# Efficient Processing With Apply() Exercises

**R-exercises**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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The `apply()`

function is an alternative to writing loops, via applying a function to columns, rows, or individual values of an array or matrix.

The structure of the `apply()`

function is:

`apply(X, MARGIN, FUN, ...)`

The matrix variable used for the exercises is:

`dataset1 <- cbind(observationA = 16:8, observationB = c(20:19, 6:12))`

Answers to the exercises are available here.

**Exercise 1**

Using `apply()`

, find the row means of `dataset1`

**Exercise 2**

Using `apply()`

, find the column sums of `dataset1`

**Exercise 3**

Use `apply()`

to sort the columns of `dataset1`

**Exercise 4**

Using `apply()`

, find the product of `dataset1`

rows

**Exercise 5**

Required function:

`DerivativeFunction <- function(x) { log10(x) + 1 }`

Apply “`DerivativeFunction`

” on the rows of `dataset1`

**Exercise 6**

Re-script the formula from Exercise 5, in order to define “`DerivativeFunction`

” inside the `apply()`

function

**Exercise 7**

Round the output of the Exercise 6 formula to 2 places

**Exercise 8**

Print the columns of `dataset1`

with the `apply()`

function

**Exercise 9**

Find the length of the `dataset1`

columns

**Exercise 10**

Use `apply()`

to find the range of numbers

within the `dataset1`

columns

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