R is a programming language and environment widely used for data analysis and visualization. It was created by Robert Gentleman and Ross Ihaka at the University of Auckland in the early 1990s. It has since become one of the most popular statistical analysis and data visualization programming languages.
R is particularly well-suited for data visualization for several reasons:
- Data manipulation: R provides powerful data manipulation and transformation tools. Users can easily clean, reshape, and preprocess data before creating visualizations.
- Graphics capabilities: R has a rich ecosystem of packages for creating a wide variety of static and interactive data visualizations. The most commonly used package for static graphics is ggplot2, which provides a flexible and intuitive syntax for creating high-quality visualizations.
- Data integration: R can easily integrate with various data sources, including databases, spreadsheets, and web APIs, making it convenient for working with real-world data.
- Community and packages: R has a large and active user community, which means numerous packages and libraries are available for various data visualization needs. Users can access and contribute to a vast repository of packages via CRAN (Comprehensive R Archive Network).
- Reproducibility: R promotes good practices for reproducible research. Users can create scripts or documents using tools like R Markdown, which allows them to combine code, data, and narrative in a single document.
- Cross-platform compatibility: R is available for the commonly used operating systems – Windows, Linux, and macOS – making it a versatile option for data visualization across different operating systems.
List of recommended resources #
For a broad overview #
Comparative Study of Big data Analytics Tools: R and Tableau
This paper, by C. Rajeswari, Dyuti Basu and Namita Maurya, does a comparative analysis of the two popular data analytics tools: Tableau and R.
Data Visualization Using R for Researchers Who Do Not Use R
This research article by Emily Nordmann, Phil McAleer and Lisa M. DeBruine provides a practical introduction to data visualization using R specifically aimed at researchers who have little to no prior experience of using R.
This database contains a collection of charts made using the R programming language.
For in depth understanding #
Data Visualisation with R: 100 Examples
This book by Thomas Rahlf gives a comprehensive introduction to creating presentation graphics with R. It includes step-by-step explanations of the programming of figures based on real data as well as the complete code of 100 examples from different fields.
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
This book by Hadley Wickham, Mine Cetinkaya-Rundel & Garrett Grolemund gives a practicum of skills for data science. It teaches the grammar of graphics, literate programming, and reproducible research to save time.
Case study #
Basic Data Visualization in R and Python
Case Study: New York Taxi Cabs – This case study uses ggplot2 library of R programming language to plot data containing information on every single trip taken with a yellow New York City taxi cab in the month of June, 2015.
Data visualization with the programming language R
This article by Paul Brennan uses the programming language R for reproducible data visualizations.
References #
Data Science with R: Getting Started
Introduction to Data Visualization in R
