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59 lines
1.4 KiB
59 lines
1.4 KiB
source("readData.R")
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library(tidyverse)
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RPEData <-readNArpeData()
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numDays <- max(RPEData$TimeSinceAugFirst)
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dayList <- 0:numDays
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workLoad <- c()
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averageWorkLoad <- c()
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for(day in dayList)
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{
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total <- 0
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daylyActivities <- subset(RPEData, TimeSinceAugFirst == day)
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cat("day: ", day, "\n",sep="")
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cat("Activity count:", length(daylyActivities$DailyLoad), "\n", sep="")
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averageWorkLoad <- c(averageWorkLoad, mean(daylyActivities$SessionLoad, na.rm = T))
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workLoad <- c(workLoad, sum(daylyActivities$SessionLoad, na.rm = T))
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}
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plot(dayList, averageWorkLoad, main="Average Work Load")
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plot(dayList, workLoad, main="Daily Total Work Load")
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slidingAverage <- c()
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window <- 7 - 1
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for(day in window:numDays)
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{
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windowAverage <- mean(workLoad[c((day-window):day)])
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slidingAverage <- c(slidingAverage, windowAverage)
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}
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plot(window:numDays, slidingAverage, main="Sliding Average")
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plot(density(slidingAverage), main="Sliding Average Density")
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plot(density(workLoad), main="Total Work Load Average")
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dataTibble <- tibble(TimeSinceAugFirst = window:numDays, slidingWorkAverage = slidingAverage)
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ggplot(data = dataTibble) +
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theme(plot.title = element_text(hjust = 0.5)) +
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ggtitle("Team's 7 Day Moving Average") +
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geom_point(mapping = aes(x=TimeSinceAugFirst, y=slidingWorkAverage)) +
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labs(x = "Days Since August Seventh 2017", y = "Teams Total Daily Load")+
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theme_bw()
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write.csv(dataTibble, "cleaned/slidingWorkAverageSevenDay.csv")
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