Learn how to use the `cross()`

function from the dplyr package in R to apply functions to multiple columns in a data frame. This blog post will show you different ways of using the `cross()`

function with examples and code snippets. Whether you want to apply the same function to multiple columns or different functions to different columns, the `cross()`

function can help you manipulate your data in R.

Contents

### Introduction

The `cross()`

function is a powerful tool in the `dplyr`

package that allows you to apply the same function to multiple columns in a data frame. This can be especially useful when you have a large data set and want to perform the same operation on multiple columns.

In this blog post, we will explore different ways of using the `cross()`

function and provide examples of how it can be used to manipulate data in R.

### Installing and Loading dplyr

Before we can use the `cross()`

function, we need to install and load the `dplyr`

package. You can do this by running the following code:

*# install.packages("dplyr")*
**library**(dplyr)

### Creating a Data Frame

To demonstrate how the `cross()`

function works, we will create a simple data frame with three columns (`x`

, `y`

, and `z`

). We can do this using the following code. This will create a data frame that looks like as shown below:

`df <- `**data.frame**(
x = **c**(1, 2, 3),
y = **c**(4, 5, 6),
z = **c**(7, 8, 9)
)

### Applying a Function to Multiple Columns

Now that we have our data frame, letâ€™s say we want to multiply each column by 2. We can do this using the following code. This will give us the following output:

`df `**%>%**
**mutate**(**across**(**c**(x, y, z), **~** . ***** 2))

# x y z # 1 2 8 14 # 2 4 10 16 # 3 6 12 18

As you can see, each column has been multiplied by 2.

You can also use the `.cols`

argument instead of `c(x, y, z)`

. This will give us the same result as before.

`df `**%>%**
**mutate**(**across**(.cols = x**:**z, **~** . ***** 2))

# x y z # 1 2 8 14 # 2 4 10 16 # 3 6 12 18

### Applying Different Functions to Different Columns

In addition to applying the same function to multiple columns, you can also apply different functions to different columns using the `across()`

function. For example, letâ€™s say we want to multiply column `x`

by 2 and column `y`

by 3. We can do this using the following code. This will give us the following output:

`df `**%>%**
**mutate**(**across**(**c**(x), **~** . ***** 2),
**across**(**c**(y), **~** . ***** 3))

# x y z # 1 2 12 7 # 2 4 15 8 # 3 6 18 9

As you can see, column `x`

has been multiplied by 2 and column `y`

has been multiplied by 3.

### Applying Multiple Functions to Multiple Columns

Finally, you can also apply multiple functions to multiple columns using the `across()`

function. For example, letâ€™s say we want to multiply column `x`

by 2 and add column `y`

and column `z`

. We can do this using the following code. This will give us the following output:

`df `**%>%**
**mutate**(**across**(**c**(x), **~** . ***** 2),
**across**(**c**(y,z), **~** . **+** x))

# x y z # 1 2 6 9 # 2 4 9 12 # 3 6 12 15

As you can see, column `x`

has been multiplied by two and added to columns `y`

and `z`

.

### Conclusion

The `cross()`

function in the `dplyr`

package allows you to apply functions to multiple columns in a data frame. Whether you want to apply the same function to multiple columns or different functions to different columns, the `across()`

function makes it easy to manipulate your data in R.

I hope this blog post has been helpful.

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