How to preProcess features when some of them are factors?

Question

My question is related to this one regarding categorical data (factors in R terms) when using the Caret package. I understand from the linked post that if you use the "formula interface", some features can be factors and the training will work fine. My question is how can I scale the data with the preProcess() function? If I try and do it on a data frame with some columns as factors, I get this error message:

Error in preProcess.default(etitanic, method = c("center", "scale")) : 
  all columns of x must be numeric

See here some sample code:

library(earth)
data(etitanic)

a <- preProcess(etitanic, method=c("center", "scale"))
b <- predict(etitanic, a)

Thank you.

Answer 1

It is really the same issue as the post you link to. preProcess works only on numeric data and you have:

> str(etitanic)
'data.frame':   1046 obs. of  6 variables:
 $ pclass  : Factor w/ 3 levels "1st","2nd","3rd": 1 1 1 1 1 1 1 1 1 1 ...
 $ survived: int  1 1 0 0 0 1 1 0 1 0 ...
 $ sex     : Factor w/ 2 levels "female","male": 1 2 1 2 1 2 1 2 1 2 ...
 $ age     : num  29 0.917 2 30 25 ...
 $ sibsp   : int  0 1 1 1 1 0 1 0 2 0 ...
 $ parch   : int  0 2 2 2 2 0 0 0 0 0 ...

You can't center and scale pclass or sex as-is so they need to be converted to dummy variables. You can use model.matrix or caret's dummyVars to do this:

 > new <- model.matrix(survived ~ . - 1, data = etitanic)
 > colnames(new)
 [1] "pclass1st" "pclass2nd" "pclass3rd" "sexmale"   "age"      
 [6] "sibsp"     "parch"  

The -1 gets rid of the intercept. Now you can run preProcess on this object.

btw making preProcess ignore non-numeric data is on my "to do" list but it might cause errors for people not paying attention.

Max

Answer 2

Here's a quick way to exclude factors or whatever you'd like from consideration:

set.seed(1)
N <- 20
dat <- data.frame( 
    x = factor(sample(LETTERS[1:5],N,replace=TRUE)),
    y = rnorm(N,5,12),
    z = rnorm(N,-5,17) + runif(N,2,12)
)

#' Function which wraps preProcess to exclude factors from the model.matrix
ppWrapper <- function( x, excludeClasses=c("factor"), ... ) {
    whichToExclude <- sapply( x, function(y) any(sapply(excludeClasses, function(excludeClass) is(y,excludeClass) )) )
    processedMat <- predict( preProcess( x[!whichToExclude], ...), newdata=x[!whichToExclude] )
    x[!whichToExclude] <- processedMat
    x
}

> ppWrapper(dat)
   x          y           z
1  C  1.6173595 -0.44054795
2  A -0.2933705 -1.98856921
3  C  1.2177384  0.65420288
4  D -0.8710374  0.62409408
5  D -0.4504202 -0.34048640
6  D -0.6943283  0.24236671
7  E  0.7778192  0.91606677
8  D  0.2184563 -0.44935163
9  C -0.3611408  0.26075970
10 B -0.7066441 -0.23046073
11 D -1.5154339 -0.75549761
12 D  0.4504825  0.38552988
13 B  1.5692675  0.04093040
14 C  0.4127541  0.13161807
15 D  0.5426321  1.09527418
16 B -2.1040322 -0.04544407
17 C  0.6928574  1.12090541
18 B  0.3580960  1.91446230
19 E  0.3619967 -0.89018040
20 A -1.2230522 -2.24567237

You can pass anything you want into ppWrapper and it will get passed along to preProcess.