The Rise of R Programming Language and Its Usefulness in Data Science

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R Programming Language

If you have ever attended any conference on data science or statistics, you will see hundreds of people wearing the badges of ‘I love R’. These people are the ones who love R more than anything. ‘R’, the eighteenth letter of the alphabet has managed to become one of the favorite letters of statisticians and data scientists throughout the world. It has become a perfect language for the people who show interest in statistics or data analysis.

Thanks to the robust environment of R programming language which helps in analyzing and visualizing the large chunks of data without any difficulties. Moreover, this programming language is backed by a community with over a million users who are always ready to make statistical computing more efficient and effective for everyone. Currently, it has gained immense popularity and is the best choice for people who want to make a career in the data science field. Already big names like Google, Facebook, Wipro and others are using R.

If you lack prior knowledge of R but want to pursue it, you can try out the “R programming guide for beginners” online tutorial for FREE!

So what makes R so popular?

Before, diving into its feature lets first know about its history and R itself…

R Programming Language: Over the Years

It was the year 1992 when Robert Gentleman, a professor at the University of Waterloo in Canada traveled to Auckland in New Zealand to lecture for 3 months. There he met Ross Ihaka, also a professor at the university. After a while, both had questions about programming languages that they wanted to answer and both knew common programming languages called ‘Scheme’ and ‘S’. As Scheme had its own benefits but lacked the desired functionality while S had the syntax they wanted.

As there were no blends of these 2 languages available, they decided to create a new language. Meanwhile, the University of Auckland was also looking for a programming language that can be used in the statistics courses for undergraduates. But the University has one condition that the program should run on Macintosh. After the work of these two professors on the new language, the Department of Statistics from the university realized that the new programming language was better than their current language. Later, the professors named it R as a reference to their names and a nod to S.

Despite R being as a free programming language, in the 1990s, both the professors were thinking of making R as a commercial product. But after the advice of Dr. Martin Machler, they agreed with the idea of making R as free software which everyone can access it regardless of their income.

In 1995, the duo made the code for R available under a free software license. After which, Machler joined the professors as a primary developer. Together they fixed all the bugs, issues and implemented the improvements which users drew from the source code. Initially, the majority of implementations were related to fixing the crashes and ensuring correct calculation but later, users started adding new functionality which made R faster, simple, easier to use and of course, it could handle more data.

Although the 1st version was released in 1994, the 1st stable beta version was made available in February 2000. As of now, R is an open-source, cross-platform programming language which is one of the most suited packages for data analysis.

You can also read A Quick Guide To Cloud Computing Utilizing R Programming which lists the advantages of using R in the context of Cloud Computing.

What is the R Programming Language?

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Now, the majority of statisticians and data scientists use R for data mining and statistics mainly because of its ability to work with big data. It is packed with numerous built-in functions and variables which make analysis way easier. Furthermore, it is highly extensible, includes many packages for specific analysis tasks and provides graphics-generation tools for producing high-quality data visualizations. It can work on any platform and runs on Unix, Linux, Mac OS, and Windows.

What makes R different from other programming languages?

Among many, few of the top benefits which separate R from other programming languages are as follows:
• It is an open-source programming language i.e. free for everyone to use.
• Cross-platform i.e. Codes for R can be used on Unix, Linux, Windows, and Mac OS.
• R is very powerful which is used heavily for statistical computing.
• R has accessible and clear programming tools.
• Can investigate, refine and analyze data more effectively and efficiently.
• It comes with a huge catalog of statistical and graphical methods.
• Made from a collection of numerous libraries designed especially for data science and statistics applications.
• R is used for machine learning, classification analysis, and drawing graphs like a histogram, line plot, box plot, density curve, and others.
• Includes different machine learning algorithms, time series, statistical inference, linear regression, and others.
These are all the pros, what about the disadvantages?

Major Issues with R Programming Language

Similar to any other things ever made by humankind, this programming language also had its own limitations. The three major limitations of R programming language are its inconsistency, scalability, and documentation.
• Inconsistency
Any algorithm which is implemented has its own parameters and naming conventions. For some, this can be frustrating as it may require reading and understanding the documentation of each package that is being used.
• Documentation
Despite the availability of hundreds of documentation, initially, these rarely help as generally, they are direct and abrupt or concise. This drives the programmers to the internet for complete working examples.
• Scalability
It was initially intended for use on data that fits into 1 machine. R is not suitable for working with data present across multiple machines.

‘R’ Making ‘Data Science’ Easy

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The world has entered the era of big data where data is playing a major role in helping industries to transform as a whole. Healthcare, financial sector, e-commerce, and every major industry is heavily dependent on data for making accurate predictions. With the tremendous increase in data, the need for its storage and analysis has also grown.

Hadoop and other frameworks have somehow managed to solve the storage problem, the focus in for data has now shifted towards the data processing and analyzing. Here, Data Science comes into the picture. It is a field which has become a necessity and several experts are claiming this field as our future.

Data Science is a field wherein, numerous algorithms, tools, and machine learning are applied with the aim of discovering hidden patterns in raw data. Generally, it is a multidisciplinary field where you can find a blend of data inference, algorithm development, and technology for solving complex analytical problems. Among others, one such tool which makes data science easy is the R programming language.

Now the question comes, what makes R the option choice for Data Science?

Why you should choose R for Data Science?

As already said, we are witnessing the era of big data, wherein, the two big challenges have emerged. One, how to store data and second, how to process the very same data. The data storage dilemma has already been solved, now for processing data; R comes in a picture. Below are the main reasons which give R a clear edge over other programming languages for Data Science.
1. R is Open-source
Being an open-source programming language already gives you an added advantage over any other competitors. Since it is free with no subscription cost limits, R is very cost effective with developments taking place at a rapid scale. Furthermore, the majority of its libraries are free but some libraries are designed for commercial use by enterprises dealing with terabytes of data.
2. Popular Among Researchers and Scholars
R is very popular in academia mainly because of its heavy use by thousands of statisticians, researchers, and scholars of data science. Because of this, a huge number of people having knowledge of R are connected to each other. Today, some of the famous books, manuals, and guides on data science are using R for statistical analysis of data.
3. Ultimate Statistical Analysis Kit
It is packed with all the standard and special tools for data analysis. You can find all the conventional and modern tools like Regression, ANOVA, Tree, GLM, and others for making data extraction a lot easy. These are designed to access data in different formats. Tools help in performing data manipulation like merges, transformation, and aggregations.
4. Data Wrangling
Data Wrangling is a term used for cleaning the complex sets of data for the analysis. It is usually one of the most time-consuming and essential processes in the field of data science. For this, R comes with an extensive library of tools or packages for database manipulation or wrangling. Among others, some of the notable packages for data wrangling include the dplyr package, data.table package and readr package.
5. Data Visualization and Analysis
A process of representing data in the graphical form is called data visualization. It helps data scientists to analyze data in different angles which initially was not possible in unorganized or tabulated data. R comes with many tools for performing data visualization, analyzation, and representation efficiently and effectively. Some of the most used packages are ggplot 2 and ggedit.
6. R and Machine Learning

Machine Learning Algorithms

Machine Learning is a subset of Artificial Intelligence, one of the most in-demand technologies of the current time. Machine Learning helps computer systems to perform a specific task without any instructions. It consists of various algorithms that help machines to learn and make accurate predictions by analyzing the data through instruction and long-term experience.
R comes with some useful tools for the developers for training and evaluating machine learning algorithms. Some of the best machine learning packages are MICE, CARET, randomFOREST, rpart & PARTY and others.
7. R Community
Thanks to its community which made R one of the most popular and sophisticated programming language for data science and statistics. R boasts one of the quickest, vibrant, robust and consistent online community.

R or Python for Data Science?

When it comes to data science, the two languages which always pops every data scientist’s mind is R and Python. Another most commonly asked and hotly debated question by people is that which one is better for data science, R or Python?

Actually, both of them are important and both of them come with their own sets of advantages and disadvantages. However, as per me, in regard to data science, R has a clear edge.

To go any further, we should know that both of the programming languages were developed in the 1990s, are free and open-source. Both are crucial for machine learning and large data as well. As learning, both languages will definitely give you a bonus point over anyone else but before choosing any one of these languages, you must know that Python is a general-purpose language having readable syntax while R is built by statisticians for their specific needs.

In comparison, R is one of the pioneers in statistics with more than 6000 packages available for the public in order to perform advanced exploratory analytics. You can integrate R with Java and Hadoop distributed framework. R includes extensive packages for data visualizations and statistical modeling and is easier for non-programmers and mathematicians. Packages like dplyr and ggplot 2 will help in data manipulation and visualization with just a few lines of code.

Contrary to R, Python is a general-purpose programming language which object-oriented programmers find it very easy. It includes packages like Numpy, Scipy, Seaborn, and Pandas for data analytics. Python can be used for data scraping from the web and cleaning of unstructured data. Opposite to R, it is very good in memory management and has extensive machine learning packages like scikit-learn. With Python, you can also reuse your code, develop web applications and helps you to use the results or analysis in a website.
Now, to choose from R or Python totally depends upon you and your needs. So whether to learn R or Python for data science and statistics is totally on you.

Bottom’s Line

Data Vizualization

Thus, this was all about the R, a programming language that was created by the professors solely for statistics and data science. From the number of packages specially designed for data analysis to the vast community behind this programming language, R has managed to outperform other programming languages at least in the field of data science. However, just like any other thing, it also has its own sets of limitations but the benefits which it is providing to the data scientists successfully overshadows the downsides. Lastly, while comparing with Python, the choice can vary depending upon a person to person depending upon the requirements.

Going after all its features and the demand for data science, one thing which is clear is the fact that R programming language will constantly increase in the upcoming years. So, if you are intrigued by R programming and its uses in Data Sciences, you can try the “Introduction To Data Science Using R Programming” online course which offers 7 hours of video and covers 7 sections.


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