Why should you learn R programming language?

The importance of Big data is on an ever-increasing trend and as more and more Software as a service companies prefer it, it is becoming a great playfield for professionals to come in and earn big bucks. The field of Big Data analytics is emerging as a great rewarding option both in terms of learning and earning.
In order to initiate of boost a career in the Big Data and data science industry, it is wise to learn a programming language that will aid in big data analysis. Out of numerous language options, R is a great choice.

Designed by statisticians, R is tailor-made to suit statistical computing. With the improvement in technology, researched data is increasing in complexity and thus R is being preferred more for data analysis. It is a comprehensive approach that greatly serves the purpose of machine learning, data visualization and analysis.

Here is how learning R can be a great boost to your career:

• It offers a great tool to statisticians and hence all data scientists can benefit from it for they are passionate programmers cum statisticians
• R has established itself as a standard among all statistical programming languages
• R programming language is accepted in the industry as a coveted skill. Big data analysts and data scientists with knowledge of R, are in great demand for employment at big brands such as Google, Facebook etc.
• It is a very versatile language that is diversifying its commercial applications each day

Below are ways that it can enhance your skillset:

• R is an open source programming language that is available for free
• Once can freely install, use, update, duplicate, edit, redistribute and resell it.
• It is an economical choice for companies
• It can be updated easily

Some features that make it a highly functional and amazing language are as follows:

• R offers great cross-platform compatibility
• It is versatile in terms of operating systems and works seamlessly on Windows, Mac OSX and Linux
• It is compatible with database programs such as Microsoft Excel access, MySQL, SQLite, Oracle etc. It is easy to import data.
• It offers great scripting support as it can handle huge data volumes, complex in nature.
• High-performance computers and simulations requiring resources can be managed perfectly with R.
• It is majorly accepted and quite popular in the programming languages space.
  It is highly receptible to change. All major statistics developments reflect initially as R packages
• It can be easily combined with document preparation systems. This implies that output and graphics from R can be combined with word-processing documents.
• The resource bank and R community are quite large. A huge community of R users is pretty active on forums and physical conferences.
• It has a huge library space with over 2000 libraries that can be used unlimited and cover statistics in areas such as finance, computing etc.

The scope of R is high because it is not very easily understandable and not many people are well versed with the syntax and rules. It has a steep learning curve owing to difficult syntax, rules and symbol definitions. It is not readable like English.
The inbuilt functions and tools make R capable of performing all tasks in a straightforward way. R is perfect for statistical computing and data visualization is better and more convenient with R than python.

Despite the comprehensive features and awesome statistical support that R offers, it also faces certain scalability issues. They are as below:

• Low speed: The basic design of R is not supposed to be fast and is thus annoying when large datasets are operated upon for analysis. It however does not impact functionality as R projects cannot be easily integrated into web apps.
• Memory limitations: Analyzing huge datasets is quite resource expensive because R uses huge chunks of memory that uses harddrive if RAM isn’t very large. This may make the computation even slower.

Huge passionate community:

As a passionate programmer you can derive immense support from the huge community that R has. All developer communities function by giving and taking help as it helps to build useful tools that make coding in that language easier. Here are some facts about the R community:

• R is a popular language on StackOverflow. It is the 11th most followed and it is important because a lot of coders use it as a go-to for their programming queries and networking.
• Networking and learning with fellow coders are easy when you have a platform to meetup. These meetups also offer amazing mentoring opportunities. R has the 8th largest meetup community made up of over 130000 members around the world. It is a pretty active community despite not being comparable in size to other huge communities.

A large volume of GitHub Projects:
R boasts of over 199000 GitHub products which may not seem to be huge as compared to Python. One of the features that strengthen R greatly is its CRAN repository that addresses all data analysis needs with over 7700 packages. These packages are highly sophisticated and hence the quality of R tools is pretty high and greatly functional.

Wide career opportunities:
With the rise in the amount of data gathered by companies, the demand for data scientists is also on the rise. The demand for R developers is certainly going to increase as it is a great tool for data analysis.

R enables tapping the complete potential of data analysis for helping businesses maintain a good rapport with customers. The TIOBE Index indicates R as 18th most popular language.

Google trends also indicate a significant rise in the number of R enthusiasts. It is a great tide to get on in order to get a position in the data analysis and statistics space and learning the language is promising on many levels. It not only offers a great career but also provides sustained support throughout the career. It is a good opportunity to look at though it may require considerable effort to learn.