machine learning - SVM classification - normalization in R -

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i want work svm classification. how possible normalize (or scale) features per column in dataset before use svm model? 

train <- read.csv("train.csv") test <- read.csv("test.csv")  svm.fit=svm(as.factor(type)~ ., data=train, core="libsvm",kernel="linear",cross=10, probability=true)

you can use scale function in sapply:

scaleddf <- as.data.frame(sapply(train, function(i) if(is.numeric(i)) scale(i) else i))

if data contains variables nan values or 0 variance, first process , subset original dataset before using function above.

# vector of variables drop dropvars <- sapply(train, function(i) {               if((is.numeric(i) & !any(is.nan(i)) & sd(i) > 0) | is.factor(i) | is.character(i)) true               else false               } # subset test dropping columns don't fit criteria smallerdf <- test[, dropvars]

then apply original sapply function above smallerdf


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