datafest competition 2019
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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)
ggplot(data = dataTibble) +
theme(plot.title = element_text(hjust = 0.5)) +
ggtitle("Team's Total Daily Load Moving Average") +
geom_point(mapping = aes(x=TimeSinceAugFirst, y=slidingWorkAverage)) +
labs(x = "Days Since August Twenty First 2017", y = "Teams Total Daily Load")+
theme_bw()
write.csv(dataTibble, "cleaned/slidingWorkAverage.csv")