# 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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