data-raw/seasons.R
In bozhinvi/volleystat: Detailed Statistics on Volleyball Matches
#### Load libraries #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #####
library(dplyr)
library(tidyr)
library(readr)
library(purrr)
library(stringr)
#### Load season list #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ###
df_seasons <- read_csv("./data-raw/seasons/seasons.csv")
#### Download *.csv season files from VBL website #### #### #### #### #### #### #### #### #### #### #### #### #### #####
# vbl_id <- df_seasons$vbl_id
#
# for (id in 1:length(vbl_id)){
#
# try(download.file(
# paste("https://www.volleyball-bundesliga.de/servlet/league/PlayingScheduleCsvExport?matchSeriesId=",
# vbl_id[id], sep=""),
# paste0("./data-raw/seasons/", vbl_id[id],".csv")),
# silent = TRUE)
#
# }
#### Importing seasons #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ##
# Importing main round
vbl_id_main <- df_seasons[df_seasons$competition_stage == "Main",]$vbl_id
df_main_round <- data.frame()
for (num in 1:length(vbl_id_main)){
tmp <- read.csv(paste0("./data-raw/seasons/", vbl_id_main[num], ".csv"),
stringsAsFactors = FALSE, fileEncoding = "iso-8859-1", sep = ";")
tmp$vbl_id <- vbl_id_main[num]
df_main_round <- rbind(df_main_round, tmp, stringsAsFactors = FALSE)
rm(tmp)
}
# Importing playoffs
vbl_id_play <- df_seasons[df_seasons$competition_stage != "Main",]$vbl_id
df_play_off <- data.frame()
for (num in 1:length(vbl_id_play)){
tmp <- read.csv(paste0("./data-raw/seasons/", vbl_id_play[num], ".csv"),
stringsAsFactors = FALSE, fileEncoding = "iso-8859-1", sep = ";")
tmp$vbl_id <- vbl_id_play[num]
df_play_off <- rbind(df_play_off, tmp, stringsAsFactors = FALSE)
rm(tmp)
}
#### Select relevant columns and join seasons #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
# Select columns in df_main round
df_main_round <- df_main_round %>% select(-c(Geschlecht, Saison, X, X..1, X..2, X..3, X..4, X..5,
X..6, Spielrunde, Ergebnis, Satzpunkte, Ballpunkte,
Austragungsort, Wochentag, Austragungsort.Ergebnis))
# Rename variables
df_main_round <- df_main_round %>% rename(date = Datum) %>%
rename(time = Uhrzeit) %>%
rename(match_id = X.) %>%
rename(match_day = ST) %>%
rename(team_1 = Mannschaft.1) %>%
rename(team_2 = Mannschaft.2) %>%
rename(home_team = Gastgeber) %>%
rename(match_duration = Spieldauer) %>%
rename(spectators = Zuschauerzahl) %>%
rename(set_won_team_1 = Satzpunkte.1) %>%
rename(set_won_team_2 = Satzpunkte.2) %>%
rename(set_1_pt_team_1 = Satz.1...Ballpunkte.1) %>%
rename(set_1_pt_team_2 = Satz.1...Ballpunkte.2) %>%
rename(set_1_duration = Satz.1...Satzdauer) %>%
rename(set_2_pt_team_1 = Satz.2...Ballpunkte.1) %>%
rename(set_2_pt_team_2 = Satz.2...Ballpunkte.2) %>%
rename(set_2_duration = Satz.2...Satzdauer) %>%
rename(set_3_pt_team_1 = Satz.3...Ballpunkte.1) %>%
rename(set_3_pt_team_2 = Satz.3...Ballpunkte.2) %>%
rename(set_3_duration = Satz.3...Satzdauer) %>%
rename(set_4_pt_team_1 = Satz.4...Ballpunkte.1) %>%
rename(set_4_pt_team_2 = Satz.4...Ballpunkte.2) %>%
rename(set_4_duration = Satz.4...Satzdauer) %>%
rename(set_5_pt_team_1 = Satz.5...Ballpunkte.1) %>%
rename(set_5_pt_team_2 = Satz.5...Ballpunkte.2) %>%
rename(set_5_duration = Satz.5...Satzdauer)
# Select columns in df_play_off round
df_play_off <- df_play_off %>% select(-c(Geschlecht, Saison, X, X..1, X..2, X..3, X..4, X..5,
X..6, Ergebnis, Wochentag, Spielfeld,
Austragungsort, Austragungsort.Ergebnis, Satzpunkte,
Ballpunkte, Spielrunde, Gruppen.Ebene))
# Rename variables
df_play_off <- df_play_off %>% rename(date = Datum) %>%
rename(time = Uhrzeit) %>%
rename(match_id = X.) %>%
rename(competition_stage = Spielgruppe) %>%
rename(team_1 = Mannschaft.1) %>%
rename(team_2 = Mannschaft.2) %>%
rename(home_team = Gastgeber) %>%
rename(match_duration = Spieldauer) %>%
rename(spectators = Zuschauerzahl) %>%
rename(set_won_team_1 = Satzpunkte.1) %>%
rename(set_won_team_2 = Satzpunkte.2) %>%
rename(set_1_pt_team_1 = Satz.1...Ballpunkte.1) %>%
rename(set_1_pt_team_2 = Satz.1...Ballpunkte.2) %>%
rename(set_1_duration = Satz.1...Satzdauer) %>%
rename(set_2_pt_team_1 = Satz.2...Ballpunkte.1) %>%
rename(set_2_pt_team_2 = Satz.2...Ballpunkte.2) %>%
rename(set_2_duration = Satz.2...Satzdauer) %>%
rename(set_3_pt_team_1 = Satz.3...Ballpunkte.1) %>%
rename(set_3_pt_team_2 = Satz.3...Ballpunkte.2) %>%
rename(set_3_duration = Satz.3...Satzdauer) %>%
rename(set_4_pt_team_1 = Satz.4...Ballpunkte.1) %>%
rename(set_4_pt_team_2 = Satz.4...Ballpunkte.2) %>%
rename(set_4_duration = Satz.4...Satzdauer) %>%
rename(set_5_pt_team_1 = Satz.5...Ballpunkte.1) %>%
rename(set_5_pt_team_2 = Satz.5...Ballpunkte.2) %>%
rename(set_5_duration = Satz.5...Satzdauer)
#### Cleaning variables main round matches #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ##
# date.time variable
df_main_round <- df_main_round %>% unite(date, time, col = "date_time", sep = " ")
df_main_round$date_time <- as.POSIXct(strptime(df_main_round$date_time, format = "%d.%m.%Y %H:%M:%S"))
# match.day
df_main_round$match_day <- as.integer(str_replace(df_main_round$match_day, "Spieltag", ""))
# durations
df_main_round <- df_main_round %>% mutate_each_(funs(as.numeric), c("set_1_duration", "set_2_duration",
"set_3_duration", "set_4_duration",
"set_5_duration")) %>%
mutate(match_duration = as.integer(match_duration))
# Delete match information of "CLOUD&HEAT VOLLEY DRESDEN" (team went bankrupt during the season)
df_main_round <- df_main_round %>% filter((team_1 != "CLOUD&HEAT VOLLEY DRESDEN") &
(team_2 != "CLOUD&HEAT VOLLEY DRESDEN") &
(home_team != "CLOUD&HEAT VOLLEY DRESDEN"))
# Competition
df_main_round$competition_stage <- "Main round"
#### Cleaning variables playoff matches #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #####
# date.time variable
df_play_off <- df_play_off %>% unite(date, time, col = "date_time", sep = " ")
df_play_off$date_time <- as.POSIXct(strptime(df_play_off$date_time,
format = "%d.%m.%Y %H:%M:%S"))
# competition_stage
df_play_off$competition_stage <- gsub(".*Viertelfinale.*", "Quarterfinal",
df_play_off$competition_stage)
df_play_off$competition_stage <- gsub(".*Halbfinale.*", "Semifinal",
df_play_off$competition_stage)
df_play_off$competition_stage <- gsub(".*Pre-Playoff.*", "Pre-Playoff",
df_play_off$competition_stage)
df_play_off$competition_stage <- gsub(".*Finale.*", "Finale",
df_play_off$competition_stage)
df_play_off$competition_stage <- gsub(".*Playdown.*", "Playdown",
df_play_off$competition_stage)
# Replace missings in home team
df_play_off$home_team[df_play_off$vbl_id == 8750462 &
df_play_off$match_id == 1510] <- "NETZHOPPERS KW-Bestensee"
df_play_off$home_team[df_play_off$vbl_id == 8750462 &
df_play_off$match_id == 1507] <- "CV Mitteldeutschland"
df_play_off$home_team[df_play_off$vbl_id == 21637369 &
df_play_off$match_id == 1510] <- "TSV Herrsching"
# Generate matchday == NA
df_play_off$match_day <- NA
#### Join datasets and add team_ids #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
# Load team_ids (file has been generated using the names of all teams). Note that since VCO Berlin has a team in
# the men's and women's league, it is necessary to join via team name and legaue gender.
df_teamids <- read_csv2("./data-raw/seasons/team_ids.csv")
# Joining data frames
df_seasons <- df_seasons %>% select(-competition_stage) %>% right_join(bind_rows(df_main_round, df_play_off)) %>%
select(-vbl_id) %>%
left_join(df_teamids, by = c("team_1" = "team_name", "league_gender" = "league_gender")) %>%
rename(team_id_1 = team_id) %>%
left_join(df_teamids, by = c("team_2" = "team_name", "league_gender" = "league_gender")) %>%
rename(team_id_2 = team_id)
#### Match level data #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ###
# Select relevant information on match level
# Gather team names, generate match, team_id and set_won variables
# Remove unnecessary variables
matches <- df_seasons %>%
select(league_gender, season_id, match_id,
competition_stage, date_time, match_day,
spectators, match_duration, team_id_1, team_id_2, team_1, team_2,
set_won_team_1, set_won_team_2) %>%
gather(team_1, team_2, key = "match", value = "team_name") %>%
mutate(match = replace(match, match == "team_1", "home"),
match = replace(match, match == "team_2", "away"),
team_id = ifelse(match == "home", team_id_1, team_id_2),
set_won = ifelse(match == "home", set_won_team_1, set_won_team_2)) %>%
select(league_gender, season_id, competition_stage,
match_id, match, match_day, date_time, spectators, match_duration,
team_id, team_name, set_won) %>%
mutate(match = factor(match))
#save(matches, file = "./data/matches.rda")
devtools::use_data(matches, matches, overwrite = TRUE)
#### Set level data #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #####
# Gather set duration and set points for each team, spread it into two columns.
# Gather team names, generate team_id and pt_set for each team
# Remove unnecessary variables and all sets which do not exists because match was
# shorter than 5 sets.
sets <- df_seasons %>%
select(-home_team, -set_won_team_1, -set_won_team_2, -match_duration,
-spectators, match_day, competition_stage, date_time) %>%
gather(set_1_duration, set_2_duration, set_3_duration, set_4_duration, set_5_duration,
set_1_pt_team_1, set_2_pt_team_1, set_3_pt_team_1, set_4_pt_team_1, set_5_pt_team_1,
set_1_pt_team_2, set_2_pt_team_2, set_3_pt_team_2, set_4_pt_team_2, set_5_pt_team_2,
key="key", value="points") %>%
mutate(set = as.integer(str_extract(key, "[:digit:]")))%>%
mutate(key = str_extract(key, "pt_team_[:digit:]")) %>%
replace_na(list(key = "set_duration")) %>%
spread(key, points) %>%
gather(team_1, team_2,
key = "match", value = "team_name") %>%
mutate(match = replace(match, match == "team_1", "home"),
match = replace(match, match == "team_2", "away"),
team_id = ifelse(match == "home", team_id_1, team_id_2),
pt_set = ifelse(match == "home", pt_team_1, pt_team_2),
match = factor(match)
) %>%
select(league_gender, season_id, match_id, match, team_id, team_name,
set, set_duration, pt_set) %>%
filter(!is.na(pt_set))
#save(sets, file = "./data/sets.rda")
devtools::use_data(sets, sets, overwrite = TRUE)
bozhinvi/volleystat documentation built on May 31, 2019, 10:01 a.m.
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#### Load libraries #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #####
library(dplyr)
library(tidyr)
library(readr)
library(purrr)
library(stringr)
#### Load season list #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ###
df_seasons <- read_csv("./data-raw/seasons/seasons.csv")
#### Download *.csv season files from VBL website #### #### #### #### #### #### #### #### #### #### #### #### #### #####
# vbl_id <- df_seasons$vbl_id
#
# for (id in 1:length(vbl_id)){
#
# try(download.file(
# paste("https://www.volleyball-bundesliga.de/servlet/league/PlayingScheduleCsvExport?matchSeriesId=",
# vbl_id[id], sep=""),
# paste0("./data-raw/seasons/", vbl_id[id],".csv")),
# silent = TRUE)
#
# }
#### Importing seasons #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ##
# Importing main round
vbl_id_main <- df_seasons[df_seasons$competition_stage == "Main",]$vbl_id
df_main_round <- data.frame()
for (num in 1:length(vbl_id_main)){
tmp <- read.csv(paste0("./data-raw/seasons/", vbl_id_main[num], ".csv"),
stringsAsFactors = FALSE, fileEncoding = "iso-8859-1", sep = ";")
tmp$vbl_id <- vbl_id_main[num]
df_main_round <- rbind(df_main_round, tmp, stringsAsFactors = FALSE)
rm(tmp)
}
# Importing playoffs
vbl_id_play <- df_seasons[df_seasons$competition_stage != "Main",]$vbl_id
df_play_off <- data.frame()
for (num in 1:length(vbl_id_play)){
tmp <- read.csv(paste0("./data-raw/seasons/", vbl_id_play[num], ".csv"),
stringsAsFactors = FALSE, fileEncoding = "iso-8859-1", sep = ";")
tmp$vbl_id <- vbl_id_play[num]
df_play_off <- rbind(df_play_off, tmp, stringsAsFactors = FALSE)
rm(tmp)
}
#### Select relevant columns and join seasons #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
# Select columns in df_main round
df_main_round <- df_main_round %>% select(-c(Geschlecht, Saison, X, X..1, X..2, X..3, X..4, X..5,
X..6, Spielrunde, Ergebnis, Satzpunkte, Ballpunkte,
Austragungsort, Wochentag, Austragungsort.Ergebnis))
# Rename variables
df_main_round <- df_main_round %>% rename(date = Datum) %>%
rename(time = Uhrzeit) %>%
rename(match_id = X.) %>%
rename(match_day = ST) %>%
rename(team_1 = Mannschaft.1) %>%
rename(team_2 = Mannschaft.2) %>%
rename(home_team = Gastgeber) %>%
rename(match_duration = Spieldauer) %>%
rename(spectators = Zuschauerzahl) %>%
rename(set_won_team_1 = Satzpunkte.1) %>%
rename(set_won_team_2 = Satzpunkte.2) %>%
rename(set_1_pt_team_1 = Satz.1...Ballpunkte.1) %>%
rename(set_1_pt_team_2 = Satz.1...Ballpunkte.2) %>%
rename(set_1_duration = Satz.1...Satzdauer) %>%
rename(set_2_pt_team_1 = Satz.2...Ballpunkte.1) %>%
rename(set_2_pt_team_2 = Satz.2...Ballpunkte.2) %>%
rename(set_2_duration = Satz.2...Satzdauer) %>%
rename(set_3_pt_team_1 = Satz.3...Ballpunkte.1) %>%
rename(set_3_pt_team_2 = Satz.3...Ballpunkte.2) %>%
rename(set_3_duration = Satz.3...Satzdauer) %>%
rename(set_4_pt_team_1 = Satz.4...Ballpunkte.1) %>%
rename(set_4_pt_team_2 = Satz.4...Ballpunkte.2) %>%
rename(set_4_duration = Satz.4...Satzdauer) %>%
rename(set_5_pt_team_1 = Satz.5...Ballpunkte.1) %>%
rename(set_5_pt_team_2 = Satz.5...Ballpunkte.2) %>%
rename(set_5_duration = Satz.5...Satzdauer)
# Select columns in df_play_off round
df_play_off <- df_play_off %>% select(-c(Geschlecht, Saison, X, X..1, X..2, X..3, X..4, X..5,
X..6, Ergebnis, Wochentag, Spielfeld,
Austragungsort, Austragungsort.Ergebnis, Satzpunkte,
Ballpunkte, Spielrunde, Gruppen.Ebene))
# Rename variables
df_play_off <- df_play_off %>% rename(date = Datum) %>%
rename(time = Uhrzeit) %>%
rename(match_id = X.) %>%
rename(competition_stage = Spielgruppe) %>%
rename(team_1 = Mannschaft.1) %>%
rename(team_2 = Mannschaft.2) %>%
rename(home_team = Gastgeber) %>%
rename(match_duration = Spieldauer) %>%
rename(spectators = Zuschauerzahl) %>%
rename(set_won_team_1 = Satzpunkte.1) %>%
rename(set_won_team_2 = Satzpunkte.2) %>%
rename(set_1_pt_team_1 = Satz.1...Ballpunkte.1) %>%
rename(set_1_pt_team_2 = Satz.1...Ballpunkte.2) %>%
rename(set_1_duration = Satz.1...Satzdauer) %>%
rename(set_2_pt_team_1 = Satz.2...Ballpunkte.1) %>%
rename(set_2_pt_team_2 = Satz.2...Ballpunkte.2) %>%
rename(set_2_duration = Satz.2...Satzdauer) %>%
rename(set_3_pt_team_1 = Satz.3...Ballpunkte.1) %>%
rename(set_3_pt_team_2 = Satz.3...Ballpunkte.2) %>%
rename(set_3_duration = Satz.3...Satzdauer) %>%
rename(set_4_pt_team_1 = Satz.4...Ballpunkte.1) %>%
rename(set_4_pt_team_2 = Satz.4...Ballpunkte.2) %>%
rename(set_4_duration = Satz.4...Satzdauer) %>%
rename(set_5_pt_team_1 = Satz.5...Ballpunkte.1) %>%
rename(set_5_pt_team_2 = Satz.5...Ballpunkte.2) %>%
rename(set_5_duration = Satz.5...Satzdauer)
#### Cleaning variables main round matches #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ##
# date.time variable
df_main_round <- df_main_round %>% unite(date, time, col = "date_time", sep = " ")
df_main_round$date_time <- as.POSIXct(strptime(df_main_round$date_time, format = "%d.%m.%Y %H:%M:%S"))
# match.day
df_main_round$match_day <- as.integer(str_replace(df_main_round$match_day, "Spieltag", ""))
# durations
df_main_round <- df_main_round %>% mutate_each_(funs(as.numeric), c("set_1_duration", "set_2_duration",
"set_3_duration", "set_4_duration",
"set_5_duration")) %>%
mutate(match_duration = as.integer(match_duration))
# Delete match information of "CLOUD&HEAT VOLLEY DRESDEN" (team went bankrupt during the season)
df_main_round <- df_main_round %>% filter((team_1 != "CLOUD&HEAT VOLLEY DRESDEN") &
(team_2 != "CLOUD&HEAT VOLLEY DRESDEN") &
(home_team != "CLOUD&HEAT VOLLEY DRESDEN"))
# Competition
df_main_round$competition_stage <- "Main round"
#### Cleaning variables playoff matches #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #####
# date.time variable
df_play_off <- df_play_off %>% unite(date, time, col = "date_time", sep = " ")
df_play_off$date_time <- as.POSIXct(strptime(df_play_off$date_time,
format = "%d.%m.%Y %H:%M:%S"))
# competition_stage
df_play_off$competition_stage <- gsub(".*Viertelfinale.*", "Quarterfinal",
df_play_off$competition_stage)
df_play_off$competition_stage <- gsub(".*Halbfinale.*", "Semifinal",
df_play_off$competition_stage)
df_play_off$competition_stage <- gsub(".*Pre-Playoff.*", "Pre-Playoff",
df_play_off$competition_stage)
df_play_off$competition_stage <- gsub(".*Finale.*", "Finale",
df_play_off$competition_stage)
df_play_off$competition_stage <- gsub(".*Playdown.*", "Playdown",
df_play_off$competition_stage)
# Replace missings in home team
df_play_off$home_team[df_play_off$vbl_id == 8750462 &
df_play_off$match_id == 1510] <- "NETZHOPPERS KW-Bestensee"
df_play_off$home_team[df_play_off$vbl_id == 8750462 &
df_play_off$match_id == 1507] <- "CV Mitteldeutschland"
df_play_off$home_team[df_play_off$vbl_id == 21637369 &
df_play_off$match_id == 1510] <- "TSV Herrsching"
# Generate matchday == NA
df_play_off$match_day <- NA
#### Join datasets and add team_ids #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
# Load team_ids (file has been generated using the names of all teams). Note that since VCO Berlin has a team in
# the men's and women's league, it is necessary to join via team name and legaue gender.
df_teamids <- read_csv2("./data-raw/seasons/team_ids.csv")
# Joining data frames
df_seasons <- df_seasons %>% select(-competition_stage) %>% right_join(bind_rows(df_main_round, df_play_off)) %>%
select(-vbl_id) %>%
left_join(df_teamids, by = c("team_1" = "team_name", "league_gender" = "league_gender")) %>%
rename(team_id_1 = team_id) %>%
left_join(df_teamids, by = c("team_2" = "team_name", "league_gender" = "league_gender")) %>%
rename(team_id_2 = team_id)
#### Match level data #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ###
# Select relevant information on match level
# Gather team names, generate match, team_id and set_won variables
# Remove unnecessary variables
matches <- df_seasons %>%
select(league_gender, season_id, match_id,
competition_stage, date_time, match_day,
spectators, match_duration, team_id_1, team_id_2, team_1, team_2,
set_won_team_1, set_won_team_2) %>%
gather(team_1, team_2, key = "match", value = "team_name") %>%
mutate(match = replace(match, match == "team_1", "home"),
match = replace(match, match == "team_2", "away"),
team_id = ifelse(match == "home", team_id_1, team_id_2),
set_won = ifelse(match == "home", set_won_team_1, set_won_team_2)) %>%
select(league_gender, season_id, competition_stage,
match_id, match, match_day, date_time, spectators, match_duration,
team_id, team_name, set_won) %>%
mutate(match = factor(match))
#save(matches, file = "./data/matches.rda")
devtools::use_data(matches, matches, overwrite = TRUE)
#### Set level data #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #####
# Gather set duration and set points for each team, spread it into two columns.
# Gather team names, generate team_id and pt_set for each team
# Remove unnecessary variables and all sets which do not exists because match was
# shorter than 5 sets.
sets <- df_seasons %>%
select(-home_team, -set_won_team_1, -set_won_team_2, -match_duration,
-spectators, match_day, competition_stage, date_time) %>%
gather(set_1_duration, set_2_duration, set_3_duration, set_4_duration, set_5_duration,
set_1_pt_team_1, set_2_pt_team_1, set_3_pt_team_1, set_4_pt_team_1, set_5_pt_team_1,
set_1_pt_team_2, set_2_pt_team_2, set_3_pt_team_2, set_4_pt_team_2, set_5_pt_team_2,
key="key", value="points") %>%
mutate(set = as.integer(str_extract(key, "[:digit:]")))%>%
mutate(key = str_extract(key, "pt_team_[:digit:]")) %>%
replace_na(list(key = "set_duration")) %>%
spread(key, points) %>%
gather(team_1, team_2,
key = "match", value = "team_name") %>%
mutate(match = replace(match, match == "team_1", "home"),
match = replace(match, match == "team_2", "away"),
team_id = ifelse(match == "home", team_id_1, team_id_2),
pt_set = ifelse(match == "home", pt_team_1, pt_team_2),
match = factor(match)
) %>%
select(league_gender, season_id, match_id, match, team_id, team_name,
set, set_duration, pt_set) %>%
filter(!is.na(pt_set))
#save(sets, file = "./data/sets.rda")
devtools::use_data(sets, sets, overwrite = TRUE)
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