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R for Data Science by Garrett Grolemund, Hadley Wickham

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Chapter 2. Workflow: Basics

You now have some experience running R code. I didn’t give you many details, but you’ve obviously figured out the basics, or you would’ve thrown this book away in frustration! Frustration is natural when you start programming in R, because it is such a stickler for punctuation, and even one character out of place will cause it to complain. But while you should expect to be a little frustrated, take comfort in that it’s both typical and temporary: it happens to everyone, and the only way to get over it is to keep trying.

Before we go any further, let’s make sure you’ve got a solid foundation in running R code, and that you know about some of the most helpful RStudio features.

Coding Basics

Let’s review some basics we’ve so far omitted in the interests of getting you plotting as quickly as possible. You can use R as a calculator:

1 / 200 * 30
#> [1] 0.15
(59 + 73 + 2) / 3
#> [1] 44.7
sin(pi / 2)
#> [1] 1

You can create new objects with <-:

x <- 3 * 4

All R statements where you create objects, assignment statements, have the same form:

object_name <- value

When reading that code say “object name gets value” in your head.

You will make lots of assignments and <- is a pain to type. Don’t be lazy and use =: it will work, but it will cause confusion later. Instead, use RStudio’s keyboard shortcut: Alt-– (the minus sign). Notice that RStudio automagically surrounds <- with spaces, which is a good code formatting practice. Code is miserable to read on a good ...

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