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.

Structure:

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:

1. • 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:

1. 1. • 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:

1. • 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

1. • 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

Resources

• 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.