2025 ANNUAL AMMNET SENEGAL MEETING/TRAINING

Basics: Introduction to R and Data Visualization

Facilitator: Dennis K. Muriithi

Center for Data Analytics & Modeling, Chuka University - Kenya

INTRODUCTION TO R AND DATA VISUALIZATION TRAINING

Course Overview

This training is designed for beginners who want to learn the fundamentals of R programming and apply it to data visualization. Participants will gain hands-on experience working with a malaria dataset, learning how to analyze, and visualize data effectively.

Training Aims

1. Understand the Basics of R Programming (Data Import and Exploratory Data Analysis).
2. Develop proficiency in creating data visualizations in R using ggplot2.
3. Enhance Data Storytelling Through Visualization.
4. Introduction to Animated and Interactive Visualizations.
5. Equip participants with skills to apply R for real-world malaria data analysis.

Learning Objectives

By the end of the training, participants should be able to perform:

• ✔️ Data Import and Exploratory Data Analysis.
• ✔️ Data Visualization (bar charts, scatterplots, histograms etc) with ggplot2, Patchwork etc.
• ✔️ Interpret and customize visualizations for clarity and impact.

Training Structure

A blend of interactive and hands-on methods:

1. Presentations: Concise theory and examples.
2. Practical Work: Guided coding exercises.
3. Group Work: Collaborative problem-solving.
4. Discussions: Q&A and best practices.

Topic Covered

1. Section 1: Introduction to R Programming
2. Section 2: Exploring Data Analysis
3. Section 3: Data Visualization with ggplot2
4. Section 4: Advanced Topics and Best Practices

Section 1: Introduction to R and Rstudio Programming

• ✔️R is a programming language for statistical computing and graphics.
• ✔️ Rstudio is an integrated development environment (IDE) for R that provides tools for coding, data visualization, and project management.

Installing R and RStudio

• ✔️ Download R latest version 4.4.3 from the CRAN website.
• ✔️ Download RStudio from the RStudio website.

RStudio Interface

1. Scripts: Write and save R code here..
2. Console : Execute commands interactively.
3. Environment: View variables, data frames, and functions in memory.
4. Plots: Display graphical outputs.

Section 2: Exploring Data Analysis (EDA)

EDA is a critical step before building models, as it helps in:

• ✅ Understanding the data structure and identifying inconsistencies.
• ✅ Detecting missing values, outliers, and unusual patterns.
• ✅ Selecting appropriate features for predictive modeling.
• ✅ Improving data preprocessing and transformation steps.
• ✅ Summarize key characteristics of a dataset.

Section 3: Data Visualization

Data visualization helps in understanding patterns, trends, and relationships in data.

Types of Data Visualization

1. Univariate Data Visualizations (Single Variable)

• ✔️ Histogram: Used for understanding the distribution of a single variable.
• ✔️ Box Plot: Used for Detecting outliers and understanding the spread of data.

2. Bivariate Data Visualizations (Two Variables)

• ✔️ Scatter Plot: UUsed for understanding relationships between two numerical variables.
• ✔️ Line Plot: UUsed for showing trends over time or continuous data.
• ✔️ Bar Chart: Used for comparing categorical data.

3. Multivariate Data Visualizations (More than Two Variables)

• ✔️ Heatmap: Used for visualizing correlations between multiple numerical variables.
• ✔️ Pair Plot: Used for visualizing pairwise relationships in the dataset.
• ✔️ Violin Plot: Used for understanding the distribution of a variable across categories.

4. Specialized Data Visualizations

• ✔️ Pie Chart: Used for representing proportions.
• ✔️ Bubble Chart: Used for adding a third variable to a scatter plot(Comparing three numerical variables)
• ✔️ Word Cloud: used to highlight keywords, trends, or themes in textual data (Text Analysis : Highlighting key terms in articles, reviews, or social media posts.)

5. Time Series Visualizations

• ✔️ Time Series Line plot: Used to analyze trends, patterns, or changes in data over a continuous period (e.g., days, months, years).
• ✔️ Autocorrelation Plot: Used for finding patterns in time series data.

Section 4: Advanced Topics and Best Practices

Advanced Topics

• ✅ Interactive Visualizations : –Use tools like Plotly and Dash to enable user interaction.
• ✅ High-Dimensional Data Visualization : Use PCA, t-SNE, and UMAP for dimensionality reduction.
• ✅ Time Series Visualization : – Apply line plots, heatmaps, and rolling averages for trends.
• ✅ Network Graph Visualization : –Use NetworkX and Gephi for social and supply chain analysis.
• ✅ Big Data Visualization : – Handle large datasets with Dask, Vaex, and Apache Superset.

Best Practices

• ✅ Choose the Right Chart Type (e.g., bar charts for categories, line charts for trends).
• ✅ Follow Design Principles (simplicity, consistency, accessibility).
• ✅ Use Storytelling to highlight key insights and structure visuals logically.
• ✅ Avoid Common Pitfalls (misleading scales, cluttered visuals, unnecessary 3D charts).