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152 lines
4.6 KiB
152 lines
4.6 KiB
source("readData.R")
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library(tidyverse)
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RPEData <-readNArpeData()
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wellnessData <- readWellnessData()
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normalizedWellnessData <- readNormalizedMetrics()
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RPEData
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playerIDS <- playerIds <-unique(RPEData$PlayerID)
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numDays <- max(RPEData$TimeSinceAugFirst)
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dayList <- 0:numDays
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dayCol <- c()
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playerid <- c()
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dailyLoadCol <- c()
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acuteChronicRatioCol <- c()
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trainDuration <- c()
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sleepHoursCol <- c()
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fatigueRawCol <- c()
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sleepQualityCol <- c()
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sorenessCol <- c()
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normFatCol <-c()
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normSoreCol <- c()
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normSleepHours <- c()
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normSleepQuality <- c()
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notatAllCol <- c()
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absCol <-c()
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somewhatCol <- c()
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unknownCol <- c()
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for(day in dayList)
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{
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for(id in playerIDS)
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{
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cat("Player:", id, "Day:", day, "\n", sep=" ")
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trainDay <- subset(RPEData, TimeSinceAugFirst == day & PlayerID == id)
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#workLoad <- c(workLoad, sum(daylyActivities$SessionLoad, na.rm = T))
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wellnessDay <- subset(wellnessData, TimeSinceAugFirst == day & PlayerID == id)
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normalizedDay <- subset(normalizedWellnessData, TimeSinceAugFirst == day & playerID == id)
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#if(length(normalizedDay$playerID) > 0)
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#{
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# print("good")
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#}
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dayCol <- c(dayCol, day)
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playerid <- c(playerid, id)
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if(length(wellnessDay$SleepHours) > 0)
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{
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fatigueRawCol <- c(fatigueRawCol, mean(wellnessDay$Fatigue, na.rm =T))
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sleepQualityCol <- c(sleepQualityCol, mean(wellnessDay$SleepQuality, na.rm = T))
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sleepHoursCol <- c(sleepHoursCol, sum(wellnessDay$SleepHours, na.rm = T))
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sorenessCol <- c(sorenessCol, mean(wellnessDay$Soreness, na.rm = T))
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}
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else
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{
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sleepQualityCol <- c(sleepQualityCol, median(wellnessData$SleepQuality, na.rm = T))
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sleepHoursCol <- c(sleepHoursCol, median(wellnessData$SleepHours))
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fatigueRawCol <- c(fatigueRawCol, median(wellnessData$Fatigue))
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sorenessCol <- c(sorenessCol, median(wellnessData$Soreness))
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}
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if(length(normalizedDay$normSoreness) > 0)
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{
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normFatCol <- c(normFatCol, mean(normalizedDay$normFatigue, na.rm=T))
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normSoreCol <- c(normSoreCol, mean(normalizedDay$normSoreness, na.rm = T))
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normSleepHours <- c(normSleepHours, mean(normalizedDay$normSleepHours, na.rm =T))
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normSleepQuality <- c(normSleepQuality, mean(normalizedDay$normSleepQuality, na.rm=T))
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}
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else
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{
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normFatCol <- c(normFatCol, mean(normalizedWellnessData$normFatigue, na.rm=T))
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normSoreCol <- c(normSoreCol, mean(normalizedWellnessData$normSoreness, na.rm = T))
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normSleepHours <- c(normSleepHours, mean(normalizedWellnessData$normSleepHours, na.rm =T))
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normSleepQuality <- c(normSleepQuality, mean(normalizedWellnessData$normSleepQuality, na.rm=T))
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}
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if(length(trainDay$SessionLoad) > 0)
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{
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dailyLoadCol <- c(dailyLoadCol, mean(trainDay$DailyLoad,na.rm = T))
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acuteChronicRatioCol <- c(acuteChronicRatioCol, mean(trainDay$AcuteChronicRatio, na.rm =T))
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trainDuration <- c(trainDuration, sum(trainDay$Duration, na.rm = T))
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notatAllCol <- c(notatAllCol, max(trainDay$BestOutOfMyselfNotAtAll))
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absCol <- c(absCol, max(trainDay$BestOutOfMyselfAbsolutely))
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somewhatCol <- c(somewhatCol, max(trainDay$BestOutOfMyselfSomewhat))
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unknownCol <- c(unknownCol, max(trainDay$BestOutOfMyselfUnknown))
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}
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else
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{
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dailyLoadCol <- c(dailyLoadCol, 0)
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acuteChronicRatioCol <- c(acuteChronicRatioCol, 0)
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trainDuration <- c(trainDuration, 0)
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notatAllCol <- c(notatAllCol, 0)
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absCol <- c(absCol, 0)
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somewhatCol <- c(somewhatCol, 0)
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unknownCol <- c(unknownCol, 1)
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}
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}
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}
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dailyLoadCol[is.na(dailyLoadCol)] <- 0
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acuteChronicRatioCol[is.na(acuteChronicRatioCol)] <- 0
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massiveTibble <- tibble(day = dayCol,
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playerID = playerid,
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DailyLoad = dailyLoadCol,
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acuteChronicRatio = acuteChronicRatioCol,
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trainDuration = trainDuration,
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sleepHours = sleepHoursCol,
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fatigue = fatigueRawCol,
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sleepQuality = sleepQualityCol,
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soreness = sorenessCol,
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fatigueNorm = normFatCol,
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sorenessNorm = normSoreCol,
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sleepHoursNorm = normSleepHours,
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sleepQualityNorm = normSleepQuality,
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BestOutOfMyselfNotAtAll = notatAllCol,
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BestOutOfMyselfAbsolutely = absCol,
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BestOutOfMyselfSomewhat = somewhatCol,
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BestOutOfMyselfUnknown = unknownCol)
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write.csv(massiveTibble, "cleaned/personal.csv")
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