Data Analytics & Visualization Using R
Master the art of data analysis with R! In this course, you’ll learn how to explore, visualize, and analyze data using R, a powerful tool for statistical computing.
Data Analytics & Visualization Using R This focuses on turning raw data into meaningful insights through structured analysis and clear visual communication. R is a powerful statistical programming language widely used in academia, research, and industry for data analysis, modeling, and visualization.
On this page, you will find practical examples and explanations covering the full analytics workflow, including:
- Data exploration and understanding using summary statistics and exploratory plots
- Data cleaning and transformation with tidy, readable code using packages such as
dplyr - Exploratory Data Analysis (EDA) to identify patterns, trends, and potential outliers
- Data visualization using
ggplot2to create clear, informative, and publication-ready charts - Interpretation of results, with emphasis on translating numbers and visuals into insights
The content emphasizes hands-on, script-based learning, making it suitable for beginners while remaining useful to intermediate users. Examples are drawn from education, health, and business datasets, particularly within the Nigerian and African context, to ensure relevance and practical value.
Rather than focusing only on tools, this page highlights analytical thinking—how to ask the right questions, choose appropriate visualizations, and communicate findings effectively. All analyses are designed to be reproducible, allowing users to rerun and adapt the code for their own datasets.
This page also supports the learning activities of the Ekiti R-Users Group, where R is used as a core tool for building data literacy, research capacity, and professional analytics skills through free, hands-on community trainings.