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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!
Simplifying Iteration and Lists With purrrFree
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.
More complex iterations
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 lists50 xpNames & pipe refresher100 xpSetting names100 xpPipes in map()100 xpMore map()50 xpSimulating Data with Purrr100 xpRun a linear model100 xpmap_chr()100 xpmap_dbl() and map_int()100 xpmap2() and pmap()50 xpSimulating data with multiple inputs using map2()100 xpSimulating data 3+ inputs with pmap()100 xp
Troubleshooting lists with purrr
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()50 xpsafely() replace with NA100 xpConvert data to numeric with purrr100 xpFinding the problem areas100 xpAnother way to possibly() purrr50 xpReplace safely() with possibly()100 xpConvert values with possibly()100 xppurrr is a walk() in the park50 xpComparing walk() vs. no walk() outputs100 xpwalk() for printing cleaner list outputs100 xp
Problem solving with purrr
Now that you have the building blocks, we will start tackling some more complex data problems with purrr.
In the following tracksIntermediate Tidyverse Toolbox
DatasetsSimulated data 1990-2005
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