R Programming Language: From Statistical Roots to Data Science Powerhouse

The R programming language has become a cornerstone in various fields like statistics, bioinformatics, and data science. Over the course of nearly three decades, R has evolved into one of the most popular tools for data analysis, holding a spot among the top 10 programming languages globally. Its rise is a testament to its versatility, ease of use, and a thriving community that continually drives its growth. This blog post explores R’s history, core features, applications, and its far-reaching impact on the world of data science and beyond.

The Origins of R: From S to Open-Source Brilliance

R traces its roots back to the S language, developed at Bell Laboratories in the 1970s by John Chambers and his team. S was designed as an interactive language for statistical computing, offering a more flexible alternative to the Fortran-based statistical tools of its time. Inspired by the simplicity and functionality of S, Ross Ihaka and Robert Gentleman created R in the early 1990s, with the goal of providing a free and open-source alternative to the proprietary statistical software like S-PLUS.

The name “R” was chosen both as an alphabetical successor to S and as a nod to the initials of the creators. Released in 1995 under the GNU General Public License, R quickly gained traction, becoming a highly successful open-source project. Its free accessibility was instrumental in building a global community of users and developers, propelling its widespread adoption and growth.

The timeline of the main events 

Core Features and Capabilities

R is widely celebrated for its powerful features, making it a versatile tool for various applications. Some of the standout features include:

• Extensive Package Ecosystem: R’s Comprehensive R Archive Network (CRAN) is home to over 19,000 packages. These packages extend R’s capabilities in statistical analysis, machine learning, data visualization, and high-performance computing. The ability to access these packages for free has been a key factor in R’s popularity.
• Bioconductor: For bioinformatics researchers, Bioconductor is an invaluable resource. This specialized repository provides thousands of packages specifically designed for the analysis of biological data, solidifying R’s place in the field of computational biology.
• Tidyverse: A collection of powerful R packages including ggplot2 for data visualization and dplyr for data manipulation. The Tidyverse is known for its user-friendly syntax and cohesive approach to data analysis, making it a go-to for data science professionals.
• RStudio: The Integrated Development Environment (IDE) for R, RStudio enhances productivity by offering a code editor, visualization tools, and integrated support for Rmarkdown. RStudio has become the preferred environment for R users, especially for collaborative research.
• Shiny: One of R’s game-changing features is the ability to build interactive web applications using Shiny. This framework enables users to create dynamic, data-driven web interfaces that make complex data accessible to a broader audience.
• Reproducible Research with Rmarkdown: R promotes transparency and reproducibility in scientific research. Rmarkdown allows users to seamlessly integrate code, results, and narrative text into dynamic reports that can be shared and reproduced by others, fostering an open scientific environment.

Installing R

To install the R programming language, follow these step-by-step instructions based on your operating system.

For Windows:

1. Download R:
   • Go to the official R project website: https://cran.r-project.org/.
   • Click on “Download R for Windows”.
2. Select R Version:
   • Click on “base” under the “Subdirectory” section.
   • Download the latest version of R by clicking on “Download R x.x.x for Windows” (where x.x.x represents the latest version).
3. Run the Installer:
   • Once the download is complete, run the .exe installer file.
   • Follow the installation prompts:
     • Select Language: Choose your preferred language for installation.
     • Select Installation Folder: You can accept the default location or choose a different folder.
     • Select Components: You can generally leave the default options selected.
     • Select Start Menu Folder: Choose the default or specify a different folder for shortcuts.
     • Select Additional Tasks: You can leave the defaults as they are and click Next.
4. Complete Installation:
   • Click Install to begin the installation process.
   • Once installation is complete, click Finish.
5. Verify Installation:
   • To check if R is installed correctly, open the R Console (which is installed by default).
   • Type version in the console, and it should show the R version details.

For macOS:

1. Download R:
   • Go to the official R project website: https://cran.r-project.org/.
   • Click on “Download R for macOS”.
2. Download R Installer:
   • Select the appropriate version for your macOS (latest version recommended) and click the link to download the .pkg file.
3. Run the Installer:
   • Once the download is complete, double-click the .pkg file to begin installation.
   • Follow the instructions in the installation wizard.
     • Click Continue on the welcome screen.
     • Agree to the license agreement.
     • Select the installation destination (you can accept the default location).
     • Click Install to begin the installation.
4. Complete Installation:
   • Once installation is complete, click Close to finish.
5. Verify Installation:
   • Open the Terminal and type R to start the R console.
   • Type version to check that R is correctly installed.

For Linux (Ubuntu):

1. Update Package List:
   • Open a terminal and run the following command to update the package list:
     bash

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     sudo apt update

2. Install R:
   • Install the R package from the repository by running:
     bash

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     sudo apt install r-base

3. Verify Installation:
   • Once the installation is complete, you can check if R was installed correctly by typing R in the terminal.
   • Type version in the R console to verify the installation.

Install RStudio (Optional but Recommended)

While R can be run directly through the R console, RStudio provides a more user-friendly interface. To install RStudio:

1. Download RStudio:
   • Visit the RStudio website: https://posit.co/download/rstudio-desktop/.
   • Choose the version appropriate for your operating system (Windows, macOS, or Linux).
2. Install RStudio:
   • For Windows and macOS: Run the installer you downloaded and follow the installation prompts.
   • For Linux: You can download the appropriate package and install it using your package manager, or run commands like:
     bash

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     sudo apt install rstudio

3. Open RStudio:
   • Once installed, open RStudio. It will automatically detect the R installation on your system.

Additional Notes:

• R is free and open-source software, and it is updated frequently. Make sure to check for the latest version from the CRAN website.
• To install packages in R, you can use the following command in the R console:
  R

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  install.packages("package_name")


  Replace "package_name" with the name of the package you want to install.

Now you’re ready to start using R for data analysis!

R in Action: Practical Examples

R’s versatility is demonstrated through its various practical applications:

• Data Import: R can import data from a variety of sources such as CSV files, databases, and web data using functions like read.csv().
• Statistical Analysis: R is equipped with numerous functions for conducting statistical tests, from basic tests like the Shapiro-Wilk test for normality to more advanced analyses such as ANOVA, regression modeling, and hypothesis testing.
• Data Visualization: R is renowned for its data visualization capabilities. With packages like ggplot2, users can create a wide range of plots, from simple bar charts to complex multi-variable visualizations.
• Machine Learning: R’s capabilities extend into machine learning with packages like caret and tidymodels, which provide interfaces to a variety of machine learning methods, including classification, regression, and clustering.

The R Community: A Pillar of Success

R owes much of its success to its vibrant community, which has continuously contributed to its growth. This community is supported by several key resources:

• Mailing Lists: The R-announce, r-devel, and r-help mailing lists allow users to communicate, share ideas, and seek help from the global community.
• Online Resources: There is a wealth of online material available for R learners, including blogs like “R-bloggers,” courses on platforms like Coursera, and YouTube channels such as “The R-Podcast.”
• The R Foundation: Formed in 2003, the R Foundation provides ongoing support for the language’s development, maintaining its core values of openness and accessibility.

Reproducibility and Transparency: R’s Role in Scientific Research

R has become a vital tool for scientific reproducibility. The combination of Rmarkdown and the ability to embed code into documents ensures that research findings are not just shared but can be reproduced and verified by others. This level of transparency is particularly important in fields like bioinformatics, where data analysis pipelines can be complex and difficult to replicate.

Comparing R with Other Programming Languages

While Python is currently the most popular language in data science, R holds a special place in bioinformatics and statistics. R’s specialized functionality for statistical analysis, coupled with its dedicated packages for computational biology, makes it a preferred tool in these fields. While Python offers broader general-purpose capabilities, R is still seen as the go-to language for statistical analysis and bioinformatics.

R also has its advantages over other languages like MATLAB, particularly due to its open-source nature. MATLAB, though efficient, is not free, which makes R an attractive alternative, especially for educational purposes and smaller research labs.

Conclusion

From its humble beginnings as a derivative of the S language, R has evolved into one of the most powerful and widely used programming languages for data analysis, statistics, and bioinformatics. Its vast ecosystem of packages, dedicated community, and commitment to reproducible research make R an essential tool for both beginners and seasoned professionals. Whether you’re working in machine learning, scientific research, or bioinformatics, R offers the tools you need to turn your data into actionable insights.

As R continues to grow and adapt to new technological advancements, its place in the world of data science is secure. With its open-source nature, powerful capabilities, and thriving community, R remains an indispensable resource for data analysts, researchers, and educators worldwide.

FAQ on the R Programming Language for Data Science and Bioinformatics

What is R and why is it popular in data science and bioinformatics?

R is a programming language and environment primarily used for statistical computing, data analysis, and graphical representation. Its popularity stems from its origins in statistics, making it an ideal tool for statistical modeling, data manipulation, and visualization. The R community has created a vast ecosystem of packages catering to fields like machine learning, bioinformatics, and data analysis, making it versatile and powerful for scientific research and data-driven applications. R’s accessibility, combined with its robust statistical capabilities and user-friendly plotting functionalities, makes it a go-to tool for both beginners and experts in these domains. It also emphasizes reproducibility in data analysis, which is crucial in scientific research.

What are the origins of R, and how did it become what it is today?

R’s origins lie in the S language developed at Bell Labs in the 1970s. The S language aimed to provide an interactive environment for statistical analysis, moving beyond batch processing. In the early 1990s, Ross Ihaka and Robert Gentleman, inspired by S and Scheme, created R as a free alternative to commercial implementations of S. R was quickly adopted by the academic community, and it has since grown into one of the top programming languages in the world. Its open-source nature and the subsequent contributions of the community were crucial to its success, along with an active development team that continuously updates the language and adds new features.

What is CRAN, and how does it relate to R?

CRAN, the Comprehensive R Archive Network, is the official repository for R packages. It serves as a central hub for a vast collection of user-contributed extensions that expand R’s capabilities. CRAN hosts thousands of packages covering a wide spectrum of applications from general statistics to specialized tasks like machine learning, web development (through packages like Shiny), and data manipulation (like the tidyverse). CRAN ensures the quality of packages through rigorous testing, providing R users with a trusted source of tools and libraries. The install.packages() function in R is used to install libraries from CRAN, while repositories for specialized fields such as bioinformatics also exist.

What is Bioconductor, and what distinguishes it from CRAN?

Bioconductor is a specialized repository for R packages focused specifically on bioinformatics and biological data analysis. In contrast to CRAN’s broad scope, Bioconductor offers tools for analysis of genomics data, including microarrays, RNA sequencing, and other types of biological assays. It is a crucial resource for computational biologists, offering rigorous standards and guidelines for package development and focusing on interoperability of methods and tools for biological analysis. This makes it the primary R resource for the computational analysis of genomic data.

How does R support data interaction, analysis and visualization?

R provides a cohesive framework for data analysis. For data interaction, R allows for importing data from diverse sources (e.g., CSV, Excel files) using functions like read.csv and read.xlsx. It also offers R specific data formats like RDS and RDA for faster input/output operations. For analysis, R has a vast array of built-in functions for statistical testing (shapiro.test, t.test), correlation analysis (cor and cor.test), and descriptive statistics (summary). Finally, regarding visualization, R includes base plotting functions (plot, boxplot) and allows extensions from packages like ggplot2 and vioplot to generate informative and high quality graphics, often directly integrated with the results of statistical tests. R’s capability to combine these operations in a single environment is a core strength of the language.

What are RMarkdown and why are they important in scientific research?

RMarkdown is a markup language and system for producing dynamic documents that combine narrative text, code, and results in a structured and reproducible way. It is used to create reports, presentations, and other documents where R code is executed and outputs (including plots and tables) are embedded directly into the document. This allows for a more fluid way of reporting results, ensuring that all analysis and code used are clear and documented. RMarkdown greatly supports reproducibility in scientific research, since others can easily verify your research methods by reviewing your code. Tools like RStudio have seamless integration of the technology to help the users to this end.

What are the main Integrated Development Environments (IDEs) and text editors available for R?

R offers a variety of Integrated Development Environments (IDEs) and text editors tailored for its programming language. Popular IDEs include RStudio, a dedicated tool combining an editor, console, and graphical output in one workspace, and Jupyter Notebook, an interactive environment used across different languages. Other notable IDEs include RKWard, a GUI-focused environment, and StatET an Eclipse based plugin. As for text editors for a more streamlined experience, Vi/Vim and Emacs, which are long standing code editors with plugins that offer integration with R, and also more modern editors like Sublime Text and Notepad++ that also allow R code integration through plugins. The choice between these environments depends on user preference, familiarity, and the complexity of their programming projects.

How does R compare to other programming languages like Python for data science, and what role does it play in Machine Learning and AI?

While Python has become increasingly popular, R maintains a strong position in data science, especially in the areas of statistics, bioinformatics, and computational biology. Both Python and R have their own strengths, and often people will use both together. R has a specific advantage in statistical operations and graphical visualization, while Python shines in more general purpose software engineering and database operations. For Machine Learning and AI, R offers a plethora of packages, like caret and tidymodels, for model training, selection, and evaluation. R’s inherent focus on statistical methods makes it well-suited for developing and applying machine learning algorithms. Also, R supports access to more modern Machine Learning libraries like Keras and Scikit-learn, allowing complex neural network models to be developed using the language.

Glossary of Key Terms

• R: A programming language and free software environment for statistical computing and graphics.
• S Language: The precursor language to R, developed at Bell Labs for statistical analysis.
• CRAN (Comprehensive R Archive Network): A repository of R packages and source code.
• Bioconductor: A specialized repository of R packages focused on bioinformatics and biological data analysis.
• R-Forge: A collaborative platform for developing R packages, used for experimenting with new packages before uploading them to CRAN or Bioconductor.
• GitHub: A web-based platform for version control and collaboration, where many R packages are also hosted.
• Tidyverse: A collection of R packages (like ggplot2, dplyr, and tibble) that share a common design philosophy and syntax, mainly used for data manipulation and visualization.
• ggplot2: An R package for creating graphics based on the grammar of graphics.
• dplyr: An R package for data manipulation, providing tools for filtering, selecting, and transforming data.
• RStudio: A popular Integrated Development Environment (IDE) for R.
• Jupyter Notebook: A web-based interactive computing environment that can execute code from various languages like Python and R.
• Rmarkdown: A markup language for R that integrates text, code, and outputs for creating dynamic reports and documents.
• Shiny: An R package for building interactive web applications with R.
• IDE (Integrated Development Environment): A software application that provides comprehensive facilities to computer programmers for software development.
• Package/Library: A collection of functions and datasets that extend the functionality of the core R language.
• Machine Learning: A type of artificial intelligence that allows computer systems to learn from data without explicit programming.
• Artificial Intelligence (AI): A broad field of computer science focused on creating systems that can perform tasks that typically require human intelligence.

R Programming Language Study Guide

Quiz

1. What is the relationship between the S language and the R language? R is based on the S language, and many of R’s core functions originated from the earlier S language, with R being considered an implementation of S. Many functions in R and S can be interchanged, and R still cites “The New S Language” as the reference for its base functions.
2. Who are the main developers credited with the creation of R? Ross Ihaka and Robert Gentleman are the main developers of R, having created it in the early 1990s while at the University of Auckland, inspired by S and the functional language Scheme. Their initials also give the R language its name.
3. What is CRAN and what role does it play in the R ecosystem? CRAN (Comprehensive R Archive Network) is the official software repository for R packages. It plays a vital role by hosting a vast number of R packages, ensuring quality, and providing a system for sharing and managing R code.
4. What are the main differences between CRAN and Bioconductor? CRAN is a general-purpose repository for all types of R packages, while Bioconductor is a specialized repository focused on bioinformatics and biological data analysis tools and packages. Bioconductor also follows stricter rules than CRAN.
5. What is the tidyverse? The tidyverse is a collection of R packages, like ggplot2 and dplyr, designed to make data manipulation and visualization more intuitive and consistent. It introduced the concept of piping and is maintained and promoted by the RStudio company.
6. What is Rmarkdown and how does it support reproducibility in research? Rmarkdown is a markup language for R that combines narrative text, R code, and output into a single document. It helps support reproducibility by creating dynamic documents where code and results are directly embedded, ensuring consistent and verifiable analysis.
7. Name two popular IDEs (Integrated Development Environments) for R. RStudio is a very popular, R-specific open-source IDE, and Jupyter Notebook is also used extensively in data science, supporting multiple programming languages like R and Python.
8. What is the Shiny framework and how can it be useful for researchers? Shiny is an R framework that allows the creation of interactive web applications directly from R code. It is useful for researchers to easily deploy their analysis tools for broader use, even for those without programming expertise.
9. What are the three main ways the article suggests R code can be used? R is designed for data interaction, data analysis, and results visualization. It allows users to import and work with data, conduct statistical tests, and plot results in several ways.
10. Name one of the primary books for learning R cited in the article. “R for Data Science” by Hadley Wickham is a popular book for learning R, focusing on the tidyverse and practical data science applications. “The R Book” by Michael J. Crawley is also a good option for learning R.

Answer Key

1. R is based on the S language, and many of R’s core functions originated from the earlier S language, with R being considered an implementation of S. Many functions in R and S can be interchanged, and R still cites “The New S Language” as the reference for its base functions.
2. Ross Ihaka and Robert Gentleman are the main developers of R, having created it in the early 1990s while at the University of Auckland, inspired by S and the functional language Scheme. Their initials also give the R language its name.
3. CRAN (Comprehensive R Archive Network) is the official software repository for R packages. It plays a vital role by hosting a vast number of R packages, ensuring quality, and providing a system for sharing and managing R code.
4. CRAN is a general-purpose repository for all types of R packages, while Bioconductor is a specialized repository focused on bioinformatics and biological data analysis tools and packages. Bioconductor also follows stricter rules than CRAN.
5. The tidyverse is a collection of R packages, like ggplot2 and dplyr, designed to make data manipulation and visualization more intuitive and consistent. It introduced the concept of piping and is maintained and promoted by the RStudio company.
6. Rmarkdown is a markup language for R that combines narrative text, R code, and output into a single document. It helps support reproducibility by creating dynamic documents where code and results are directly embedded, ensuring consistent and verifiable analysis.
7. RStudio is a very popular, R-specific open-source IDE, and Jupyter Notebook is also used extensively in data science, supporting multiple programming languages like R and Python.
8. Shiny is an R framework that allows the creation of interactive web applications directly from R code. It is useful for researchers to easily deploy their analysis tools for broader use, even for those without programming expertise.
9. R is designed for data interaction, data analysis, and results visualization. It allows users to import and work with data, conduct statistical tests, and plot results in several ways.
10. “R for Data Science” by Hadley Wickham is a popular book for learning R, focusing on the tidyverse and practical data science applications. “The R Book” by Michael J. Crawley is also a good option for learning R.

Essay Questions

1. Discuss the evolution of R, highlighting key milestones and the role of the R community in its development. How has the language adapted to the changing needs of its users over the past 30 years?
2. Compare and contrast the different R package repositories (CRAN, Bioconductor, R-Forge, and GitHub). How do their focus and governance models influence the types of packages they host and their quality?
3. Explain how the concepts of data interaction, analysis, and visualization are integrated in R. Provide examples of core R functions and packages that demonstrate these principles, and discuss how this integration is useful in scientific research.
4. Evaluate the role of Rmarkdown in promoting reproducible research practices. How does it facilitate the integration of code, narrative, and results, and what are its advantages over traditional approaches to scientific reporting?
5. Discuss the role of R in machine learning and artificial intelligence, including how its development intersects with the growth of these fields. How can R be effectively used for training and implementing machine learning models, and what are some of the packages or tools that are most useful for AI applications?