Building a model for statistical analysis was a daunting task – playing around with C and C++ and painstakingly building a lengthy program. Even if one thing goes wrong, you would have to pull the whole thing down or start all over – a truly crushing realization.
Thankfully, in the world of technology, things move quickly and in the world of statistical analysis, things have moved faster. It’s now possible to create a system for analyzing data that’s constantly adapting, evolving and improving. One of the best ways to do that is with R.
So, what exactly is R?
R is a programming language with rich features and can be used for all kinds of data science, statistics and visualization projects. R’s popularity is also due to its open source nature with an extensive support network and free tutorials. R is known to be so versatile and powerful that everyone from Google to Facebook is using it to either analyze the news feeds or to assess the effectiveness of its ads. R has enough provisions to implement machine learning too.
Benefits of using R
Here are some of the benefits of using R:
- The coding is quite easy and can be implemented easily too.
- It’s open source and no need to pay any subscription charges – no recurring costs of any kind.
- Availability of instant access to over 7800 packages that are customized to perform various computation tasks.
- R has an overwhelming community support and there are a number of forums that will help you in case of any issues.
- High performance computing experience.
Beginning in the 90’s, a number of endless efforts have been made to improve the interface and the journey of this language from being a text editor to an interactive studio that can enable data science and statistical analysis is quite a journey in itself!