Selecting columns based on type
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The tidyverse
and, in particular, dplyr
, provides functions to select columns
from a data frame. There are three scoped functions available: select_all
, select_if
and select_at
. In this post, we’ll look at a particular application of
select_if
, i.e., capturing the names of numeric variables.
A quick search using Google finds a few solutions to this problem. As an example data set, I’ll use the diamonds
data set from the ggplot2
package.
names(diamonds)[sapply(diamonds, is.numeric)] ## [1] "carat" "depth" "table" "price" "x" "y" "z"
or, equivalently
names(diamonds)[map_lgl(diamonds, is.numeric)] ## [1] "carat" "depth" "table" "price" "x" "y" "z"
However, there is an elegant (to me) pipeline based solution using select_if
.
diamonds %>% select_if(is.numeric) %>% names() ## [1] "carat" "depth" "table" "price" "x" "y" "z"
However, the elegance is at the expense of some efficiency.
library(microbenchmark) microbenchmark(names(diamonds)[sapply(diamonds, is.numeric)], names(diamonds)[map_lgl(diamonds, is.numeric)], diamonds %>% select_if(is.numeric) %>% names()) %>% autoplot() ## Coordinate system already present. Adding new coordinate system, which will replace the existing one.
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