R Programming Training for Research

Course Objectives:
  • Equip students with the fundamental skills of R programming for data analysis.
  • Foster confidence in data manipulation, statistical analysis, and visualization using R.
  • Enable students to apply R to their research projects across various fields.
  • Promote R as a powerful tool for data-driven research and innovation.

The course is structured into three modules: Basic, Intermediate, and Advanced, progressively building upon the acquired knowledge and skills.

Why we promote R:
    • Open-source and free
    • Large and active community
    • Extensive package ecosystem for diverse tasks
    • Powerful statistical and graphical capabilities
Value for Money:
  • We emphasize hands-on exercises and case studies to solidify understanding.
  • We incorporate real-world datasets relevant to various research fields.
  • We promote active learning and collaborative problem-solving.
  • We invite various guest lecturers from researchers using R in their specific fields.
  • We provide continuous guidance and support from our instructors and teaching assistants.
  • We draw conclusions from Data Analysis

Module 1: Basic (Free)


Introduction to R and RStudio:
    • Installation and set-up of R and RStudio
    • Introduction to the R console and workspace
    • Basic data types and structures (vectors, matrices, lists, data frames)
    • Object manipulation and operators
    • Managing R scripts and projects
Data Input and Output:
      • Importing data from various sources (CSV, Excel, text files, databases)
      • Data cleaning and manipulation using dplyr
      • Data export and reporting
Programming Fundamentals:
        • Control flow statements (if, else, for, while loops)
        • Functions and arguments
        • Debugging and error handling
Exploratory Data Analysis (Descriptive Statistics and Data Visualization):
    • Calculating summary statistics for numerical and categorical variables
    • Creating basic visualizations (histograms, boxplots, scatter plots)
    • Introduction to ggplot2 for advanced data visualization.

Module 2: Intermediate 

Data Manipulation and Cleaning:
    • Advanced data wrangling techniques
    • Dealing with missing values and outliers
    • String manipulation and regular expressions
Statistical Inference and Hypothesis Testing:
      • Introduction to probability and statistical concepts
      • Hypothesis testing (parametric and non-parametric tests)
      • Confidence intervals and statistical significance
Linear Regression:
    • Understanding linear regression models
    • Model fitting and evaluation
    • Model diagnostics and assumptions
Data Visualization for Deeper Insights:
    • Advanced ggplot2 features (faceting, themes, customization)
    • Creating informative and publication-quality visualizations
    • Communicating results visually
Reproducible Research
    • Version control and code documentation for reproducible research practices
    • Writing R Markdown reports for clean and reproducible data analysis
    • Creating dynamic reports
    • Version control with Git and GitHub
Capstone Project:
    • Apply learned concepts to real-world research project
    • Emphasize collaboration and communication skills
    • Present findings using dynamic reports and Shiny apps

 Module 3: Advanced 

 Advanced Statistical Models
  • Linear and logistic regression
  • Generalized linear models.
  • Time series analysis
Machine Learning with R
    • Supervised learning techniques
    • Unsupervised learning techniques
    • Model evaluation and selection
Text Analysis:
      • Text cleaning and preprocessing
      • Sentiment analysis and topic modeling
      • Working with natural language processing (NLP) packages
Shiny App Development
    • Introduction to Shiny
    • Building interactive web applications with R
    • Deploying Shiny apps
Introduction to R Packages:
    • Utilizing specialized R packages for specific research domains
    • Package installation and basic usage
    • Building custom R functions and packages
Capstone Project:
    • Apply learned concepts to real-world research project
    • Emphasize collaboration and communication skills
    • Present findings using dynamic reports and Shiny apps


  • Comprehensive course textbook and materials.
  • Access to online coding platforms and learning resources.
  • List of suggested R packages and learning materials for further exploration.

Training Cost

While our Module 1 training is offered for free, the training cost for Modules 2 and 3 is US$85 per module of your choice. However, we offer discounts if you are interested in both modules 2 and 3. The cost is for full remote training, and it covers the tuition fees, learning materials, and certification. Those interested in physical training incur an additional US$25 to include meals and refreshments at the training venue. Further information will be provided upon inquiry.

Application Process

Applicants should complete the LERIS Course-Application-Form.

Personal Information Form

Appliction Form

Bio Data

Education and Qualification

Work Experience

Module(s) of Interest

Special Considerations