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R Academy

Unlock the power of data analysis and statistics with R Language Academy. A CoddyKit learning path of 43 short courses and 159 interactive lessons, with a 24/7 AI tutor at your side.

Courses
43
Lessons
159
Interactive steps
1,953
Free to start
Course 1
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About the mobile app: Learn R Programming & Analysis →

Curriculum

43 courses in this path

Every course in the R Academy path, in learning order. The first one is free.

1

A13 lessonsFree

Discover what R is, why scientists, analysts, and data teams worldwide rely on it, and what kinds of insights and visualisations you will produce in this track.

  1. 1What is R?
  2. 2Why R for Data Science?
  3. 3What You Can Do with R
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2

A13 lessonsPRO

This course introduces the R environment, RStudio interface, and the fundamentals of writing and running R code. It covers basic arithmetic operations, object creation, and workspace setup to build a…

  1. 1Getting Started with RStudio
  2. 2Basic Arithmetic and Variables
  3. 3Workspaces and Projects
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3

A14 lessonsPRO

Master arithmetic, logical, and comparison operators in R with hands-on exercises to build solid foundational skills.

  1. 1Arithmetic and Comparison Operators
  2. 2Logical Operators and Boolean Logic
  3. 3Assignment and Special Operators
  4. +1 more
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4

A24 lessonsPRO

Generate numeric sequences and repeated values using R's built-in tools for data creation.

  1. 1The Colon Operator for Integer Ranges
  2. 2seq() for Custom Sequences
  3. 3rep() for Repeating Values
  4. +1 more
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5

A24 lessonsPRO

Create and manipulate two-dimensional matrix structures, the building block of numerical computing in R.

  1. 1Creating Matrices with matrix()
  2. 2Matrix Indexing and Subsetting
  3. 3Matrix Arithmetic and Operations
  4. +1 more
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6

A24 lessonsPRO

Understand how R handles missing data and learn reliable strategies for detecting and treating NAs.

  1. 1Understanding NA in R
  2. 2Detecting and Counting Missing Values
  3. 3Removing and Replacing NA Values
  4. +1 more
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7

A24 lessonsPRO

Navigate R's implicit and explicit type conversion rules to avoid common data type bugs.

  1. 1R's Type System Overview
  2. 2Converting Between Numeric Types
  3. 3Logical and Character Conversion
  4. +1 more
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8

A24 lessonsPRO

Rearrange vectors and data frames using R's sorting and ranking functions for data analysis tasks.

  1. 1Sorting Vectors with sort()
  2. 2order() for Flexible Ordering
  3. 3Ranking Values with rank()
  4. +1 more
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9

A24 lessonsPRO

Build, format, and display text output using paste(), sprintf(), and other R string tools.

  1. 1Building Strings with paste() and paste0()
  2. 2Formatted Output with sprintf()
  3. 3Displaying Output with cat() and print()
  4. +1 more
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10

A23 lessonsPRO

In this course, you will explore the fundamental data types in R such as numeric, character, logical, and the common data structures like vectors, matrices, lists, and data frames.

  1. 1Understanding Data Types
  2. 2Working with Vectors and Matrices
  3. 3Lists and Data Frames
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11

A23 lessonsPRO

Learn to write custom functions, control the flow of your program with conditional statements and loops, and debug your code efficiently.

  1. 1Writing Functions in R
  2. 2Control Flow Statements
  3. 3Debugging Techniques
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12

A23 lessonsPRO

This course covers essential string operations and date/time manipulations in R, providing the skills to handle textual and temporal data accurately.

  1. 1String Manipulation
  2. 2Regular Expressions
  3. 3Dates and Times in R
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13

A23 lessonsPRO

Master the fundamental plotting capabilities of base R to visualize data effectively. Topics include creating bar charts, histograms, scatter plots, and customizing graphical parameters.

  1. 1Basic Plots in R
  2. 2Customization of Plots
  3. 3Saving and Exporting Graphics
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14

B14 lessonsPRO

Write robust R code that gracefully handles errors, warnings, and messages using tryCatch and related tools.

  1. 1Errors, Warnings, and Messages in R
  2. 2tryCatch() for Error Recovery
  3. 3withCallingHandlers() and Restarts
  4. +1 more
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15

B14 lessonsPRO

Understand lexical scoping and R's environment system to write predictable, side-effect-free code.

  1. 1What Is an Environment in R?
  2. 2Lexical Scoping Rules
  3. 3Global vs Local Scope
  4. +1 more
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16

B14 lessonsPRO

Organize R code into maintainable scripts and projects using best practices for file structure and sourcing.

  1. 1Using source() to Load Scripts
  2. 2Comments, Style, and Readability
  3. 3Working Directories and File Paths
  4. +1 more
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17

B14 lessonsPRO

Go beyond basic data wrangling with grouped window functions, multi-table joins, and tidy evaluation in dplyr.

  1. 1Grouped Summaries and group_by()
  2. 2Window Functions: lag, lead, cumsum
  3. 3Multi-table Joins in dplyr
  4. +1 more
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18

B14 lessonsPRO

Transform messy wide data into tidy long format and back using tidyr's pivot functions and nesting tools.

  1. 1Wide to Long with pivot_longer()
  2. 2Long to Wide with pivot_wider()
  3. 3separate() and unite() for String Columns
  4. +1 more
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19

B14 lessonsPRO

Read CSV, TSV, Excel, and fixed-width files efficiently into R with robust parsing and type inference.

  1. 1Reading CSV Files with read_csv()
  2. 2Parsing TSV and Fixed-Width Files
  3. 3Importing Excel Files with readxl
  4. +1 more
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20

B14 lessonsPRO

Query relational databases from R using DBI, RSQLite, and write SQL-style queries through dbplyr.

  1. 1DBI and RSQLite Basics
  2. 2Connecting to PostgreSQL and MySQL
  3. 3dbplyr: SQL via dplyr Syntax
  4. +1 more
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21

B14 lessonsPRO

Extract structured data from HTML pages using rvest's CSS selector and XPath tools.

  1. 1HTML Structure and CSS Selectors
  2. 2html_element() and html_text() Basics
  3. 3Scraping Tables and Links
  4. +1 more
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22

B14 lessonsPRO

Generate reproducible random samples and simulate statistical processes for data analysis and testing.

  1. 1set.seed() and Reproducibility
  2. 2Generating Random Distributions
  3. 3Monte Carlo Simulation Basics
  4. +1 more
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23

B14 lessonsPRO

Parse JSON responses, build HTTP requests, and integrate R workflows with external REST APIs.

  1. 1Parsing JSON with jsonlite
  2. 2Making HTTP Requests with httr2
  3. 3Consuming REST APIs in R
  4. +1 more
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24

B13 lessonsPRO

Learn how to import, clean, transform, and combine data from different sources using base R techniques. This course covers essential data wrangling tasks for real-world data analysis.

  1. 1Importing Data
  2. 2Data Cleaning Basics
  3. 3Combining and Reshaping Data
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25

B13 lessonsPRO

Expand your visualization skills with the ggplot2 package, building layered graphics and customizing them for powerful, publication-quality plots.

  1. 1ggplot2 Basics
  2. 2Layers and Geoms
  3. 3Theme Customization
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26

B13 lessonsPRO

Dive into the Tidyverse, a collection of R packages designed for data science. You will learn to use dplyr, tidyr, and other packages for streamlined data manipulation.

  1. 1Introduction to Tidyverse
  2. 2Data Wrangling with dplyr
  3. 3Tidying Data with tidyr
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27

B13 lessonsPRO

Focus on techniques for summarizing, visualizing, and deriving insights from data sets. Learn best practices for quick data discovery and storytelling.

  1. 1Data Summaries and Visualization
  2. 2Identifying Patterns and Outliers
  3. 3EDA Best Practices
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28

B24 lessonsPRO

Replace for-loops with elegant, composable functional programming patterns using purrr's map family.

  1. 1map() and Typed Variants
  2. 2map2() and pmap() for Multiple Inputs
  3. 3reduce(), accumulate(), and walk()
  4. +1 more
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29

B24 lessonsPRO

Perform matrix operations, solve linear systems, and compute eigenvalues using R's built-in linear algebra tools.

  1. 1Matrix Multiplication and Determinants
  2. 2Solving Linear Systems with solve()
  3. 3Eigenvalues and Eigenvectors
  4. +1 more
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30

B23 lessonsPRO

Build on basic statistics concepts to conduct hypothesis testing, explore distributions, and understand the fundamentals of probability in R.

  1. 1Descriptive Statistics
  2. 2Probability Distributions
  3. 3Hypothesis Testing
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31

B23 lessonsPRO

Discover how to deploy R solutions into real-world environments using tools like R Markdown, Shiny, and Docker. Learn to create dynamic reports and interactive dashboards.

  1. 1R Markdown Basics
  2. 2Introduction to Shiny Apps
  3. 3Deployment and Best Practices
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32

B24 lessonsPRO

Build end-to-end ML workflows using the tidymodels ecosystem: recipes, parsnip, workflows, and tune.

  1. 1Feature Engineering with recipes
  2. 2Model Specifications with parsnip
  3. 3Workflows: Combining Recipes and Models
  4. +1 more
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33

B24 lessonsPRO

Build production-grade Shiny apps with modules, dynamic UI, reactive programming, and deployment strategies.

  1. 1Reactive Programming Deep Dive
  2. 2Shiny Modules for Code Reuse
  3. 3Dynamic UI with renderUI and insertUI
  4. +1 more
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34

B24 lessonsPRO

Turn R functions into REST API endpoints using Plumber, with authentication, routing, and production deployment.

  1. 1Introduction to Plumber and REST
  2. 2Creating GET and POST Endpoints
  3. 3Authentication and API Security
  4. +1 more
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35

B24 lessonsPRO

Manipulate spatial vector and raster data, perform spatial joins, and create interactive maps with R.

  1. 1Simple Features with the sf Package
  2. 2Coordinate Reference Systems and Projections
  3. 3Spatial Joins and Operations
  4. +1 more
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36

B24 lessonsPRO

Apply NLP techniques to text corpora using tidytext: tokenization, TF-IDF, sentiment analysis, and topic modeling.

  1. 1Tokenization and Stop Word Removal
  2. 2TF-IDF and Term Frequency Analysis
  3. 3Sentiment Analysis in R
  4. +1 more
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37

B24 lessonsPRO

Build, document, test, and publish your own R packages using devtools, roxygen2, and testthat.

  1. 1Package Structure with usethis and devtools
  2. 2Documenting Functions with roxygen2
  3. 3Unit Testing with testthat
  4. +1 more
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38

C14 lessonsPRO

Identify bottlenecks in R code and apply vectorization, benchmarking, and profiling techniques for speed.

  1. 1system.time() and proc.time()
  2. 2Profiling Code with Rprof and profvis
  3. 3Vectorization for Speed
  4. +1 more
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39

C13 lessonsPRO

Delve into more complex techniques like regression models, time series analysis, and machine learning fundamentals. Gain the skills to handle sophisticated data scenarios.

  1. 1Regression Models
  2. 2Time Series Analysis
  3. 3Machine Learning Fundamentals
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40

C14 lessonsPRO

Build and tune ensemble models using ranger for random forests and xgboost for gradient boosting in R.

  1. 1Decision Trees: Foundation of Ensembles
  2. 2Random Forests with ranger
  3. 3Gradient Boosting with xgboost
  4. +1 more
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41

C14 lessonsPRO

Train neural networks in R using the Keras API backed by TensorFlow for image and sequence tasks.

  1. 1Setting Up Keras and TensorFlow in R
  2. 2Building Sequential Models
  3. 3Convolutional Neural Networks Basics
  4. +1 more
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42

C14 lessonsPRO

Speed up R workflows with multicore parallelism using the parallel package, future framework, and furrr.

  1. 1parallel Package and detectCores()
  2. 2The future Framework
  3. 3furrr: Parallel purrr Operations
  4. +1 more
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43

C14 lessonsPRO

Build and fit probabilistic models using Stan from R, interpreting posteriors and validating with MCMC diagnostics.

  1. 1Introduction to Bayesian Thinking
  2. 2Writing Stan Models in R
  3. 3MCMC Sampling and Diagnostics
  4. +1 more
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CoddyKit PRO

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Yearly$29.90 / yearAbout $2.49 a monthPay onceLifetime$69.99 onceNo renewals. PRO forever.
Course Overview

R: The Language Built for Data Analysis and Statistics

R is a programming language designed specifically for statistical computing, data analysis, and visualization. It is the standard tool in academic research, biostatistics, finance, and data science teams that need rigorous statistical methods. With packages like ggplot2, tidyverse, and tidymodels, R combines expressive data manipulation with publication-quality graphics and reproducible analysis workflows — making it a practical choice for anyone working seriously with data.

What You Will Learn

You will start with R fundamentals: data types, operators, control flow, functions, and working with matrices and sequences. From there you move into data wrangling with dplyr and tidyr, importing data via readr and readxl, and connecting to databases and REST APIs. Visualization is covered thoroughly — from base R graphics through ggplot2 and full exploratory data analysis. Advanced topics include functional programming with purrr, statistical analysis and probability, machine learning with tidymodels, random forests and gradient boosting, geospatial analysis, text mining with tidytext, building web apps with Shiny, creating APIs with Plumber, and deep learning with Keras in R.

The Learning Path

Forty-two courses span A1 through C1. The first twelve courses establish core R and RStudio fundamentals — data structures, string formatting, type coercion, sorting, and base R visualization. The B1 and B2 tiers build practical skills: web scraping with rvest, the Tidyverse, simulation, linear algebra, and production-ready reporting. The track closes at C1 with Performance and Profiling in R, Advanced Data Analysis, Parallel Computing, and Bayesian Statistics with RStan — topics that reflect real production and research use.

How It Works

Each course is split into short, hands-on lessons you complete in the built-in code editor with real-time feedback. An AI tutor is available whenever you get stuck, explaining errors and guiding you toward the correct solution without just giving away the answer.

Tools

Practice with AI, not just read

Supercharge your learning with AI-powered tools and features

AI Code GeneratorGenerate code with AIAI Bug FixFind & fix code errorsPROAI InsightDeep code analysisPROAI ChatChat with AI tutorAsk Super QuestionGet expert-level answersPROCreate LessonAI-powered lesson builderPROStart Certification ExamEarn your certificatePROCode EditorWrite & run codeStart ChallengeTest your skills

How You'll Learn

  1. 01

    Interactive Lessons

    Hands-on coding exercises with real-time feedback

  2. 02

    AI Tutor

    Get instant help from our AI when you're stuck

  3. 03

    Built-in Editor

    Write and run code directly in your browser

  4. 04

    Certificate

    Earn a certificate when you complete the course

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FAQ

Frequently Asked Questions

Is the R Academy course free?

Yes. You can start the R Academy course for free and complete its interactive lessons at no cost. An optional PRO subscription unlocks advanced AI tools and a shareable certificate.

Do I need prior experience to learn R?

No. The course begins with the fundamentals and gradually moves to more advanced topics, so you can start even with no prior R experience.

How will I learn R on CoddyKit?

You learn by doing. Short interactive lessons pair a clear explanation with a hands-on coding exercise that runs in real time, and a 24/7 AI tutor gives personalized help whenever you get stuck.

Do I get a certificate for completing R Academy?

Yes. PRO learners can take an exam and earn a shareable certificate of completion with a verifiable code for the R Academy course.

Can I learn R on my phone?

Yes. CoddyKit is available on the web and as native iOS and Android apps, so you can learn R on any device and your progress syncs across them.

How much does CoddyKit PRO cost?

The first course of R Academy is free. CoddyKit PRO unlocks every course: $5.90 per week, $7.90 per month, $29.90 per year, or $69.99 once for lifetime access with no renewals.

How long does it take to complete R Academy?

R Academy has 43 courses and 159 lessons. Most lessons take about 5–10 minutes, so the whole path is roughly 19 hours of hands-on practice, at your own pace.

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