Today job portals like LinkedIn, Indeed, Glassdoor, Monster, Naukri, etc., are flooded with jobs for R developers. There are over 1 million jobs for R programmers across the globe.
With expertise in R programming, you can find jobs in the domains such as:
- Data Science
- Data Analytics
- Business Analysis
- Data Visualization
- Business Intelligence
- Quantitative Analysis
Today R programming language is used in almost every sector such as finance, retail, healthcare, academics, IT, government agencies, banking, e-commerce, social media, search engines, and public welfare. Moreover, there are over 3 million users across the globe, making a huge community for R programming language.
There are a large number of companies in every sector that are using R. some of them are:
- Tech Mahindra
And many more.
The average annual salary of an R programmer is around USD 65,000 at entry-level. With such a diverse field, you would wish to know more about R. the extensive applicability of R in so many fields has resulted in a surge in demand for trained and certified professionals in R. In this article, we will explore more about R and its features. By the end of this article, you will come across the fact that taking up an R programming course and making a career in this domain can be really rewarding.
What is R?
Developed in 1993 by Robert Gentleman and Ross Ihaka in the University of Auckland, Auckland, New Zealand, R is an open-source programming language that is extensively used as a statistical software and data analysis tool. This programming language is widely used by Data Scientists, Software Programmers, Data Miners, and Statisticians.
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As far as Data Science is considered, R is the most popular language in this field. The most important feature of R is that it is capable of analyzing the data, whether it is structured, unstructured, or semi-structured. R provides a wide range of statistical techniques such as linear and non-linear modeling, time-series analysis, classical statistical tests, classification, clustering, and more. It also provides graphical techniques and is highly extensible. This is the reason that R has become the standard language for carrying out statistical operations.
The R environment
R is a unified suite of software facilities that provide for data manipulation, computation, and graphic display, that allows for the implementation of statistical techniques. The term ‘environment’ here implies that R is a well-planned and coherent system meant for data analysis. It consists of:
- A powerful data handling and storage capacity
- A suite of operators for computations on arrays, particularly matrices
- A huge, coherent, integrated assembly of intermediate tools meant for data analysis.
- Graphical features for data analysis and interpretation on hard copy or on-screen.
- A well-developed, simple yet effective programming language that consists of loops, conditionals, user-defined recursive functions, and input and output facilities.
Programming Features of R
There are various features of R that make it on the top of preferred programming languages. They are:
- It provides for data input facilities such as keyboard typing, data types, and importing data as well.
- Data management tools as in data variables and operators.
- R has a distributed computing environment which is an open-source and high-performance platform. The tasks are split between various processing nodes so that the execution time is minimized and large datasets can be analyzed effectively.
- There are packages in R that are a suite of R functions, sample data, and compiled code. R installs a set of packages at the time of installation by default.
- R can easily associate with different databases and performs really well when it comes to bringing information from Microsoft Access, Microsoft Excel, Oracle, MySQL, SQLite, etc.
- It is a flexible language that is capable of bridging the gap between Software development and Data Analysis.
- It has cross-platform interoperability, which implies that it can run efficiently on Windows, Mac, and Linux. You can R code from one platform to another easily.
- There are a wide variety of packages with a diversity of codes, functions, and features specifically tailored for statistical modeling, data analysis, visualization, machine learning, and importing and manipulating data.
Why should you learn R?
Let’s jump to the reasons for learning R.
R is important in Data Science
R plays a crucial role in Data Science because R enables you to run your code without any compiler as it is an interpreted language. R interprets the code, thus making the development of code easier.
R is a vector language that allows you to add functions to a single vector without putting it in a loop.
Apart from being a statistical language, R can be used for genetics and biology as well.
R for Businesses
R can be modified and redistributed according to the requirements of the user, just because R is open-source. It is excellent when you need to perform visualization.
When the businesses are data-driven, Data-Scientists are required to manage the data. It is here when trained, and skilled professionals in R are in demand.
R is open-source
R is an open-source language that is maintained by a community of active users, enabling you to avail of R for free. There are no restrictions on its usage as it is published under the GNU (General Public License).
Learning R can give you a lucrative career
Data Scientists are required in almost every sector. Data Scientists who are trained and proficient in R can have a lucrative career as Data Science is referred to as the “sexiest job of the century.”
A Data Scientist trained in R can earn an average of over USD 117,000 annually.
R has a robust visualization library
R is popular for its excellent visualizations, which makes it ahead of other statistical languages. There are libraries like ggplot2, plotly, that offer aesthetic graphical plots to the users.
R is used in almost every industry
From portfolio management to banking industries, from healthcare to academics, nearly every industry requires Data analysts or data scientists who are proficient in R. so a trained and certified professional is always a preferred candidate for the recruiters.
The Bottom Line
R is considered to be the lingua franca for Data Science. To make a career in the domain which is ever-growing and lucrative, all you need is to get trained and certified in the skills required. When you look for the skills required in Data Science, you will find that proficiency in R is required.
Though self-study is always an option, it is strongly recommended that you take up an online training course from a reputed online training institute. The training goes trouble-free, and to ensure that the training is going well, there are industry experts to help you with the doubts and make the learning-go-easy.