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PerryXDeng-wheatyeeters
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Implemented fancy exp smoothing to sequence data for fatigue and work load.

master
Jeffery Russell 5 years ago
parent
1dda04e6d7
commit
6190974fc3
6 changed files with 461 additions and 16 deletions
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  1. +356
    -0
      data_preparation/cleaned/expSmoothWorkAndFatigueData.csv
  2. +105
    -16
      data_preparation/createWorkSequenceData.R
  3. BIN
      findings/Rplot.png
  4. BIN
      findings/Rplot01.png
  5. BIN
      findings/SmoothedFatigue.png
  6. BIN
      findings/SmoothedWork.png

+ 356
- 0
data_preparation/cleaned/expSmoothWorkAndFatigueData.csv View File

@ -0,0 +1,356 @@
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+ 105
- 16
data_preparation/createWorkSequenceData.R View File

@ -27,6 +27,92 @@ plot(dayList, averageWorkLoad, main="Average Work Load")
plot(dayList, workLoad, main="Daily Total Work Load") plot(dayList, workLoad, main="Daily Total Work Load")
fatigueFunction <- function(workLoad, index)
{
if(index == 1)
{
return(workLoad[1])
}
else
{
return(workLoad[index] + (exp(1)^(-1/15))*fatigueFunction(workLoad, index -1))
}
}
smoothedWork <- c()
for(day in dayList)
{
smoothedWork <- c(smoothedWork, fatigueFunction(workLoad, day + 1))
}
plot(dayList, smoothedFatigue)
fatigueData <- readFatigueSums()
dayNum <- max(fatigueData$TimeSinceAugFirst)
dayList <- 0:dayNum
slidingAverage <- c()
window <- 21 - 1
for(day in window:dayNum)
{
windowAverage <- mean(fatigueData$fatigueSum[c((day-window):day)], na.rm = T)
slidingAverage <- c(slidingAverage, windowAverage)
}
smoothedFatigueData <- c()
for(day in dayList)
{
smoothedFatigueData <- c(smoothedFatigueData, fatigueFunction(fatigueData$fatigueSum, day + 1))
}
plot(dayList, smoothedFatigueData)
workTibble <- tibble(day = dayList, totalWork = workLoad,
averageWorkLoad = averageWorkLoad,
smoothedWork = smoothedWork,
smoothedFatigueData = smoothedFatigueData)
workGraph <- ggplot(data = workTibble) +
theme(plot.title = element_text(hjust = 0.5)) +
ggtitle("Team's Smoothed Work") +
geom_point(mapping = aes(x=day, y=smoothedWork)) +
labs(x = "Days Since August First 2017", y = "Teams Training Work")+
theme_bw()
fatGraph <- ggplot(data = workTibble) +
theme(plot.title = element_text(hjust = 0.5)) +
ggtitle("Team's Percieved Fatigue") +
geom_point(mapping = aes(x=day, y=smoothedFatigueData)) +
labs(x = "Days Since August First 2017", y = "Teams Average Normalized Fatigue")+
theme_bw()
for(gameDay in games$Date)
{
fatGraph <- fatGraph + geom_vline(xintercept = gameDay, linetype="dotted",
color = "blue", size=1.0)
workGraph <- workGraph + geom_vline(xintercept = gameDay, linetype="dotted",
color = "blue", size=1.0)
}
workGraph
fatGraph
write.csv(dataTibble, "cleaned/expSmoothWorkAndFatigueData.csv")
slidingAverage <- c() slidingAverage <- c()
window <- 31 - 1 window <- 31 - 1
@ -46,7 +132,7 @@ plot(density(workLoad), main="Total Work Load Average")
dataTibble <- tibble(TimeSinceAugFirst = window:numDays, slidingWorkAverage = slidingAverage) dataTibble <- tibble(TimeSinceAugFirst = window:numDays, slidingWorkAverage = slidingAverage)
ggplot(data = dataTibble) +
workGraph <- ggplot(data = dataTibble) +
theme(plot.title = element_text(hjust = 0.5)) + theme(plot.title = element_text(hjust = 0.5)) +
ggtitle("Team's 7 Day Moving Average") + ggtitle("Team's 7 Day Moving Average") +
geom_point(mapping = aes(x=TimeSinceAugFirst, y=slidingWorkAverage)) + geom_point(mapping = aes(x=TimeSinceAugFirst, y=slidingWorkAverage)) +
@ -54,37 +140,40 @@ ggplot(data = dataTibble) +
theme_bw() theme_bw()
for(gameDay in games$Date)
{
workGraph <- workGraph + geom_vline(xintercept = gameDay, linetype="dotted",
color = "blue", size=1.0)
}
write.csv(dataTibble, "cleaned/slidingWorkAverageSevenDay.csv")
workGraph
################################ Wellness Data ###################################
fatigueData <- readFatigueSums()
write.csv(dataTibble, "cleaned/slidingWorkAverageSevenDay.csv")
dayNum <- max(fatigueData$TimeSinceAugFirst)
dayList <- 0:dayNum
################################ Wellness Data ###################################
slidingAverage <- c()
window <- 21 - 1
for(day in window:dayNum)
{
windowAverage <- mean(fatigueData$fatigueSum[c((day-window):day)], na.rm = T)
slidingAverage <- c(slidingAverage, windowAverage)
}
graphingTib <- tibble(slidingAverage = slidingAverage, days = window:dayNum) graphingTib <- tibble(slidingAverage = slidingAverage, days = window:dayNum)
ggplot(data = graphingTib) +
fGraph <- ggplot(data = graphingTib) +
theme(plot.title = element_text(hjust = 0.5)) + theme(plot.title = element_text(hjust = 0.5)) +
ggtitle("Team's Average Normalized Fatigue") + ggtitle("Team's Average Normalized Fatigue") +
geom_point(mapping = aes(x=days, y=slidingAverage)) + geom_point(mapping = aes(x=days, y=slidingAverage)) +
labs(x = "Days Since August Twenty First 2017", y = "Teams Average Normalized Fatigue")+ labs(x = "Days Since August Twenty First 2017", y = "Teams Average Normalized Fatigue")+
theme_bw() theme_bw()
for(gameDay in games$Date)
{
fGraph <- fGraph + geom_vline(xintercept = gameDay, linetype="dotted",
color = "blue", size=1.0)
}
fGraph
plot(density(slidingAverage)) plot(density(slidingAverage))
plot(window:dayNum, slidingAverage) plot(window:dayNum, slidingAverage)

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findings/SmoothedWork.png View File

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