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Catalog / All levels / R Developer Essentials – Writing, Structuring, and Packaging Efficient Code with 10 Exams
Mammoth Club All levels 18 sections 65 lectures

R Developer Essentials – Writing, Structuring, and Packaging Efficient Code with 10 Exams

Human-made tutorials enhanced with AI and polished to a high standard you can trust. Writing R code is one thing—writing efficient, reusable R code is another.

MC
Created by Team Mammoth
01
Skill level
All levels
02
Sections
18
03
Lectures
65
04
Instructor
Team Mammoth
What's inside

This course includes.

✓
18
Sections
✓
65
Lectures
✓
47
Quizzes
✓
Certificate of completion
Included
✓
Mobile and desktop access
Included
✓
AI learning assistance
Included
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Course content

Curriculum & lectures.

8 sections · 55 lectures
+ Section 1: Getting Started with R 5 lectures
Lecture 1.01: Why Learn R for Development? Locked
Lecture 1.02: Installing R and RStudio Locked
Lecture 1.03: Using R in the Cloud and Alternative Setups Locked
Lecture 1.04: First Steps in R Locked
Lecture 1.05: Navigating R Help and Documentation Locked
+ Section 2: Core R Programming Foundations 10 lectures
Lecture 2.01: Variables and Assignment Locked
Lecture 2.02: Understanding Data Types – Part 1 (Numeric, Character, Logical) Locked
Lecture 2.03: Understanding Data Types – Part 2 (Special Values) Locked
Lecture 2.04: Arithmetic and Relational Operators Locked
Lecture 2.05: Logical Operators and Boolean Algebra Locked
Lecture 2.06: Conditional Statements – if and ifelse Locked
Lecture 2.07: Loops – for and while Locked
Lecture 2.08: Loops – repeat and Control Statements Locked
Lecture 2.09: Writing Functions – Basics Locked
Lecture 2.10: Writing Functions – Scope and Reusability Locked
+ Section 3: Working with R Data Structures 8 lectures
Lecture 3.01: What Are Vectors? Locked
Lecture 3.02: How to Use and Modify Vectors Locked
Lecture 3.03: What Are Lists? Locked
Lecture 3.04: Working with Lists and Nested Lists Locked
Lecture 3.05: What Are Matrices? Locked
Lecture 3.06: Arrays — Extending Beyond 2D Locked
Lecture 3.07: What Are Data Frames? Locked
Lecture 3.08: Factors — Handling Categories in R Locked
+ Section 4: Data Input, Output, and Manipulation 10 lectures
Lecture 4.01: Importing CSV and Text Data Locked
Lecture 4.02: Importing Excel and Other File Types Locked
Lecture 4.03: Checking Imported Data Locked
Lecture 4.04: Exporting Data to CSV and Text Locked
Lecture 4.05: Saving and Reloading R Objects Locked
Lecture 4.06: Data Cleaning – Handling Missing Values Locked
Lecture 4.07: Data Cleaning – Renaming and Reshaping Columns Locked
Lecture 4.08: Data Cleaning – Changing Data Types Locked
Lecture 4.09: Data Wrangling with dplyr – Core Verbs Locked
Lecture 4.10: Data Wrangling with dplyr – Piping and Chaining Locked
+ Section 5: Visualization and Reporting 7 lectures
Lecture 5.01: Introduction to Base R Plotting Locked
Lecture 5.02: Exploring Base R Plot Types Locked
Lecture 5.03: Getting Started with ggplot2 Locked
Lecture 5.04: Customizing ggplot2 Visuals Locked
Lecture 5.05: Advanced ggplot2 Techniques Locked
Lecture 5.06: Exporting Visualizations Locked
Lecture 5.07: Reporting with R Markdown Locked
+ Section 6: Professional R Development Practices 6 lectures
Lecture 6.01: Writing Clean Code in R Locked
Lecture 6.02: Commenting and Documenting R Code Locked
Lecture 6.03: Debugging R Programs Locked
Lecture 6.04: Error Handling and Defensive Coding in R Locked
Lecture 6.05: Organizing R Code into Scripts Locked
Lecture 6.06: Testing R Code with testthat Locked
+ Section 7: Creating and Sharing R Packages 6 lectures
Lecture 7.01: What is an R Package? Locked
Lecture 7.02: Setting Up an R Package Structure Locked
Lecture 7.03: Documenting R Packages with roxygen2 Locked
Lecture 7.04: Adding Dependencies and Metadata in R Packages Locked
Lecture 7.05: Building and Installing R Packages Locally Locked
Lecture 7.06: Testing and Maintaining R Packages Locked
+ Section 8: Real-World Applications 3 lectures
Lecture 8.01: Automating Workflows with R Locked
Lecture 8.02: R for Data Science and Analytics Locked
Lecture 8.03: Next Steps Locked
Description

About this course.

This course shows how to move beyond quick scripts into structured development practices that scale.


✅ Learn to organize code into clear functions and packages

✅ Improve readability and reusability with best practices

✅ Debug and troubleshoot with confidence

✅ Reinforce skills with 10 structured exams


From exploratory work to production-ready tools, you’ll gain habits that make your R development sharper.


🎁 Build a toolkit that helps your R code work smarter, not harder.

Ready to start building?

Human-made tutorials enhanced with AI and polished to a high standard you can trust. Writing R code is one thing—writing efficient, reusable R code is another.

Buy lifetime access →