#Setup

setwd("C:/Users/Jeff/Desktop/TagPro Analytics")
library("RJSONIO", lib.loc="~/R/win-library/3.2")
players <- fromJSON("bulkplayers (1).json")
matches <- fromJSON("bulkmatches (1).json")
maps <- fromJSON("bulkmaps (1).json")

#Creating List of Grouped Matches of 10 Minutes

players_sample <- NULL

x <- 1
for(i in 1:223302){
  if(matches[[i]]$ group == "redacted" && matches[[i]]$ timeLimit == 10){
    players_sample[[x]] <- i
    x <- x + 1
  }
}

#Players

PlayersID <- NULL
Auth <- NULL
Name <- NULL
Flair <- NULL
Degree <- NULL
Score <- NULL
Points <- NULL
Team <- NULL
Events <- NULL

x <- 1
for(i in players_sample){
  for(n in players[[i]]){
    PlayersID[[x]] <- i
    Auth[[x]] <- n$ auth
    Name[[x]] <- n$ name
    Flair[[x]] <- n$ flair
    Degree[[x]] <- n$ degree
    Score[[x]] <- n$ score
    Points[[x]] <- n$ points
    Team[[x]] <- n$ team
    Events[[x]] <- n$ events
    x <- x + 1
  }
}

df_players <- data.frame(PlayersID, Auth, Name, Flair, Degree, Score, Points, Team, stringsAsFactors=FALSE, check.rows=FALSE)

#Matches

MatchesID <- NULL
Port <- NULL
Group <- NULL
Date <- NULL
TimeLimit <- NULL
Duration <- NULL
MapID <- NULL
Team1Name <- NULL
Team1Score <- NULL
Team2Name <- NULL
Team2Score <- NULL
Result <- NULL

x <- 1
for(i in players_sample){
  MatchesID[[x]] <- i
  Port[[x]] <- matches[[i]]$ port
  Group[[x]] <- matches[[i]]$ group
  Date[[x]] <- matches[[i]]$ date
  TimeLimit[[x]] <- matches[[i]]$ timeLimit
  Duration[[x]] <- matches[[i]]$ duration
  MapID[[x]] <- matches[[i]]$ mapId
  Team1Name[[x]] <- matches[[i]]$ teams[[1]]$ name
  Team1Score[[x]] <- matches[[i]]$ teams[[1]]$ score
  Team2Name[[x]] <- matches[[i]]$ teams[[2]]$ name
  Team2Score[[x]] <- matches[[i]]$ teams[[2]]$ score
  if(Team1Score[[x]] > Team2Score[[x]]){
    Result[[x]] <- 1
  } else if(Team1Score[[x]] < Team2Score[[x]]){
    Result[[x]] <- 2
  } else {
    Result[[x]] <- 1.5
  }
  x <- x + 1
}

df_matches <- data.frame(MatchesID, Port, Group, Date, TimeLimit, Duration, MapID, Team1Name, Team1Score, Team2Name, Team2Score, Result, stringsAsFactors=FALSE, check.rows=FALSE)

#Matches -> Players

Port <- NULL
Group <- NULL
Date <- NULL
TimeLimit <- NULL
Duration <- NULL
MapID <- NULL
Team1Name <- NULL
Team1Score <- NULL
Team2Name <- NULL
Team2Score <- NULL
Result <- NULL

for(i in 1:nrow(df_matches)){
  Port[df_players$PlayersID == df_matches$MatchesID[[i]]] <- df_matches$Port[[i]]
  Group[df_players$PlayersID == df_matches$MatchesID[[i]]] <- df_matches$Group[[i]]
  Date[df_players$PlayersID == df_matches$MatchesID[[i]]] <- df_matches$Date[[i]]
  TimeLimit[df_players$PlayersID == df_matches$MatchesID[[i]]] <- df_matches$TimeLimit[[i]]
  Duration[df_players$PlayersID == df_matches$MatchesID[[i]]] <- df_matches$Duration[[i]]
  MapID[df_players$PlayersID == df_matches$MatchesID[[i]]] <- df_matches$MapID[[i]]
  Team1Name[df_players$PlayersID == df_matches$MatchesID[[i]]] <- df_matches$Team1Name[[i]]
  Team1Score[df_players$PlayersID == df_matches$MatchesID[[i]]] <- df_matches$Team1Score[[i]]
  Team2Name[df_players$PlayersID == df_matches$MatchesID[[i]]] <- df_matches$Team2Name[[i]]
  Team2Score[df_players$PlayersID == df_matches$MatchesID[[i]]] <- df_matches$Team2Score[[i]]
  Result[df_players$PlayersID == df_matches$MatchesID[[i]]] <- df_matches$Result[[i]]
}

df_players <- cbind(df_players, Port)
df_players <- cbind(df_players, Group)
df_players <- cbind(df_players, Date)
df_players <- cbind(df_players, TimeLimit)
df_players <- cbind(df_players, Duration)
df_players <- cbind(df_players, MapID)
df_players <- cbind(df_players, Team1Name)
df_players <- cbind(df_players, Team1Score)
df_players <- cbind(df_players, Team2Name)
df_players <- cbind(df_players, Team2Score)
df_players <- cbind(df_players, Result)

#Maps

ID <- NULL
Name <- NULL
Author <- NULL
MarsBalls <- NULL
Width <- NULL
Tiles <- NULL

for(i in 1:1145){
  ID[[i]] <- i
  Name[[i]] <- maps[[i]]$ name
  Author[[i]] <- maps[[i]]$ author
  MarsBalls[[i]] <- maps[[i]]$ marsballs
  Width[[i]] <- maps[[i]]$ width
  Tiles[[i]] <- maps[[i]]$ tiles
}

df_maps <- data.frame(ID, Name, Author, MarsBalls, Width, Tiles, stringsAsFactors=FALSE, check.rows=FALSE)

#Wins

for(i in 1:nrow(df_players)){
  if(df_players$Team[[i]] == df_players$Winner[[i]]) {
    Result[i] <- "Win"
  } else if(df_players$Winner[[i]] == 1.5) {
    Result[i] <- "Tie"
  } else if(df_players$Team[[i]] == 0) {
    Result[i] <- "None"
  } else {
    Result[i] <- "Loss"
  }
}

df_players <- cbind(df_players, Result)

#Degree

df_degrees <- data.frame(unique(df_players$Degree, stringsAsFactors=FALSE, check.rows=FALSE))
names(df_degrees)[1] <- "Degree"

Wins <- NULL
Losses <- NULL
Ties <- NULL
LateJoin <- NULL
Games <- NULL
WinPerc <- NULL
WinPercMod <- NULL

for(i in 1:nrow(df_degrees)){
  Wins[[i]] <- length(which(df_players$Degree == df_degrees$Degree[[i]] & df_players$Result == "Win"))
  Losses[[i]] <- length(which(df_players$Degree == df_degrees$Degree[[i]] & df_players$Result == "Loss"))
  Ties[[i]] <- length(which(df_players$Degree == df_degrees$Degree[[i]] & df_players$Result == "Tie"))
  LateJoin[[i]] <- length(which(df_players$Degree == df_degrees$Degree[[i]] & df_players$Result == "None"))
  Games[[i]] <- Wins[[i]] + Losses[[i]] + Ties[[i]]
  WinPerc[[i]] <- Wins[[i]] / Games[[i]]
  WinPercMod[[i]] <- (Wins[[i]] + 0.5*Ties[[i]])/ Games[[i]]
}

df_degrees <- cbind(df_degrees, Games)
df_degrees <- cbind(df_degrees, Wins)
df_degrees <- cbind(df_degrees, Losses)
df_degrees <- cbind(df_degrees, Ties)
df_degrees <- cbind(df_degrees, LateJoin)
df_degrees <- cbind(df_degrees, WinPerc)
df_degrees <- cbind(df_degrees, WinPercMod)

#Plot: Degrees vs. Win Percentage

plot(df_degrees$Degree[df_degrees$Degree < 150 & df_degrees$Degree > 20], df_degrees$WinPercMod[df_degrees$Degree < 150 & df_degrees$Degree > 20])
title(main = "Win Percentage vs. Degrees")
abline(lm(df_degrees$WinPercMod[df_degrees$Degree < 150 & df_degrees$Degree > 20]~df_degrees$Degree[df_degrees$Degree < 150 & df_degrees$Degree > 20]))

#Bar Plot

for(i in 1:20){
  for(n in 1:10){
    WinPercMod[[n]] <- df_degrees$WinPercMod[[((i-1)*10)+n]]
    WinPercMean[[i]] <- mean(WinPercMod)
  }
}

barplot(WinPercMean)