You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
181 lines
4.3 KiB
181 lines
4.3 KiB
source("readData.R")
|
|
|
|
library(tidyverse)
|
|
|
|
|
|
RPEData <-readNArpeData()
|
|
|
|
|
|
numDays <- max(RPEData$TimeSinceAugFirst)
|
|
|
|
|
|
dayList <- 0:numDays
|
|
workLoad <- c()
|
|
averageWorkLoad <- c()
|
|
|
|
|
|
for(day in dayList)
|
|
{
|
|
daylyActivities <- subset(RPEData, TimeSinceAugFirst == day)
|
|
cat("day: ", day, "\n",sep="")
|
|
cat("Activity count:", length(daylyActivities$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")
|
|
|
|
|
|
|
|
|
|
|
|
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()
|
|
|
|
window <- 31 - 1
|
|
for(day in window:numDays)
|
|
{
|
|
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)
|
|
|
|
|
|
workGraph <- ggplot(data = dataTibble) +
|
|
theme(plot.title = element_text(hjust = 0.5)) +
|
|
ggtitle("Team's 7 Day Moving Average") +
|
|
geom_point(mapping = aes(x=TimeSinceAugFirst, y=slidingWorkAverage)) +
|
|
labs(x = "Days Since August Seventh 2017", y = "Teams Total Daily Load")+
|
|
theme_bw()
|
|
|
|
|
|
for(gameDay in games$Date)
|
|
{
|
|
workGraph <- workGraph + geom_vline(xintercept = gameDay, linetype="dotted",
|
|
color = "blue", size=1.0)
|
|
}
|
|
|
|
workGraph
|
|
|
|
|
|
write.csv(dataTibble, "cleaned/slidingWorkAverageSevenDay.csv")
|
|
|
|
|
|
################################ Wellness Data ###################################
|
|
|
|
|
|
|
|
graphingTib <- tibble(slidingAverage = slidingAverage, days = window:dayNum)
|
|
|
|
fGraph <- ggplot(data = graphingTib) +
|
|
theme(plot.title = element_text(hjust = 0.5)) +
|
|
ggtitle("Team's Average Normalized Fatigue") +
|
|
geom_point(mapping = aes(x=days, y=slidingAverage)) +
|
|
labs(x = "Days Since August Twenty First 2017", y = "Teams Average Normalized Fatigue")+
|
|
theme_bw()
|
|
|
|
|
|
for(gameDay in games$Date)
|
|
{
|
|
fGraph <- fGraph + geom_vline(xintercept = gameDay, linetype="dotted",
|
|
color = "blue", size=1.0)
|
|
}
|
|
|
|
fGraph
|
|
|
|
plot(density(slidingAverage))
|
|
plot(window:dayNum, slidingAverage)
|
|
|
|
|
|
|