Your Turn
- Start with the
enrollment_18_19data frame select()thedistrict_idvariable as well as those about number of students by race/ethnicity and get rid of all others (hint: use thecontains()helper function withinselect())- Use
pivot_longer()to convert all of the race/ethnicity variables into one variable - Within
pivot_longer(), use the names_to argument to call that variablerace_ethnicity - Within
pivot_longer(), use the values_to argument to call that variablenumber_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. David or Charlie Hadley will help you out!
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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”)
Hi IIbrah. Try changing
pivot_longer(cols = “district_id”,
to
pivot_longer(-district_id ,
The code you entered was pivoting the district ID in addition to the other columns.
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?
I don’t know the answer to that! Does it give the same result?
Looks like it gives the same answer.
Ok, I guess it does do the same thing then. Thanks for teaching me something new!
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.
I now see that we don’t need for (Y)our turn. was just trying to follow along
Hi David, is there a way to enter the name of each column we want to remove within one select(-contains(” “)) argument rather than writing each one separately?
Do you mean like doing
select(-c(column1, column2))?Yes! Thank you, that worked.