Selecting only numeric columns from a data frame

in case you are interested only in column names then use this :

names(dplyr::select_if(train,is.numeric))

Filter() from the base package is the perfect function for that use-case: You simply have to code:

Filter(is.numeric, x)

It is also much faster than select_if():

library(microbenchmark)
microbenchmark(
    dplyr::select_if(mtcars, is.numeric),
    Filter(is.numeric, mtcars)
)

returns (on my computer) a median of 60 microseconds for Filter, and 21 000 microseconds for select_if (350x faster).

The dplyr package's select_if() function is an elegant solution:

library("dplyr")
select_if(x, is.numeric)

EDIT: updated to avoid use of ill-advised sapply.

Since a data frame is a list we can use the list-apply functions:

nums <- unlist(lapply(x, is.numeric), use.names = FALSE)

Then standard subsetting

x[ , nums]

## don't use sapply, even though it's less code
## nums <- sapply(x, is.numeric)

For a more idiomatic modern R I'd now recommend

x[ , purrr::map_lgl(x, is.numeric)]

Less codey, less reflecting R's particular quirks, and more straightforward, and robust to use on database-back-ended tibbles:

dplyr::select_if(x, is.numeric)

Newer versions of dplyr, also support the following syntax:

x %>% dplyr::select(where(is.numeric))

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