Easy way to separate categorical and continuous variables from a data frame in R

separate_categorical_continuous_variables.r

# Ensure the data is read as a dataframe and that the categorical variables are read as factors and not characters.
# A minimum reprex is given below

# load the adult dataset from the UCI ML repo.
library(data.table)
dt<- fread("https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data",
           header = FALSE, sep = ",", stringsAsFactors = TRUE)
# coerce data table to data frame
dt<- as.data.frame(dt)
head(dt)
class(dt)

# use sapply()
dt.cat<-dt[,sapply(dt, is.factor)]
dt.cont<-dt[,!sapply(dt, is.factor)]

> str(dt.cat)
'data.frame':	32561 obs. of  9 variables:
 $ V2 : Factor w/ 9 levels "?","Federal-gov",..: 8 7 5 5 5 5 5 7 5 5 ...
 $ V4 : Factor w/ 16 levels "10th","11th",..: 10 10 12 2 10 13 7 12 13 10 ...
 $ V6 : Factor w/ 7 levels "Divorced","Married-AF-spouse",..: 5 3 1 3 3 3 4 3 5 3 ...
 $ V7 : Factor w/ 15 levels "?","Adm-clerical",..: 2 5 7 7 11 5 9 5 11 5 ...
 $ V8 : Factor w/ 6 levels "Husband","Not-in-family",..: 2 1 2 1 6 6 2 1 2 1 ...
 $ V9 : Factor w/ 5 levels "Amer-Indian-Eskimo",..: 5 5 5 3 3 5 3 5 5 5 ...
 $ V10: Factor w/ 2 levels "Female","Male": 2 2 2 2 1 1 1 2 1 2 ...
 $ V14: Factor w/ 42 levels "?","Cambodia",..: 40 40 40 40 6 40 24 40 40 40 ...
 $ V15: Factor w/ 2 levels "<=50K",">50K": 1 1 1 1 1 1 1 2 2 2 ...
> str(dt.cont)
'data.frame':	32561 obs. of  6 variables:
 $ V1 : int  39 50 38 53 28 37 49 52 31 42 ...
 $ V3 : int  77516 83311 215646 234721 338409 284582 160187 209642 45781 159449 ...
 $ V5 : int  13 13 9 7 13 14 5 9 14 13 ...
 $ V11: int  2174 0 0 0 0 0 0 0 14084 5178 ...
 $ V12: int  0 0 0 0 0 0 0 0 0 0 ...
 $ V13: int  40 13 40 40 40 40 16 45 50 40 ...