R is a powerful programming language and software environment widely used for statistical analysis, data visualization, and machine learning. It provides a vast array of tools and libraries that make it a popular choice among data scientists, statisticians, and researchers.
R excels in statistical analysis and is equipped with a rich set of functions for descriptive statistics, hypothesis testing, regression analysis, time series analysis, and multivariate techniques. This makes it a preferred choice for researchers and analysts working with data from various fields, such as social sciences, finance, healthcare, and environmental studies.
Moreover, R offers exceptional data visualization capabilities. Its default plotting system allows users to create a wide variety of static and interactive visualizations to explore and present data effectively. Additionally, packages like ggplot2 provide a grammar of graphics approach, enabling users to construct complex and customizable plots with ease.
In recent years, R has gained popularity in the field of machine learning. Packages such as caret, randomForest, and keras offer powerful tools for building and evaluating predictive models. R's integration with other languages, such as Python, allows users to leverage popular machine learning frameworks like TensorFlow and scikit-learn within their R workflow.
Download and install both R and RStudio: https://posit.co/download/rstudio-desktop/
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Daphne Janelyn L. Go
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Mark Edward M. Gonzales
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These materials were originally created for the Basic R Workshop, jointly organized by the Bioinformatics Lab with the Systems and Computational Biology Unit, De La Salle University, last July 12, 2023.