R Foundations: Iteration, A Hands-On Interactive Course

Almost every R task repeats one operation across many values. This four-lesson interactive course teaches the idiomatic ways to do that, from operating on a whole vector at once to mapping a function over a list and keeping a long run going past its failures.

Beginners reach for a for loop because it is the first tool they learn, but R rewards a different habit: describe the operation once and let it run across the whole collection. This course builds that habit in the order you need it, starting with the simplest case (a single vector) and ending with iteration over awkward, nested data that does not fit a neat rectangle.

Each lesson is a guided, interactive experience: you run R right in the page, answer checkpoints, and write code as you go. No setup, no installs.

The four lessons

Lesson 1: Why Vectorization Beats Loops

Operate on a whole vector in one expression instead of visiting each element with a loop, learn how recycling applies one number (or a matching list of numbers) across the vector, and see why the vectorized version is both shorter and far faster.

Start Lesson 1: Why Vectorization Beats Loops

Lesson 2: The apply Family

apply over the rows or columns of a matrix, lapply and sapply over a list, and the type-safe vapply for when you want to state the result shape up front and catch surprises early.

Start Lesson 2: The apply Family

Lesson 3: The purrr map Family

map and its typed variants (map_dbl, map_chr and friends), map2 and pmap for iterating over several inputs in step, and walk for the side-effect case where you want the action, not a returned value.

Start Lesson 3: The purrr map Family

Lesson 4: Resilient and Nested Iteration

Keep a long run going past errors with safely and possibly, then work with list-columns: pack each group into a column of tables with nest, map a function over each, and unnest back to a flat frame.

Start Lesson 4: Resilient and Nested Iteration

Who this is for

You can write a for loop and call a function, and that is enough. You do not need any prior experience with functional programming. By the end you will reach for a vectorized expression or a map call by default, and fall back to a loop only when it is genuinely the clearer choice.

What you will be able to do

• Rewrite an element-by-element loop as a single vectorized expression, and explain why it runs faster
• Choose between apply, lapply, sapply and vapply for iterating over matrices and lists
• Map a function over one or several inputs with map, map2 and pmap, and pick the right typed output
• Keep a long iteration running past failures with safely and possibly
• Iterate over grouped data using list-columns with nest and unnest

This course is part of the free New to R foundations track.

Ready? Begin with Lesson 1: Why Vectorization Beats Loops.