Pakeisti visi NA reikšmės tinklo ar kitą turinį būtina duomenis iš yra įvesties duomenų rinkinio išvesties duomenų rinkinio, naudojant funkciją
rxDataStep. Toliau pateiktas scenarijaus pavyzdys pakeisti visus NA xdf faile "AirlineDemoSmall.xdf"# Create a data frame with missing valuesset.seed(17)
myDataF <- data.frame(x = rnorm(100), y = runif(100), z = rgamma(100, shape = 2)) xmiss <- seq.int(from = 5, to = 100, by = 5) ymiss <- seq.int(from = 2, to = 100, by = 5) myDataF$x[xmiss] <- NA myDataF$y[ymiss] <- NA # Convert into a xdf myDataNA<-file.path(getwd(),"myDataNA.xdf") trsfxdf<-rxDataStep(inData=myDataF,outFile=myDataNA,overwrite=TRUE) writeLines("\n\nXdf Generated with random NA values") print(rxGetInfo(myDataF, n = 15)$data) # Test ouput data ## ## Use from here if there is an existing xdf. ## replace myDataNA with your xdf file ## writeLines("\n\nVariables that contains NA values (Missing Observations)") (mySum <- rxSummary(~., data = myDataNA)$sDataFrame) # Find variables that are missing transVars <- mySum$Name[mySum$MissingObs > 0] print(transVars) #Test detected variables # create a function to replace NA vals with mean NAreplace <- function(dataList) { replaceFun <- function(x) { x[is.na(x)] <- replaceValue return(x) } dataList <- lapply(dataList, replaceFun) return(dataList) } # myDataRMV<-file.path(getwd(),"myDataRMV.xdf") # Replace Missing Value trsfxdf<- rxDataStep(inData = myData1, outFile = myDataRMV, transformFunc = NAreplace, transformVars = transVars, transformObjects = list(replaceValue = "REPLACED MISSING VALUE"), overwrite=TRUE) writeLines("\n\nTransformed xdf with NA replaced by Value") print(rxGetInfo(myDataRMV, n=15)$data) # Test output data