Introduction to R
Learn R basics for practical data analysis
Introduction to R is an entry-level course on DataCamp that teaches the fundamentals of R programming for practical data analysis. It walks learners through core topics such as vectors, matrices, factors, data frames, and lists using short, interactive exercises designed to make syntax and basic workflows memorable.
The course focuses on hands-on practice so you can quickly apply R to tidy, summarize, and inspect real datasets, preparing you for further study in statistics or data science. Alongside syntax and data structures, the material emphasizes in-browser coding and immediate feedback to reinforce learning and accelerate proficiency with R-based analysis.
At a Glance
Introduction to R is an entry-level course offered on DataCamp and taught by DataCamp’s instructional team, designed to introduce the R programming language for data analysis.
The course covers core R building blocks — vectors, matrices, factors, data frames and lists — and emphasizes interactive practice to prepare learners for basic data-analysis workflows.
| Level | Beginner |
| Rating | 4.8 out of 5 |
| Duration | 4+ hours |
| Languages | English |
| Learners | Not publicly disclosed |
| Certificate | Statement of Accomplishment |
| Access | Access for as long as your DataCamp subscription is active; introductory content available free to preview |
| Course includes |
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| Price | Subscription-based; free introductory access with optional paid certificate |
What This Course Teaches
Learners will gain measurable competencies for basic R data analysis: identify core R data types, perform vector and matrix operations, differentiate and apply factors, and evaluate outputs from data frames and lists.
How the Course Is Structured
The course is organized as a sequence of six focused modules that present core R concepts through short, interactive chapters on the DataCamp platform.
Expect a compact program totaling around 4+ hours of guided instruction and hands-on exercises across six modules.
Curriculum overview
01Intro to basics▾
Introduces basic R operations, using the console as a calculator and an overview of core data types.
02Vectors▾
Covers creating, naming, subsetting, and comparing one-dimensional vectors, with applied practice on a sample Las Vegas betting dataset.
03Matrices▾
Shows how to construct matrices and perform arithmetic operations and summary calculations on two-dimensional data.
04Factors▾
Explains nominal and ordered factors and how to create, set levels, and compare categorical data in R.
- What’s a factor and why would you use it?
- Factor levels
- Summarizing a factor
- Battle of the sexes
- Ordered factors
- Comparing ordered factors
05Data frames▾
Introduces data frames and demonstrates subsetting, summarizing, and preparing tabular datasets for analysis.
06Lists▾
Covers creation and manipulation of lists to store heterogeneous objects and support flexible data structures.
Audience & Requirements
This course is aimed at learners who need a practical introduction to R for data analysis and statistics, including students, researchers, and early-career analysts transitioning into data work.
It assumes no prior programming experience and emphasizes hands-on, in-browser exercises to develop fundamental R skills applicable to academic and applied data tasks.
- Aspiring data analysts and early-career data scientists building R fundamentals.
- Researchers and students who perform statistical analysis in academic settings.
- Business analysts and domain specialists who need to manipulate and summarize tabular data.
- Career changers exploring entry-level data workflows and tools.
- No prior programming or R experience required.
- A DataCamp account (free preview available; subscription required for full access and certificate).
- Comfort with basic arithmetic and familiarity with tabular data (e.g., spreadsheets).
- Willingness to work inside an interactive, browser-based coding environment.
Final Verdict
Introduction to R delivers compact, practice-oriented coverage of fundamental R concepts through interactive exercises and targeted assessments.
Given its solid user rating, broad learner adoption, and the availability of an official completion statement within a subscription model, it offers clear value as a first R course.
It is a strong choice for beginners, students, researchers, and analysts who want a practical, low-friction way to start using R for data tasks.
Those aiming for advanced statistical modeling or production-grade programming should treat this as a foundation course and plan to follow it with intermediate or specialized R offerings.
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