data-raw/teams.R
In bozhinvi/volleystat: Detailed Statistics on Volleyball Matches
#### Load libraries #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #####
library(dplyr)
library(tidyr)
library(readr)
library(purrr)
library(countrycode)
#library(stringi)
library(textclean)
library(tools)
#### Load team list #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #####
df_teamlist <- read_csv2("./data-raw/teams/teams.csv")
#### Download *.csv team files from VBL website #### #### #### #### #### #### #### #### #### #### #### #### #### #### ##
vbl_id <- df_teamlist$vbl_id
# for (i in 1:length(vbl_id)){
#
# try(download.file(
# paste0("https://www.volleyball-bundesliga.de/servlet/sportsclub/TeamMemberCsvExport?teamId=", vbl_id[i]),
# paste0("./data-raw//teams/", vbl_id[i],".csv")),
# silent = TRUE)
#
# }
#### Importing teams #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
df_teams <- data.frame()
for (id in 1:length(vbl_id)){
tmp <- read.csv(paste0("./data-raw/teams/", vbl_id[id], ".csv"),
stringsAsFactors = FALSE, fileEncoding = "iso-8859-1", sep = ";")
tmp$vbl_id <- vbl_id[id]
df_teams <- rbind(df_teams, tmp, stringsAsFactors = FALSE)
rm(tmp)
}
df_teams <- as_tibble(df_teams)
#### Select relevant columns and join teams and remove vbl.id #### #### #### #### #### #### #### #### #### #### #### ###
df_teams <- df_teams %>%
left_join(df_teamlist, by = c("vbl_id" = "vbl_id")) %>%
select(season_id, league_gender, team_id, team_name, Nachname,
Vorname, Größe, Geschlecht, Geburtsdatum, Trikot, Staatsangehörigkeit,
NAT, Position.Funktion.Offizieller, Staatsangehörigkeit)
#### Rename columns #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #####
names(df_teams) <- c("season_id", "league_gender", "team_id", "team_name", "lastname", "firstname",
"height", "gender", "birthdate", "shirt_number", "nat_long", "nat_short",
"position")
#### Recoding variables #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #
# Firstname
df_teams[df_teams$firstname == "Bart?omiej",]$firstname <- "Bartlomiej"
# Lastname
df_teams[df_teams$lastname == "Bo??d?",]$lastname <- "Bolqdzl"
df_teams[df_teams$lastname == "Sláde?ek",]$lastname <- "Sladecek"
# Remove non-ASCII codes
showNonASCII(df_teams$lastname)
df_teams$lastname <- gsub("ü", "ue", df_teams$lastname)
df_teams$lastname <- gsub("ö", "oe", df_teams$lastname)
df_teams$lastname <- gsub("ß", "ss", df_teams$lastname)
df_teams$lastname <- gsub("ä", "ae", df_teams$lastname)
df_teams$lastname <- gsub("á", "a", df_teams$lastname)
df_teams$lastname <- gsub("é", "e", df_teams$lastname)
df_teams$lastname <- gsub("ó", "o", df_teams$lastname)
df_teams$lastname <- gsub("ú", "u", df_teams$lastname)
df_teams$lastname <- gsub("ø", "oe", df_teams$lastname)
df_teams$lastname <- gsub("", "s", df_teams$lastname)
df_teams$lastname <- replace_non_ascii(df_teams$lastname)
showNonASCII(df_teams$firstname)
df_teams$firstname <- gsub("ü", "ue", df_teams$firstname)
df_teams$firstname <- gsub("ö", "oe", df_teams$firstname)
df_teams$firstname <- gsub("ß", "ss", df_teams$firstname)
df_teams$firstname <- gsub("ä", "ae", df_teams$firstname)
df_teams$firstname <- gsub("á", "a", df_teams$firstname)
df_teams$firstname <- gsub("é", "e", df_teams$firstname)
df_teams$firstname <- gsub("ó", "o", df_teams$firstname)
df_teams$firstname <- gsub("ú", "u", df_teams$firstname)
df_teams$firstname <- gsub("ø", "oe", df_teams$firstname)
df_teams$firstname <- gsub("Ó", "O", df_teams$firstname)
df_teams$firstname <- gsub("ë", "e", df_teams$firstname)
df_teams$firstname <- gsub("", "s", df_teams$firstname)
# Gender
df_teams[df_teams$gender == "weiblich",]$gender <- "female"
df_teams[df_teams$gender == "männlich",]$gender <- "male"
df_teams$gender <- factor(x = df_teams$gender, levels = c("female", "male"))
# Birthdate
df_teams <- df_teams %>% mutate(birthdate = ifelse(birthdate != "01.01.2018", birthdate, NA))
df_teams$birthdate <- as.Date(df_teams$birthdate, format = "%d.%m.%Y")
# Position
df_teams[df_teams$position == "Diagonal",]$position <- "Diagonal"
df_teams[df_teams$position == "Mittelblock",]$position <- "Middle block"
df_teams[df_teams$position == "Außenangriff",]$position <- "Outside spiker"
df_teams[df_teams$position == "Libero",]$position <- "Libero"
df_teams[df_teams$position == "Zuspiel",]$position <- "Setter"
df_teams[df_teams$position == "Trainer",]$position <- "Coach"
df_teams[df_teams$position == "Co-Trainer",]$position <- "Assistant coach"
df_teams[df_teams$position == "Arzt",]$position <- "Doctor"
df_teams[df_teams$position == "Physiotherapeut",]$position <- "Physiotherapist"
df_teams[df_teams$position == "Statistiker",]$position <- "Statistician"
df_teams[df_teams$position == "Co-Trainer (Scout)",]$position <- "Assistant coach (scout)"
df_teams$position <- factor(df_teams$position)
# Role
df_teams$role <- "Player"
df_teams[df_teams$position == "Coach",]$role <- "Staff"
df_teams[df_teams$position == "Assistant coach",]$role <- "Staff"
df_teams[df_teams$position == "Doctor",]$role <- "Staff"
df_teams[df_teams$position == "Physiotherapist",]$role <- "Staff"
df_teams[df_teams$position == "Statistician",]$role <- "Staff"
df_teams[df_teams$position == "Assistant coach (scout)",]$role <- "Staff"
df_teams$role <- factor(df_teams$role)
# Nationality
df_teams <- df_teams %>% mutate(nationality = countrycode(nat_long, "country.name.de", "country.name"))
# List countries with NA
df_teams %>% select(nationality, nat_long) %>% filter(is.na(nationality))
# Replace countries with NA and remove German nationality variables
df_teams <- df_teams %>% select(-nat_long, -nat_short) %>%
mutate(nationality = replace(nationality, is.na(nationality), c("United Kingdom"))) %>%
mutate(nationality = factor(nationality))
#### Correcting missings #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #####
# Checking for NA's in height/birthdate/shirt_number
df_teams %>% filter(role != "Staff") %>%
select(height, birthdate, shirt_number) %>%
summary()
# Missing height
df_teams %>% filter(role != "Staff" & is.na(height))
# Missing height for Players
# Iane Henke
df_teams[(is.na(df_teams$height) == TRUE) &
(df_teams$lastname == "Henke") &
(df_teams$team_id == 2001),]$height <- 185
# Marie Haenle
df_teams[(is.na(df_teams$height) == TRUE) &
(df_teams$lastname == "Haenle") &
(df_teams$team_id == 2001),]$height <- 185
# Missing height belongs to staff according to web search
df_teams[(is.na(df_teams$height) == TRUE) & (df_teams$role != "Staff"),]$role <- "Staff"
# Missing position
df_teams %>% filter(role != "Staff" & position == "")
# Missing position in Men's league is setter according to web search
df_teams[(df_teams$position == "") &
(df_teams$team_id == 1016) &
(df_teams$league_gender == "Men") &
(df_teams$season_id == "1314"),]$position <- "Setter"
# Missing position in Women's league is setter according to web search
df_teams[(df_teams$position == "") &
(df_teams$team_id == 2007) &
(df_teams$league_gender == "Women") &
(df_teams$season_id == "1314"),]$position <- "Outside spiker"
# Missing names
# Missing names of players where checked in match reports and replaced.
# Missing player nmes mean that player were never fielded in the season.
# Therefore, it is not possible to find the name.
df_teams[df_teams$lastname == "privat - ausgeblendet",]
df_teams[(df_teams$lastname == "privat - ausgeblendet" &
df_teams$team_id == 1014 &
df_teams$shirt_number == 5 &
df_teams$season_id == "1314" &
df_teams$league_gender == "Men"),]$lastname <- "Grosche"
df_teams[(df_teams$firstname == "privat - ausgeblendet" &
df_teams$team_id == 1014 &
df_teams$shirt_number == 5 &
df_teams$season_id == "1314" &
df_teams$league_gender == "Men"),]$firstname <- "Eric"
df_teams[(df_teams$lastname == "privat - ausgeblendet" &
df_teams$team_id == 1018 &
df_teams$shirt_number == 7 &
df_teams$season_id == "1314" &
df_teams$role == "Player" &
df_teams$league_gender == "Men"),]$lastname <- "Foyer"
df_teams[(df_teams$firstname == "privat - ausgeblendet" &
df_teams$team_id == 1018 &
df_teams$shirt_number == 7 &
df_teams$season_id == "1314" &
df_teams$role == "Player" &
df_teams$league_gender == "Men"),]$firstname <- "Kevin"
df_teams[(df_teams$lastname == "privat - ausgeblendet" &
df_teams$team_id == 2011 &
df_teams$shirt_number == 5 &
df_teams$season_id == "1516" &
df_teams$league_gender == "Women"),]$lastname <- "Salancoiva"
df_teams[(df_teams$firstname == "privat - ausgeblendet" &
df_teams$team_id == 2011 &
df_teams$shirt_number == 5 &
df_teams$season_id == "1516" &
df_teams$league_gender == "Women"),]$firstname <- "Erika"
# Missing gender
df_teams %>% filter(role != "Staff" & is.na(gender))
df_teams[(df_teams$lastname == "Valentine" &
df_teams$team_id == 2010 &
df_teams$shirt_number == 9 &
df_teams$season_id == "1314" &
df_teams$league_gender == "Women"),]$gender <- "female"
#### Generate player_id #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ######
# Generate player_id which is equal to the shirt number except of cases where shirt numbers wihin a team
# season are duplicates. In these cases the player_id is equal to a 3-digit number starting with 100 for all
# but one player which keeps his original shirt number.
df_teams$player_id <- df_teams$shirt_number
# Generate data frame with duplicate shirt numbers
duplicate_shirts <- df_teams %>%
filter(role == "Player") %>%
group_by(season_id, league_gender, team_id, shirt_number) %>%
summarize(n = n()) %>%
filter(n > 1) %>%
left_join(df_teams) %>%
select(-n, -role)
#### Season 2013/2014 MEN
# Check which player is fielded with this shirt number in the season:
# read_csv("./data-raw/matchreports/MATCHSTAT_MEN_1314.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 9 & team.id == 1016) %>% select(player.name) %>% table()
# Change shirt number of Hopt to 100
df_teams[(df_teams$lastname == "Hopt" & df_teams$season_id == "1314"),]$player_id <- 100
# Check which player is fielded with this shirt number in the season:
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELMEN1314.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 10 & team.id == 1014) %>% select(player.name) %>% table()
df_teams[(df_teams$lastname == "Kross" & df_teams$season_id == "1314"),]$player_id <- 100
df_teams[(df_teams$lastname == "Pinheiro" & df_teams$season_id == "1314"),]$player_id <- 101
#### Season 2014/2015 MEN
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELMEN1415.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 2 & team.id == 1016) %>% select(player.name) %>% table()
df_teams[(df_teams$lastname == "Guenthoer" &
df_teams$firstname == "Jakob" &
df_teams$season_id == "1415"),]$player_id <- 100
#### Season 2013/2014 WOMEN
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1314.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 1 & team.id == 2002) %>% select(player.name) %>% table()
# All players are neverfielded in this season.
df_teams[(df_teams$lastname == "Gruetze" &
df_teams$team_id == 2002 &
df_teams$season_id == "1314" &
df_teams$league_gender == "Women"),]$player_id <- 100
df_teams[(df_teams$lastname == "Kirsten" &
df_teams$team_id == 2002 &
df_teams$season_id == "1314" &
df_teams$league_gender == "Women"),]$player_id <- 101
df_teams[(df_teams$lastname == "Petter" &
df_teams$team_id == 2002 &
df_teams$season_id == "1314" &
df_teams$league_gender == "Women"),]$player_id <- 102
df_teams[(df_teams$lastname == "Plath" &
df_teams$team_id == 2002 &
df_teams$season_id == "1314" &
df_teams$league_gender == "Women"),]$player_id <- 103
df_teams[(df_teams$lastname == "Wolf" &
df_teams$team_id == 2002 &
df_teams$season_id == "1314" &
df_teams$league_gender == "Women"),]$player_id <- 104
#### Season 2014/2015 WOMEN
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1415.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 2 & team.id == 2002) %>% select(player.name) %>% table()
# All players are neverfielded in this season.
df_teams[(df_teams$lastname == "Kirsten" &
df_teams$team_id == 2002 &
df_teams$season_id == "1415" &
df_teams$league_gender == "Women"),]$player_id <- 100
df_teams[(df_teams$lastname == "Petter" &
df_teams$team_id == 2002 &
df_teams$season_id == "1415" &
df_teams$league_gender == "Women"),]$player_id <- 101
df_teams[(df_teams$lastname == "Plath" &
df_teams$team_id == 2002 &
df_teams$season_id == "1415" &
df_teams$league_gender == "Women"),]$player_id <- 102
df_teams[(df_teams$lastname == "Wolf" &
df_teams$team_id == 2002 &
df_teams$season_id == "1415" &
df_teams$league_gender == "Women"),]$player_id <- 103
#### Season 2015/2016 WOMEN
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1516.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 6 & team.id == 2002) %>% select(player.name) %>% table()
# All players are neverfielded in this season.
df_teams[(df_teams$lastname == "Lowke" &
df_teams$team_id == 2002 &
df_teams$season_id == "1516" &
df_teams$league_gender == "Women"),]$player_id <- 100
df_teams[(df_teams$lastname == "Plath" &
df_teams$team_id == 2002 &
df_teams$season_id == "1516" &
df_teams$league_gender == "Women"),]$player_id <- 101
df_teams[(df_teams$lastname == "Wolf" &
df_teams$team_id == 2002 &
df_teams$season_id == "1516" &
df_teams$league_gender == "Women"),]$player_id <- 102
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1516.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 18 & team.id == 2002) %>% select(player.name) %>% table()
df_teams[(df_teams$lastname == "Onyejekwe" &
df_teams$team_id == 2002 &
df_teams$season_id == "1516" &
df_teams$league_gender == "Women"),]$player_id <- 103
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1516.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 16 & team.id == 2003) %>% select(player.name) %>% table()
df_teams[(df_teams$lastname == "Senger" &
df_teams$team_id == 2003 &
df_teams$season_id == "1516" &
df_teams$league_gender == "Women"),]$player_id <- 100
#### Season 2016/2017 WOMEN
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1617.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 1 & team.id == 2002) %>% select(player.name) %>% table()
df_teams[(df_teams$firstname == "Elena" &
df_teams$team_id == 2002 &
df_teams$season_id == "1617" &
df_teams$league_gender == "Women"),]$player_id <- 100
df_teams[(df_teams$lastname == "Weitzel" &
df_teams$team_id == 2002 &
df_teams$season_id == "1617" &
df_teams$league_gender == "Women"),]$player_id <- 101
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1617.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 17 & team.id == 2002) %>% select(player.name) %>% table()
df_teams[(df_teams$lastname == "Mancuso" &
df_teams$team_id == 2002 &
df_teams$season_id == "1617" &
df_teams$league_gender == "Women"),]$player_id <- 102
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1617.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 13 & team.id == 2012) %>% select(player.name) %>% table()
df_teams[(df_teams$lastname == "Wiesner" &
df_teams$team_id == 2012 &
df_teams$season_id == "1617" &
df_teams$league_gender == "Women"),]$player_id <- 101
#### Season 2017/2018 WOMEN
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1718.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 17 & team.id == 2002) %>% select(player.name) %>% table()
df_teams[(df_teams$lastname == "Koemmling" &
df_teams$team_id == 2002 &
df_teams$season_id == "1718" &
df_teams$league_gender == "Women"),]$player_id <- 100
df_teams[(df_teams$lastname == "Nestler" &
df_teams$team_id == 2002 &
df_teams$season_id == "1718" &
df_teams$league_gender == "Women"),]$player_id <- 101
df_teams[(df_teams$lastname == "Nitsche" &
df_teams$team_id == 2002 &
df_teams$season_id == "1718" &
df_teams$league_gender == "Women"),]$player_id <- 102
df_teams[(df_teams$lastname == "Scholz" &
df_teams$team_id == 2002 &
df_teams$season_id == "1718" &
df_teams$league_gender == "Women"),]$player_id <- 103
df_teams[(df_teams$lastname == "Stemmler" &
df_teams$team_id == 2002 &
df_teams$season_id == "1718" &
df_teams$league_gender == "Women"),]$player_id <- 104
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1718.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 18 & team.id == 2012) %>% select(player.name) %>% table()
df_teams[(df_teams$lastname == "Schieder" &
df_teams$team_id == 2012 &
df_teams$season_id == "1718" &
df_teams$league_gender == "Women"),]$player_id <- 100
# read_csv("./data-raw/Matchreports/MATCHSTAT_WOMEN_1819.csv") %>%
# select(match_id, team_id, shirt_number, player_name) %>%
# filter(shirt_number == 3 & team_id == 2002) %>% select(player_name) %>% table()
df_teams[(df_teams$lastname == "Lieb" &
df_teams$team_id == 2002 &
df_teams$season_id == "1819" &
df_teams$league_gender == "Women"),]$player_id <- 100
df_teams[(df_teams$lastname == "Nestler" &
df_teams$team_id == 2002 &
df_teams$season_id == "1819" &
df_teams$league_gender == "Women"),]$player_id <- 101
df_teams[(df_teams$lastname == "Scholz" &
df_teams$team_id == 2002 &
df_teams$season_id == "1819" &
df_teams$league_gender == "Women"),]$player_id <- 102
df_teams[(df_teams$lastname == "Strubbe" &
df_teams$team_id == 2002 &
df_teams$season_id == "1819" &
df_teams$league_gender == "Women"),]$player_id <- 103
# Remove tibble with duplicates
rm(duplicate_shirts)
#### Split and save datasets #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #
# Split dataset into Staff and Players
staff <- df_teams[df_teams$role == "Staff",] %>%
select(-role, -shirt_number, -height, -player_id) %>%
mutate(position = droplevels(position),
nationality = droplevels(nationality)) %>%
mutate(firstname = iconv(firstname, to = "utf8"),
lastname = iconv(lastname, to = "utf8"))
#save(staff, file = "./data/staff.rda")
devtools::use_data(staff, staff, overwrite = TRUE)
players <- df_teams[df_teams$role != "Staff",] %>%
select(-role, league_gender, season_id, team_id, team_name,
player_id, shirt_number, position, firstname, lastname,
gender, birthdate, height, nationality) %>%
mutate(position = droplevels(position),
nationality = droplevels(nationality)) %>%
mutate(firstname = iconv(firstname, to = "utf8"),
lastname = iconv(lastname, to = "utf8"))
#save(players, file = "./data/players.rda")
devtools::use_data(players, players, 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(countrycode)
#library(stringi)
library(textclean)
library(tools)
#### Load team list #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #####
df_teamlist <- read_csv2("./data-raw/teams/teams.csv")
#### Download *.csv team files from VBL website #### #### #### #### #### #### #### #### #### #### #### #### #### #### ##
vbl_id <- df_teamlist$vbl_id
# for (i in 1:length(vbl_id)){
#
# try(download.file(
# paste0("https://www.volleyball-bundesliga.de/servlet/sportsclub/TeamMemberCsvExport?teamId=", vbl_id[i]),
# paste0("./data-raw//teams/", vbl_id[i],".csv")),
# silent = TRUE)
#
# }
#### Importing teams #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ####
df_teams <- data.frame()
for (id in 1:length(vbl_id)){
tmp <- read.csv(paste0("./data-raw/teams/", vbl_id[id], ".csv"),
stringsAsFactors = FALSE, fileEncoding = "iso-8859-1", sep = ";")
tmp$vbl_id <- vbl_id[id]
df_teams <- rbind(df_teams, tmp, stringsAsFactors = FALSE)
rm(tmp)
}
df_teams <- as_tibble(df_teams)
#### Select relevant columns and join teams and remove vbl.id #### #### #### #### #### #### #### #### #### #### #### ###
df_teams <- df_teams %>%
left_join(df_teamlist, by = c("vbl_id" = "vbl_id")) %>%
select(season_id, league_gender, team_id, team_name, Nachname,
Vorname, Größe, Geschlecht, Geburtsdatum, Trikot, Staatsangehörigkeit,
NAT, Position.Funktion.Offizieller, Staatsangehörigkeit)
#### Rename columns #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #####
names(df_teams) <- c("season_id", "league_gender", "team_id", "team_name", "lastname", "firstname",
"height", "gender", "birthdate", "shirt_number", "nat_long", "nat_short",
"position")
#### Recoding variables #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #
# Firstname
df_teams[df_teams$firstname == "Bart?omiej",]$firstname <- "Bartlomiej"
# Lastname
df_teams[df_teams$lastname == "Bo??d?",]$lastname <- "Bolqdzl"
df_teams[df_teams$lastname == "Sláde?ek",]$lastname <- "Sladecek"
# Remove non-ASCII codes
showNonASCII(df_teams$lastname)
df_teams$lastname <- gsub("ü", "ue", df_teams$lastname)
df_teams$lastname <- gsub("ö", "oe", df_teams$lastname)
df_teams$lastname <- gsub("ß", "ss", df_teams$lastname)
df_teams$lastname <- gsub("ä", "ae", df_teams$lastname)
df_teams$lastname <- gsub("á", "a", df_teams$lastname)
df_teams$lastname <- gsub("é", "e", df_teams$lastname)
df_teams$lastname <- gsub("ó", "o", df_teams$lastname)
df_teams$lastname <- gsub("ú", "u", df_teams$lastname)
df_teams$lastname <- gsub("ø", "oe", df_teams$lastname)
df_teams$lastname <- gsub("", "s", df_teams$lastname)
df_teams$lastname <- replace_non_ascii(df_teams$lastname)
showNonASCII(df_teams$firstname)
df_teams$firstname <- gsub("ü", "ue", df_teams$firstname)
df_teams$firstname <- gsub("ö", "oe", df_teams$firstname)
df_teams$firstname <- gsub("ß", "ss", df_teams$firstname)
df_teams$firstname <- gsub("ä", "ae", df_teams$firstname)
df_teams$firstname <- gsub("á", "a", df_teams$firstname)
df_teams$firstname <- gsub("é", "e", df_teams$firstname)
df_teams$firstname <- gsub("ó", "o", df_teams$firstname)
df_teams$firstname <- gsub("ú", "u", df_teams$firstname)
df_teams$firstname <- gsub("ø", "oe", df_teams$firstname)
df_teams$firstname <- gsub("Ó", "O", df_teams$firstname)
df_teams$firstname <- gsub("ë", "e", df_teams$firstname)
df_teams$firstname <- gsub("", "s", df_teams$firstname)
# Gender
df_teams[df_teams$gender == "weiblich",]$gender <- "female"
df_teams[df_teams$gender == "männlich",]$gender <- "male"
df_teams$gender <- factor(x = df_teams$gender, levels = c("female", "male"))
# Birthdate
df_teams <- df_teams %>% mutate(birthdate = ifelse(birthdate != "01.01.2018", birthdate, NA))
df_teams$birthdate <- as.Date(df_teams$birthdate, format = "%d.%m.%Y")
# Position
df_teams[df_teams$position == "Diagonal",]$position <- "Diagonal"
df_teams[df_teams$position == "Mittelblock",]$position <- "Middle block"
df_teams[df_teams$position == "Außenangriff",]$position <- "Outside spiker"
df_teams[df_teams$position == "Libero",]$position <- "Libero"
df_teams[df_teams$position == "Zuspiel",]$position <- "Setter"
df_teams[df_teams$position == "Trainer",]$position <- "Coach"
df_teams[df_teams$position == "Co-Trainer",]$position <- "Assistant coach"
df_teams[df_teams$position == "Arzt",]$position <- "Doctor"
df_teams[df_teams$position == "Physiotherapeut",]$position <- "Physiotherapist"
df_teams[df_teams$position == "Statistiker",]$position <- "Statistician"
df_teams[df_teams$position == "Co-Trainer (Scout)",]$position <- "Assistant coach (scout)"
df_teams$position <- factor(df_teams$position)
# Role
df_teams$role <- "Player"
df_teams[df_teams$position == "Coach",]$role <- "Staff"
df_teams[df_teams$position == "Assistant coach",]$role <- "Staff"
df_teams[df_teams$position == "Doctor",]$role <- "Staff"
df_teams[df_teams$position == "Physiotherapist",]$role <- "Staff"
df_teams[df_teams$position == "Statistician",]$role <- "Staff"
df_teams[df_teams$position == "Assistant coach (scout)",]$role <- "Staff"
df_teams$role <- factor(df_teams$role)
# Nationality
df_teams <- df_teams %>% mutate(nationality = countrycode(nat_long, "country.name.de", "country.name"))
# List countries with NA
df_teams %>% select(nationality, nat_long) %>% filter(is.na(nationality))
# Replace countries with NA and remove German nationality variables
df_teams <- df_teams %>% select(-nat_long, -nat_short) %>%
mutate(nationality = replace(nationality, is.na(nationality), c("United Kingdom"))) %>%
mutate(nationality = factor(nationality))
#### Correcting missings #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #####
# Checking for NA's in height/birthdate/shirt_number
df_teams %>% filter(role != "Staff") %>%
select(height, birthdate, shirt_number) %>%
summary()
# Missing height
df_teams %>% filter(role != "Staff" & is.na(height))
# Missing height for Players
# Iane Henke
df_teams[(is.na(df_teams$height) == TRUE) &
(df_teams$lastname == "Henke") &
(df_teams$team_id == 2001),]$height <- 185
# Marie Haenle
df_teams[(is.na(df_teams$height) == TRUE) &
(df_teams$lastname == "Haenle") &
(df_teams$team_id == 2001),]$height <- 185
# Missing height belongs to staff according to web search
df_teams[(is.na(df_teams$height) == TRUE) & (df_teams$role != "Staff"),]$role <- "Staff"
# Missing position
df_teams %>% filter(role != "Staff" & position == "")
# Missing position in Men's league is setter according to web search
df_teams[(df_teams$position == "") &
(df_teams$team_id == 1016) &
(df_teams$league_gender == "Men") &
(df_teams$season_id == "1314"),]$position <- "Setter"
# Missing position in Women's league is setter according to web search
df_teams[(df_teams$position == "") &
(df_teams$team_id == 2007) &
(df_teams$league_gender == "Women") &
(df_teams$season_id == "1314"),]$position <- "Outside spiker"
# Missing names
# Missing names of players where checked in match reports and replaced.
# Missing player nmes mean that player were never fielded in the season.
# Therefore, it is not possible to find the name.
df_teams[df_teams$lastname == "privat - ausgeblendet",]
df_teams[(df_teams$lastname == "privat - ausgeblendet" &
df_teams$team_id == 1014 &
df_teams$shirt_number == 5 &
df_teams$season_id == "1314" &
df_teams$league_gender == "Men"),]$lastname <- "Grosche"
df_teams[(df_teams$firstname == "privat - ausgeblendet" &
df_teams$team_id == 1014 &
df_teams$shirt_number == 5 &
df_teams$season_id == "1314" &
df_teams$league_gender == "Men"),]$firstname <- "Eric"
df_teams[(df_teams$lastname == "privat - ausgeblendet" &
df_teams$team_id == 1018 &
df_teams$shirt_number == 7 &
df_teams$season_id == "1314" &
df_teams$role == "Player" &
df_teams$league_gender == "Men"),]$lastname <- "Foyer"
df_teams[(df_teams$firstname == "privat - ausgeblendet" &
df_teams$team_id == 1018 &
df_teams$shirt_number == 7 &
df_teams$season_id == "1314" &
df_teams$role == "Player" &
df_teams$league_gender == "Men"),]$firstname <- "Kevin"
df_teams[(df_teams$lastname == "privat - ausgeblendet" &
df_teams$team_id == 2011 &
df_teams$shirt_number == 5 &
df_teams$season_id == "1516" &
df_teams$league_gender == "Women"),]$lastname <- "Salancoiva"
df_teams[(df_teams$firstname == "privat - ausgeblendet" &
df_teams$team_id == 2011 &
df_teams$shirt_number == 5 &
df_teams$season_id == "1516" &
df_teams$league_gender == "Women"),]$firstname <- "Erika"
# Missing gender
df_teams %>% filter(role != "Staff" & is.na(gender))
df_teams[(df_teams$lastname == "Valentine" &
df_teams$team_id == 2010 &
df_teams$shirt_number == 9 &
df_teams$season_id == "1314" &
df_teams$league_gender == "Women"),]$gender <- "female"
#### Generate player_id #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### ######
# Generate player_id which is equal to the shirt number except of cases where shirt numbers wihin a team
# season are duplicates. In these cases the player_id is equal to a 3-digit number starting with 100 for all
# but one player which keeps his original shirt number.
df_teams$player_id <- df_teams$shirt_number
# Generate data frame with duplicate shirt numbers
duplicate_shirts <- df_teams %>%
filter(role == "Player") %>%
group_by(season_id, league_gender, team_id, shirt_number) %>%
summarize(n = n()) %>%
filter(n > 1) %>%
left_join(df_teams) %>%
select(-n, -role)
#### Season 2013/2014 MEN
# Check which player is fielded with this shirt number in the season:
# read_csv("./data-raw/matchreports/MATCHSTAT_MEN_1314.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 9 & team.id == 1016) %>% select(player.name) %>% table()
# Change shirt number of Hopt to 100
df_teams[(df_teams$lastname == "Hopt" & df_teams$season_id == "1314"),]$player_id <- 100
# Check which player is fielded with this shirt number in the season:
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELMEN1314.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 10 & team.id == 1014) %>% select(player.name) %>% table()
df_teams[(df_teams$lastname == "Kross" & df_teams$season_id == "1314"),]$player_id <- 100
df_teams[(df_teams$lastname == "Pinheiro" & df_teams$season_id == "1314"),]$player_id <- 101
#### Season 2014/2015 MEN
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELMEN1415.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 2 & team.id == 1016) %>% select(player.name) %>% table()
df_teams[(df_teams$lastname == "Guenthoer" &
df_teams$firstname == "Jakob" &
df_teams$season_id == "1415"),]$player_id <- 100
#### Season 2013/2014 WOMEN
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1314.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 1 & team.id == 2002) %>% select(player.name) %>% table()
# All players are neverfielded in this season.
df_teams[(df_teams$lastname == "Gruetze" &
df_teams$team_id == 2002 &
df_teams$season_id == "1314" &
df_teams$league_gender == "Women"),]$player_id <- 100
df_teams[(df_teams$lastname == "Kirsten" &
df_teams$team_id == 2002 &
df_teams$season_id == "1314" &
df_teams$league_gender == "Women"),]$player_id <- 101
df_teams[(df_teams$lastname == "Petter" &
df_teams$team_id == 2002 &
df_teams$season_id == "1314" &
df_teams$league_gender == "Women"),]$player_id <- 102
df_teams[(df_teams$lastname == "Plath" &
df_teams$team_id == 2002 &
df_teams$season_id == "1314" &
df_teams$league_gender == "Women"),]$player_id <- 103
df_teams[(df_teams$lastname == "Wolf" &
df_teams$team_id == 2002 &
df_teams$season_id == "1314" &
df_teams$league_gender == "Women"),]$player_id <- 104
#### Season 2014/2015 WOMEN
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1415.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 2 & team.id == 2002) %>% select(player.name) %>% table()
# All players are neverfielded in this season.
df_teams[(df_teams$lastname == "Kirsten" &
df_teams$team_id == 2002 &
df_teams$season_id == "1415" &
df_teams$league_gender == "Women"),]$player_id <- 100
df_teams[(df_teams$lastname == "Petter" &
df_teams$team_id == 2002 &
df_teams$season_id == "1415" &
df_teams$league_gender == "Women"),]$player_id <- 101
df_teams[(df_teams$lastname == "Plath" &
df_teams$team_id == 2002 &
df_teams$season_id == "1415" &
df_teams$league_gender == "Women"),]$player_id <- 102
df_teams[(df_teams$lastname == "Wolf" &
df_teams$team_id == 2002 &
df_teams$season_id == "1415" &
df_teams$league_gender == "Women"),]$player_id <- 103
#### Season 2015/2016 WOMEN
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1516.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 6 & team.id == 2002) %>% select(player.name) %>% table()
# All players are neverfielded in this season.
df_teams[(df_teams$lastname == "Lowke" &
df_teams$team_id == 2002 &
df_teams$season_id == "1516" &
df_teams$league_gender == "Women"),]$player_id <- 100
df_teams[(df_teams$lastname == "Plath" &
df_teams$team_id == 2002 &
df_teams$season_id == "1516" &
df_teams$league_gender == "Women"),]$player_id <- 101
df_teams[(df_teams$lastname == "Wolf" &
df_teams$team_id == 2002 &
df_teams$season_id == "1516" &
df_teams$league_gender == "Women"),]$player_id <- 102
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1516.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 18 & team.id == 2002) %>% select(player.name) %>% table()
df_teams[(df_teams$lastname == "Onyejekwe" &
df_teams$team_id == 2002 &
df_teams$season_id == "1516" &
df_teams$league_gender == "Women"),]$player_id <- 103
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1516.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 16 & team.id == 2003) %>% select(player.name) %>% table()
df_teams[(df_teams$lastname == "Senger" &
df_teams$team_id == 2003 &
df_teams$season_id == "1516" &
df_teams$league_gender == "Women"),]$player_id <- 100
#### Season 2016/2017 WOMEN
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1617.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 1 & team.id == 2002) %>% select(player.name) %>% table()
df_teams[(df_teams$firstname == "Elena" &
df_teams$team_id == 2002 &
df_teams$season_id == "1617" &
df_teams$league_gender == "Women"),]$player_id <- 100
df_teams[(df_teams$lastname == "Weitzel" &
df_teams$team_id == 2002 &
df_teams$season_id == "1617" &
df_teams$league_gender == "Women"),]$player_id <- 101
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1617.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 17 & team.id == 2002) %>% select(player.name) %>% table()
df_teams[(df_teams$lastname == "Mancuso" &
df_teams$team_id == 2002 &
df_teams$season_id == "1617" &
df_teams$league_gender == "Women"),]$player_id <- 102
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1617.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 13 & team.id == 2012) %>% select(player.name) %>% table()
df_teams[(df_teams$lastname == "Wiesner" &
df_teams$team_id == 2012 &
df_teams$season_id == "1617" &
df_teams$league_gender == "Women"),]$player_id <- 101
#### Season 2017/2018 WOMEN
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1718.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 17 & team.id == 2002) %>% select(player.name) %>% table()
df_teams[(df_teams$lastname == "Koemmling" &
df_teams$team_id == 2002 &
df_teams$season_id == "1718" &
df_teams$league_gender == "Women"),]$player_id <- 100
df_teams[(df_teams$lastname == "Nestler" &
df_teams$team_id == 2002 &
df_teams$season_id == "1718" &
df_teams$league_gender == "Women"),]$player_id <- 101
df_teams[(df_teams$lastname == "Nitsche" &
df_teams$team_id == 2002 &
df_teams$season_id == "1718" &
df_teams$league_gender == "Women"),]$player_id <- 102
df_teams[(df_teams$lastname == "Scholz" &
df_teams$team_id == 2002 &
df_teams$season_id == "1718" &
df_teams$league_gender == "Women"),]$player_id <- 103
df_teams[(df_teams$lastname == "Stemmler" &
df_teams$team_id == 2002 &
df_teams$season_id == "1718" &
df_teams$league_gender == "Women"),]$player_id <- 104
# read_csv("./Matchreports/SEASONSMATCHPLAYERLEVELWOMEN1718.csv") %>%
# select(match.id, team.id, shirt.number, player.name) %>%
# filter(shirt.number == 18 & team.id == 2012) %>% select(player.name) %>% table()
df_teams[(df_teams$lastname == "Schieder" &
df_teams$team_id == 2012 &
df_teams$season_id == "1718" &
df_teams$league_gender == "Women"),]$player_id <- 100
# read_csv("./data-raw/Matchreports/MATCHSTAT_WOMEN_1819.csv") %>%
# select(match_id, team_id, shirt_number, player_name) %>%
# filter(shirt_number == 3 & team_id == 2002) %>% select(player_name) %>% table()
df_teams[(df_teams$lastname == "Lieb" &
df_teams$team_id == 2002 &
df_teams$season_id == "1819" &
df_teams$league_gender == "Women"),]$player_id <- 100
df_teams[(df_teams$lastname == "Nestler" &
df_teams$team_id == 2002 &
df_teams$season_id == "1819" &
df_teams$league_gender == "Women"),]$player_id <- 101
df_teams[(df_teams$lastname == "Scholz" &
df_teams$team_id == 2002 &
df_teams$season_id == "1819" &
df_teams$league_gender == "Women"),]$player_id <- 102
df_teams[(df_teams$lastname == "Strubbe" &
df_teams$team_id == 2002 &
df_teams$season_id == "1819" &
df_teams$league_gender == "Women"),]$player_id <- 103
# Remove tibble with duplicates
rm(duplicate_shirts)
#### Split and save datasets #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #### #
# Split dataset into Staff and Players
staff <- df_teams[df_teams$role == "Staff",] %>%
select(-role, -shirt_number, -height, -player_id) %>%
mutate(position = droplevels(position),
nationality = droplevels(nationality)) %>%
mutate(firstname = iconv(firstname, to = "utf8"),
lastname = iconv(lastname, to = "utf8"))
#save(staff, file = "./data/staff.rda")
devtools::use_data(staff, staff, overwrite = TRUE)
players <- df_teams[df_teams$role != "Staff",] %>%
select(-role, league_gender, season_id, team_id, team_name,
player_id, shirt_number, position, firstname, lastname,
gender, birthdate, height, nationality) %>%
mutate(position = droplevels(position),
nationality = droplevels(nationality)) %>%
mutate(firstname = iconv(firstname, to = "utf8"),
lastname = iconv(lastname, to = "utf8"))
#save(players, file = "./data/players.rda")
devtools::use_data(players, players, overwrite = TRUE)
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