Introduction: Overcoming the Fear of Coding

• The mindset shift: From apprehension to confidence

• Why R and RStudio?

• Course structure and expectations

Session 1: RStudio Infrastructure (July 21, 2025 | 4 hours)

• Installing R and RStudio

• Navigating the interface: Script, Console, Environment, and Help windows

• Customizing layouts and shortcuts

• How to access help (?function, R Documentation, online forums)

• Core data types: numeric, character, logical, factors

• Data structures: vectors, lists, matrices, and data frames

• Checking and converting data types

• Hands-on exercises: Creating and inspecting structures

Session 2: Data Manipulation (July 22, 2025 | 4 hours)

• Subsetting and filtering data

• Reshaping data: gather() and spread() (from tidyr)

• Handling missing values

• Introduction to dplyr verbs (select(), filter(), mutate())

• Hands-on exercises: Data Manipulation

• Descriptive statistics (mean, median, variance)

• Basic visualizations: histograms, boxplots, scatter plots (ggplot2)

• Identifying patterns and outliers

• Hands-on exercises: Exploring a dataset

Session 3: Deep dive into Data Visualization (July 23, 2025 | 4 hours)

•  Principles of Effective Visualization

• Overview of R’s Visualization Systems (Base R, Lattice, GGPLOT2)

• Lattice Package

  1. Basics: Syntax and Formula Interface (~x | g)

  2. Key Plot Types: Scatter, Histogram, Boxplot, Density

  3. Multi-Panel Conditioning (facet_wrap, facet_grid)

  4. Customization: Colors, Axes, and Legends

•  GGPLOT2 Package

  1. Grammar of Graphics (Layers: Data, Aesthetics, Geoms)

  2. Essential Geoms: geom_point(), geom_line(), geom_bar()

  3. Themes, Scales, and Annotations

  4. Interactive Visuals with plotly/shiny (Optional)

  5. Exporting Plots (PDF/PNG) & Reproducibility

  6. Mini-Projects: Real-World Datasets

Session 4: Statistical Data Analysis (July 24, 2025 | 4 hours)

• Hypothesis testing: 

  1. Parametric and non-parametric tests

• Correlation analysis

• Interpreting and presenting results

• Hands-on exercises: Test selected hypotheses

Session 5: Linear Regression Analysis  (July 25, 2025 | 4 hours)

• From correlation to simple linear regression

• Multiple Linear Regression

• Model Selection and Interpretation

• Packages for advanced analysis (e.g., lme4, caret)

• R Markdown for reproducible reports

Target Audience: Are you a researcher or a programming enthusiast? Then this course is for you. You need no prior knowledge in statistics or programming. However, knowledge of basic statistics will be an advantage. 

Dates: 

Mon to Fri : 21-25 July 2025: 9:00h-12:30h

Register using the form below: