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Using the dplyr package in R, it is possible to select only numeric columns from a dataframe by using the select_if() function. This function takes a predicate as an argument, which in the case of selecting only numeric columns is is.numeric(), and returns a dataframe containing only the columns which fulfill the predicate. This is a useful tool for quickly subsetting a dataframe and selecting only the desired columns.
You can use the following function from the package to select only numeric columns from a data frame in R:
df %>% select(where(is.numeric))
The following example shows how to use this function in practice.
Example: Select Only Numeric Columns Using dplyr
Suppose we have the following data frame in R that contains information about various basketball players:
#create data frame df <- data.frame(team=c('A', 'B', 'C', 'D', 'E'), points=c(22, 34, 30, 12, 18), assists=c(7, 9, 9, 12, 14), rebounds=c(5, 10, 10, 8, 8)) #view data frame df team points assists rebounds 1 A 22 7 5 2 B 34 9 10 3 C 30 9 10 4 D 12 12 8 5 E 18 14 8
We can use the following syntax to select only the numeric columns from the data frame:
library(dplyr)
#select only the numeric columns from the data frame
df %>% select(where(is.numeric))
points assists rebounds
1 22 7 5
2 34 9 10
3 30 9 10
4 12 12 8
5 18 14 8 Notice that only the three numeric columns have been selected – points, assists, and rebounds.
We can verify that these columns are numeric by using the str() function to display the data type of each variable in the data frame:
#display data type of each variable in data frame
str(df)
'data.frame': 5 obs. of 4 variables:
$ team : chr "A" "B" "C" "D" ...
$ points : num 22 34 30 12 18
$ assists : num 7 9 9 12 14
$ rebounds: num 5 10 10 8 8
From the output we can see that team is a character variable while points, assists, and rebounds are all numeric.
Related:
The following tutorials explain how to perform other common tasks using dplyr: