Conclusion
By completing these tutorials, you’ve built a solid foundation in understanding five of the most ubiquitous and useful data types in R:
- Vectors
- Matrices
- Lists
- Data frames
- Factors
Additional Reading
For more information on the topics we’ve covered in this module, the following chapters from Hadley Wickham’s R for Data Science are excellent resources.
- Vectors in the First Edition.
- Factors in the Second Edition.
Advanced Reading
For a much more technical deep-dive into the topics we’ve covered in this module, check out the following two chapters from Hadley Wickham’s Advanced R.
Next Steps
Now that you have a good introduction to the fundamental elements of the R statistical programming language, you’re ready to start thinking about how to best organize your data analysis projects. In the next module, we’ll explore different aspects of effective R workflows–—scripts, working directories, R projects, file paths, and coding style—–to help you create reproducible, organized, and efficient data analysis projects.