Iteration is a powerful way to make the computer do the work for you. It can also be an area of coding where it is easy to make lots of typos and simple mistakes. The purrr package helps simplify iteration so you can focus on the next step, instead of finding typos.

The power of iteration

Introduction to iteration

Iteration with purrr

More iteration with for loops

More iteration with purrr

Subsetting lists

List structure

Subsetting list elements

The many flavors of map()

map() argument alternatives

map_*

Simplifying Iteration and Lists With purrr

purrr is much more than a for loop; it works well with pipes, we can use it to run models and simulate data, and make nested loops!

Working with unnamed lists

Names & pipe refresher

Setting names

Pipes in map()

More map()

Simulating Data with Purrr

Run a linear model

map_chr()

map_dbl() and map_int()

map2() and pmap()

Simulating data with multiple inputs using map2()

Simulating data 3+ inputs with pmap()

More complex iterations

Like anything in R, understanding how to troubleshoot issues is an important skill set. This can be particularly important with lists, where finding the problem can be tricky.

How to purrr safely()

safely() replace with NA

Convert data to numeric with purrr

Finding the problem areas

Another way to possibly() purrr

Replace safely() with possibly()

Convert values with possibly()

purrr is a walk() in the park

Comparing walk() vs. no walk() outputs

walk() for printing cleaner list outputs

Troubleshooting lists with purrr

Now that you have the building blocks, we will start tackling some more complex data problems with purrr.

Using purrr in your workflow

Name review

Asking questions from a list

Even more complex problems

Questions about gh_repos

Graphs in purrr

ggplot() refresher

purrr and scatterplots

purrr and histograms

Wrap Up

Problem solving with purrr

Simulated data 1990-2005

Lists can be difficult to both understand and manipulate, but they can pack a ton of information and are very powerful. In this course, you will learn to easily extract, summarize, and manipulate lists and how to export the data to your desired object, be it another list, a vector, or even something else! Throughout the course, you will work with the purrr package and a variety of datasets from the repurrrsive package, including data from Star Wars and Wes Anderson films and data collected about GitHub users and GitHub repos. Following this course, your list skills will be purrrfect!

Introduction to the Tidyverse

Intermediate R

Learn how to work with the purrr package and a variety of datasets from the repurrrsive package to extract, summarize, manipulate, and troubleshoot lists.

Foundations of Functional Programming with purrr

Learn to easily summarize and manipulate lists using the purrr package.

Caixa de ferramentas intermediária do Tidyverse

Desenvolvedor R

`map_*()`	Output
`map_chr()`	character vector
`map_lgl()`	logical vector [TRUE or FALSE]
`map_int()`	integer vector
`map_dbl()`	double vector

map_dbl() and map_int()

Foundations of Functional Programming with purrr

Exercício interativo prático