Why R has been so popular among data scientists throughout the world? What are the advantages of R? How I can learn R programming languages? Having all these questions in mind! Well, let’s dive more into Data Science and R programming language…
In the last few years, this world has become a digital space dealing with zetta or yottabytes of data daily. Every now and then, it has become common to hear or read “explosion of data”, “data is the new gold”, “huge data” and so on. Most of you reading this article knows how data and data science has become important for everyone. From big organizations to the students; from LinkedIn to Tinder; from the banking sector to the healthcare, data science is now being used everywhere. To deal with the big data, often these big organizations or other companie hire developers knowing the programming language which best fit for data science. Here, “R programming language” comes in the picture. Now, most of you’ll think “Why R?”, as already there are a lot of programming language present!
R is one of the programming languages which has gained immense popularity among the data scientists. The biggest tech giants like Google, Twitter, Microsoft, New York Times and others are already using R for analyzing the data and predicting the outcomes.
Understanding R Programming Language…
Among other programming languages, it is one of the most popular one which helps with statistical computing, data analytics and scientific research. This language is widely used by data analysts, researchers, statisticians and marketers for data analysis by retrieving, cleaning, analyzing and visualizing the data.
Coming back to its history, it was created and developed in 1993 by Ross Ihaka and Robert Gentleman. Going by the first letters of their names, the name ‘R’ was chosen for this programming language. In 1995, its initial version was first released for everyone so that they can perform complex data statistical computations and its analysis with visual graphical representations.
R is free, cross-platform and consists of a powerful set of tools for data analysis. Some of these are linear and nonlinear modeling, time-series analysis, classical statistical tests, classification, clustering and other graphical techniques. It can be used for producing well-designed plots including mathematical formulas and symbols. Moreover, R also includes machine learning algorithms. Because of this, it has become one of the best programming language for statistics, data science and machine learning. R has an expressive syntax and easy-to-use interface and, also lets you create objects, functions and packages.
Steps for data analysis using R
In R, data analysis is done in a series of steps which includes programming, transforming, discovering, modeling and communicating the results.
Any programmer first programs then transform their data using various libraries which are designed specifically for data science. After this, data is investigated and refined for the hypothesis and analysis then you can choose the right model for your data using a wide array of tools provided by R. Finally, the results can be communicated through the report with R markdown or apps having all the codes graphs and outputs.
Why R Programming Language Is Important For Data Science?
When it comes to data science and machine learning, R is the choice of a programming language among all the data scientists, researchers, and programmers. It comes with a myriad of features designed specifically for data science and machine learning algorithms which are enough to back this belief.
Read More: R features for data science applications
Among many, some of the most talked benefits of R programming language are:
1. Open-Source & Cross-Platform
The fact that R is free for everyone and is a cross-platform (meaning it can run on Linux, Windows and Mac) makes it ideal for developers, data geeks and students. Moreover, you can have free access to most of its libraries; however, some commercial libraries are created for enterprises dealing with terabytes of data.
2. Comprehensive
It offers over 4000 bundles for Bioinformatics, Econometrics, Data Mining and Spatial Investigation from different stores. R also includes wide range of graphical strategies, characterization, measurable demonstrating, factual tests, bunching, information control, etc.
3. Statistical Analysis Kit
It is a programming language which includes ultimate statistical analysis kit having all the standard tools for data analysis. This helps in accessing data in varied formats for data manipulation operations like merges, transformations and aggregations. Some of the most widely used tools are ANOVA, Regression, GLM and Tree which makes it easy to extract and merge information.
4. Data Wrangling
Thanks to its various packages which makes data wrangling very easy. It simplifies the process of data preparation and its analysis. R can load various types of files like .csv, .txt, SAS or any other with just one line of code. Further, the process of data cleaning and transforming is simple too. Ultimately, these features can significantly reduce your time for data preparation.
5. Advanced Visualization
Its basic features can also let you create scatter plots, line plots and histograms. These come handy for visualizing your data instantly to get insights which are not possible from just the tabulated data. And, if you are ready to spend some more time for understanding the advanced visualization packages like ggplot2 or any other than it’ll greatly help you to create some impressive and professional looking graphs. You can also be intrigued by some of its features like adding maps or animation to your visualizations making it more appealing.
Read More: Master Data Visualization using R Programming Language with ggplot2
6. Charting
As we have already talked about how easy data visualization is in R programming language but now, to aid visualization it also has some great tools for creating graphs, bar charts, multi-panel lattice charts and even custom designed graphics.
7. Reproducible Research
Just imagine, you’ve uploaded the data then arranged it, inspected it and deleted some of it with the missing or incorrect value. Further, after running the model you’ve observed strange or incorrect results. Now, in a normal scenario, one has to start the analysis from the beginning in order to find the error but R allows you to create scripts including all the steps from loading the data to the finished prepared graphs and tables. This helps in trying different ideas, correcting any issues and updating the analysis just by changing a few lines of code.
8. Consistent Online Support
Its quick and consistent online support makes R easy for everyone. Thanks to its loyal user base, all the statisticians, scientists and engineers throughout the world can easily use it without having the proper computer programming language.
Best Resources To Learn R
1. Eduonix (E-Learning Platform)
It is an outstanding E-learning platform which has recently gained popularity. Eduonix includes some of the best tutorials on R created by professionals from different parts of the world. Additionally, it also includes highly rated courses on data science, machine learning, programming languages, AWS and several more categories.
- R Programming for Absolute Beginners
- Free
- Rating: 4.4
- R Programming for Beginners
- Rating: 4.7
- 30 Days Money-Back Guarantee
- Certificate of Completion
- Introduction To Data Science Using R Programming
- Rating: 4.5
- 30 Days Money-Back Guarantee
- Certificate of Completion
2. Udemy (E-Learning Platform)
It is another very popular platform for online learning. It includes courses over a multitude of topics created by professionals all over the world. Web Development, Game development, mobile app development, database, programming languages, WordPress, DevOps, blockchain, game design, animation are some of the few categories of Udemy.
It provides hundreds of courses on R but the two best ones are:
- R Programming A-Z For Data Science With Real Exercises
- Rating: 4.6
- Students Enrolled: +80K
- R Programming: Advanced Analytics In R For Data Science
- Rating: 4.7
- Students Enrolled: +25K
3. Books
- R for Data Science
This book is one of the greatest books for beginners who wants to learn data science with R. It was written by Hadley Wickham and Garrett Grolemund who have included different topics like R programming, RStudio (free and open source IDE) and the tidyverse which is generally a suite of R packages. It is available online but you can also buy its hard copy.
- R Cookbook
It is written by Paul Teetor and offers various techniques for quick and efficient data analysis in R programming language. It covers various topics including statistics, time series analysis, probability, data pre-processing and others. This book focuses more on practical aspects than to theoretical explanation by providing over 200 practical recipes along with tips and tricks.
- The Art of R Programming
The Art of R Programming teaches you from the very basic topics to the advanced one such as closures, recursion and anonymous functions. This book will teach you about functional & object-oriented programming, rearranging complex data into a simpler form, interface R with C/C++ and Python, visualization, running mathematical simulations, and much more.
4. Blog
Today’s internet is filled with blogs. Literally, there are millions of bloggers and their websites present today across different niches. Some of these are useful and others are either copy-pasted or rewritten. This is also true for the R programming language as you can find hundreds of blogs. Some of the websites which I found most valuable for R programming language are:
Wrapping Up!
So there you go, this was R, a programming language which gained its popularity among the data scientists throughout the world. It has become an indispensable tool which has the capability of doing anything when it comes to data or data science. Thanks to its features, now R is being used not only in data science but for statistical computations and machine learning too. Already big names like Facebook, Twitter, Google, Microsoft, Uber, Airbnb, IBM, Accenture and other companies are using it for different purposes like behavior analysis, advertisement effectiveness, statistical analysis and so on.
