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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")
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