[r] Selecting only numeric columns from a data frame

Suppose, you have a data.frame like this:

x <- data.frame(v1=1:20,v2=1:20,v3=1:20,v4=letters[1:20])

How would you select only those columns in x that are numeric?

Another way could be as follows:-

#extracting numeric columns from iris datset
(iris[sapply(iris, 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).

This doesn't directly answer the question but can be very useful, especially if you want something like all the numeric columns except for your id column and dependent variable.

numeric_cols <- sapply(dataframe, is.numeric) %>% which %>% 
                   names %>% setdiff(., c("id_variable", "dep_var"))

dataframe %<>% dplyr::mutate_at(numeric_cols, function(x) your_function(x))

Numerical_variables <- which(sapply(df, is.numeric))
# then extract column names 
Names <- names(Numerical_variables)

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

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

The library PCAmixdata has functon splitmix that splits quantitative(Numerical data) and qualitative (Categorical data) of a given dataframe "YourDataframe" as shown below:

install.packages("PCAmixdata")
library(PCAmixdata)
split <- splitmix(YourDataframe)
X1 <- split$X.quanti(Gives numerical columns in the dataset) 
X2 <- split$X.quali (Gives categorical columns in the dataset)

iris %>% dplyr::select(where(is.numeric)) #as per most recent updates

Another option with purrr would be to negate discard function:

iris %>% purrr::discard(~!is.numeric(.))

If you want the names of the numeric columns, you can add names or colnames:

iris %>% purrr::discard(~!is.numeric(.)) %>% names

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

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

library(purrr)
x <- x %>% keep(is.numeric)

This an alternate code to other answers:

x[, sapply(x, class) == "numeric"]

with a data.table

x[, lapply(x, is.numeric) == TRUE, with = FALSE]

If you have many factor variables, you can use select_if funtion. install the dplyr packages. There are many function that separates data by satisfying a condition. you can set the conditions.

Use like this.

categorical<-select_if(df,is.factor)
str(categorical)