🗓️ Session 4: Object types in R

Author

Affiliation

Claudius Gräbner-Radkowitsch

EUF and JKU

In the first part of this session you learn some additional details about R packages and about the most important and most fundamental object types in R, such as decimal numbers or words. While this might look a bit boring at first, understanding these basic types is fundamental for all the more advanced (and exiting) stuff in the future!

In the second part of this session, you are then introduced to two more advanced object types in R. The two object types covered, factors and data frames, are advanced in the sense that they can be thought of extensions of some of the basic object types you encountered before: factors and special kinds of integers, and data frames are special kinds of lists.

👨‍🏫 Lecture Slides

For the slides of the first part of this session (Packages and basic object types) on click on the slide area below or click here to download the slides.

TipSolutions to the intermediate exercises of part 1

For the slides of the second part of this session (Advanced object types) see the slide area below or click here to download the slides.

TipNotes and solutions for the second part of this session

🎥 Lecture videos

All the videos for the first part on basic object types are available via this playlist.

All the videos for the second part on advanced object types are available via this playlist.

📚 Mandatory Reading

Read the following tutorials for the first part on basic object types:

• Fundamental object types in R I: Functions
• Fundamental object types in R II: Vectors

The topics of the second part on advanced object types are covered here:

• Fundamental object types in R III: Factors and data frames

🏆 Further readings

I suggest you read these references after you learned about data wrangling techniques in session 10.

• Sections 1-3 in Chapter 12 of Wickham et al. (2023).
• Chapter 13 in Wickham et al. (2023).
• Chapter 14 in Wickham et al. (2023).
• Chapter 16 in Wickham et al. (2023).

✍️ Coursework

• Do the ObjectTypes1 exercises of the package DataScienceExercises for the first part on basic object types:

TipQuick code for starting the exercises

learnr::run_tutorial(
  name = "ObjectTypes1", 
  package = "DataScienceExercises", 
  shiny_args=list("launch.browser"=TRUE))

• For the second part do the ObjectTypes2 exercises of the package DataScienceExercises

TipQuick code for starting the exercises

learnr::run_tutorial(
  name = "ObjectTypes2", 
  package = "DataScienceExercises", 
  shiny_args=list("launch.browser"=TRUE))

• If you have questions or problems, please post them in the Moodle forum

References

Wickham, H., Çetinkaya-Rundel, M. and Grolemund, G. (2023) R for data science: Import, tidy, transform, visualize, and model data, 2nd edition., Beijing et al.: O’Reilly, available at https://r4ds.hadley.nz/.