Updated June 19, 2023

What is R?

R is the most powerful and widely used programming language for computational statistics, visualization, and data science. Many data scientists and statisticians use R to solve their problems in the data analytics field in all the sections, from computational biology to quantitative marketing. R is a GNU project which is more like the S language. Therefore, it is considered a dialect of the S language.

R provides a wide range of statistical and graphical techniques. As a result, r is highly extensible and offers a lot of choices for research people. R is free software on various platforms like UNIX, Windows, Linux, and MacOS. R is also the most popular and successful language with some powerful features. Top-rated companies like Google, Facebook, Shell, Merck, Bank of America, Pfizer, and LinkedIn use R.

R Environment

R is an integrated suite of software facilities developed for data analysis, statistical modeling, simulation, and graphics. R is a simple and effective language that includes conditionals, loops, user-defined recursive functions, and input and output facilities.

R includes the following –

Effective data handling and storage facility.
Set of operators for the calculation of arrays.
A huge integrated collection of intermediate tools for data analysis.
You can display graphical facilities in R on-screen or hardcopy format.
Allows users to add additional functionality.
C, C++, and Fortran code can be linked to R and called at run time.
R can be extended through packages easily.
R has its Latex-like documentation format, which supplies comprehensive documentation.

Why Use R Analytics Tool?

There is a lot of software available for data analysis today, but still, R stands ahead of all the other tools. There are many reasons why data scientists choose R.

A few reasons are listed below –

1. R is Free

R is an open-source project which you can download and use for free. You don’t have to worry about subscription fees, license fees, license renewals, or user limits as R is an open-source software you can inspect and make changes to the code as you like. So many experts have contributed to R in such a way that it now benefits millions of people who use it today.

2. R is not just a Statistical Package; it is a Language

R is an easy language to learn. In R, you analyze data by writing functions and scripts rather than relying solely on simple clicks. It offers a natural and expressive way for data analysis. There are many advantages to learning this language. It is the most interactive language when compared with other programming languages. R provides an innovative way of data analysis and leads to better results that otherwise would not have been done. A script records all your work which can be re-run at any time. These scripts also make it easy for you to automate tasks that can be integrated into other processes.

3. Data Analysis Techniques at your Fingertips

R contains all the traditional and recently developed data manipulation techniques. It also offers you different statistical models and charts that all the data scientists feel are essential to them. You can easily download any method free of cost.

4. Powerful Graphics and Data Visualization

One of the main features of R is the visualization of data through charts and graphs. R offers special tools for producing graphics, from information provided in the form of bar charts and scatter plots to multi-panel Lattice charts. Experts in data visualization, like Bill Cleveland and Edward Tufte, also believe in R’s graphical system. Reputed places like the New York Times, the Economist, and the FlowingData Blog utilize R due to its excellent graphical properties.

5. Faster and Better Results

R stands ahead of the usual black-box procedures of other data analytics tools. R is designed especially for data analysis to create faster results than statistical software programs. It also offers you to mix and match models for better results. The automation of R scripts enables you to deliver reproducible research.

6. Cutting Edge Statistics with R

Leading data scientists and researchers worldwide use R for statistics, machine learning, and predictive modeling. As a result, R has a lot of packages that are not available in any other statistical tool in the data analysis market. More than 2000 packages are available in the R language in every domain for free download. And more packages are added frequently. Because of such packages, R is widely used in fields like finance, genomics, etc. One example of such a package is “glmnet.” This exclusive modeling package handles the prediction of interval and categorical dependent variables efficiently.

7. Flexible Data Analysis Tool

R has built a standard data analysis tool that includes accessing data in various formats, data manipulation, and traditional and modern statistical models. Statistical models include regression, ANOVA, GLM, and tree models. All the statistical tools are available in an object-oriented framework that will help you to extract only the information you need from the results. It will thus reduce unwanted statistics, and you don’t have to produce a cut-and-paste static report.

8. Robust Community

If you have any R-related questions, there is an online answer. There are a lot of community resources available on the internet for R. R has thousands of contributors and more than two million users from around the world. This is one of the main reasons you can find answers to all your R-related questions online.

9. Unlimited Possibilities

R is an open-source community; many people contribute directly to the R language. It does not have a pre-defined set of codes. Instead, you can use codes that others contribute to the R community. You can also extend the R functions. R mingles easily with applications like MySQL database, Apache web server, and Google Maps.

10. Wide Acceptance of R Analytics Tool

R is used widely by most industries in India and abroad. R is becoming a leader in the business analytics field. Recently SAS is also being integrated with R and is included in SAS/ML Studio 3.2 version. The government primarily uses R in clinical research.

Special Features of R Analytics Tool

R is available for Windows, Linux, and MacOS platforms.
R lets you input data from SAS, Excel, SPSS, STATA, etc.
R creates complicated plots and charts of great quality, making it an excellent tool.
R lets you display statistical concepts using animations.
R can be embedded in Excel. This will provide you with a new range of statistics and graphical properties.
R has good community support, which answers all your queries. Most of the responses come from experts in the data analysis field. The community gives you a detailed insight into the R language for newbies.
R provides a data analytics solution on the web by embedding R on a server through the Apache server.
CRAN contains numerous packages in R, each written for a specific purpose and divided based on their target domain.
R contains codes to perform all the basic and advanced statistics.
R has Revolution Computing which is an enterprise R provider.
R has a blog that discusses the latest developments, usages of R in various fields, and other matters related to R.

Why does R Analytics Tool get so much Attention?

Many languages are used for data analysis, but most prefer using R for business. The reasons why R is preferred more than other languages are listed below.

More interactive language – Interactivity and Language are more important for data analysis. R, in other words, is an interactive language, making it more special than other languages.
Data structures – R also offers an excellent way to create data structures. You don’t need to put all your data in pre-defined structures. You can create your structure and put your data in a natural form.
Excellent graphical properties – Human brains are more attracted to pictures than numbers or text. R gives you a high-quality graphical representation of data.
Missing values – Real data always have missing values. R has several functions that will help you deal with missing values. Missing values are an integral part of R.
Lots of packages – R has many packages that allow you to add your functionality to make it unique. These packages help you do extraordinary things with R.
Support and Community – R has a very supportive community that helps improve data analysis through the R language.
First-class objects – Objects here mean functions like mean and median. You can shift between the functions more quickly in the R language.
Vector oriented – Objects in R are considered as a whole rather than treating it as a collection of individual numbers. For example, if you want to change the volume from liters to milliliters, then a simple code change has to be done.
Flexibility – R does not compel you to work only within its language. You can mix different tools and select the best tool for each task. You can also call C and C++ functionality.
Changes made easy – The codes written in the R language are accessible to the users. You can make minor changes to the task and modify the code in a shorter period.

Applications of R Analytics Tool

R applications are used in various fields, from astronomy, finance, and chemistry to medicine. R’s packages are dedicated to specific applications. You can also create your R application and don’t need to be a genius.

Some of the R packages for applications are listed below –

Clinical Trials
Cluster Analysis
Computational Physics
Differential Equations
Environmental Studies
Finance
Genetics
Graphical Models
Graphics and Visualization
Machine Learning
Medical Imaging
Natural Language Processing
Official Statistics

R Community

R is a global community where many people spend time and effort maintaining and developing the R language and its environment. This community plays an important role in making R a high-quality and transparent software. R community does the following activities.

Supports and enhances the R language
Enlarge the scope and power of the R language
Create new applications in different fields using R
Talk about the benefits and advantages of R
Provide help for beginners to start with R

Contributors to R Analytics Tool

R Analytics Tool has thousands of contributors from different parts of the world. The heavy lifters of R are the members of the R group. They make changes, fix bugs, test the code, maintain the CRAN, and keep it going on all the supported platforms.

Conclusion

From this article, R Analytics Tool, you can clearly understand R. Research has proven that R is the most critical platform for developing new statistical methods. It stands ahead of other data analysis tools. If you are a data scientist, you should learn R like any other language. If you love programming, you will love working with R. If you want to become a master of the R language, then you need to practice the language often and use it for performing important tasks.

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