R programming, the widely used free software environment for statistical computing, is our topic today. As we have found that, there are so many queries on this topic among the users. The users of this language are statisticians and data miners, and the R Programming Language is one of the best choices for them for statistical analysis and analysis.
R was designed by Ross Ihaka and Robert Gentleman from the R core team in the year of 1993. It has released its latest version on June 2020. In this article, we will provide you with all the basic details of this language. So, let’s start.
What is the R Programming Language?
As we have told you, R is a free programming language. And the environment is written in C, FORTRAN, and R and licensed by GNU general public license company. As R is written in the R language itself, so it is a partially self-hosting programming language.
R possesses both the command line interface and third-party graphical user interfaces. Some of these third-party graphical user interfaces are RStudio, an integrated development environment, and Jupyter, a notebook interface.
Features of R Programming Language
Now we will discuss the features provided by the R programming language. There are several features from where we have shortlisted some attractive features. So let’s start.
R is an open-source environment that provides its service free of cost. Thus, it allows you to adjust your budget if you are a new-comer.
And the great part about this language is, it provides all the basic requirements which are expected from a software environment. You can make improvements and also can add packages as per your need.
2. Strong Graphical Capabilities
R offers data visualization and presentation super easy for you. The secrete behind these features are extended libraries of R. Thus, this allows R to produce static graphics which are very much attractive.
3. Highly Active Community
This is a much-needed feature, which should be present in all the libraries. As the R language is in the market for 27 years, it has a large community of users. As per our research, the community of the R language is very much active and responsive.
4. A Wide Choice of Packages
R offers you more than 10000 different packages and extensions that help you to solve most of the problems of data analyzing. There is at least one package available for all the fields, i.e., high-quality interactive graphics, development of web applications, quantitative analysis or machine learning procedures.
R has packages to satisfy the need of almost all the fields like astronomy, physics, mathematics, etc. Previously it found uses in academic purpose, its enormous features allow this to use in industries as well.
5. R Can Perform Complex Statistical Calculations
R is an effective tool to perform simple and complex mathematical calculations. Hence, large data sets perform this calculation.
6. R Can Run Code Without a Compiler
R is an interpreted language. As you all know, an interpreted language does not need a compiler to make a program from the code.
7. Cross-Platform Support
R is machine-independent, which signifies that it could be used on different platforms.
8. Compatible with Other Programming Languages
R shows very high compatibility with other languages like Java, C, C++, and FORTRAN. So, these languages can also be used easily to manipulate objects directly.
9. Generates Reports in Any Format
R language offers this unique feature. It can produce reports in form like web pages, word documents, or PowerPoint presentations. R also has its markdown package for report generation, which is considered as one of the best packages for report generation.
These are the features that you can’t skip when you are using the R language. There are some more features like pulling data from APIs, servers, SPSS files, useful for web scraping, capacity to perform multiple complex mathematical calculations through a single command, and many more.
Why Should You Use R Programming Language for Statistical Computing and Graphics?
R is offering you a wide variety of statistical and graphical techniques which include linear and non-linear modelling, classic tests of statistics, analysis of time series, classification, clustering.
We have already mentioned the lots of general features that are provided by the R language. So, in a nutshell, R is offering you something free of cost, which is far better than most of the highly-paid languages.
The statisticians and data miners are smart enough to choose the best.
Is R Programming Easy to Learn?
This is the field where the performance of R is a little disappointing. R also has a reputation for being a hard language to learn. It takes some time and effort, and unfortunately, there are no shortcuts to learn the language.
But as we have seen, there are some things which could be better than what they are now. Overuse of GUIs, so many commands, misleading function or parameter name, inconsistent function names, unpredictable syntax, absence of the way to clear memory, these are some of those fields where the R core company should think more.
The overall learning takes some extra effort initially, but once you learn to handle these things, R will be your best friend.
Also Read: Reasons to Learn Swift Programming Language
Applications of the R Language in the Real World
As R is introducing an ocean of benefits without a cost, it is used by all sectors. Statistical computation and data analyzing is the biggest market of the R language. It also finds uses in machine learning, research and deep learning.
The IT giants like Microsoft, IBM, Infosys, Wipro, TCS, and Paytm also use R language to perform their statistical analysis and machine learning-related tasks.
Google uses this to make their search engine better. E-commerce leaders like Amazon and Flipkart also use this language for data analysis. Healthcare, banking, and social media also uses R language.
As we promised, we have provided you with all the information which you need to about the R language. However, if you can overcome the initial challenges of learning, it will be the best language you ever learned.