1) What R and RStudio Are, and Why You Use Both

  • R is a programming language designed for statistics, data analysis, simulation, and visualization.
  • RStudio (now under Posit) is an IDE (Integrated Development Environment): a more convenient interface for working with R.

Key point:

  • You must install R first (because R is the engine).
  • Then you can install RStudio (because it is the user-friendly workspace that runs R).

In practice, most people write and run R code inside RStudio because it makes scripting, plotting, and managing files much easier.

2) Installing R and RStudio (Practical Notes)

After installation, one concept you should understand early is the working directory:

  • The working directory is the default folder where R looks for files (data files, scripts, outputs) unless you provide full file paths.
  • If R “cannot find” a file you are trying to read, the cause is often that the working directory is not pointing to the right folder.

Typical workflow:

  • Decide a clear folder for your project (for example, time_series_project/).
  • Save scripts and datasets there.
  • Set your working directory to that folder, so file loading becomes easy and consistent.

3) Understanding the RStudio Interface (The 4 Main Panes)

When you open RStudio, you typically see four panes. Each has a specific role.

3.1 Console (bottom-left by default)

  • This is where you can type R commands one line at a time.
  • Press Enter to execute the command immediately.
  • It also works like a calculator (for quick checks).

Use the Console for:

  • Quick experiments
  • Testing a command
  • Simple calculations

3.2 Script Editor (top-left by default)

Typing everything in the Console is inefficient for real work. The Script Editor is where you write code in a file you can save and reuse.

How to create a script:

  • File → New File → R Script

Why scripts matter:

  • You keep a record of your work.
  • You can rerun the same analysis later.
  • You can debug and revise code more systematically.

How to run code from a script:

  1. Run a single line or selected lines
    • Put your cursor on the line (or highlight a block).
    • Click Run or use:
      • Windows: Ctrl + Enter
      • Mac: Cmd + Enter
  2. Run the entire script
    • Click Source (runs all code in the script).
    • “Source with Echo” also prints the commands to the Console so you can see what ran.

There is also a command version:

  • source("filename.R", echo = TRUE)
    • echo = TRUE prints the code as it runs.
    • If the file is not in your working directory, you must provide a full path.

3.3 Environment and History (top-right by default)

Environment

  • Shows objects currently stored in memory: variables, datasets, models, functions you created.
  • Example: if you run x <- 10, you will see x appear here.
  • This is useful for confirming what data is loaded and what objects exist.

History

  • Stores a list of commands you executed previously in the current RStudio session.
  • Helpful when you want to reuse a command without retyping.

3.4 Plots / Packages / Help (bottom-right by default)

This pane has multiple tabs. Three core ones:

Packages

  • R’s capabilities expand through packages (add-on libraries).
  • Some packages come with base R; others must be installed.

Two different actions matter:

  1. Install a package (usually done once per computer):
    • install.packages("packageName")
  2. Load a package (usually done each time you start a new R session):
    • library(packageName)

Common mistake:

  • People install a package but forget to load it with library(...), then wonder why commands are not found.

Plots

  • Any graph you create appears here.
  • R has a simple built-in plotting function: plot().
  • Many people later use more advanced visualization tools like ggplot2, but basic plot() is enough for many tasks.

Help

  • R has built-in documentation for commands and datasets.
  • You can access help in two equivalent ways:
?plot
help("plot")

This opens a help page explaining:

  • What the command does
  • What inputs (arguments) it accepts
  • Examples of usage

4) Packages and Built-in Example Datasets (A Simple Demonstration)

R includes a package called datasets (often available by default). It contains sample datasets useful for learning.

4.1 Listing available datasets

You can list datasets in that package using:

data(package = "datasets")

This typically opens a list of dataset names and descriptions.

4.2 Getting information about a dataset

One dataset in datasets is AirPassengers, a classic monthly time series of airline passengers.

To view documentation:

help(AirPassengers)

This gives:

  • A description of what the data represents
  • The time range and frequency (monthly)
  • The source and other notes

4.3 Plotting the dataset

You can plot it using:

plot(
  AirPassengers,
  main = "International Airline Passengers, 1949–1960",
  ylab = "Number of Passengers (in thousands)",
  xlab = ""
)

What these arguments mean:

  • AirPassengers (first input): the data you want to plot.
  • main: the plot title shown at the top.
  • ylab: label for the vertical axis (y-axis).
  • xlab: label for the horizontal axis (x-axis). Here it is set to empty ("") to hide the label.

This plot is a standard time series line plot, showing how passenger volume changes over time and typically revealing patterns like trend and seasonality.

5) The Main Practical Takeaways

  • Install R first, then RStudio.
  • Use the Script Editor for real work (reproducible code), and the Console for quick tests.
  • Learn the difference between:
    • install.packages() (install once)
    • library() (load each session)
  • Use Help (?command) whenever you forget syntax.
  • Use Plots to visually inspect data quickly, especially for time series.