2. Workflow basics

Learning outcomes

• I am comfortable with the second chapter of R for Data Science
• I can recommend the swirl package to the right person
• I can assign a value to a variable
• I can use snake case when naming a variable
• I can write a comment
• I can correct typos in simple code

For teachers

Teaching goals are:

• Learners have read part the second chapter of R for Data Science

Prior question:

• What is R?
• What is RStudio?
• What is a variable?
• What is an assignment?
• What is an assignment operator?
• How does it look like?
• What is a comment?
• Why use a comment?
• What is meant with 'snake case'?

Feedback questions:

• What is a variable?
• Why use a comment?
• What is meant with 'snake case'?
• Why use variable names with snake case?

Why basic R is important

Basic R applies to all programming in R.

Exercises

Exercise 1: Chapter 2

• Read R for data science, chapter 2 until the exercises of 2.5.
• Do these exercises

Answers

See the answers here.

Note that the answers use chapter 3.5.

(Optional) Exercise 2: swirl

swirl is an R package to teach R. The goal of this exercise is to see how useful it may be to you.

• Install the swirl R package

Answer

install.packages("swirl")

• Load the `swirl package

Answer

library(swirl)

This will show:

| Hi! Type swirl() when you are ready to begin.

• Start the `swirl package. Tip: you've gotten a hint in the previous step

Answer

swirl()

• Give your name
• Pick the R Programming course
• Pick the 1: Basic Building Blocks lesson
• Do 1 exercise, so you know how swirl behaves: the goal is to know whether to recommend/use swirl.