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Going Deeper with R

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  1. Welcome to Going Deeper with R
  2. Advanced Data Wrangling and Analysis
    Overview
  3. Importing Data
  4. Tidy Data
  5. Reshaping Data
  6. Dealing with Missing Data
  7. Changing Variable Types
  8. Advanced Variable Creation
  9. Advanced Summarizing
  10. Binding Data Frames
  11. Functions
  12. Merging Data
  13. Renaming Variables
  14. Quick Interlude to Reorganize our Code
  15. Exporting Data
  16. Advanced Data Visualization
    Data Visualization Best Practices
  17. Tidy Data
  18. Pipe Data Into ggplot
  19. Reorder Plots to Highlight Findings
  20. Line Charts
  21. Use Color to Highlight Findings
  22. Declutter
  23. Use the scales Package for Nicely Formatted Values
  24. Use Direct Labeling
  25. Use Axis Text Wisely
  26. Use Titles to Highlight Findings
  27. Use Color in Titles to Highlight Findings
  28. Use Annotations to Explain
  29. Tweak Spacing
  30. Customize Your Theme
  31. Customize Your Fonts
  32. Try New Plot Types
  33. Advanced RMarkdown
    Advanced Markdown Text Formatting
  34. Tables
  35. Advanced YAML
  36. Inline R Code
  37. Making Your Reports Shine: Word Edition
  38. Making Your Reports Shine: HTML Edition
  39. Making Your Reports Shine: PDF Edition
  40. Presentations
  41. Dashboards
  42. Other Formats
  43. Wrapping Up
    You Did It!
Going Deeper with R Reshaping Data
Lesson 5 of 43
In Progress

Reshaping Data

Your Turn
  1. Start with the enrollment_18_19 data frame
  2. select() the district_id variable as well as those about number of students by race/ethnicity and get rid of all others (hint: use the contains() helper function within select())
  3. Use pivot_longer() to convert all of the race/ethnicity variables into one variable
  4. Within pivot_longer(), use the names_to argument to call that variable race_ethnicity
  5. Within pivot_longer(), use the values_to argument to call that variable number_of_students
Solutions
Learn More

The best place to learn more about pivot_longer() and pivot_wider() is the pivoting vignette from the tidyr package.

There’s also a nice article by Gavin Simpson of University College, London about pivoting. That article includes the animations below, made by Garrick Aden-Buie and Mara Averick, that gave a visual demonstration of pivoting.

RStudio has a nice primer on reshaping data, complex with a few exercises.

Finally, a heads up: if you ever see references to the functions gather() and spread(), these are the previous iterations of the pivot functions. They still work (as the tweet below from tidyverse developer Hadley Wickham indicates), but the pivot functions are, in my view (and the view of many others), much easier to use.

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

  1. IBRAH SENINDE April 21, 2021

    Hi David, I typed the following code, but the new data frame still the original structure. What could be the problem?
    enrollment_by_race_ethnicity_18_19 %
    select(-contains(“grade”)) %>%
    select(-contains(“kindergarten”)) %>%
    select(-contains(“percent”)) %>%
    pivot_longer(cols = “district_id”,
    names_to = “race_ethnicity”,
    values_to = “number_of_students”)

  2. Harold Stanislaw April 21, 2021

    I used select(!contains (“percent”)) instead of select(-contains (“percent”)), mainly because the helper page listed the exclamation option rather than the minus sign. Are there any differences between the two?

  3. Matt M November 8, 2021

    I only have enrollment_17_18 and enrollment_18_19 files. Where did the math scores files come from that is shown at 5:40 of this lesson? My code matches what’s in the Solutions for the Importing Data lesson and I don’t think there was anything we downloaded in the Tidy Data lesson.

      1. Nadia Chang November 29, 2022

        What I understand from pivot_longer documentation page is that it will rearrange the data by decreasing the number of columns, so my question is: if pivot longer will always tidy the data into 3 columns? In this case there is just one column for “the values”, one column for “the names or columns that have characters” and “id column” (this last one we kept it by adding the “-“).

        1. David Keyes November 30, 2022

          It will be 3 columns if you only have 1 column that you are not pivoting (in this case, the id column). However, if you are not pivoting multiple columns then you will have more than 3. Does that make sense?