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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
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- Try New Plot Types
- Bring it All Together (Advanced Data Visualization)
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Quarto
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- Making Your Reports Shine: Word Edition
- Making Your Reports Shine: PDF Edition
- Making Your Reports Shine: HTML Edition
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- Quarto Extensions
- Parameterized Reporting, Part 1
- Parameterized Reporting, Part 2
- Parameterized Reporting, Part 3
- Wrapping up Going Deeper with R
Going Deeper with R
Use the scales Package for Nicely Formatted Values
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This lesson is called Use the scales Package for Nicely Formatted Values, part of the Going Deeper with R course. This lesson is called Use the scales Package for Nicely Formatted Values, part of the Going Deeper with R course.
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Transcript
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Your Turn
Make a new variable called
percent_displaythat shows thepercent_of_total_enrollmentvariable as a nicely formatted percent (rounded to the nearest whole number)Make sure you save this as
highlight_district(i.e. don’t just display the result)
Learn More
The best place to learn more about the scales package is its documentation website. You’ll see that you can format a wide range of values using this package, including dollar values, dates, times, and more.
Dana Siedel gave a nice talk at rstudio::conf 2020 about the scales package that is well worth a 20-minute watch!
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Juan Clavijo
November 28, 2021
Hello,
I had already rounded my proportions and multiplied by 100 so I now have 39.4, for example. when running this I get 3940%. Is there a way to make it a 39.4% without undoing the previous rounding? Also, do I need to do this if I already have the 39.4 for graphing purposes?
Matt M
December 6, 2021
When I run highlight_dataframe <- mutate(percent_display = percent('Percent of Total', accuracy = 1)) I get the error: Error in UseMethod("round_any") : no applicable method for 'round_any' applied to an object of class "character"
If i remove the ", accuracy = 1" I get the error: "Error in x * scale : non-numeric argument to binary operator"
but the 'Percent of Total' variable is numeric (when I mouseover, it says "numeric with range 0-1"
Mark Lewis
December 5, 2022
I work with a step like this pretty often, but always seem to run into some complication or other. I like the idea here of making a completely new column for the display value. I see that
scales::percent()has been deprecated in favor ofscales::label_percent(). Butlabel_percent()says it's designed to be used in alabelsargument in a ggplot scale. Do you have a sense of what the consequences are (if any) of using it outside that context, like in a simplemutate()of the sort you're doing here?