Added r scripts for generating individual metrics to the git repository.

data_preparation/IndividualMetrics.R

@@ -1,4 +1,5 @@
 source("readData.R")
+source("exponentialSmoothing.R")
 
 library(tidyverse)
 
@@ -6,6 +7,9 @@ library(tidyverse)
 RPEData <-readNArpeData()
 wellnessData <- readWellnessData()
 normalizedWellnessData <- readNormalizedMetrics()
+
+
+
 RPEData
 
 
@@ -130,17 +134,26 @@ dailyLoadCol[is.na(dailyLoadCol)] <- 0
 acuteChronicRatioCol[is.na(acuteChronicRatioCol)] <- 0
 
 
+accuteFatigueSliding <- slidingWindowSmooth(acuteChronicRatioCol)
+
 
 massiveTibble <- tibble(day = dayCol,
                         playerID = playerid,
                         DailyLoad = dailyLoadCol,
+                        DailyLoadSliding = slidingWindowSmooth(DailyLoad),
                         acuteChronicRatio = acuteChronicRatioCol,
+                        acuteChronicRatioSliding = slidingWindowSmooth(acuteChronicRatioCol),
                         trainDuration = trainDuration,
+                        trainDurationSliding = slidingWindowSmooth(trainDuration),
                         sleepHours = sleepHoursCol,
+                        sleepHoursSliding = slidingWindowSmooth(sleepHours),
                         fatigue = fatigueRawCol,
+                        fatigueSliding = slidingWindowSmooth(fatigue),
                         sleepQuality = sleepQualityCol,
                         soreness = sorenessCol,
+                        sorenessSliding = slidingWindowSmooth(soreness),
                         fatigueNorm = normFatCol,
+                        fatigueNormSliding = slidingWindowSmooth(fatigueNorm),
                         sorenessNorm = normSoreCol,
                         sleepHoursNorm = normSleepHours,
                         sleepQualityNorm = normSleepQuality,
@@ -150,3 +163,53 @@ massiveTibble <- tibble(day = dayCol,
                         BestOutOfMyselfUnknown = unknownCol)
 
 write.csv(massiveTibble, "cleaned/personal.csv")
+
+
+ggplot(data = massiveTibble) + 
+  theme(plot.title = element_text(hjust = 0.5)) + 
+  ggtitle("Normalized Soreness Box Plots") + 
+  geom_boxplot(na.rm = T, mapping = aes(y=sorenessNorm, group = playerID), outlier.colour = "red", outlier.shape = 1) + 
+  labs(group = "Player ID", y = "Normalized Soreness Values") +
+  coord_flip() +
+  theme_bw()
+
+ggplot(data = massiveTibble) + 
+  theme(plot.title = element_text(hjust = 0.5)) + 
+  ggtitle("Normalized Sleep Quality Box Plots") + 
+  geom_boxplot(na.rm = T, mapping = aes(y=sleepQualityNorm, group = playerID), outlier.colour = "red", outlier.shape = 1) + 
+  labs(group = "Player ID", y = "Normalized Sleep Quality") +
+  coord_flip() +
+  theme_bw()
+
+
+ggplot(data = massiveTibble) + 
+  theme(plot.title = element_text(hjust = 0.5)) + 
+  ggtitle("Soreness Box Plots") + 
+  geom_boxplot(na.rm = T, mapping = aes(y=sleepQuality, group = playerID), outlier.colour = "red", outlier.shape = 1) + 
+  labs(group = "Player ID", y = "Sleep Quality") +
+  coord_flip() +
+  theme_bw()
+
+
+ggplot(data = massiveTibble) + 
+  theme(plot.title = element_text(hjust = 0.5)) + 
+  ggtitle("Team's Percieved Fatigue") + 
+  geom_point(mapping = aes(x=day, y=fatigue)) + 
+  labs(x = "Days Since August First 2017", y = "Teams Fatigue")+ 
+  theme_bw()
+
+
+ggplot(data = massiveTibble) + 
+  theme(plot.title = element_text(hjust = 0.5)) + 
+  ggtitle("Team's Percieved Fatigue") + 
+  geom_point(mapping = aes(x=day, y=sorenessSliding)) + 
+  labs(x = "Days Since August First 2017", y = "Accute Fatugue ")+ 
+  theme_bw()
+
+
+ggplot(data = massiveTibble) + 
+  theme(plot.title = element_text(hjust = 0.5)) + 
+  ggtitle("Team's Percieved Fatigue") + 
+  geom_point(mapping = aes(x=sleepQuality, y=fatigueNorm)) + 
+  labs(x = "Days Since August First 2017", y = "Accute Fatugue ") + 
+  theme_bw()

data_preparation/createWorkSequenceData.R

@@ -23,13 +23,12 @@ for(day in dayList)
   averageWorkLoad <- c(averageWorkLoad, mean(daylyActivities$SessionLoad, na.rm = T))
   workLoad <- c(workLoad, sum(daylyActivities$SessionLoad, na.rm = T))
 }
+
 plot(dayList, averageWorkLoad, main="Average Work Load")
 plot(dayList, workLoad, main="Daily Total Work Load")
 
 
 
-
-
 fatigueFunction <- function(workLoad, index)
 {
   if(index == 1)
@@ -52,8 +51,6 @@ plot(dayList, smoothedFatigue)
 
 
 
-
-
 fatigueData <- readFatigueSums()
 
 dayNum <- max(fatigueData$TimeSinceAugFirst)
@@ -79,11 +76,13 @@ for(day in dayList)
 plot(dayList, smoothedFatigueData)
 
 
-workTibble <- tibble(day = dayList, totalWork = workLoad, 
+workTibble <- tibble(day = dayList, totalWork = workLoad,
                      averageWorkLoad = averageWorkLoad, 
                      smoothedWork = smoothedWork,
                      smoothedFatigueData = smoothedFatigueData)
 
+plot(workTibble$totalWork, fatigueData$fatigueSum[-1])
+
 workGraph <- ggplot(data = workTibble) + 
   theme(plot.title = element_text(hjust = 0.5)) + 
   ggtitle("Team's Smoothed Work") + 
@@ -99,6 +98,14 @@ fatGraph <- ggplot(data = workTibble) +
   theme_bw()
 
 
+ggplot(data = workTibble) + 
+  theme(plot.title = element_text(hjust = 0.5)) + 
+  ggtitle("Team's Percieved Fatigue") + 
+  geom_point(mapping = aes(x=smoothedWork, y=smoothedFatigueData)) + 
+  labs(x = "Smoothed Work Per Day", y = "Teams Average Normalized Fatigue")+ 
+  theme_bw()
+
+
 for(gameDay in games$Date)
 {
   fatGraph <- fatGraph + geom_vline(xintercept = gameDay, linetype="dotted", 
@@ -106,10 +113,13 @@ for(gameDay in games$Date)
   workGraph <- workGraph + geom_vline(xintercept = gameDay, linetype="dotted", 
                                         color = "blue", size=1.0)
 }
+
+
+
 workGraph
 fatGraph
 
-write.csv(dataTibble, "cleaned/expSmoothWorkAndFatigueData.csv")
+write.csv(workTibble, "cleaned/expSmoothWorkAndFatigueData.csv")
 
 
 

data_preparation/exponentialSmoothing.R

@@ -6,7 +6,7 @@ fatigueFunction <- function(workLoad, index)
   }
   else
   {
-    return(workLoad[index] + 0.7*fatigueFunction(workLoad, index -1))
+    return(workLoad[index] + (exp(1)^(-1/15))**fatigueFunction(workLoad, index -1))
   }
 }
 
@@ -23,6 +23,21 @@ smoothVector <- function(dataV)
 }
 
 
-smoothVector(c(1,2,3,4))
 
-plot(1:100, smoothVector(1:100))
+slidingWindowSmooth <- function(dataV, windowSize = 7)
+{
+  dataNew <- c()
+  
+  
+  for(i in 1:windowSize)
+  {
+    dataNew <- c(dataNew, mean(dataV[c(1:i)]))
+  }
+  
+  
+  for(i in (windowSize + 1):length(dataV))
+  {
+    dataNew <- c(dataNew, mean(dataV[(i-7):i]))
+  }
+  dataNew
+}

data_preparation/readData.R

@@ -21,3 +21,21 @@ readNArpeData <- function()
 {
   as_tibble(read.csv("./cleaned/time_series_notnormalized_with_continuousNan_rpe.csv"))
 }
+
+
+readNormalizedMetrics <- function()
+{
+  as_tibble(read.csv("./cleaned/time_series_normalized_wellness.csv"))
+}
+
+
+readFatigueSums <- function()
+{
+  as_tibble(read.csv("./cleaned/fatigue_total_sum.csv"))
+}
+
+
+readGameRandChanges <- function()
+{
+  as_tibble(read.csv("./cleaned/time_series_days_ranked.csv"))
+}