[This article was first published on R-exercises, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

The `lapply()` function applies a function to individual values of a list, and is a faster alternative to writing loops.

Structure of the `lapply()` function:
`lapply(LIST, FUNCTION, ...)`

The list variable used for these exercises:
`list1 <- list(observationA = c(1:5, 7:3), observationB=matrix(1:6, nrow=2))`

Answers to the exercises are available here.

Exercise 1

Using `lapply()`, find the length of `list1`‘s observations.

Exercise 2

Using `lapply()`, find the sums of `list1`‘s observations.

Exercise 3

Use `lapply()` to find the quantiles of `list1`.

Exercise 4

Find the classes of `list1`‘s sub-variables, with `lapply()`.

Exercise 5

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

Apply the “`DerivativeFunction`” to `list1`.

Exercise 6

Script the “`DerivativeFunction`” within `lapply()`. The dataset is `list1`.

Exercise 7

Find the unique values in `list1`.

Exercise 8

Find the range of `list1`.

Exercise 9

Print `list1` with the `lapply()` function.

Exercise 10

Convert the output of Exercise 9 to a vector, using the `unlist()`, and `lapply()`, functions.

To leave a comment for the author, please follow the link and comment on their blog: R-exercises.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

# Never miss an update! Subscribe to R-bloggers to receive e-mails with the latest R posts.(You will not see this message again.)

Click here to close (This popup will not appear again)