Data transformation and reading a file¶
Learning outcomes
- Comfortable with the third chapter of R for Data Science
- Can use the pipe operator
- Can use a filter
- Can use the logical equal operator
For teachers
Teaching goals are:
- Learners have read part the third chapter of R for Data Science
Prior question:
- Which verbs do you associate with working with data?
- What is tidy data?
- What is a pipe?
- When to use a pipe?
- What is the different between
=and==?
Feedback questions:
- What is tidy data?
- What is a pipe?
- When to use a pipe?
- What is the different between
=and==?
Why data transformations are important¶
Whenever you have your raw data, this is rarely shown in a paper as-is. Instead, you will probably show only certain columns, or certain subsets, give average over categories, etc. All these things are called 'data transformations'.
Exercises¶
Exercise 1¶
- Read R for data science, chapter 3 until the exercises of 3.2.5.
- Do these exercises