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