Free R Courses

Free R courses on Simplilearn's SkillUp platform give learners the tools to turn raw, messy data into structured insights. It involves moving from unprocessed datasets to cleaned, analyzed, and visualized outputs across real-world scenarios.

The courses address the full analytical workflow:

• Importing and structuring data using data frames and base R operations

• Cleaning and reshaping datasets using dplyr and tidyr for tidy, analysis-ready formats

• Applying statistical models, such as regression, classification, and clustering, to extract patterns from data

• Visualizing results through charts, graphs, and dashboards that communicate findings clearly

• Working with time series data for trend analysis, seasonal decomposition, and forecasting

Best for: Data analysts who want to move beyond Excel, researchers interested in creating reproducible pipelines, and business professionals who want to analyze data at scale with programming.

The main free R courses cover a complete, progressive skill set from foundational syntax through machine learning implementation.

• R Fundamentals

  • Learn variables, data types, logical operators, conditional statements, loops, and functions

  • Covered in:

    • R Programming for Beginners

• Data Structures

  • Learn vectors, lists, matrices, and data frames

  • Understand the core structures for data handling in R

  • Covered in:

    • R Programming for Beginners

    • ML with R

• Data Manipulation

  • Learn filtering, selecting, mutating, and summarizing with dplyr

  • Learn reshaping and pivoting with tidyr

  • Covered in:

    • R Programming for Beginners

    • ML with R

• Data Visualization

  • Create charts, graphs, and visual summaries using R’s visualization capabilities

  • Covered in:

    • R Programming for Beginners

    • ML with R

• Time Series Analysis

  • Learn temporal data handling

  • Understand ARIMA modeling

  • Learn seasonal decomposition and forecasting

  • Covered in:

    • R Programming for Beginners

    • R for Data Science

• Statistical Modeling

  • Learn linear regression, logistic regression, decision trees, and clustering

  • Covered in:

    • R for Data Science

    • ML with R

• Machine Learning

  • Learn random forests, support vector machines, hierarchical clustering, and ensemble methods

  • Covered in:

    • Machine Learning with R

• ML Pipeline

  • Learn the full workflow from data preparation through model training, evaluation, and interpretation

  • Covered in:

    • Machine Learning with R

The free R courses introduce the most widely used packages across data manipulation, visualization, modeling, and machine learning.

• Base R

  • Purpose:

    • Core language functions

    • Data structures

    • Statistical operations

  • Covered in:

    • All courses

• dplyr

  • Purpose:

    • Data manipulation

    • Filter, select, mutate, summarize, and join operations

  • Covered in:

    • R Programming for Beginners

    • ML with R

• tidyr

  • Purpose:

    • Tidying messy data

    • Pivoting between wide and long formats

  • Covered in:

    • R Programming for Beginners

    • ML with R

• ggplot2

  • Purpose:

    • Data visualization

    • Creating layered, publication-quality charts and graphs

  • Covered in:

    • Data visualization modules across courses

• stats

  • Purpose:

    • Statistical modeling functions

    • Regression

    • Hypothesis testing

    • Distribution analysis

  • Covered in:

    • R for Data Science

    • ML with R

• randomForest

  • Purpose:

    • Ensemble learning via bagging

    • Random forest classification and regression

  • Covered in:

    • Random Forest lessons

    • ML with R

• e1071

  • Purpose:

    • Support vector machine classification and regression in R

  • Covered in:

    • SVM lesson

    • ML with R

• forecast

  • Purpose:

    • Time series forecasting using ARIMA and exponential smoothing

  • Covered in:

    • Time Series modules

    • R for Data Science

• caret

  • Purpose:

    • Classification and regression training framework

    • Unified ML model interface

  • Covered in:

    • Referenced in Machine Learning with R

    • Deeper coverage available through follow-on resources

• Shiny

  • Purpose:

    • Interactive web app and dashboard development in R

  • Covered in:

    • Covered conceptually in some courses

R's domain versatility is one of its defining strengths. The same language used in an academic statistics department is used in pharmaceutical trials, hedge fund research, and corporate analytics teams.

• Academic Research

  • R is used for reproducible analysis workflows

  • Supports hypothesis testing

  • Helps analyze survey data

  • Used for statistical reporting in peer-reviewed publications

• Clinical Trials and Pharmaceuticals

  • R is used for survival analysis

  • Supports mixed-effects models

  • Helps with dose-response modeling

  • Produces regulatory-grade statistical outputs using validated R packages

• Financial Modeling

  • R is used for time series analysis in stock price modeling

  • Supports risk quantification

  • Helps with portfolio optimization

  • Enables ARIMA-based forecasting

• Business Intelligence

  • R is used for customer segmentation through clustering

  • Supports predictive modeling for churn and demand forecasting

  • Helps create dashboard reports

• Epidemiology and Public Health

  • R is used for disease outbreak modeling

  • Supports regression analysis on health datasets

  • Helps with spatial data visualization

• Marketing Analytics

  • R is used for A/B test analysis

  • Supports attribution modeling

  • Helps with campaign performance reporting using R’s statistical capabilities

R's domain versatility is one of its defining strengths. The same language used in an academic statistics department is used in pharmaceutical trials, hedge fund research, and corporate analytics teams.

• Primary Strength

  • R: Statistical computing, academic research, and rigorous data analysis

  • Python: General-purpose programming, machine learning, and production system development

• Ecosystem

  • R: 20,000+ CRAN packages focused on statistics, modeling, and visualization

  • Python: Broader ecosystem spanning web development, automation, ML, and deep learning

• Visualization

  • R: ggplot2 is widely regarded as a highly expressive visualization library in data science

  • Python: Matplotlib, Seaborn, and Plotly are strong but considered less intuitive for exploratory analysis

• Statistical Depth

  • R: Superior, built-in support for complex statistical models, tests, and distributions

  • Python: Requires external libraries such as scipy and statsmodels for equivalent statistical functionality

• Machine Learning

  • R: Capable through caret, tidymodels, and randomForest; less dominant than Python for deep learning

  • Python: Dominant, with scikit-learn, TensorFlow, and PyTorch as industry standards for ML and deep learning

• Primary Users

  • R: Statisticians, researchers, biostatisticians, quantitative analysts, and academics

  • Python: Software engineers, ML engineers, data engineers, and full-stack data scientists

• Covered on SkillUp

  • R: Yes, through R Programming for Beginners, R for Data Science, and Machine Learning with R

  • Python: Yes, through complementary free Python and data science courses