R programming language is now one of the most popular languages used in the world of data science. It can be used to analyse both structured and unstructured data. The reason behind the popularity of R for data science is its robust functions and open source nature. If you have been planning to learn R to pivot to a data science career, then you have reached the right place.
What is R programming language
R programming language is popular among data scientists and it is primarily used for statistical analysis, graphical analysis, and data analysis. Developed by Robert Gentleman and Ross Ihaka in 1993, R programming language has a large number of graphical and statistical methods including linear regression, machine learning algorithms, statistical inferences and time series. While most of the R libraries are written in the R language itself, some of the libraries with heavy computational requirements are written in C++, C, and Fortran codes. It is important to note that R is used by many large companies around the world for data analysis including Facebook, Uber, Airbnb, and Google. You can download it for free on from their official website R-project.
There are two main types of libraries or packages that can be used to introduce more functionalities in R — base packages and distributed packages. The base packages get automatically installed when you install the R language on your system, but you would have to download the distributed packages separately from CRAN if you need them.
R programming language plays an incredibly important role in data science. Since R is an interpreted language, you can run the code directly without any compiler, which in turn decreases the development time. R is also able to handle vectors seamlessly, so you can add functions to even a single vector without the need for any loop. This makes R faster and more powerful.
Features of R programming language
The primary use of the R programming language is machine learning, data analysis, and visualisation. Some of the many features of R include:
1. Distributed computing
R programming language also gives you access to distributed R, which is an open source and high performing platform for distributed computing. By splitting tasks between multiple processing nodes, it helps reduce the overall execution time which in turn allows you to analyse large data sets faster.
2. The multitude of libraries and packages
You get access to a wide collection of functions, sample data, and compiled code. The R programming language also has statistical libraries like ggplot2 and plotly that allow you to create aesthetic and easy to understand graphics. In fact, the simple and engaging visualisation options is what gives R programming language its edge.
3. Develop powerful web apps
The IDE in R has an interactive dashboard and console which can help you build powerful web applications. It is also possible to add visualisations to your web app in order to improve the way you showcase your data.
4. Community support
There is a vast community of developers that constantly maintain and update the R programming language. If you face any kind of problem while coding in R, you can ask for support from the community on platforms like Stack Overflow or their official website. There are also many communities that readily organise R Bootcamp and meetups.
Should you be using the R programming language?
Here are the advantages and disadvantages of using R
Advantages of the R language
- It offers a comprehensive data analysis package
- Since its open source, it can be downloaded and installed for free
- It is a cross-platform language that can run on all the major operating systems including Microsoft Windows, Mac OS, and Unix
- Developers can also contribute by offering code enhancement, bug fixes, and new packages
Disadvantages of the R language
- The quality of some the packages in R is not so great since anyone can contribute and there are no strict quality checks
- Because R is an open source language, there is no dedicated customer support. There is only community support
The R function gives you all the customisation options that you need to analyse and visualise your data in an effective way. But before you start learning the R programming language, you should nail the basics of data science. Springboard offers a Data Science Career Track course with a job guarantee with unlimited mentor calls and syllabus created by experts in the field. Take a look at the course and apply today!