About RLearn
RLearn is a comprehensive online platform designed to guide you from a beginner to a confident R programmer in just 12 weeks, focusing on the skills needed for effective data analysis and visualization.
Our curriculum is carefully structured to provide a gradual and engaging learning experience, covering fundamental R concepts, data structures, control flow, data manipulation with the Tidyverse, and data visualization with ggplot2.
Whether you're aiming for a career in data science, research, statistics, or simply want to harness the power of R for your data tasks, RLearn provides the resources and support you need.
Our 12-Week R Syllabus
Click on the links below to dive into each week's learning material:
Week 1: Introduction to R & RStudio
Intro to R, RStudio setup, basic commands.
- What is R?
- Why learn R?
- Installing R & RStudio
- RStudio Tour
- First commands
Week 2: R Data Basics
Variables, fundamental data types, vectors.
- Variables & Assignment
- Numeric, Integer, Logical, Character
- Vectors (Creation)
- Arithmetic & Logical Operators
Week 3: Data Structures - Vectors & Matrices
Vector manipulation, matrices, getting help.
- Vector Indexing & Slicing
- Vector Recycling
- Matrices (Creation, Indexing)
- Getting Help (`help()`, `?`)
Week 4: Data Structures - Lists & Data Frames
Lists, introduction to data frames, importing data.
- Lists (Creation, Indexing)
- Data Frames (Intro)
- Viewing Data (`str`, `head`)
- Importing CSV (`read.csv`)
Week 5: Control Flow - Conditionals
Making decisions with `if`, `else`, `else if`.
- `if`, `else if`, `else`
- Conditional Logic
- Vectorized `ifelse()`
Week 6: Control Flow - Loops
Repeating actions with `for` and `while`.
- `for` loops (Vectors, Lists)
- `while` loops
- `break`, `next`
Week 7: Functions in R
Writing and using reusable code blocks.
- Function Definition (`function`)
- Parameters & Arguments
- Return Values
- Variable Scope
Week 8: Intro to Data Manipulation (dplyr)
Filtering, selecting, arranging, and mutating data.
- Tidyverse Concept
- Installing Packages
- `dplyr`: `filter`, `select`
- `dplyr`: `mutate`, `arrange`
- Pipe Operator (`%>%` or `|>`)
Week 9: More Data Manipulation & Tidying
Grouping, summarising, tidying, joining data.
- `group_by()` & `summarise()`
- Intro to `tidyr`
- `pivot_longer()`, `pivot_wider()`
- Basic Joins (`dplyr`)
Week 10: Intro Data Visualization (ggplot2)
Creating basic plots with ggplot2.
- Grammar of Graphics
- `ggplot()`, `aes()`
- Geoms (`point`, `line`, `bar`)
- Mapping Aesthetics
Week 11: Customizing Visualizations & Stats
Improving plots, basic statistical functions.
- Titles, Labels, Themes
- Saving Plots
- Basic Stats (`mean`, `median`, `sd`)
- `summary()` function
Week 12: Project & Next Steps
Apply skills, R Markdown intro, further learning.
- Mini Data Analysis Project
- Intro to R Markdown
- Exploring Packages
- R Community
Contact Us
Have questions or need support? Reach out to us!
Email: akshatpcid12@gmail.com
Phone: +917682001264