 1373535 Winter… 147. LOSE 1373988 Spring… 229. 425065… TRUE
#> 7 2 1374252 Centra… 160. LOSE 1374255 Mushro… 210. 422451… TRUE
#> 8 2 1374271 Clutch… 174. WIN 1374315 Shangh… 132. 422451… TRUE
#> 9 2 1373393 Philad… 138. LOSE 1373991 Top Ci… 226. 425065… TRUE
#> 10 2 1373475 Winter… 300. WIN 1373480 Golden… 161. 422451… TRUE
#> # … with 38 more rows, 4 more variables: isDivisional <lgl>, isPlayoffs <lgl>,
#> # isThirdPlaceGame <lgl>, isChampionshipGame <lgl>, and abbreviated variable
#> # names ¹franchise_id, ²franchise_name, ³franchise_score, ⁴opponent_id,
#> # ⁵opponent_name, ⁶opponent_score, ⁷isFinalScore
# }
# \donttest{
try({ # try only shown here because sometimes CRAN checks are weird
ssb_conn <- ff_connect(platform = "mfl", league_id = 54040, season = 2020)
ff_schedule(ssb_conn)
}) # end try
#> # A tibble: 234 × 6
#> week franchise_id franchise_score result opponent_id opponent_score
#> <dbl> <chr> <dbl> <chr> <chr> <dbl>
#> 1 1 0001 123. W 0002 103.
#> 2 1 0002 103. L 0001 123.
#> 3 1 0003 128. L 0004 174.
#> 4 1 0004 174. W 0003 128.
#> 5 1 0005 144. W 0011 130.
#> 6 1 0006 173. W 0013 125.
#> 7 1 0007 145. W 0010 127.
#> 8 1 0008 185. W 0009 176.
#> 9 1 0009 176. L 0008 185.
#> 10 1 0010 127. L 0007 145.
#> # … with 224 more rows
# }
# \donttest{
try({ # try only shown here because sometimes CRAN checks are weird
jml_conn <- ff_connect(platform = "sleeper", league_id = "522458773317046272", season = 2020)
ff_schedule(jml_conn)
}) # end try
#> # A tibble: 184 × 6
#> week franchise_id franchise_score opponent_id opponent_score result
#> <int> <int> <dbl> <int> <dbl> <chr>
#> 1 1 1 97.8 12 160. L
#> 2 1 2 65.9 8 70.2 L
#> 3 1 3 103. 10 71 W
#> 4 1 4 133. 7 106. W
#> 5 1 5 82.4 6 99.3 L
#> 6 1 6 99.3 5 82.4 W
#> 7 1 7 106. 4 133. L
#> 8 1 8 70.2 2 65.9 W
#> 9 1 9 78.3 11 147 L
#> 10 1 10 71 3 103. L
#> # … with 174 more rows
# }