Why Learn R? 10 Handy Reasons to Learn R programming Language
The questions which arise in the mind of every R aspirant before starting to learn R Programming is – Why Learn R? What are the reasons to learn R for Data Science? Here, in this tutorial, we will answer all these types of questions. And, I promise after completing this tutorial, you will not be having any confusion related to the importance of R Programming. So, let’s quickly start.
R is the most popular language in the world of Data Science. It is heavily used in analyzing data that is both structured and unstructured. This has made R, the standard language for performing statistical operations. R allows various features that set it apart from other Data Science languages. In this article, we will explain why you must learn R and how it will benefit you in the domain of Data Science.
Before moving on, you must be aware of the Latest Features of R Programming so that you can master its core aspects
What is R?
R is one of the leading tools for Machine Learning, Statistics, and Data Analysis. It is a programming language using which we can create objects, functions, and packages. R language is platform-independent which means that we can use it on any operating system.
It is not only a statistic package but also an open-source tool which means anyone can examine the source code to see what exactly is being performed on the screen. You can also add a feature and fix bugs without waiting for the vendor to do this.
Thus, R allows you to integrate with other languages (C, C++). It also enables you to interact with many data sources and statistical packages (SAS, SPSS). R has a large growing community of users.
You must have a look at the comparison between R, SAS and SPSS – Data Analytics Tools – R vs SAS vs SPSS
Now let’s understand why R is used extensively in all the sectors and get to know how powerful R is.
Why Learn R Programming?
There are various reasons to learn R, we have listed the major ones that will surely answer your question to why learn R.
1. Why R is important for Data Science?
R plays a very important role in Data Science, you will be benefited with following operations in R.
- You can run your code without any compiler – R is an interpreted language. Hence we can run code without any compiler. R interprets the code and makes the development of code easier.
- Many calculations done with vectors – R is a vector language, so anyone can add functions to a single Vector without putting in a loop. Hence, R is powerful and faster than other languages.
- Statistical Language – R used in biology, genetics as well as in statistics. R is a turning complete language where any type of task can perform.
2. Why R is Good for Business?
R will just not help you in the technical fields, it will also be a great help in your business.
- Here, the major reason is that R is open-source, therefore it can be modified and redistributed as per the user’s need. It is great for visualization and has far more capabilities as compared to other tools.
- For data-driven businesses, lack of Data Scientists is a huge concern. Companies are using R programming as their core platform and are recruiting trained R programmers.
3. R is a gateway to Lucrative Career
R language is used extensively in Data Science. This field offers some of the highest-paying jobs in the world today. Data Scientists who are proficient in R make more than $117,000 (Rs 80,56,093) on an average per year. If you want to enter the field of Data Science and earn a lucrative salary, then you must definitely learn R.
Wondering why R is important for Data Science? Then, do check out the article on – Reasons to Choose R for Data Science
R is an open-source language. It is maintained by a community of active users and you can avail R for free. You can modify various functions in R and make your own packages. Since R is issued under the General Public Licence (GNU), there are no restrictions on its usage.
R has become one of the most popular programming languages in the industries. Conventionally, R was mostly used in academia but with the emergence of Data Science, the need for R in the industries became evident. R is used at Facebook for social network analysis. It is being used at Twitter for semantic analysis as well as visualizations.
6. Robust Visualization Library
R comprises of libraries like ggplot2, plotly that offer aesthetic graphical plots to its users. R is most widely recognized for its stunning visualizations which gives it an edge over other Data Science programming languages.
7. With R, you can develop amazing Web-Apps
R provides you with the ability to build aesthetic web-applications. Using the R Shiny package, you can develop interactive dashboards straight from the console of your R IDE. Using this, you can embed your visualizations and enhance the storytelling of your data analysis through aesthetic visualizations.
Any queries in why learn R article till now? Please comment below.
8. R enjoys a vast Community Support
R Programming is supported by a vast community that maintains and updates R. If you face any trouble with the code in R, you can avail the support of the community on places like Stack Overflow (of course you can also ask us any queries in the comment section below, DataFlair is always there for you!). There are several communities around the world that organize bootcamps and R meetups.
9. A go-to language for Statistics and Data Science
R is the standard language for Statistics and Data Science. R was developed for statistics, by statisticians. It has been in use even before the word “Data Science” was coined. Statisticians and Data Scientists are most familiar with R than any other programming language. R facilitates various statistical operations through its thousands of packages.
Its the right time to be aware of Statistical Programming in R
10. R is being used in almost every industry
R is one of the most widely used programming languages in the world today. It is used in almost every industry, ranging from finance, banking to medicine and manufacturing. R is used for portfolio management, risk analytics in finance and banking industries. It is used for carrying out an analysis of drug discovery and genomic analysis in bioinformatics. R is also used to implement various statistical measures to optimize industrial processes.
Programming Features of R
R has various programming features which we will discuss below:
1. Data Inputs and Data Management
- Data inputs such as data type, importing data, keyboard typing.
- Data management such as data variables, operators.
2. Distributed Computing and R Packages
- Distributed Computing – Distributed computing is an open-source, high-performance platform for the R language. It splits tasks between multiple processing nodes to reduce execution time and analyze large datasets.
- R Packages – R packages are a collection of R functions, compiled code and sample data. By default, R installs a set of packages during installation.
Not aware about R Packages? Don’t worry, here is the R Packages Tutorial for you.
Advantages and Disadvantages of R Programming
There are several benefits and some limitations of the R programming language. Let us discuss them one by one:
Pros of R Language
- R is the most comprehensive statistical analysis package, as new technology and ideas often appear first in R.
- R is an open-source that’s why you can run R anywhere any time, and even sell it under conditions of the license.
- It is cross-platform which runs on many operating systems. It’s best for GNU/Linux and Microsoft Windows.
- In R, everyone is welcomed to provide bug fixes, code enhancements, and new packages.
Cons of R Language
- The quality of some packages in R is less than perfect.
- There’s no customer support of R Language whom you can complain if something doesn’t work.
- R commands hardly concerns over memory management, and so R can consume all the available memory.
Data Science is the most popular technology in the world today. Since it is mostly comprised of statistics, R is the lingua franca of this field. We went through the various points which delineate why learning R is the first choice for mastering Data Science. In the end, we conclude that learning R will have immense benefits that will provide you with the right tools to deal with data on a large scale.
Now your next step should be – Career Opportunities and Scope of R Programming
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