R is the most popular language for data analysis, modeling and visualization. It is great for linear and non-linear models, parametric and non-parametric test, clustering and time-series models. It provides advanced graphics functionality for presentation and analysis.

And how was it made?

R was created in 1991 by New Zealand-based Ross Ihaka (who is currently working on a new programming language that contains faster and more data) and Robert Gentleman. R is based on S, which was created by John Chambers and others at Bell Labs in 1976. S was originally created as a series in the Fortran Library to assist with internal statistical requirements but was rewritten in C in 1988. This basically makes R 40. – Year old technology. It was released to the public in 1993 and soon became the standard language for visualization and statistical analysis. R is free and open source, such as Python. It follows the terms of the Free Software Foundation under the GNU License Agreement. This means you have full access to source code to study how programs work, adapt to your own needs, and share your improvements with other people on sites like GitHub.

R is named after the first alphabetical name of its creators – Ross and Robert.

This has led to the formation of an active R developer community, consisting of a core group of 20 developers who, since 1997, have been publishing updates on R every year and responding to packages submitted to the group. Packages are programs stored in script files that can run automatically without any additional coding. There are currently over 4000 packages available on the R website, although some of them may be for specialized use and may only benefit a specific community, such as people who sequence genomes. Whatever you need, chances are there is a package for it. R Website packages go through a quality control process by the core group, but you can always write your own package for personal use and share it online for others to use.

Default R language download is very basic.

Why is it so important today?

R has applications in healthcare, pharmaceuticals, biochemistry, genetics, molecular biology, computer science, AI and social sciences. It has packages for almost everything an analyst needs. Healthcare and pharmaceutical professionals can use R to model the effectiveness of drug treatments, biologists can compare and contrast genomes and therapies, social scientists can use it to model behavior, computer scientists can use it to create better predictive algorithms, and business researchers can use it. Can use. To find patterns in model marketing, finance, HR and operations data and to help businesses. R is specialized in this use because it offers packages that allow users to clean data and run models or tests on the data input. The models provide outputs that show how well they fit with the data model and also graphs and charts useful for understanding the data. Such special functionality makes R an ideal language for testing statistical concepts and goes hand in hand with learning statistics.

Not surprisingly, R has had huge success in data science, even surpassing Python in utilities. Of course, Python, which is pretty, or more recently Julia, that lets you work with algorithms in a way that other languages ​​can’t, has been around for a long time. At the time of publication R was the only language that was written exclusively for statistical analysis, the implementation of S gave it an edge over other languages ​​and tools that later entered the market. Since it was the first statistical tool of the scene, it gained a trusted user base who felt comfortable with it and wrote packages to constantly improve it. It has been around for over 10 years, and has amassed a huge library ready for statistical programming.

But that’s just part of the story. This means that R is constantly updated, retaining the latest statistical techniques and, in addition to the basic statistical models, a program for machine learning techniques – pattern recognition, neural network algorithms, clustering, random forest and deep learning.

And the studio It has a good look and feel.

What makes it better?

In a word – customization. There are other languages ​​and applications that analyze data, but the packages do not offer the flexibility or range that R does. Applications like MATLAB, S-PLUS, Stata, SPSS and SAS hide their command prompts behind the fancy GUI. These products are owned and developed by corporations such as IBM that design and copyright the source code behind their GUI. This obviously simplifies most data modeling because the learning curve is faster. The user just has to learn which button to click on to get the desired result and how to interpret the result. However, this only works as long as the user does not need any additional customization that requires changing the source code, which is impossible to access because it is proprietary software. R has a huge advantage in this case as it is free source and open source code.

Languages ​​like Python, Java, and Julia can also model and visualize data, but they didn’t exist as long as R had them, and so R lacks the huge library of statistical and graphics packages it offers. For example, Python does not yet have the ability to run multiple regression scripts. It will take a few years to catch up with the R of the library.

ggplot2 is one of R’s most popular graphics packages.

How else do you get it?

The Base R programming language is available as a free download from their website. R can be installed on Windows, Mac, Unix, Linux systems and even PlayStation 3 (although this is only for serious geeks who don’t mind missing out on gaming time). After downloading R you can also choose to download R Studio, which has a better interface with multiple screens for working. You can write scripts in a separate text box, a list of defined variables, check past commands, view graphs and help files, and view codes at the same time from the command prompt. To download R, go to their website, click ‘Download R’> click on a mirror link to download from the nearest server where you live. For India, it will be located at IIT Chennai (still known as Madras on R site).

And to download Studio, go to the download page and click on the latest installer for your system. Note that you need to download R in advance. Known as the Base System, the R download contains the R programming language, a compiler, debugger, useful datasets, and many simple data analytics and graphics packages that you will need. You can download additional packages directly from the command prompt in R using the install.packages and library commands.
> install.packages (“package name”)
> Library (package name)

Resources for learning

A huge advantage of using R is the built-in help function. For example, to learn more about boxplot, open R and type “? Boxplot” on the command console. You will find details about Boxplot usage and arguments for passing with examples and references. A great way to learn R is a short interactive learning resource called Swirl that lets you learn about basic R operations through the R console.
To install Vortex, open R> type install.packages (“swirl”)> library (“swirl”)> swirl ()

A long way to learn R applied in data science is to work through nine courses of data science specialization offered by Johns Hopkins University on the course.
Go to the course website> click on Specialization> Click on Data Science
If you are looking for a more basic course, take a look at Duke University’s Data Analysis and Statistical Estimation course, or the Foundation of Data Analysis course at the University of Texas at EdX.

Here are some of the best books to use for learning:
Victor A. Bloomfield. Using R for numerical analysis in science and engineering. Chapman & Hall / CRC, 2014. ISBN 978-1439884485
Sarah Stowell. Using R for statistics. Express, 2014. ISBN 978-1484201404.
Steven Murray. Learn more in one day. SJ Murray, 2013.
Robert J. Nell. Introductory R: A Beginner’s Guide to Data Visualization and Analysis using R. March 2013. ISBN 978-0-9575971-0-5.
Jesse Nash. Nonlinear parameter optimization using R tool. Wiley, 2014. ISBN 9781118883969.

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