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Advanced Data Wrangling
- Downloading and Importing Data
- Overview of Tidy Data
- Tidy Data Rule #1: Every Column is a Variable
- Tidy Data Rule #3: Every Cell is a Single Value
- Tidy Data Rule #2: Every Row is an Observation
- Changing Variable Types
- Dealing with Missing Data
- Advanced Summarizing
- Binding Data Frames
- Functions
- Data Merging
- Exporting Data
- Bring It All Together (Advanced Data Wrangling)
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Advanced Data Visualization
- Best Practices in Data Visualization
- Tidy Data
- Pipe Data into ggplot
- Reorder Plots to Highlight Findings
- Line Charts
- Use Color to Highlight Findings
- Declutter
- Add Descriptive Labels to Your Plots
- Use Titles to Highlight Findings
- Use Annotations to Explain
- Tweak Spacing
- Create a Custom Theme
- Customize Your Fonts
- Try New Plot Types
- Bring it All Together (Advanced Data Visualization)
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Quarto
- Advanced Markdown
- Advanced YAML and Code Chunk Options
- Tables
- Inline R Code
- Making Your Reports Shine: Word Edition
- Making Your Reports Shine: PDF Edition
- Making Your Reports Shine: HTML Edition
- Presentations
- Dashboards
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- Publishing Your Work
- Quarto Extensions
- Parameterized Reporting, Part 1
- Parameterized Reporting, Part 2
- Parameterized Reporting, Part 3
- Wrapping up Going Deeper with R
Going Deeper with R
Dealing with Missing Data
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This lesson is called Dealing with Missing Data, part of the Going Deeper with R course. This lesson is called Dealing with Missing Data, part of the Going Deeper with R course.
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Transcript
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Heads up! The code that I demonstrate in the video worked when I wrote it, but no longer does. The reason why is a bit complicated so I recommend watching both videos below to understand why this happened (info on the change to tidyr that I mention in the video is here).
Your Turn
Convert all of the missing values in the
number_of_studentsvariable to NA usingna_if()Convert all of the NA values you just made to 0 using
replace_na().
Learn More
The best place to learn about replace_na() is on the tidyr website, which has an excellent documentation page about the function.na_if() comes from the dplyr package so check it out there.
I referenced the read_csv() function having an na argument early on in the video. The read_excel() function has an na argument as well. The na arguments in these two functions can help you deal with missing data on import. However, replace_na() and na_if() are good to know because you don’t always have this option!
If you want to go deep on exploring missing data in your datasets in R, there is a package called naniar that will help. Allison Horst also has a really nice tutorial on using it here.
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Erin Guthrie
April 20, 2021
Anyone have a solution for the following error message? I know I am likely doing something very stupid... Error in length_x %in% c(1L, n) : object 'number_of_students' not found
Abby Isaacson
April 21, 2021
Why is the last mutate line in your code added in the solution code (not the video)? What would that do? Does this have to do with making sure the variable is numeric?
Oindrila Bhattacharyya
April 22, 2021
I received the same error message as Erin. However, when I ran the codes from beginning till the end selecting all together, it worked fine!
Louise Blight
May 7, 2021
Looks like the last line of code here belongs in the next lesson instead: mutate(number_of_students = as.numeric(number_of_students))
Elan Sykes
December 28, 2021
is there a data maintenance reason not to replace all "-" with 0s directly in 1 step instead of 2? Or is it code legibility?
Carolyn Ford
April 22, 2022
For line 9 - when I put in the number 0 I get error message: Error in
mutate(): ! Problem while computingnumber_of_students = replace_na(number_of_students, 0). Caused by error invec_assign(): ! Can't convertreplaceto match type ofdata. Runrlang::last_error()to see where the error occurred.But when I offset the 0 as a character, everything works: enrolment_by_race_ethnicity % select(-contains("grade")) %>% select(-contains("kindergarten")) %>% select(-contains("percent")) %>% pivot_longer(cols = -district_id, names_to = "race_ethnicity", values_to = "number_of_students") %>% mutate(number_of_students = na_if(number_of_students,"-")) %>% mutate(number_of_students = replace_na(number_of_students, "0"))
Josh Gutwill
November 4, 2022
Why replace the dashes with NAs and then replace the NAs with zeros? Why not skip a step and replace the dashes with zeros? Is there no way to find/replace in the values of a variable?