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R Architecture

R Architecture

Understanding How R Works

R is an interpreted programming language. It evaluates instructions when you execute your code and displays the results.

Three important concepts help you understand how R works:

  • Objects: Named items that store values, such as numbers, text, or tables.
  • Functions: Reusable operations that perform specific tasks.
  • Environment: The space where R stores and manages objects created during your session.

For example, you can store a student's mark in an object and display its value:

marks <- 85

print(marks)

Output:

[1] 85

Here, marks is the object name, <- assigns the value, and print() displays it.

R, RStudio, and R Packages

R, RStudio, and packages serve different purposes in your workflow.

Think of a car:

  • R is the engine: It executes code and performs calculations.
  • RStudio is the dashboard: It provides a convenient interface with a code editor, Console, and Plots pane.
  • Packages are additional tools: They extend R with functions for specific tasks.

For example:

  • dplyr helps you select, filter, and transform data.
  • ggplot2 helps you create data visualizations.

You need to install R first. RStudio makes it easier to work with R, while packages provide additional functionality when required.

R Project Structure

An R project is a folder that keeps the files for a particular analysis together. A consistent structure makes your work easier to manage, understand, and share.

A typical project structure looks like this:

student-analysis/

├── data/                  # Raw and cleaned CSV files

├── scripts/               # R scripts

├── output/                # Charts and reports

└── student-analysis.Rproj

Each folder has a specific purpose:

  • data/ stores raw and cleaned datasets.
  • scripts/ contains the R code used for analysis.
  • output/ stores generated charts and reports.
  • .Rproj file helps RStudio manage the project and its working directory.

Best Practice: Keep your data, code, and generated outputs organized separately. This makes the project easier to maintain and reproduce.


R for Data Science

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