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Congratulations!

1. Congratulations!

Congratulations! You learned so much in this course! Let's do a quick recap.

2. Separating messy string columns

We jumped into the action with the separate() and separate_rows() functions to turn messy string columns into variables or observations. The first chapter also taught us different ways of dealing with missing values.

3. Pivoting data

The second chapter was all about pivoting data. The pivot_longer() function allowed you to move variables hidden in column headers into columns, while pivot_wider() did the opposite.

4. Expanding data

We then moved on to expanding data in chapter three, where the complete() function allowed you to add observations that were missing in the data.

5. Unnesting data

In the final chapter, we saw how to turn nested data structures into tidy data frames using the unnest_wider() and unnest_longer() functions. We also saw an upside to nested data structures, as they allowed us to elegantly train multiple models in a single pipeline.

6. The end

It's now up to you to use the tools in the tidyr package to tidy your datasets. Remember that the trick to solving a complex problem is to divide it into many smaller ones. My name is Jeroen Boeye, and I thank you for sticking with me to the end. Well done!

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