Learn R Programming for Data Science
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research. It is one of the most popular languages used by statisticians, data analysts, researchers and marketers to retrieve, clean, analyze, visualize and present data.
Due to its expressive syntax and easy-to-use interface, it has grown in popularity in recent years.
Things to Know Before Start Learning R
Why use R for statistical computing and graphics?
-
R is open source and free!
R is free to download as it is licensed under the terms of GNU General Public license.You can look at the source to see what’s happening under the hood. There’s more, most R packages are available under the same license so you can use them, even in commercial applications without having to call your lawyer.
-
R is popular – and increasing in popularity
IEEE publishes a list of the most popular programming languages each year. R was ranked 5th in 2016, up from 6th in 2015. It is a big deal for a domain-specific language like R to be more popular than a general purpose language like C#.This not only shows the increasing interest in R as a programming language, but also of the fields like Data Science and Machine Learning where R is commonly used.
-
R runs on all platforms
You can find distributions of R for all popular platforms – Windows, Linux and Mac.R code that you write on one platform can easily be ported to another without any issues. Cross-platform interoperability is an important feature to have in today’s computing world – even Microsoft is making its coveted .NET platform available on all platforms after realizing the benefits of technology that runs on all systems.
-
Learning R will increase your chances of getting a job
According to the Data Science Salary Survey conducted by O’Reilly Media in 2014, data scientists are paid a median of $98,000 worldwide. The figure is higher in the US – around $144,000.Of course, knowing how to write R programs won’t get you a job straight away, a data scientist has to juggle a lot of tools to do their work. Even if you are applying for a software developer position, R programming experience can make you stand out from the crowd.
-
R is being used by the biggest tech giants
Adoption by tech giants is always a sign of a programming language’s potential. Today’s companies don’t make their decisions on a whim. Every major decision has to be backed by concrete analysis of data.
Also Read:
Why you should learn R for data science today ?
Companies Using R
R is the right mix of simplicity and power, and companies all over the world use it to make calculated decisions. Here are a few ways industry stalwarts are using R and contributing to the R ecosystem.
Company Application/Contribution Twitter Monitor user experience Ford Analyse social media to support design decisions for their cars New York Times Infographics, data journalism Microsoft Released Microsoft R Open, an enhanced R distribution and Microsoft R server after acquiring Revolution Analytics in 2015 Human Rights Data Analysis Group Measure the impact of war Google Created the R style guide for the R user community inside Google
Also Read:
List of Big Companies using R
Applications of R Programming in Real World
- Data Science
Harvard Business Review named data scientist the “sexiest job of the 21st century”. Glassdoor named it the “best job of the year” for 2016. With the advent of IoT devices creating terabytes and terabytes of data that can be used to make better decisions, data science is a field that has no other way to go but up.Simply explained, a data scientist is a statistician with an extra asset: computer programming skills. Programming languages like R give a data scientist superpowers that allow them to collect data in realtime, perform statistical and predictive analysis, create visualizations and communicate actionable results to stakeholders.Most courses on data science include R in their curriculum because it is the data scientist’s favourite tool. - Statistical computing
R is the most popular programming language among statisticians. In fact, it was initially built by statisticians for statisticians. It has a rich package repository with more than 9100 packages with every statistical function you can imagine.R’s expressive syntax allows researchers – even those from non computer science backgrounds to quickly import, clean and analyze data from various data sources.
R also has charting capabilities, which means you can plot your data and create interesting visualizations from any dataset. - Machine Learning
R has found a lot of use in predictive analytics and machine learning. It has various package for common ML tasks like linear and non-linear regression, decision trees, linear and non-linear classification and many more.Everyone from machine learning enthusiasts to researchers use R to implement machine learning algorithms in fields like finance, genetics research, retail, marketing and health care.
Recommended Books
Hands-On Programming with R
This book is written by RStudio’s chief trainer Garrett Grolemundand is filled with several practical data analysis projects of a casino (Weighted Dice, Playing Cards, Slot Machine).
It is not a comprehensive guide to the R programming language, but an excellent primer to get started with the language.
The Art of R Programming – A Tour of Statistical Software Design
If you are a software engineer looking to build software in R, this is the book for you. On the other hand, the book might not be ideal if you are a statistician trying to learn programming through R.
Beginning R: The Statistical Programming Language
An excellent book for beginners who want to learn the notoriously complex R concepts through clear simple statistical examples.
