Why R is used in data analysis?

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.

The R environment

R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes

• an effective data handling and storage facility,
• a suite of operators for calculations on arrays, in particular matrices,
• a large, coherent, integrated collection of intermediate tools for data analysis,
• graphical facilities for data analysis and display either on-screen or on hardcopy, and
• a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.

The term “environment” is intended to characterize it as a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, as is frequently the case with other data analysis software.

R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.

Many users think of R as a statistics system. We prefer to think of it as an environment within which statistical techniques are implemented. R can be extended (easily) via packages. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very wide range of modern statistics.

R has its own LaTeX-like documentation format, which is used to supply comprehensive documentation, both on-line in a number of formats and in hardcopy.

Those interested in data science may be interested in learning the R programming language. R for data science can be used for statistical analysis and other functions. There are a number of ways to embark on your path to learn R. Keep reading to learn more about R in data science, R vs. Python, real-world applications of R, the best add-on packages for R and more.

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The R Foundation, a nonprofit focused on supporting the continued development of R through the R Project, describes R as “a language and environment for statistical computing and graphics.” But, if you’re familiar with R for data science, you probably know it’s a lot more than that. 

R was created in the 1990s by Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand. The R language was modeled based on the S language developed at Bell Laboratories by John Chambers and other employees. Today, R is an open-source language; it’s accessible as a free software compatible with many systems and platforms. 

Here are some important things to know about R in data science:

• R is an open-source software. R is free and adaptable because it’s an open-source software. R’s open interfaces allow it to integrate with other applications and systems. Open-source softwares have a high standard of quality, since multiple people use and iterate on them. 
• R is a programming language. As a programming language, R provides objects, operators and functions that allow users to explore, model and visualize data.
• R is used for data analysis. R in data science is used to handle, store and analyze data. It can be used for data analysis and statistical modeling.
• R is an environment for statistical analysis. R has various statistical and graphical capabilities. The R Foundation notes that it can be used for classification, clustering, statistical tests and linear and nonlinear modeling. 
• R is a community. R Project contributors include individuals who have suggested improvements, noted bugs and created add-on packages. While there are more than 20 official contributors, the R community extends to those using the open-source software on their own. 

Python and R are both open-source software languages that have been around for a while. When comparing R vs. Python, some feel that Python is a more general programming language. Python is often taught in introductory programming courses and is the primary language for multiple machine learning workflows, RStudio reports. R is typically used in statistical computing. RStudio notes that R is often taught in statistics and data science courses. It adds that many machine learning interfaces are written in Python, while many statistical methods are written in R. 

In terms of R vs. Python environments, the R environment is ideal for data manipulation and graphing. Some Python applications include web development, numeric computing and software development. Additionally, while R has numerous packages, Python has many libraries devoted to data science. 

Whether or not R vs. Python is better may come down to what you’re using each for. Being knowledgeable in both languages can be beneficial in data science. In fact, RStudio notes that many data science teams are “bilingual,” using both R and Python. 

R for data science focuses on the language’s statistical and graphical uses. When you learn R for data science, you’ll learn how to use the language to perform statistical analyses and develop data visualizations. R’s statistical functions also make it easy to clean, import and analyze data.

It may be equipped with an Integrated Development Environment (IDE). According to computer software company GitHub, the purpose of an IDE is to make writing and working with software packages easier. RStudio is an IDE for R that improves the accessibility of graphics and includes a syntax-highlighting editor that helps with code execution. This may be helpful as you begin to learn R for data science.

R for data science is used in industries such as banking, telecommunications and media. Below we explore examples of data visualization in R through real-life projects.

There are many packages you may consider installing to help use R. Below are some R packages for data science, based on the list of recommended packages from RStudio.

• DBI helps basic communication between R and database management systems. 
• RMySQL, RSQLite and other database drivers assist with loading and reading data from a database.
• stringr includes user-friendly tools that work with character strings and regular expressions.
• dplyr offers functions for summarizing, connecting and rearranging datasets. 
• lubridate facilitates working with dates and times across various periods. 
• ggplot2 is well known for making it easy to produce visually appealing plots and graphics.
• rgl enables three-dimensional, interactive visualizations with R in which you can rotate and zoom in on parts of a visualization. 
• randomForest is a machine learning package that can also be used in unsupervised learning.
• caret is helpful for training classification and regression models. 
• shiny is an R package for data science that helps you create web apps.
• xtable provides HTML or latex code when you need to paste your R project into the final document.
• ggmap is one of multiple R packages for data science that helps with spatial data; it lets you download map areas from Google Maps and integrate them into ggplots.
• xts includes tools for working with time series datasets.
• XML assists in working with XML documents.
• httr assists in working with http connections.
• devtools helps you create your own R package.

Want to learn about more R packages for data science? Browse the complete list of recommended packages from RStudio.

• Data Science Bootcamp Guide: Use this guide if you aspire to become a data scientist or are looking to learn programming languages like Python or R for data science.
• Data Analytics Bootcamp Guide: Learn more about data analytics bootcamps if you’re interested in helping companies manage and gain insights from data.
• Coding Bootcamp Guide: Look into coding bootcamps if you want to gain web development skills and coding language knowledge.
• FinTech Bootcamp Guide: Discover bootcamps that focus on financial technology, blockchain and cryptocurrencies. 

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Below are some online R courses to consider. These courses focus on fundamental R concepts to help you learn the basics of this programming language. 

• Learn R from Codecademy: This course begins by teaching the fundamentals of R. It consists of 10 lessons covering topics such as data frames, data cleaning, aggregates, variance and standard deviation. Codecademy’s course may take about 20 hours to complete. There are no prerequisites.
• R Programming Fundamentals from Pluralsight: This online R course may help teach you about R variables, data structures, functions, packages and more. It also includes demonstrations and opportunities for hands-on practice. This course may take about seven hours to complete.
• Data Analysis with R from Udacity: This course begins by discussing exploratory data analysis (EDA). Lessons build upon EDA knowledge and focus on R basics, quantifying and visualizing variables, and predictive modeling. The self-paced course may take approximately two months to complete.
• Introduction to R and Visualization from Data Society: This online R course from Data Society teaches you about data science and how it’s used in companies, how to use R, and how to create visualizations with R. It includes two hours and 40 minutes of instruction and around 25 hours of practice.

Happy coding!

Last updated: November 2020