Getting Started with R & RStudio

CEU, AIIMS Bhopal

What are R and RStudio?

We use two programs that work together.
R is the Engine.
- The programming language.
- It does all the calculations.
- It’s the “kitchen” (oven, knives, etc.).
RStudio is the Dashboard.
- An “Integrated Development Environment” (IDE).
- It’s the “organized countertop & recipe book” you use to operate the kitchen.

The RStudio Interface

What it is: Your text editor.
Use: Write and save your R Scripts (.R) and Quarto docs (.qmd). This is your “recipe book.”
Key Action: Ctrl + Enter (or Cmd + Enter) to run the current line.

What it is: Where R actually runs. You’ll see the > prompt.
Use: Test code, see outputs, and view error messages. This is like a “calculator” or “testing a single step.”
Note: Code typed here is not saved.

What it is: Your R session’s “memory.”
Use: See all objects (data, variables) you’ve created. These are your “prepped ingredients.”
History tab shows all your past commands.

Files: Browse your computer’s folders and files.
Plots: Where your graphs appear.
Packages: Manage your installed “appliances.”
Help: Look up help files for functions.

Saving Your Work: R Scripts

Your Console history is not saved. To save your work, you must use an R Script (.R file).

Go to File > New File > R Script.
Write your code in the Source Editor. This is your reproducible recipe.
Use comments (#) to explain your code.
Run code line-by-line using Ctrl + Enter.

my_recipe.R

# This is a comment
x <- 10 + 5
print(x)

➡️

Console

> x <- 10 + 5
> print(x)
[1] 15
> _

The “Where Am I?” Problem

This is the #1 source of confusion for new R users.

The Problem: You try to load a file read_csv("data.csv") and R says: “File not found!”
The Reason: R is always “standing” in one folder, its Working Directory.
Analogy: R is a cook at a single workbench. When you ask for "data.csv", R only looks on that workbench. If the file is in the pantry (your Desktop), R won’t find it.

The “Bad Solution” (Don’t Do This!)

You will see this code online. Do not use it.

# This is an ABSOLUTE path
setwd("C:/Users/YourName/Desktop/MyFolder")

Why it’s bad: This “hard-codes” your exact folder path.
Analogy: It’s like writing your specific home address in a recipe.
The Result: When you send your script to a colleague (who has a different “address”), your script breaks. It is not reproducible.

The “Good Solution”: RStudio Projects

We will NEVER use setwd(). We will ALWAYS use RStudio Projects.

What it is: A single file (.Rproj) that lives in your project’s main folder.
The Magic: When you double-click the .Rproj file to open RStudio, it automatically sets your Working Directory to that folder.
Analogy: It’s a self-contained “kitchen-in-a-box.” All your recipes, ingredients, and final dishes live inside one organized, shareable folder.

The RStudio Project Workflow

This is the Golden Rule for all your work.

Your script my_recipe.R can use relative paths like read_csv("data/ingredients.csv").
This will work for anyone who downloads your project folder.

Expanding Your Power: Packages

The Concept: “Base R” is your basic kitchen. Packages are “fancy appliances” you get for specific jobs.
Analogy:
- Want to wrangle data? Get the dplyr “food processor.”
- Want to make graphs? Get the ggplot2 “stand mixer with piping kit.”
- Want a bunch of tools? Get the tidyverse “full professional kitchen suite.”

Packages: The 2-Step Process

This is a critical concept.

Step 1: INSTALL

What: “Buy the appliance” from the store (CRAN) and have it delivered to your kitchen.
When: Only ONCE per computer.
How:

install.packages("tidyverse")

(Run this in your Console)

Step 2: LOAD

What: “Plug the appliance in” to the wall to use it for this cooking session.
When: EVERY TIME you restart R.
How:

library(tidyverse)

(Put this at the top of your R Script)

Session Recap: Best Practices

Always organize your work using RStudio Projects (your “kitchen-in-a-box”).
Write your code in R Scripts (your “recipes”).
Run code from your script using Ctrl + Enter (or Cmd + Enter).
Use comments (#) to explain why you are doing something.
Load all your required packages (using library()) at the very top of your script.