The R programming language is an open-source programming language. It is widely used as a statistical software and data analysis tool. The language contains an extensive catalog of statistical and graphical methods.
It also gives you numerous tools for machine learning algorithms, linear regression, analyzing time series, and drawing statistical inferences. Is R even popular? Well, according to IEEE Spectrum’s survey, the R programming language ranked seventh among the top ten programming languages in 2018.
Programs in R can be written using an IDE like R Studio, Rattle, or Tinn-R. Many R users prefer to use a simple text editor to write small programs and scripts. After you have written your program, you can save it to a file with an extension.r.
If you want to run the program, you simply go to the command prompt and execute the following command:
This runs your file.
It has an extensive library that has been mostly written in R but surprisingly, many components have been written in C, C++, and even Fortran. The language is widely used among academicians and even multinational companies like Facebook, Google and Uber have implemented many R programming-based solutions in their modules.
Interestingly, although the language is majorly known for statistical purposes, many of its statistical features come from different packages that you can avail when writing routines in R As mentioned above, the biggest users of the R programming language come from the academic circles, and after that, it is the healthcare sector that uses this programming language.
There are many government departments that use the R programming language. Aside from being a language, it is also a software environment that allows you to achieve many statistical tasks from within the interface.
In fact, its original promoters prefer to define R as an “integrated suite of software facilities for data manipulation, calculation, and graphical display.” It works on a command prompt, though, you can run under the Windows operating system.
Once the R software is running it appears the same as the UNIX command prompt or the shell command prompt and gives most of the commands and runs most of the statistical operations from the prompt itself.
The R programming language has been derived from the S programming language. In fact, it is considered to be an implementation of the S language which was developed at Bell Labs.
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The difference between the R programming language and other programming languages
As mentioned above, R was primarily designed to do data analysis. Hence, it gives you lots of features for heavy graphics, statistical functionality, creating and manipulating data structures as well as object manipulation, and numerical computation.
It is an interpreter-based language and it is also vector-oriented (as opposed to object-oriented, unlike C++). Although it is known as a programming language, R is more of a statistical programming package compared to a standard language such as Python or Java.
Some also prefer to call it a statistical tool rather than a full-fledged programming language. Hence, can you write a complete program using the R programming language as you can write in PHP or C++? You cannot.
Can you imagine someone writing WordPress in the R programming language? No. This is because R is not a general-purpose programming language. You will find most of the comparisons with the Python programming language because on many occasions, Python and R are combined to achieve complex statistical and mathematical formulations.
It has a steep learning curve. For example, you can start learning a language like Python on your own but to leverage R you need to know the exact functions and the exact libraries as well as the exact environment under which you need to derive results.
Instead, SQL (structured query language) would be a good comparison. Although you can issue commands in SQL, all these commands are centered around manipulating databases, you cannot write independent software applications or mobile apps just using SQL.
The same goes for the R programming language. If you want to write a comprehensive, low-level program then you would use another language like C or Python, or PHP but if you only need a few lines of code without many decision loops and multiple parameters and arrays, you can easily depend on the R programming language.
The main features of the R programming language
Every programming language survives because it gives something that the other programming languages do not give. Hence, there are ardent users of the R programming language.
They vouch for it. They claim that what this language can achieve, no other language can achieve. Let’s throw some light on the features of R to see why thousands of people all over the world use the language to solve complex statistical and mathematical problems.
R is open source
It is an open-source software environment or a programming language. The biggest benefit of a software tool being open source is that you can write your own features and packages and add them to the overall software.
Consequently, there are thousands of libraries and packages available that you can use with the R programming language. You can simply download, install and start using the software immediately.
Strong data visualization capabilities
You can generate all sorts of graphs, pie charts, bars, density plots, scatterplot matrix, trellis graphs, kernel density plots, and the horde of other graphs. It has so many visualization capabilities that you can create almost any type of graph using the R programming language.
Thousands of additional packages
It is a wide availability of libraries and packages. One of the most widely used R libraries is CRAN which holds more than 100,000 packages. They can provide you the functionality of possibly every data field and visualization.
From specialized statistical techniques to graphical devices to import-export capabilities and massive reporting tools, you can get everything from these packages. Although most of these packages are developed in R, many are also developed, as already mentioned above, in Java, C, C++, and Fortran.
If you download and install CRAN on your computer, you get a core set of packages. Within CRAN (comprehensive R archive network) you get 15,000 additional packages. Many of its capabilities are also contributing towards the genome project under the Bioconductor project, and in fact, under this project, some object-oriented capabilities have also been introduced into the language.
It comes with cross-platform capabilities
The R programming language requires an interpreter to deliver performance. All its commands work from a shell prompt. If you can install the shell prompt on a Windows machine or a Mac machine, you can run these commands.
This makes the R programming language cross-platform. The basic set of commands are the same whether you’re using UNIX, Windows, or Mac. It is just like Java. Once you have installed the runtime environment, you can run your code anywhere. The same can be done in R.
A strong and vibrant online community
Post the Internet, every language has grown on the shoulders of its community and the same is the case with the R programming language. It is open-source, thousands of programmers and ardent users have added to the packages and libraries and they are still adding.
There are more than 100,000 packages available that you can download and start using. All these packages have been developed by the community. There is also ample help available online through various forums and social networking groups.
It supports distributed computing
The R programming language has packages like ddR and multiDplyr that allow it to use distributed computing to process large datasets. In distributed computing, tasks and processes can be distributed among multiple nodes (even computers scattered all over the world) to reduce processing time and increase efficiency.
It does not need a compiler
The R programming language does not need a compiler. It is an interpreter-based language. The software that you install to run the R commands does all the interpretation.
Ample capabilities to interact with databases
Since the R programming language works with datasets, it is but natural that it should be able to work with all the mainstream database packages. Although to run data analysis and statistics R does not require a relational database system, sometimes big chunks of data need to be stored before they can be processed and analyzed and for that, the data needs to be stored in a database.
R works with MySQL. It also works with the open database connectivity protocol, Roracle, and RmySQL.
It is the process of reorienting, cleaning, and restructuring raw data into the desired format so that it can be processed better, in less time. If you supply raw data to the R programming language, it can turn it into a well-structured dataset.
For other programming languages, it can be a difficult task to process but the R programming language has inbuilt capabilities to understand complex patterns even in unprocessed data and then render structures to it.
The R programming language can be ideal for machine learning because it is one of the most powerful machines learning platforms and it is being used by top data scientists all over the world.
It is state-of-the-art for machine learning because it is extensively used by academics and these academics are constantly adding new machine learning algorithms to the R packages. What is machine learning after all?
It is high-grade data analysis and intelligence gathering, which is already inbuilt in the R programming language.
Integration with other programming languages
A great thing about the R programming language is that if you want to perform some heavy tasks, you can easily integrate high-level & low-level programming languages such as Python, C, C++, Fortran, Java, and. NET.
As mentioned above, for handling complex databases, you can also integrate it with SQL. Integration is possible because, whereas, many of its packages have been written in R itself, many more packages have also been written in Fortran, C++, and C.
You can either run pre-saved scripts to generate complex reports in R, or you can generate them on the fly. If you want to generate reports in the R programming language, you will need to use it with the package “officer”.
It can also help you generate Word and PowerPoint documents.
Some more features include:
- The R language can perform operations directly on vectors and consequently, you don’t need to execute multiple loops.
- R can extract data from multiple APIs, servers, SPSS files, and any other data format files.
- Interested in web scraping? You can easily achieve it with the R programming language.
- It can perform multiple and complex mathematical operations with a single command.
- R Markdown can be used to create attractive and complex reports combining plain text and data visualization and then these reports can be exported to Word and PowerPoint documents.
Which industries majorly use the R programming language
The R programming language is used in major industries, especially in the field of data science. It has an extensive set of libraries that allow you to carry out highly complex statistical functions.
With big data analytics becoming popular among various companies, there is a great demand for data analysts and programmers who are proficient with the R programming language. The following industries use R programming language based on their needs, with academics gaining the biggest chunk, followed by healthcare.
Big technology companies like Google, Bing, Accenture, and Wipro are using the R programming language for their data analysis needs and even for machine learning purposes. Many RDBMS companies and SaaS-based services also use R for different needs.
Career opportunities in R programming
The R programming language is mostly used by data scientists, analysts, and programmers who need to handle data for statistics and mathematical computations. Approximately 2 million people are using the R programming language right now for different projects.
If you are seeking a job in companies like Google and Twitter, we have already mentioned that these companies are extensively using the R programming language for various purposes. How do these companies use the R programming language?
Twitter uses R for statistical analysis. Google uses its open-source analytical tool to monitor the returns from its various advertising campaigns. Even the US government uses it to forecast flooding.
Even the carmaker Ford uses the R programming language to analyze feedback and reports for its various products being used in the market. IBM is using it for machine learning. Accenture is using it for human resource analysis.
According to a 2019 survey among the Stack Overflow developers, an average R developer makes $ 64,000 per annum, which is $ 1000 more than an average Python programmer. US and UK are right now the biggest markets for this.
If you can improve your R skills, you can easily increase your salary to $ 89,596 per annum in the US. Data scientists conversant in R are known to make $ 126,226 per annum. Most of the R developers come with additional skills such as data analysis, Microsoft Excel, SQL, Java programming, and statistical analysis.
Consequently, even as an R programming, you can get a job as a software engineer, a software developer, a business analyst in information technology, a team leader in information technology, an IT consultant, a senior business analyst, and a software developer.
In whichever industry data analysis and statistical computations are required, the R programming language can be invaluable. If you want to make your career as a data scientist then extensive knowledge of data wrangling, data analysis, and data visualization is mandatory.
Aside from these, you need some good foundation in a higher-level programming language along with SQL and statistical computing languages like R. Since big data analysis is fast emerging as one of the prominent fields, students and professionals who are proficient in the R programming language are certainly going to have an edge.
Want to become a data analyst? Then the R programming language gives you a strong mathematical foundation. Also, if you want to become a business analyst, the knowledge of R can be invaluable.
Through it, you can create multiple technical solutions for business analysis for your own company or for other businesses. From forecasting to market research to consumer behavior, everything can be analyzed using the tools available in the R programming language.
What are the benefits of using the R programming language?
Aside from the feature-related benefits, one of the biggest benefits of using the R programming language is that it is open source. As it happens in most of the popular open-source languages, thousands of programmers are constantly adding new code to the packages.
Also, since the programming language is extensively used by academicians, they are constantly adding new features to it. You can start using the R programming language immediately.
It is open source means once you have become proficient in the language and you can develop your own packages, you can go on adding new features to the language making it richer not just for yourself, but also for other developers, analysts, and common users.
Want to inject more power? You can easily use other low-level languages to make up for the features that may be missing in the R programming language. The language is custom-made for mathematical and statistical analysis.
Large swaths of data can be analyzed extremely fast to draw intelligence. Yes, it can be slow but that is not the inherent fault of the R programming language. It is just the way it has been built to make it more compatible and more user-friendly.
Since it is an interpreter-based language, it is platform-independent. As long as you have an interpreter running, you can run all the commands under any operating system. Even career-wise, the benefits of knowing the R programming language are manifold.
You can become a data scientist. You can get a job as an analyst and a forecaster. Even if you want to work as a systems analyst, the R programming language can be a great asset to you.
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You write scripts or a chain of commands. To be able to write this chain of commands, you must know all these commands. Yes, you can have the reference manual with you, but even then, it may take lots of time to find the right commands and then use them appropriately to solve problems.
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