5.5 Resources

Here are some pointers on tibbles and related data tables:

5.5.1 Help on tibbles

For more details on tibbles and the tibble package (Müller & Wickham, 2023):

• study the vignette("tibble") and the documentation for ?tibble;

• study https://tibble.tidyverse.org and its examples;

• read Chapter 10: Tibbles of the r4ds textbook (Wickham & Grolemund, 2017) and complete the corresponding exercises;

• study the back of the Posit cheatsheets on Data Import:

Figure 5.3: Essential tibble commands on the back of the Posit cheatsheets on Data Import.

• Another way of creating tibbles is by importing data files in various formats (e.g., with the read_csv() or read_delim() functions). The readr package (Wickham, Hester, et al., 2024) will be covered in the next chapter on Importing data (Chapter 6).

5.5.2 Miscellaneous

Alternative tools

• See the data.table package and its homepage for an alternative extension of the base R data.frame.

• See the R package datapasta and its vignette for a clipboard-based solution for cut-and-pasting data into the RStudio IDE.

Blogs etc.

• See this thread on the R-pkg-devel mailing list for a discussion about the pros and cons of tibbles being data frames.

• The blog post data.frame vs data.table vs tibble in R (by Megapteraphile, 2020-03-25) traces the R history of three rectangular data structures.

• The blog post Base R, the tidyverse, and data.table (by Jason Merger, 2020-01-27) compares different R dialects (or tools?) to wrangle rectangular data structures.

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[05_tibbles.Rmd updated on 2024-12-20 by hn.]

References

Müller, K., & Wickham, H. (2023). tibble: Simple data frames. Retrieved from https://CRAN.R-project.org/package=tibble

Wickham, H., & Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. Retrieved from http://r4ds.had.co.nz

Wickham, H., Hester, J., & Bryan, J. (2024). readr: Read rectangular text data. Retrieved from https://CRAN.R-project.org/package=readr