Master the Basics of R Programming from Scratch

Overview

Python dominates general programming discourse, but R still anchors statistics departments, clinical research, survey shops, and many analytics teams—especially where ggplot2 and specialized packages lead. Learning R as a second language widens jobs you can credibly apply for and sharpens statistical thinking through a tool built for it.\n\nThis course starts at zero in RStudio: scripts, the console, vectors, factors, lists, data frames, importing CSV and Excel, base plotting, and an ggplot2 introduction. You write readable scripts others can reproduce—critical in research and regulated industries where copy-paste chaos is unacceptable.\n\nNo prior R required; prior programming in any language accelerates week one. Expect vocabulary overlap with Python (lists versus vectors, DataFrame versus data.frame) and deliberate contrast so neither language confuses the other later.\n\nMany job postings still list R or Python—meaning both—and this course gives you a credible answer when a hiring manager asks whether you can open an existing .Rmd or script and extend it without starting from scratch.

What you will learn

• RStudio workflow: projects, scripts, console, and reproducible organization
• Vectors, factors, lists, data frames, and tidy manipulation basics
• Import CSV and Excel with readr/readxl patterns and column types
• Base R plotting quicklooks and ggplot2 grammar of graphics introduction
• Summary statistics, tables, and simple hypothesis tests in context
• Writing scripts with comments, chunks, and clear naming for collaborators
• Translating common Excel analyses into repeatable R code
• Knowing when R fits versus Python for a given workplace task

Syllabus outline

1. Module 1: R and RStudio — installation, interface, and first script
2. Module 2: Objects and Types — vectors, factors, lists, and atomic rules
3. Module 3: Data Frames — import, subset, filter, and mutate patterns
4. Module 4: Visualization — base plots and ggplot2 layers, aesthetics, geoms
5. Module 5: Statistics Intro — summaries, tables, and common tests responsibly
6. Module 6: Scripting Discipline — projects, paths, and reproducibility habits
7. Module 7: Applied Lab — end-to-end analysis from raw file to chart and table

Who this course is for

Statistics and social science students, researchers publishing reproducible work, Python analysts expanding hiring flexibility, and data professionals joining teams with legacy R codebases.

Prerequisites

None strictly. Comfort with basic math and spreadsheets helps. Any prior programming experience is a bonus, not a requirement.

How to study this course

Use RStudio projects from day one—scattered working directories cause most beginner pain. Re-type examples; R’s bracket syntax rewards muscle memory. Compare each new R function to a Python equivalent if you know Python; a small bilingual notebook helps. Run sessionInfo or equivalent habits before sharing work. Finish the lab module by redoing one analysis you previously did in Excel—time yourself; R wins on repeat runs.

Why this guide exists on the site

R courses proliferate; learners need a concise map of what basics means in this catalog versus Python tracks. Videos on Udemy walk through RStudio; this page clarifies outcomes, modules, and who should pair R with existing Python skills—orientation the platform listing alone does not provide.

How enrollment works

Sharad Khare hosts structured video lessons on Udemy for convenient playback, progress tracking, and certificates. Use the button above to open the official course page. Pricing and promotions are set by Udemy. Pair lessons with free note packs on the Notes page and related reading in Articles.