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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!
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This lesson is called Line Charts, part of the Going Deeper with R course. This lesson is called Line Charts, part of the Going Deeper with R course.
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enrollment_by_race_ethnicity %>% filter(race_ethnicity == "Hispanic/Latino") %>% ggplot(aes(x = year, y = percent_of_total_at_school, group = district)) + geom_line()
Make a line chart that shows the growth in the Hispanic/Latino population in school districts from 2017-2018 to 2018-2019
There are also spins on line charts that you can easily make by adjusting your geom. For example, you can use
geom_area()to make area charts.
There are also extensions that take idea of line charts further. Here is a plot made with the
ggbump package, for instance, which enables you to make slopegraphs (another cousin of line charts) with curves.