1.1 Getting Started with R and RStudio

1. Install R, then RStudio

R is the language. RStudio (Posit) is an editor that makes R easier to use.

Install them in this order:

1. R for Windows, macOS, or Linux.
2. RStudio Desktop.

RStudio will not work properly if R is not installed first.

We will work in RStudio, not in the older R application that installs with R.

R was designed for statistics. It is free, it is built around packages, and it is a good language for analysis you can rerun. Python is also widely used in data science. This course uses R.

2. The RStudio Layout

When you open RStudio, you will typically see several panes:

• the console, where R evaluates code immediately
• a source editor, where you write scripts and documents you want to keep
• an environment / history pane, which lists objects in the current session
• a files / plots / packages / help pane

The source pane stays closed until you open or create a file.

A few habits that save time:

• The Files tab is a limited file manager. You can open, copy, and delete files, and you can set the console working directory from More.
• The Environment tab shows objects you have created in this session.
• The Help tab shows documentation. You can also type help(mean) or ?mean in the console.

3. Configure RStudio Once

Open Tools → Global Options… → General.

Under Workspace:

• uncheck Restore .RData into workspace at startup
• set Save workspace to .RData on exit to Never

If RStudio restores yesterday’s objects, you can accidentally use results you no longer have code for. That looks convenient and is bad for reproducibility.

Other useful options:

• Code → Editing: turn on soft wrapping
• Appearance: choose a font size you can read

4. The Console

The console is the pane with the > prompt.

Type:

1 + 1

## [1] 2

and press Enter.

R prints:

# [1] 2

The [1] is not part of the answer. It is the index of the first element R is showing you.

The up and down arrow keys cycle through recent console commands. That is useful when you mistype a line.

R is a calculator:

3 * 7

## [1] 21

9 / 3

## [1] 3

(3 + 5) * 6

## [1] 48

3 ^ 2

## [1] 9

%% is remainder after integer division:

1 %% 2

## [1] 1

# 1

4 %% 2

## [1] 0

# 0

The console is useful for short experiments. It is a poor place to keep your work. When you close RStudio, console history is not a script.

If a command is incomplete, R shows a + prompt and waits. Press Esc to get back to >.

5. Scripts

A script is a text file of R code, usually with a .R extension.

Create one with File → New File → R Script.

Type this into the script:

x <- 10
x * 2

## [1] 20

Place the cursor on a line and run it with Ctrl+Enter (Windows/Linux) or Cmd+Enter (macOS). You can also click Run.

The code is sent to the console, and the console shows the result.

A useful habit is:

Type in a script. Run from the script. Treat the console as a scratch pad.

Save the script. You can reopen it in a later session.

Lab 1 is mostly a script (lab01.R). Section G is a short Quarto file; see note 1.5.

6. Comments

R ignores anything after # on a line:

# This line is only for humans.

x <- 10   # store 10 in x

Comments are how you explain why you wrote something, not how you hide code you might need later.

7. Assignment

We store a value with <-:

greeting <- "Hello"
greeting

## [1] "Hello"

In this course:

• <- stores a value in an object
• = names an argument inside a function call, for example mean(x, na.rm = TRUE)

R is case sensitive. x and X are different objects. TRUE is not true. sum() is not Sum().

8. Getting Help

Almost every function has a help page:

help(mean)
?mean
help("%%")

??topic searches help pages. Adding "in R" to a web search is often faster than memorizing function names.

9. Organize Your Files

Do not keep every course file on the Desktop with names like file 1. You will not be able to tell R where the data is, and other people will not be able to rerun your work.

Create one folder for the course, for example data_612, and keep lecture work and assignments in separate subfolders. Use snake_case names. Avoid spaces and special characters.

data_612/ students.csv lab01.R data/ students.csv extra/ R/ output/ assignments/ lab_01/

Inside a project, a common pattern is:

• R/ for .R scripts
• data/ for files you import
• output/ for files you write

You do not need every folder every week. You do need a place that is not a pile.

Back up that folder to Google Drive or another cloud service. A crashed laptop is a common way to lose a course.

10. Where Are Your Files?

R does not search your whole computer for a file. It looks in one folder, called the working directory, unless you tell it a path.

getwd()

## [1] "C:/Users/Lenovo/Documents/Cursor/Website-Mousavi/files/lectures"

That path is “where R is standing.” In RStudio you can also read it just under the Console tab.

A better habit for the course is File → New Project. An RStudio project pins the working directory to that folder. Do not nest one project inside another.

Note 1.3 is the full treatment: relative vs absolute paths, . and .., choosing an import function, and saving results.

11. Errors Are Information

If R cannot run a line, it prints an error. Read it from the top.

Common first-week mistakes include:

• a missing quotation mark
• a missing parenthesis
• running install.packages(ggplot2) without quotes
• calling a function from a package you have not loaded
• looking for a file that is not in the working directory

You do not need to memorize every error. You do need to read it.

12. What to Do Next

Once R and RStudio are installed and you can run a line from a script, continue with:

• 1.2 R Packages and the Tidyverse
• 1.3 Working with Files and Paths
• 1.4 Introduction to R Concepts
• 1.5 R Markdown and Quarto
• Lab 1, which asks you to predict what R will do before you run the code. Work in a .R script.