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56 lines
1.4 KiB
56 lines
1.4 KiB
source("readData.R")
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library(tidyverse)
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# file to replace NA values with the median for thet column
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trainingData <- readRPEData()
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#duration
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trainingData$Duration[is.na(trainingData$Duration)] <- median(trainingData$Duration, na.rm=TRUE)
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print(trainingData$Duration)
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#RPE
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trainingData$RPE[is.na(trainingData$RPE)] <- median(trainingData$RPE, na.rm=TRUE)
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# acute load
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trainingData$AcuteLoad[is.na(trainingData$AcuteLoad)] <- median(trainingData$AcuteLoad, na.rm=TRUE)
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# chronic load
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trainingData$ChronicLoad[is.na(trainingData$ChronicLoad)] <- median(trainingData$ChronicLoad, na.rm=TRUE)
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# ratio
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trainingData$AcuteChronicRatio[is.na(trainingData$AcuteChronicRatio)] <- median(trainingData$AcuteChronicRatio, na.rm=TRUE)
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# objective rating
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trainingData$ObjectiveRating[is.na(trainingData$ObjectiveRating)] <- median(trainingData$ObjectiveRating, na.rm=TRUE)
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# focus rating
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trainingData$FocusRating[is.na(trainingData$FocusRating)] <- median(trainingData$FocusRating, na.rm=TRUE)
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# session load
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trainingData$SessionLoad[is.na(trainingData$SessionLoad)] <- median(trainingData$SessionLoad, na.rm=TRUE)
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# daily load
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trainingData$DailyLoad[is.na(trainingData$DailyLoad)] <- median(trainingData$DailyLoad, na.rm=TRUE)
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trainingData$RPE[is.na(trainingData$RPE)] <- median(trainingData$RPE, na.rm=TRUE)
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write.csv(as.data.frame(trainingData), "cleaned/time_series_rpe_NA_ReplacedWithMedian.csv")
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head(as.data.frame(trainingData), 100)
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trainingData
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