.Rhistory

"Quarter 4 [15:00-10:00]",
"Quarter 4 [10:00-5:00]",
"Quarter 4 [5:00-End]",
"Overtime") -> game_clock_chunked
# Pull in png pixel sizes
df_team_info$Logo_Path_Height <- 0
df_team_info$Logo_Path_Width <- 0
df_team_info$Shadow_Logo_Path_Height <- 0
df_team_info$Shadow_Logo_Path_Width <- 0
df_team_info$Wordmark_Path_Height <- 0
df_team_info$Wordmark_Path_Width <- 0
for(i in 1:nrow(df_team_info)) {
png_dim <- dim(readPNG(paste("www/", df_team_info$Logo_Path[i], sep = "")))
df_team_info$Logo_Path_Height[i] <- png_dim[1]
df_team_info$Logo_Path_Width[i] <- png_dim[2]
png_dim <- dim(readPNG(paste("www/", df_team_info$Shadow_Logo_Path[i], sep = "")))
df_team_info$Shadow_Logo_Path_Height[i] <- png_dim[1]
df_team_info$Shadow_Logo_Path_Width[i] <- png_dim[2]
png_dim <- dim(readPNG(paste("www/", df_team_info$Wordmark_Path[i], sep = "")))
df_team_info$Wordmark_Path_Height[i] <- png_dim[1]
df_team_info$Wordmark_Path_Width[i] <- png_dim[2]
}
View(df_team_info)
View(df_team_info)
df_team_info <- read.csv("Team_Info.csv", header = T, stringsAsFactors = F)
View(df_team_info)
shiny::runApp()
library(rsconnect)
library(tidyverse)
# Update Master Files
file.copy(from = "/Users/robweber/Documents/Professional/First Line/World Lacrosse/Master File Backup/MLAX_Worlds_Master_File.csv",
to = "/Users/robweber/Documents/Professional/First Line/World Lacrosse/MLAX_Germany_Reporting_Dashboard_1/", overwrite = TRUE)
file.copy(from = "/Users/robweber/Documents/Professional/First Line/World Lacrosse/Master File Backup/WLAX_Worlds_Master_File.csv",
to = "/Users/robweber/Documents/Professional/First Line/World Lacrosse/WLAX_Germany_Reporting_Dashboard_1/", overwrite = TRUE)
runApp()
deployApp("/Users/robweber/Documents/Professional/First Line/World Lacrosse/WLAX_Germany_Reporting_Dashboard_1",
appName = "WLAX_Germany_Reporting_Dashboard_1", account = "robertweber98", forceUpdate = TRUE)
library(rsconnect)
library(tidyverse)
# Update Master Files
file.copy(from = "/Users/robweber/Documents/Professional/First Line/World Lacrosse/Master File Backup/MLAX_Worlds_Master_File.csv",
to = "/Users/robweber/Documents/Professional/First Line/World Lacrosse/MLAX_Germany_Reporting_Dashboard_1/", overwrite = TRUE)
file.copy(from = "/Users/robweber/Documents/Professional/First Line/World Lacrosse/Master File Backup/WLAX_Worlds_Master_File.csv",
to = "/Users/robweber/Documents/Professional/First Line/World Lacrosse/WLAX_Germany_Reporting_Dashboard_1/", overwrite = TRUE)
# Deploying
runApp()
runApp()
deployApp("/Users/robweber/Documents/Professional/First Line/World Lacrosse/WLAX_Germany_Reporting_Dashboard_1",
appName = "WLAX_Germany_Reporting_Dashboard_1", account = "robertweber98", forceUpdate = TRUE)
##################### 2024 Germany Women's Dashboard #####################
############## Setup ##############
#### Librarying ####
library(shiny) # base
library(tidyverse) # data manipulation, etc
library(shinydashboard) # aesthetics
library(shinyWidgets) # fancy boxes and inputs
library(shinycssloaders) # loading spinners
library(plotly) # viz
library(png) # reading png files
library(scales) # for "percent" function
library(lubridate) # to treat game dates correctly
library(DT) #datatable controls
library(leaflet) # munual continuous color scales
#### Options ####
options(warn=-1)
options(dplyr.summarise.inform = FALSE)
#### Reading in External Data/Info ####
df_master <- read.csv("WLAX_Worlds_Master_File.csv", header = T, stringsAsFactors = F)
df_team_info <- read.csv("Team_Info.csv", header = T, stringsAsFactors = F)
goal_marker_coords <- read.csv("Lacrosse-Goal-2_MarkerCoords.csv", header = T, stringsAsFactors = F)
field_marker_coords <- read.csv("Lacrosse-Field-2_Grid_MarkerCoords.csv", header = T, stringsAsFactors = F)
#### Plot Pictures ####
goal_base <- "www/Lacrosse-Goal-2.png"
field_base <- "www/Lacrosse-Field-2.png"
field_grid <- "www/Lacrosse-Field-2_Grid.png"
field_transparent <- "www/Lacrosse-Field-2_Transparent.png"
shot_angle <- "www/Release Point for Tracking_6zones.png"
#### Parameters ####
# Report team
report_team_row <- which(df_team_info$Team == "Germany")
# Game list
df_master$Game <- paste(df_master$Game_Date, ": ", df_master$Home_Team, " vs. ", df_master$Away_Team, sep = "")
game_list <- df_master %>%
group_by(Game_Date, Game) %>%
count() %>%
ungroup() %>%
select(-n) %>%
mutate(Game_Date = as.Date(Game_Date, "%m/%d/%Y")) %>%
arrange(desc(Game_Date), Game)
game_list <- game_list$Game
# Starting Game Row
starting_game_row <- 1
# dates data frame object for server
df_dates <- df_master %>%
group_by(Game_Date, Game, Home_Team, Away_Team) %>%
count() %>%
ungroup() %>%
select(-n)
# Goalie List
all_goalies <- unique(c(df_master$Home_Goalie_Name, df_master$Away_Goalie_Name))
# Field Location Bin df
fieldloc_bins <- data.frame("Bin" = c("Back Right", "Back Left", "Right Back", "Left Back",
"1", "2", "3",
"4", "5", "6",
"7", "8", "9",
"Right Front", "Left Front", "Crease Right", "Crease Left",
"Behind the Net", "Right Under", "Left Under"),
"x_max" = c(0.729, 0.729, 0.845, 0.845,
0.788, 0.788, 0.788,
0.845, 0.845, 0.845,
0.902, 0.902, 0.902,
0.99, 0.99, 0.99, 0.99,
2, 2, 2),
"x_min" = c(-1, -1, 0.729, 0.729,
0.729, 0.729, 0.729,
0.788, 0.788, 0.788,
0.845, 0.845, 0.845,
0.845, 0.845, 0.902, 0.902,
0.99, 0.99, 0.99),
"y_max" = c(2, 0.49, 2, 0.15,
0.372, 0.6181, 0.8505,
0.372, 0.6181, 0.8505,
0.372, 0.6181, 0.8505,
2, 0.15, 0.8505, 0.49,
0.696, 2, 0.299),
"y_min" = c(0.49, -1, 0.8505, -1,
0.15, 0.372, 0.6181,
0.15, 0.372, 0.6181,
0.15, 0.372, 0.6181,
0.8505, -1, 0.49, 0.15,
0.299, 0.696, -1),
stringsAsFactors = F)
# Shot On, Off, and Post lists
shot_on <- c("5-Hole", "Left Leg", "Left Shoulder", "Under Left Arm", "Right Leg", "Right Shoulder", "Under Right Arm")
shot_off <- c("Left Miss", "Right Miss", "Top Miss")
shot_post <- c("Crossbar", "Left Post", "Right Post")
# General shooting percentages
gen_shoot_per <- length(which(df_master$Event_Type == "Shot" & (df_master$Result == "Goal" | df_master$Result == "Miss")))
gen_shoot_per <- length(which(df_master$Event_Type == "Shot" & df_master$Result == "Goal")) / gen_shoot_per
shoton_shoot_per <- length(which(df_master$Event_Type == "Shot" & df_master$Net_Location %in% shot_on))
shoton_shoot_per <- length(which(df_master$Result == "Goal")) / shoton_shoot_per
# Need to add some image sourcing objects to the team info df
df_team_info$HTML <- "none"
for(i in 1:nrow(df_team_info)) {
df_team_info$HTML[i] <- sprintf(paste("<img src = '", df_team_info$Logo_Path[i],
"' width = 30px><div class = 'jhr'>%s</div></img>", sep = ""),
df_team_info$Full_Name[i])
}
# Field bin Dataframe
field_df.b <- data.frame("Field_Location_Bin" = c("Back Right", "Back Left", "Right Back", "Left Back",
"1", "2", "3", "4", "5", "6", "7", "8", "9",
"Right Front", "Left Front", "Crease Right", "Crease Left",
"Behind the Net", "Right Under", "Left Under"),
stringsAsFactors = F)
# Shot Location Dataframe
goal_info.b <- data.frame("Net_Location" = rep(c("5-Hole", "Chest", "Crossbar", "Empty Net",
"Left Leg", "Left Miss", "Left Shoulder", "Left Post", "Under Left Arm",
"Right Leg", "Right Miss", "Right Shoulder", "Right Post", "Under Right Arm",
"Top Miss")),
stringsAsFactors = F)
# Shot Angle bin Dataframe
shotangle_info.b <- data.frame("Shot_Angle_Bin" = c("Dive/Other", "LowRight", "MidRight", "HighRight", "HighLeft", "MidLeft", "LowLeft"),
stringsAsFactors = F)
shot_angle_coords <- data.frame("Shot_Angle_Bin" = c("Dive/Other", "LowRight", "MidRight", "HighRight", "HighLeft", "MidLeft", "LowLeft"),
"Dot_Loc_x" = c(0.227, 0.74, 0.975, 0.74, 0.26, 0.025, 0.26),
"Dot_Loc_y" = c(0.435, 0.11, 0.50, 0.89, 0.89, 0.50, 0.11),
stringsAsFactors = F)
# Game Clock Chunked Ranges
c("Quarter 1 [15:00-10:00]",
"Quarter 1 [10:00-5:00]",
"Quarter 1 [5:00-End]",
"Quarter 2 [15:00-10:00]",
"Quarter 2 [10:00-5:00]",
"Quarter 2 [5:00-End]",
"Quarter 3 [15:00-10:00]",
"Quarter 3 [10:00-5:00]",
"Quarter 3 [5:00-End]",
"Quarter 4 [15:00-10:00]",
"Quarter 4 [10:00-5:00]",
"Quarter 4 [5:00-End]",
"Overtime") -> game_clock_chunked
# Pull in png pixel sizes
df_team_info$Logo_Path_Height <- 0
df_team_info$Logo_Path_Width <- 0
df_team_info$Shadow_Logo_Path_Height <- 0
df_team_info$Shadow_Logo_Path_Width <- 0
df_team_info$Wordmark_Path_Height <- 0
df_team_info$Wordmark_Path_Width <- 0
for(i in 1:nrow(df_team_info)) {
png_dim <- dim(readPNG(paste("www/", df_team_info$Logo_Path[i], sep = "")))
df_team_info$Logo_Path_Height[i] <- png_dim[1]
df_team_info$Logo_Path_Width[i] <- png_dim[2]
png_dim <- dim(readPNG(paste("www/", df_team_info$Shadow_Logo_Path[i], sep = "")))
df_team_info$Shadow_Logo_Path_Height[i] <- png_dim[1]
df_team_info$Shadow_Logo_Path_Width[i] <- png_dim[2]
png_dim <- dim(readPNG(paste("www/", df_team_info$Wordmark_Path[i], sep = "")))
df_team_info$Wordmark_Path_Height[i] <- png_dim[1]
df_team_info$Wordmark_Path_Width[i] <- png_dim[2]
}
runApp()
df_player <- df_master
df1 <- df_player %>%
filter(!is.na(Player1_Name)) %>%
group_by(Team, Player1, Player1_Name) %>%
summarise("Max_Date" = max(mdy(Game_Date))) %>%
ungroup() %>%
rename("Player_Num" = Player1,
"Player_Name" = Player1_Name)
df2 <- df_player %>%
filter(!is.na(Primary_Assist_Name)) %>%
group_by(Team, Primary_Assist, Primary_Assist_Name) %>%
summarise("Max_Date" = max(mdy(Game_Date))) %>%
ungroup() %>%
rename("Player_Num" = Primary_Assist,
"Player_Name" = Primary_Assist_Name)
df3 <- df_player %>%
filter(!is.na(Secondary_Assist_Name)) %>%
group_by(Team, Secondary_Assist, Secondary_Assist_Name) %>%
summarise("Max_Date" = max(mdy(Game_Date))) %>%
ungroup() %>%
rename("Player_Num" = Secondary_Assist,
"Player_Name" = Secondary_Assist_Name)
df4 <- df_player %>%
filter(!is.na(Rebound_Name) & Rebound_Type == "Offensive") %>%
group_by(Team, Rebound, Rebound_Name) %>%
summarise("Max_Date" = max(mdy(Game_Date))) %>%
ungroup() %>%
rename("Player_Num" = Rebound,
"Player_Name" = Rebound_Name)
df5 <- df_player %>%
filter(!is.na(Rebound_Name) & Rebound_Type == "Defensive") %>%
mutate(Defensive_Team = ifelse(Team == Home_Team, Away_Team, Home_Team)) %>%
group_by(Defensive_Team, Rebound, Rebound_Name) %>%
summarise("Max_Date" = max(mdy(Game_Date))) %>%
ungroup() %>%
rename("Player_Num" = Rebound,
"Player_Name" = Rebound_Name,
"Team" = Defensive_Team)
df6 <- df_player %>%
filter(!is.na(FO_Home_Name)) %>%
group_by(Home_Team, FO_Home, FO_Home_Name) %>%
summarise("Max_Date" = max(mdy(Game_Date))) %>%
ungroup() %>%
rename("Player_Num" = FO_Home,
"Player_Name" = FO_Home_Name,
"Team" = Home_Team)
df7 <- df_player %>%
filter(!is.na(FO_Away_Name)) %>%
group_by(Away_Team, FO_Away, FO_Away_Name) %>%
summarise("Max_Date" = max(mdy(Game_Date))) %>%
ungroup() %>%
rename("Player_Num" = FO_Away,
"Player_Name" = FO_Away_Name,
"Team" = Away_Team)
df8 <- df_player %>%
filter(!is.na(Home_Goalie_Name)) %>%
group_by(Home_Team, Home_Goalie, Home_Goalie_Name) %>%
summarise("Max_Date" = max(mdy(Game_Date))) %>%
ungroup() %>%
rename("Player_Num" = Home_Goalie,
"Player_Name" = Home_Goalie_Name,
"Team" = Home_Team)
df9 <- df_player %>%
filter(!is.na(Away_Goalie_Name)) %>%
group_by(Away_Team, Away_Goalie, Away_Goalie_Name) %>%
summarise("Max_Date" = max(mdy(Game_Date))) %>%
ungroup() %>%
rename("Player_Num" = Away_Goalie,
"Player_Name" = Away_Goalie_Name,
"Team" = Away_Team)
df_player2 <- rbind(df1, df2, df3, df4, df5, df6, df7, df8, df9)
df_player2 <- left_join(df_player2, df_team_info %>% select(Team, Team_Type), by = "Team")
df_player2 <- df_player2 %>%
mutate(Max_Date = ifelse(Team_Type == "Club", ymd("1900-01-01"), Max_Date)) %>%
mutate(Max_Date = as_date(Max_Date))
df_player2 <- df_player2 %>%
group_by(Player_Name) %>%
mutate(Player_Max_Date = max(as_date(Max_Date)))
df_player2 <- df_player2 %>%
group_by(Player_Name) %>%
summarise(Team = max(Team[which(Max_Date == Player_Max_Date)]),
Player_Num = max(Player_Num[which(Max_Date == Player_Max_Date)]))
df_player2 <- df_player2 %>%
ungroup() %>%
rowwise() %>%
mutate(LastName = str_split_fixed(Player_Name, ". ", 2)[2]) %>%
ungroup() %>%
arrange(Team, LastName)
df_player2 <- df_player2 %>%
filter(Team == "England")
View(df_player2)
df_mf <- df_master %>%
filter(Event_Type == "Shot" & (Player1_Name == "L. Catton" | Primary_Assist_Name == "L. Catton" | Secondary_Assist_Name == "L. Catton")) %>%
mutate(Home_Team = ifelse(Home_Team == Team, df_player2$Team[df_player2$Player_Name == "L. Catton"], "Opponent"),
Away_Team = ifelse(Away_Team == Team, df_player2$Team[df_player2$Player_Name == "L. Catton"], "Opponent")) %>%
mutate(Team = df_player2$Team[df_player2$Player_Name == "L. Catton"])
# need to go game by game and figure out which side of the floor each side started on and played the 3rd quarter on to figure out coord switching
df_mf_sides <- df_mf %>%
filter(Quarter %in% c(1, 3) & !is.na(Team)) %>%
group_by(Game, Team) %>%
summarise(Field_Side = ifelse(length(which(Location1_x < 0.5)) > length(which(Location1_x > 0.5)), "West", "East"))
View(df_mf_sides)
# normalize the event locations so that they're always with respect to the same side of the field for either team
# the goal is to get it so that side1 is on the east side of the field and side2 is on the west side of the field
for(i in unique(df_mf$Game)) {
for(j in which(df_mf$Game == i)[which(!is.na(df_mf$Team[which(df_mf$Game == i)]))]) {
if((df_mf$Team[j] == df_player2$Team[df_player2$Player_Name == "L. Catton"]) &
(df_mf_sides$Field_Side[df_mf_sides$Game == i & df_mf_sides$Team == df_player2$Team[df_player2$Player_Name == "L. Catton"]] == "East")) {
if(df_mf$Quarter[j] == 2) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
} else {
if(df_mf$Quarter[j] == 4) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
}
}
} else {
if((df_mf$Team[j] == df_player2$Team[df_player2$Player_Name == "L. Catton"]) &
(df_mf_sides$Field_Side[df_mf_sides$Game == i & df_mf_sides$Team == df_player2$Team[df_player2$Player_Name == "L. Catton"]] == "West")) {
if(df_mf$Quarter[j] == 1) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
} else {
if(df_mf$Quarter[j] == 3) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
} else {
if(df_mf$Quarter[j] == 5) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
}
}
}
} else {
if((df_mf$Team[j] != df_player2$Team[df_player2$Player_Name == "L. Catton"]) &
(df_mf_sides$Field_Side[df_mf_sides$Game == i & df_mf_sides$Team != df_player2$Team[df_player2$Player_Name == "L. Catton"]] == "East")) {
if(df_mf$Quarter[j] == 1) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
} else {
if(df_mf$Quarter[j] == 3) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
} else {
if(df_mf$Quarter[j] == 5) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
}
}
}
} else {
if(df_mf$Quarter[j] == 2) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
} else {
if(df_mf$Quarter[j] == 4) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
}
}
}
}
}
}
}
df_mf$Team[j] == df_player2$Team[df_player2$Player_Name == "L. Catton"]
df_mf_sides$Field_Side[df_mf_sides$Game == i & df_mf_sides$Team == df_player2$Team[df_player2$Player_Name == "L. Catton"]] == "East"
View(df_mf_sides)
View(df_mf_sides)
View(df_mf)
df_mf_sides <- df_mf %>%
filter(Quarter %in% c(1, 3) & !is.na(Team))
unique(df_mf$Game)
# need to go game by game and figure out which side of the floor each side started on and played the 3rd quarter on to figure out coord switching
df_mf_sides <- df_mf %>%
filter(Quarter %in% c(1, 3) & !is.na(Team)) %>%
group_by(Game, Team) %>%
summarise(Field_Side = ifelse(length(which(Location1_x < 0.5)) > length(which(Location1_x > 0.5)), "West", "East"))
# need to go game by game and figure out which side of the floor each side started on and played the 3rd quarter on to figure out coord switching
df_mf_sides <- df_mf %>%
filter(!is.na(Team)) %>%
group_by(Game, Team) %>%
summarise(Field_Side = ifelse((length(which(Location1_x < 0.5)) > length(which(Location1_x > 0.5)) & (Quarter %in% c(1, 3))), "West", "East"))
# need to go game by game and figure out which side of the floor each side started on and played the 3rd quarter on to figure out coord switching
df_mf_sides <- df_mf %>%
filter(!is.na(Team)) %>%
group_by(Game, Team) %>%
summarise(Field_Side = ifelse(length(which(Location1_x[Quarter %in% c(1, 3)] < 0.5)) > length(which(Location1_x[Quarter %in% c(1, 3)] > 0.5)), "West", "East"))
# normalize the event locations so that they're always with respect to the same side of the field for either team
# the goal is to get it so that side1 is on the east side of the field and side2 is on the west side of the field
for(i in unique(df_mf$Game)) {
for(j in which(df_mf$Game == i)[which(!is.na(df_mf$Team[which(df_mf$Game == i)]))]) {
if((df_mf$Team[j] == df_player2$Team[df_player2$Player_Name == "L. Catton"]) &
(df_mf_sides$Field_Side[df_mf_sides$Game == i & df_mf_sides$Team == df_player2$Team[df_player2$Player_Name == "L. Catton"]] == "East")) {
if(df_mf$Quarter[j] == 2) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
} else {
if(df_mf$Quarter[j] == 4) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
}
}
} else {
if((df_mf$Team[j] == df_player2$Team[df_player2$Player_Name == "L. Catton"]) &
(df_mf_sides$Field_Side[df_mf_sides$Game == i & df_mf_sides$Team == df_player2$Team[df_player2$Player_Name == "L. Catton"]] == "West")) {
if(df_mf$Quarter[j] == 1) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
} else {
if(df_mf$Quarter[j] == 3) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
} else {
if(df_mf$Quarter[j] == 5) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
}
}
}
} else {
if((df_mf$Team[j] != df_player2$Team[df_player2$Player_Name == "L. Catton"]) &
(df_mf_sides$Field_Side[df_mf_sides$Game == i & df_mf_sides$Team != df_player2$Team[df_player2$Player_Name == "L. Catton"]] == "East")) {
if(df_mf$Quarter[j] == 1) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
} else {
if(df_mf$Quarter[j] == 3) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
} else {
if(df_mf$Quarter[j] == 5) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
}
}
}
} else {
if(df_mf$Quarter[j] == 2) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
} else {
if(df_mf$Quarter[j] == 4) {
df_mf$Location1_x[j] <- 0.5 + -1*(df_mf$Location1_x[j] - 0.5)
df_mf$Location1_y[j] <- 0.5 + -1*(df_mf$Location1_y[j] - 0.5)
df_mf$Location2_x[j] <- 0.5 + -1*(df_mf$Location2_x[j] - 0.5)
df_mf$Location2_y[j] <- 0.5 + -1*(df_mf$Location2_y[j] - 0.5)
}
}
}
}
}
}
}
runApp()
library(rsconnect)
library(tidyverse)
# Update Master Files
file.copy(from = "/Users/robweber/Documents/Professional/First Line/World Lacrosse/Master File Backup/MLAX_Worlds_Master_File.csv",
to = "/Users/robweber/Documents/Professional/First Line/World Lacrosse/MLAX_Germany_Reporting_Dashboard_1/", overwrite = TRUE)
file.copy(from = "/Users/robweber/Documents/Professional/First Line/World Lacrosse/Master File Backup/WLAX_Worlds_Master_File.csv",
to = "/Users/robweber/Documents/Professional/First Line/World Lacrosse/WLAX_Germany_Reporting_Dashboard_1/", overwrite = TRUE)
deployApp("/Users/robweber/Documents/Professional/First Line/World Lacrosse/WLAX_Germany_Reporting_Dashboard_1",
appName = "WLAX_Germany_Reporting_Dashboard_1", account = "robertweber98", forceUpdate = TRUE)
shiny::runApp()
runApp()
runApp()
library(rsconnect)
library(tidyverse)
deployApp("/Users/robweber/Documents/Professional/First Line/World Lacrosse/MLAX_Germany_Reporting_Dashboard_1",
appName = "MLAX_Germany_Reporting_Dashboard_1", account = "robertweber98", forceUpdate = TRUE)
deployApp("/Users/robweber/Documents/Professional/First Line/World Lacrosse/WLAX_Germany_Reporting_Dashboard_1",
appName = "WLAX_Germany_Reporting_Dashboard_1", account = "robertweber98", forceUpdate = TRUE)