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This is a lesson on tidying data. Specifically, what to do when a conceptual variable is spread out over 2 or more variables in a data frame.

Data used: words spoken by characters of different races and gender in the Lord of the Rings movie trilogy

  • 01-intro shows untidy and tidy data. Then we demonstrate how tidy data is more useful for analysis and visualization. Includes references, resources, and exercises.
  • 02-tidy shows how to tidy data, using gather() from the tidyr package. Includes references, resources, and exercises.
  • 03-tidy-bonus-content is not part of the lesson but may be useful as learners try to apply the principles of tidy data in more general settings. Includes links to packages used.

Learner-facing dependencies:

  • files in the tidy-data sub-directory of the Data Carpentry data directory
  • tidyr package (only true dependency)
  • ggplot2 is used for illustration but is not mission critical
  • dplyr and reshape2 are used in the bonus content

Instructor dependencies:

  • curl if you execute the code to grab the Lord of the Rings data used in examples from GitHub. Note that the files are also included in the datacarpentry/data/tidy-data directory, so data download is avoidable.
  • rmarkdown, knitr, and xtable if you want to compile the Rmd to md and html

Possible to do's

  • Domain-specific exercises could be added instead of or in addition to the existing exercises. Instructor could show basic principles and code using the LOTR data via projector and then pose challenges for the students using completely different data.
  • Cover more common data tidying tasks, such as:
    • split a variable that contains values and units into two separate variables, e.g. 10 km_square becomes 10 and km_square
    • simple joins or merges of two data tables, e.g. add info on LOTR film duration or box office gross
    • renaming variables, revaluing factors, etc. to make data more self-documenting
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