Data transformation and reading a file¶
Learning outcomes
- Comfortable with the third and seventh chapter of R for Data Science
- Can use the pipe operator
- Can use a filter
- Can use the logical equal operator
- Can read a file
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'.
Why reading a file is important¶
Whenever you have your raw data, you have it in one or more files. To do data transformations, you will first need to read it.
Exercises¶
Exercise 1¶
- Read R for data science, chapter 3 until the exercises of 3.2.5.
- Do these exercises
Exercise 2¶
- Read R for data science, chapter 7 until the exercises of 7.2.4.
- Do these exercises