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Your Turn

  1. Read the Tidy Data vignette

  2. Take a look at your data and see which principles of tidy data it violates

Learn More

In the video, I only talk about two types of data tidying: each variable forming a column and each type of observational unit forming a table. If you want to see examples of the third type (each observation forming a row), check out the tidy data vignette from the tidyr package.

The workflow diagram I talked about is from Chapter 1 of R for Data Science.

Tidy data worfklow.

One small note unrelated to the main content of this lesson: I recorded it before dplyr 1.0 was released. If you have this version of dplyr installed, you have access to the across() function, which enables you to do summaries across rows. My example of finding it challenging to summarize German speakers data across rows would be much easier using the across() function. However, I still think that in most cases, it is easier to tidy your data and work with it in that format.

Have any questions? Put them below and we will help you out!

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Vuk Sekicki

Vuk Sekicki

April 19, 2021

Hello David,

Could you help me out understanding this: names_pattern = "(.)(.+)"

Specifically what is "(.)(.+)"


David Keyes

David Keyes

April 19, 2021

Oh man, this is an area I struggle with. With that caveat, this is a way to flexibly create variable names on the fly when you pivot. It involves using regular expressions, which, again, I'm not very good at. You can read a bit more about this here.

Vuk Sekicki

Vuk Sekicki

April 20, 2021 datacamp: String Manipulation with stringr in R

Just putting it out there if anyone needs it in future. I see this is a science by it self that requires weeks to master. I will look into this in future. Too much for now. TNX!!

David Keyes

David Keyes

April 20, 2021

Yes, it's very complex! Not to be too salesy but I actually have an in-depth data cleaning course coming out later this year. It has a whole section on regular expressions.

Vuk Sekicki

Vuk Sekicki

April 22, 2021

Not at all, looking forward!

I see you re-worded the 3 rules of tidy data from the vignette. Although I think I understand conceptually what is being sought, I'm not sure I follow what each rule means (i.e., what I need to do to make sure that I'm complying with the rule) and what a violation of each rule looks like (the third rule in particular)

The wording of the 3 rules of tidy data have changed over time. I hope some of the additional resources I shared help to make sense of the rules. Here they are for others: