diff --git a/data_preparation/cleaned/slidingWorkAverage.csv b/data_preparation/cleaned/slidingWorkAverage.csv
new file mode 100644
index 0000000..21a6be5
--- /dev/null
+++ b/data_preparation/cleaned/slidingWorkAverage.csv
@@ -0,0 +1,336 @@
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diff --git a/data_preparation/createWorkSequenceData.R b/data_preparation/createWorkSequenceData.R
new file mode 100644
index 0000000..859e120
--- /dev/null
+++ b/data_preparation/createWorkSequenceData.R
@@ -0,0 +1,50 @@
+source("readData.R")
+
+library(tidyverse)
+
+
+RPEData <-readNArpeData()
+
+
+numDays <- max(RPEData$TimeSinceAugFirst)
+
+
+dayList <- 0:numDays
+workLoad <- c()
+averageWorkLoad <- c()
+
+
+for(day in dayList)
+{
+  total <- 0
+  
+  daylyActivities <- subset(RPEData, TimeSinceAugFirst == day)
+  cat("day: ", day, "\n",sep="")
+  cat("Activity count:", length(daylyActivities$DailyLoad), "\n", sep="")
+  
+  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")
+
+
+slidingAverage <- c()
+
+window <- 21 - 1
+for(day in window:numDays)
+{
+  print(length(workLoad[c((day-window):day)]))
+  windowAverage <- mean(workLoad[c((day-window):day)])
+  
+  slidingAverage <- c(slidingAverage, windowAverage)
+}
+
+plot(window:numDays, slidingAverage, main="Sliding Average")
+plot(density(slidingAverage), main="Sliding Average Density")
+plot(density(workLoad), main="Total Work Load Average")
+
+
+dataTibble <- tibble(TimeSinceAugFirst = window:numDays, slidingWorkAverage = slidingAverage)
+
+write.csv(dataTibble, "cleaned/slidingWorkAverage.csv")
\ No newline at end of file
diff --git a/data_preparation/dataPrep.R b/data_preparation/dataPrep.R
index 4c7c694..dc0205a 100644
--- a/data_preparation/dataPrep.R
+++ b/data_preparation/dataPrep.R
@@ -7,7 +7,7 @@ library(tidyverse)
 library(DBI)
 library(RSQLite)
 
-gpsData <- read.csv("data/gps.csv")
+gpsData <- read.csv("data/gps.csv")c
 
 
 gpsDataTibble <- as_tibble(gpsData)
diff --git a/data_preparation/normalizeData.R b/data_preparation/normalizeData.R
index b07ebee..7d44f4f 100644
--- a/data_preparation/normalizeData.R
+++ b/data_preparation/normalizeData.R
@@ -83,11 +83,11 @@ for(id in playerIds)
 }
 
 
-normalWellnessData <- tibble(date = normDate, playerID = normPlayerIDs, normSoreness = normSoreness, 
+normalWellnessData <- tibble(TimeSinceAugFirst = normDate, playerID = normPlayerIDs, normSoreness = normSoreness, 
                             normFatigue = normFatigue, normDesire = normDesire, normIrritability = normIrritability,
                             normSleepHours = normSleepHours, normSleepQuality = normSleepQuality)
 
-write.csv(normalWellnessData, "cleaned/normalizedWellness.csv")
+write.csv(normalWellnessData, "cleaned/time_series_normalized_wellness.csv")
 
 
 plot()
diff --git a/data_preparation/readData.R b/data_preparation/readData.R
index 3e6bed8..f26b375 100644
--- a/data_preparation/readData.R
+++ b/data_preparation/readData.R
@@ -13,5 +13,11 @@ readWellnessData <- function()
 
 readRPEData <- function()
 {
-  as_tibble(read.csv("./cleaned/notnormalized_with_0Nan_rpe.csv"))
+  as_tibble(read.csv("./cleaned/time_series_notnormalized_with_continuousNan_rpe.csv"))
+}
+
+
+readNArpeData <- function()
+{
+  as_tibble(read.csv("./cleaned/time_series_notnormalized_with_continuousNan_rpe.csv"))
 }
diff --git a/data_preparation/replaceNanWithMedian.R b/data_preparation/replaceNanWithMedian.R
index 083203a..00a0d87 100644
--- a/data_preparation/replaceNanWithMedian.R
+++ b/data_preparation/replaceNanWithMedian.R
@@ -12,6 +12,10 @@ trainingData <- readRPEData()
 trainingData$Duration[is.na(trainingData$Duration)] <- median(trainingData$Duration, na.rm=TRUE)
 
 
+print(trainingData$Duration)
+
+
+
 #RPE
 trainingData$RPE[is.na(trainingData$RPE)] <- median(trainingData$RPE, na.rm=TRUE)
 
@@ -32,14 +36,21 @@ trainingData$ObjectiveRating[is.na(trainingData$ObjectiveRating)] <- median(trai
 trainingData$FocusRating[is.na(trainingData$FocusRating)] <- median(trainingData$FocusRating, na.rm=TRUE)
 
 
-trainingData$RPE[is.na(trainingData$RPE)] <- median(trainingData$RPE, na.rm=TRUE)
 
-write.csv(as.data.frame(trainingData), "cleaned/time_series_rpw_naReplacedWithMedian.csv")
+# session load
+trainingData$SessionLoad[is.na(trainingData$SessionLoad)] <- median(trainingData$SessionLoad, na.rm=TRUE)
 
+# daily load
+trainingData$DailyLoad[is.na(trainingData$DailyLoad)] <- median(trainingData$DailyLoad, na.rm=TRUE)
 
-head(as.data.frame(trainingData), 100)
+
+trainingData$RPE[is.na(trainingData$RPE)] <- median(trainingData$RPE, na.rm=TRUE)
+
+write.csv(as.data.frame(trainingData), "cleaned/time_series_rpe_NA_ReplacedWithMedian.csv")
 
 
 
+head(as.data.frame(trainingData), 100)
+
 
 trainingData