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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 Overview, part of the Going Deeper with R course. This lesson is called Overview, part of the Going Deeper with R course.
In this first section of the course, you'll learn to clean and tidy data as well as do some analysis of it.
As I probably don’t need to tell you, you rarely receive data in sparkling format so having the skills to wrangle your data is key to your success with R. Fortunately, R has some great tools to do this. The
tidyr packages are your main friends here, with a wide range of functions to get your data into the format you need it in.
The Learn More section on most lessons has resources to help you, well, learn more about any topic. Many of the links there are to R for Data Science , the free book that is the tidyverse bible, and to Stat 545 , a course taught by Jenny Bryan at the University of British Columbia whose materials are available for free online.
If you want to learn more about data cleaning in R more generally, here are a few resources:
Crystal Lewis gave a presentation to R-Ladies St Louis in November 2019 on data cleaning in R.
Gina Reynolds has put together a flipbook on common data cleaning techniques in R.
Sharla Gelfand has an extremely thorough overview of tidying Toronto Transit Commission data.
There are also a series of example walkthroughs starting in Chapter 7 of the book Data Science in Education Using R. They go step-by-step, importing, cleaning, tidying, and analyzing data. They're great examples to learn from.