R has several characteristics that set it apart from other Data Science languages. We’ll discuss why you should learn R and how it may help you in the field of data science in this article. Before beginning to study R programming, every R aspirant asks themselves, “Why Learn R?” What are the benefits of learning R? All of these issues will be covered in this article. And, when you’ve finished this lesson, I assure you that you won’t be confused about the relevance of R programming.

R is the most extensively used language in the field of data science. It is frequently applied in the study of both structured and unstructured data. R has become the standard language for conduct statistical procedures as a result of this.

## Why Learn R Programming?

There are many reasons to learn R; we’ve compiled a list of the most important ones that will absolutely answer your query about why you should learn R.

with no a doubt, data science course is one of the most in-demand professions. The level of prominence and demand that this sector has now demands all of our attention. With everyone turning their job focus to the magical world of data, understanding what it takes to succeed professionally has never been more important. There are a set of conditions that, if met, will set you apart from the competition. The R programming language is one of them. Without R, data science would be hard to imagine.

Before we get into why R is so important in data science, let’s take a look at what R is. R is a statistical programming words and software environment designed primarily for statistical computing and data visualisation. Statistical analysis, data visualisation, and data manipulation are the main areas in which R excels.

R is regarded as one of the best languages for analysing data. R is really simple to learn, even if you have no prior coding knowledge.

R makes learning data science a lot simpler — Though Python is often regarded as one of the most user-friendly languages, beginning with R offers its own set of advantages, the most notable of which is the way R is built to facilitate data handling and analysis. As a result, mastering the fundamentals of data science — data manipulation, data visualisation, and machine learning – may be a lot easier with R. The fact that data science incorporates both structured and unstructured data is one of the reasons R is so widely used.

### 1. Why R is important for Data Science?

R is very important in Data Science, and understanding how to conduct the following operations in R can help you a lot.

**You can run your code without any compiler** – R is an interpreted programming language. As a result, we no longer need to use a compiler to run code. R understands the code and makes it simple to create.

**Many calculations are done with vectors** – Because R is a vector programming language, anyone may add functions to a single Vector without having to use a loop. As a result, R beats other programming languages in terms of power and speed.

**Statistical Language** – R is a computer language for biology, genetics, and statistics. R is a flexible programming language that may be used for a variety of applications.

### 2. Why R is Good for Business?

R will help you not only with your technological issues but also with your company. The main reason for this is that R is open-source, which means it may be updated and redistributed as needed by the user. It’s great for visualisation and comes with a lot more capabilities than other programmes.

For data-driven businesses, finding Data Scientists is a huge difficulty. R programming is gaining traction as a crucial commercial platform, and R programmers are in great demand.

### 3. R Opens the Door to a Lucrative Career

In data research, the R programming language is commonly used. This business offers some of the highest-paying positions on the planet. R-skilled data scientists make more than $117,000 (Rs 80, 56,093) per year on average. You must learn R if you want to work in Data Science and make a good livelihood.

### 4. Open-Source

R is a free and open-source programming language. R is a free programming language that is developed by a user community. In R, you may alter many functions and create your own packages. Because R is published under the GNU General Public Licence, its usage is unlimited.

### 5. Popularity

R has quickly developed to become one of the industry’s most popular programming languages. R was traditionally only used in academics, but with the rise of Data Science, the necessity for R in the business world became clear. For social network analysis, Facebook uses R. Twitter uses it for semantic analysis as well as graphics.

### 6. Robust Visualisation Library

ggplot2 and plotly are two R packages that provide users with appealing graphical plots. R is most renowned for its eye-catching graphics, which gives it an edge over other Data Science programming languages.

### 7. You can Create Great Web-Apps Using R

You can use R to make visually stunning web applications. You can create interactive dashboards right from the console of your R IDE using the R Shiny package. You may incorporate your visualisations and use attractive visuals to improve the storytelling of your data analysis.

### 8. R enjoys a vast Community Support

R is updated and maintained by a broad community of people. If you have any issues with your R code, you may seek support from the community on sites like Stack Overflow (of course, you can always ask us questions in the comments section below; DataFlair is always accessible!). Bootcamps and R meetings are organised by a number of groups throughout the world.

### 9. A go-to Language for Data Science and Statistics

The statistical and data science programming language R is the most extensively used. R is a statistical programming language that was created by statisticians. It was in use long before the concept “Data Science” was invented. Statistics and data scientists are more familiar with R than with any other computer language. R’s hundreds of packages make doing a variety of statistical methods simple. It’s a good opportunity to learn about statistical programming in R.

### 10. R is Utilised in almost every Industry nowadays.

R is now one of the most well-liked programming languages in the world. It’s used in a variety of areas, including finance, banking, medicine, and manufacturing. R is used for portfolio management and risk evaluations in the financial and banking industries. It is used in bioinformatics to do drug discovery and genetic analytical investigations. Additionally, the synergy between R’s statistical capabilities and HCS High Content Screening technology enhances the efficiency and accuracy of drug screening assays, leading to more robust candidate selection. R is also used to include a number of statistical measurements to help in the smooth operation of industrial operations.

## Programming Features of R

R includes a number of programming capabilities that we’ll go through below:

### 1. Data Inputs and Data Management

Data inputs such as data type, data import, and keyboard typing are all examples of data inputs.

Data management such as data variables, operators.

### 2. Distributed Computing and R Packages

**Distributed Computing** – For the R programming language, distributed computing is an open-source, high-performance platform. To minimise execution time and analyse massive datasets, it divides work across numerous processing nodes.

**R Packages** – R packages contain R functions, built code, and example data. R instals a set of packages by default after installation.

With over 10,000 packages in the CRAN Repository, R offers a wide choice of options. The vast majority of these Packages are used to carry out Data Science tasks. While some of the most popular have previously been discussed, the following is a list of some of the most extensively used R Packages in Data Science:

**Ggplot2**: Ggplot2 is one of the most popular R packages for visualisation that requires only a few lines of code. It is inspired by the Grammar of Graphics. Data scientists just need to tell Ggplot2 how to map variables to aesthetics and which graphical primitives to employ, and it will take care of the rest.

**Plotly**: It’s a graphing library for creating interactive graphs that can then be simply incorporated into web applications.

**Tidyr**: Data Scientists may use this R package to clean and arrange their data. When each variable represents a column, each row represents an observation, and each cell represents a single value, the data is called tidy.

**Dplyr**: It’s the go-to package for data manipulation and wrangling. It allows Data Frames in R to perform actions such as subsetting, summarising, rearranging, and joining data sets together.

**Caret**: The Caret Package is used for Predictive Modelling and stands for Classification and Regression Training. It improves Data Splitting, Pre-Processing, Feature Selection, Variable Importance Estimate, and other tasks while also offering a common interface to a lot of Machine Learning algorithms.

**Knitr**: Knitr is primarily used for creating reports in a number of file formats, including LaTeX, HTML, Markdown, LyX, AsciiDoc, and reStructuredText documents.

**Xtable**: When Data Scientists need to put an R project into a final document, it creates HTML or LaTeX code.

**Foreign**: It has tools for importing data files into R from other applications like SAS or SPSS.

**Data.table**: During data manipulation, this package can manage a large quantity of data. It’s R’s data in a performance-optimised format. For ease of use and programming speed, a frame with enhanced syntax and functionality has been included.

**Rcpp**: It’s used to create R functions that call C++ code in order to get lightning-fast performance.

**Bioconductor**: It’s an open-source project that includes a variety of tools for analysing and understanding high-throughput genomic data.

**Parallel**: It is used for organization Parallel Processing in R to speed up codes or to crunch big data sets.

**Mlr**: It’s a fantastic piece of software for performing Machine Learning jobs. Regression, Clustering in R, Multi-Classification, and Survival Analysis algorithms are among the most important and relevant classifications.

**RCrawler**: It’s an R package for domain-based web crawling and content extraction that’s been donated. It can crawl, parse, store, and extract data from websites, as well as product data for usage in Web applications directly.

## Advantages and Disadvantages of R Programming

There are several benefits and some limits of the R programming language. Let us discuss them one by one:

**Pros of R Language**

Because new technologies and ideas generally come first in R, it is the most complete statistical analysis tool.

- Because R is open-source, you may use it anywhere, at any time, and even sell it under the terms of the licence.
- It is cross-platform, meaning it can function on a variety of operating systems. It works well with GNU/Linux and Windows.

Everyone is invited to add bug fixes, code improvements, and new packages to R.

**Cons of R Language**

- Some R packages aren’t up to standard in terms of quality.
- If anything doesn’t work, there is no way to contact R Language’s customer service.
- R instructions have no concern for memory management, therefore R can use up all of the available memory.

## Conclusion

Today, data science is the most widely used technology on the planet. R is the main language of this discipline because it is mostly constituted of statistics. We talked over the different reasons why learning R is the best way to grasp Data Science. Finally, we conclude that knowing R will give you several advantages, including the capacity to handle large volumes of data.