**DataScience+**, and kindly contributed to R-bloggers)

In this article, I will demonstrate how to use the `apply`

family of functions in R. They are extremely helpful, as you will see.

## apply

`apply`

can be used to apply a function to a matrix.

For example, let’s create a sample dataset:

data <- matrix(c(1:10, 21:30), nrow = 5, ncol = 4) data[,1] [,2] [,3] [,4] [1,] 1 6 21 26 [2,] 2 7 22 27 [3,] 3 8 23 28 [4,] 4 9 24 29 [5,] 5 10 25 30

Now we can use the apply function to find the mean of each row as follows:

apply(data, 1, mean)13.5 14.5 15.5 16.5 17.5

The second parameter is the dimension. `1`

signifies rows and `2`

signifies columns. If you want both, you can use `c(1, 2)`

.

## lapply

`lapply`

is similar to apply, but it takes a list as an input, and returns a list as the output.

Let’s create a list:

data <- list(x = 1:5, y = 6:10, z = 11:15) data$x 1 2 3 4 5 $y 6 7 8 9 10 $z 11 12 13 14 15

Now, we can use lapply to apply a function to each element in the list. For example:

lapply(data, FUN = median)$x [1] 3 $y [1] 8 $z [1] 13

## sapply

`sapply`

is the same as `lapply`

, but returns a vector instead of a list.

sapply(data, FUN = median)x y z 3 8 13

## tapply

`tapply`

splits the array based on specified data, usually factor levels and then applies the function to it.

For example, in the `mtcars`

dataset:

library(datasets) tapply(mtcars$wt, mtcars$cyl, mean)4 6 8 2.285727 3.117143 3.999214

The tapply function first groups the cars together based on the number of cylinders they have, and then calculates the mean weight for each group.

## mapply

`mapply`

is a multivariate version of `sapply`

. It will apply the specified function to the first element of each argument first, followed by the second element, and so on. For example:

x <- 1:5 b <- 6:10 mapply(sum, x, b)7 9 11 13 15

It adds 1 with 6, 2 with 7, and so on.

Let me know if you have any questions by leaving a comment below or reaching out to me on Twitter.

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