Conclusion

Congratulations! You’ve completed the Lists & Data Frames module.

Learning Goals

Now that you’ve completed this module, you should be able to:

Lists

  1. Describe the characteristics of an R list.
  2. Describe important use-cases for lists, and explain why lists are the best choice in these situations.
  3. Create new lists in R.
  4. Access and modify list elements using different methods, and explain the difference between the available selection operators.

Data Frames

  1. Describe the characteristics of data frames.
  2. Explain important use-cases for data frames, and explain why data frames are the best choice in these situations.
  3. Create new data frames in R.
  4. Explain how data frames and lists are related.
  5. Explain the differences between data frames and matrices.
  6. Use list-style and matrix-style indexing to access and modify the elements of a data frame.
  7. Explain the different effects of the available selection operators.
  8. Explain how recycling works with data frames.

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.

Advanced Reading

For a much more technical deep-dive into the topics we’ve covered in this module, check out the following sections/chapter from Hadley Wickham’s Advanced R.

Next Steps

Now that you’re comfortable with lists and data frames, you’re ready to move on to the last data structure in this series: factors, which R uses to represent categorical data.

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