Normalized the users data relative to their answers to remove some bias.

data_preparation/dataPrep.R

@@ -77,27 +77,17 @@ write.csv(compressedMetrics, "data/speedData.csv")
 
 
 
-
-
-
-rpeData <- read.csv("./data/rpe.csv")
-rpeDataTibble <- as_tibble(rpeData)
-
-
-gameData <- read.csv("data/games.csv")
-gameDataTibble <- as_tibble(gameData)
-
-
 wellnessData <- read.csv("./data/wellness_na.csv")
 wellnessDataTibble <- as_tibble(wellnessData)
 
 
-wellnesPlayer1 <- subset(wellnessDataTibble, PlayerID == 1)
 
-plot(wellnesPlayer1$Fatigue * wellnesPlayer1$Soreness * wellnesPlayer1$Irritability, wellnesPlayer1$SleepHours * wellnesPlayer1$SleepQuality)
+
+#plot(wellnesPlayer1$Fatigue * wellnesPlayer1$Soreness * wellnesPlayer1$Irritability, wellnesPlayer1$SleepHours * wellnesPlayer1$SleepQuality)
 
 
 wellnessCleaned <- as_tibble(read.csv("./cleaned/dirty_wellness.csv"))
+wellnesPlayer1 <- subset(wellnessCleaned, PlayerID == 1)
 
 
 ggplot(data = wellnessCleaned) + 
@@ -111,7 +101,7 @@ ggplot(data = wellnessCleaned) +
 
 ggplot(data = wellnessCleaned) + 
   theme(plot.title = element_text(hjust = 0.5)) + 
-  ggtitle("Hours of Sleep Box Plot") + 
+  ggtitle("Fatigue Box Plot") + 
   geom_boxplot(na.rm = T, mapping = aes(y=Fatigue, group = PlayerID), outlier.colour = "red", outlier.shape = 1) + 
   labs(group = "Player ID", y = "Fatigue Score") +
   coord_flip() +
@@ -136,6 +126,8 @@ ggplot(data = wellnessCleaned) +
   theme_bw()
 
 
+plot(density(wellnesPlayer1$SleepHours))
+
 max(wellnessCleaned$SleepHours, na.rm = T)
 min(wellnessCleaned$SleepHours, na.rm = T)
 
@@ -143,4 +135,84 @@ min(wellnessCleaned$SleepHours, na.rm = T)
 playerIdsWellness <-unique(wellnessCleaned$PlayerID)
 cat("Number of Players: ", length(playerIdsWellness), sep="")
 
-head(gpsData)
+
+
+
+rpeData <- read.csv("./data/rpe.csv")
+rpeDataTibble <- as_tibble(rpeData)
+
+
+gameData <- read.csv("data/games.csv")
+gameDataTibble <- as_tibble(gameData)
+
+
+
+par(mfrow = c(4, 5))
+
+playerIdsWellness <- sort(playerIdsWellness)
+
+for(playerID in playerIdsWellness)
+{
+  if(!is.na(playerID) && playerID < 88)
+  {
+    #print(playerID)
+    #welnessTibble <- c()
+    
+    
+    welnessTibble <- subset(wellnessCleaned,PlayerID == playerID)
+    #print(length(welnessTibble$SleepHours))
+    
+    plot(density(welnessTibble$SleepHours, kernel = "gaussian", bw=0.5), main = paste("Player ", playerID, sep=""), xlab="Hours of Sleep")
+    
+    #lines(density(welnessTibble$SleepHours))
+  }
+}
+
+
+plot(density(wellnesPlayer1$SleepHours, kernel = "gaussian", bw=0.4), ylim=c(0,.7), xlab = "Hours of Sleep", main="Team's Sleep Distribution")
+for(playerID in playerIdsWellness)
+{
+  if(!is.na(playerID) && playerID < 88)
+  {
+    #print(playerID)
+    #welnessTibble <- c()
+    
+    welnessTibble <- subset(wellnessCleaned,PlayerID == playerID)
+
+    lines(density(welnessTibble$SleepHours,kernel = "gaussian", bw=0.4))
+  }
+}
+
+
+
+plot(density(wellnesPlayer1$Fatigue, kernel = "gaussian", bw=0.4), ylim=c(0,.7), xlab = "Self Reported Fatigue", main="Team's Fatigue Distribution")
+for(playerID in playerIdsWellness)
+{
+  if(!is.na(playerID) && playerID < 88)
+  {
+    #print(playerID)
+    #welnessTibble <- c()
+    
+    welnessTibble <- subset(wellnessCleaned,PlayerID == playerID)
+    
+    lines(density(welnessTibble$Fatigue,kernel = "gaussian", bw=0.4))
+  }
+}
+
+
+
+
+
+
+head(gpsData)
+
+
+
+
+
+
+
+
+
+# Normalize Wellness data
+

data_preparation/normalizeData.R

@@ -0,0 +1,97 @@
+source("readData.R")
+
+library(tidyverse)
+
+library(bestNormalize)
+
+
+# File to normalize the user inputted data in
+# the wellness
+
+wellnessData <- readWellnessData()
+
+
+playerIds <-unique(wellnessData$PlayerID)
+cat("Number of Players: ", length(playerIds), sep="")
+
+
+
+normPlayerIDs <- c()
+normDate <- c()
+
+
+normFatigue <- c()
+normSoreness <- c()
+normDesire <- c()
+normIrritability <- c()
+normSleepHours <- c()
+normSleepQuality <- c()
+
+
+for(id in playerIds)
+{
+  wellnessDataT <- subset(wellnessData, PlayerID == id)
+  if(length(wellnessDataT$Fatigue) > 0)
+  {
+    print(id)
+    userTibble <- subset(wellnessData, PlayerID == id)
+    print(length(userTibble$Fatigue))
+    
+    #fatigueNormalized <- bestNormalize(userTibble$Fatigue)
+    fatigueNormalized <- bestNormalize(userTibble$Fatigue, standardize = TRUE)
+    
+    fatNorm <-predict(fatigueNormalized)
+    
+    print(fatigueNormalized)
+    
+    
+    sleepNormalized <- bestNormalize(userTibble$SleepHours, standardize = TRUE)
+    sleepNorm <-predict(fatigueNormalized)
+    
+    
+    soreness <- bestNormalize(userTibble$Soreness, standardize = TRUE)
+    sorenessNorm <- predict(soreness)
+    
+    
+    desire <- bestNormalize(userTibble$Desire, standardize = TRUE)
+    desireNorm <- predict(desire)
+    
+    irritability <- bestNormalize(userTibble$Irritability, standardize = TRUE)
+    irritabilityNorm <- predict(irritability)
+    
+    
+    sleepHours <- bestNormalize(userTibble$SleepHours, standardize = TRUE)
+    sleepHoursNorm <- predict(sleepHours)
+    
+    sleepQuality <- bestNormalize(userTibble$SleepQuality, standardize = TRUE)
+    sleepQualityNorm <- predict(sleepQuality)
+    
+    
+    normPlayerIDs <- c(normPlayerIDs, userTibble$PlayerID)
+    normDate <- c(normDate, userTibble$TimeSinceAugFirst)
+    normSoreness <- c(normSoreness, sorenessNorm)
+    normFatigue <- c(normFatigue, fatNorm)
+    normDesire <- c(normDesire, desireNorm)
+    normIrritability <- c(normIrritability, irritabilityNorm)
+    normSleepHours <- c(normSleepHours, sleepHoursNorm)
+    normSleepQuality <- c(normSleepQuality, sleepQualityNorm)
+    
+    #plot(density(userTibble$SleepHours))
+    #plot(density(sleepNorm))
+    
+  }
+}
+
+
+normalWellnessData <- tibble(date = normDate, playerID = normPlayerIDs, normSoreness = normSoreness, 
+                            normFatigue = normFatigue, normDesire = normDesire, normIrritability = normIrritability,
+                            normSleepHours = normSleepHours, normSleepQuality = normSleepQuality)
+
+write.csv(normalWellnessData, "cleaned/normalizedWellness.csv")
+
+
+plot()
+
+plot(normDesire, normSoreness)
+
+print(fagigueNormalized)

data_preparation/readData.R

@@ -0,0 +1,17 @@
+library(tidyverse)
+
+
+readGPSMetrics <- function()
+{
+  as_tibble(read.csv("./data/speedData.csv"))
+}
+
+readWellnessData <- function()
+{
+  as_tibble(read.csv("./cleaned/time_series_notnormalized_with_0NaN_wellness.csv"))
+}
+
+readRPEData <- function()
+{
+  as_tibble(read.csv("./cleaned/notnormalized_with_0Nan_rpe.csv"))
+}