Название: Visualizing Biological Data with ggplot2
Автор: William G. Vilchez
Издательство: CRC Press
Год: 2027
Страниц: 249
Язык: английский
Формат: True PDF, True EPUB
Размер: 41.0 MB

We are currently living in an era where technology is constantly advancing, which leads to the generation of large volumes and many types of data. Consequently, the ability to transform complex data into clear and attractive visualizations has become essential. Visualizing Biological Data with ggplot2 encourages readers to explore the power of visualization in biology, combining science and art to communicate findings clearly.

The first part of the book begins with a general overview of data visualization and then guides readers through the use of the R language, especially its visualization package called ggplot2. In the second part, the author introduces the creation of different visualizations, from basic graphs and tables to more complex representations such as phylogenetic trees, heat maps, and geographic maps, among others. Subsequently, in the third section, tips and strategies for optimizing the design of your graphs are presented. Finally, the author addresses the creation of interactive visualizations and explains the various options available for exporting and sharing your visualizations.

During my years as a college student, I first encountered the R programming language and decided to learn it on my own. In that self-taught process, I came across a book (mentioned in the Inspirational Readings section) that completely transformed my way of thinking about how data should be visualized.
In modern biology, data have multiplied and diversified at an accelerated rate, especially in the past two decades, as a result of advances in technology. Faced with this scenario, visualization becomes an essential tool to translate the complexity of biological data into understandable and accessible knowledge.

However, there are few courses or materials that teach how to design truly clear, accurate, and well-founded visualizations. With this need in mind, Visualizing Biological Data with ggplot2, a book conceived as a practical guide to manipulating and presenting various types of biological data using R and its powerful graphics package ggplot2, is born. This text is intended for a broad audience: undergraduate and graduate students, teachers, researchers, and anyone interested in communicating scientific information through effective graphics. Ggplot2 has been chosen as the main tool because of its flexibility and ability to create high-quality visualizations. However, this book does not limit itself to displaying a variety of plots, but combines it with the principles of visualization resulting in customized, publication-ready figures. Although the focus is on ggplot2, there is no harm if the reader decides to use another graphics package or software to reproduce or adapt the figures presented here.

It is important to clarify that this is not a complete R manual, for that there are excellent books that cover that subject, but a direct introduction focused on the essential aspects that will allow the reader to manipulate data with ease and transform them into real visual pieces (figures and tables) that can communicate effectively in presentations, publications, or classes.

What Is Data Visualization? It is a graphical representation of the data collected in your research. There are various ways to display these data, such as bar charts, tables, and maps, in order to communicate the results of your studies. Therefore, inadequate data visualization can diminish the quality and hinder the progress of your research. In short, proper visualization transforms data, whether simple or complex, into simple representations (plots, tables, and maps) that help people interact with and understand your data.

In the Chapter 2, we will explore the R programming language and its development environment, RStudio. R has been specifically designed to perform statistical analysis, generate high-quality graphics, and efficiently handle large datasets. RStudio acts as a user-friendly interface that enhances working with R by integrating key tools such as a code editor, graphics viewer, and package management system, among other features. We will also discuss how to prepare your documents, so they can be easily shared and replicated using R Markdown. Reproducibility is a fundamental pillar of data science.

R is a free programming language that is widely recognized and supported by the scientific community, offering functions (through code instructions) to perform various tasks, such as statistical analysis. What makes R renowned? And how does it stand apart from other programming languages? Presently, R is compatible with nearly all computer platforms, owing to its distinct attributes:
• Free access (thanks to the General Public license of Gnu1)
• A large variety of packages that perform multiple functions, for example, ggplot2.
• Supports various data types: homogeneous (list, data frame) and heterogeneous (vector, matrix, array).
• It is not that “hard” to learn R: beginners can acquire proficiency by executing basic scripts (text files with the .R extension).
• Due to its accessibility, it facilitates the exchange of scripts, so you can interact with people on the Internet and solve your problems when executing your scripts.

Features:

Introduction to data visualization
Explains the basics of R and its suite of packages called tidyverse
Tutorial on ggplot2 to create a wide range of graph types
Advice on when to avoid certain types of visualization which can lead to errors or confusion in the interpretation of results
Strategies for customizing charts, ready for publication
Numerous examples based on real biological data, which facilitate the practical application of the concepts learned

With a wide variety of biological examples and practical exercises, allowing readers to consolidate knowledge throughout this book, Visualizing Biological Data with ggplot2 is perfect for experienced students and researchers, while also serving as a graphic guide for anyone wishing to transform their data into visualizations.

Contents: