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Going Deeper with R
Advanced Data Wrangling and Analysis
- Importing Data
- Tidy Data
- Reshaping Data
- Dealing with Missing Data
- Changing Variable Types
- Advanced Variable Creation
- Advanced Summarizing
- Binding Data Frames
- Merging Data
- Renaming Variables
- Quick Interlude to Reorganize our Code
- Exporting Data
Advanced Data Visualization
- Data Visualization Best Practices
- Tidy Data
- Pipe Data Into ggplot
- Reorder Plots to Highlight Findings
- Line Charts
- Use Color to Highlight Findings
- Use the scales Package for Nicely Formatted Values
- Use Direct Labeling
- Use Axis Text Wisely
- Use Titles to Highlight Findings
- Use Color in Titles to Highlight Findings
- Use Annotations to Explain
- Tweak Spacing
- Customize Your Theme
- Customize Your Fonts
- Try New Plot Types
- Advanced Markdown Text Formatting
- Advanced YAML
- Inline R Code
- Making Your Reports Shine: Word Edition
- Making Your Reports Shine: HTML Edition
- Making Your Reports Shine: PDF Edition
- Other Formats
- You Did It!
Inline R Code
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This lesson is called Inline R Code, part of the Going Deeper with R course. This lesson is called Inline R Code, part of the Going Deeper with R course.
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The table below shows the districts with the highest growth of their `r params$race_ethnicity_category` student population from 2017-2018 to 2018-2019.
Add a line to your report that uses inline R code
To learn more about inline R code, check out lesson 4 of the RMarkdown series on the RStudio website as well as Chapter 6 of Nick Tierney's book titled RMarkdown for Scientists.
I use a few functions in this lessons you may not be familiar with. Here are links to the documentation for pull() and unique(). I also use Sys.Date() , which returns the current date in the format YYYY-MM-DD. If you want nicely formatted dates (e.g. May 22, 2020), check out the add_readable_time() function from the blastula package.