What is Linear Regression? | A Complete Guide

• In this article you will learn:
• 1.What Is R?
• 2.What Is R and What Are the Advantages?
• 3.Does R Have Any Drawbacks?
• 4.What is R Used For?
• 5.The Popularity of R by Industry.
• 6.What Are the Most Popular R Packages?
• 7.Conclusion.

What Is R?

What better place to find good definition of language than the R Foundation’s website? According to the R-Project.org, R is “… a language and environment for the statistical computing and graphics.” It’s an open-source programming language often used as data analysis and statistical software tool. The R environment consists of integrated suite of software facilities designed for a data manipulation, calculation, and graphical display. The environment features are:

• A high-performance for data storage and handling facility.
• A suite of an operators for array calculations, mainly matrices.
• A vast, simply understandable, integrated assortment of an intermediate tools dedicated to data analysis.
• Graphical facilities for the data analysis and display that work either for the on-screen or hardcopy.
• The well-developed, simple and an effective programming language, featuring are user-defined recursive functions, loops, conditionals, and input and output facilities.

The syntax of a R consists of 3 items:

• Variables, which store a data.
• Comments, which are used to improve the code readability.
• Keywords, reserved words that have special meaning for compiler.

R was developed in the 1993 by Ross Ihaka and Robert Gentleman and includes a linear regression, machine learning algorithms, statistical inference, time series, and more.R is universal programming language compatible with Windows, Macintosh, UNIX, and Linux platforms. It is often referred to as a various implementation of the S language and environment and is considered be highly extensible.

What Is R and What Are the Advantages?

• It’s an open-source. No fees or licenses are needed, so it’s a low-risk venture if developing new program.
• It’s a platform-independent. R runs on all the operating systems, so developers only need to create the one program that can work on competing systems. This independence is yet another reason for why R is cost-effective!
• It has a lots of packages. For example, R language has more than 10,000 packages stored in a CRAN repository, and the number is continuously increasing.
• It’s great for statistics. Statistics are big thing today, R shines in this regard. As result, programmers prefer it over the other languages for a statistical tool development.
• It’s well suited for the Machine Learning. R is ideal for machine learning operations like regression and classification. It even offers many features and packages for the artificial neural network development.
• R lets perform data wrangling. R offers host of packages that help to data analysts turn unstructured, messy data into structured format.
• R is a still growing. R keeps an evolving and growing, constantly updating and upgrading, thanks to solid supportive community.

Does R Have Any Drawbacks?

• It’s the complicated language. R has steep learning curve. It’s a language best suited for the people who have previous programming experience.
• It’s not as a secure. R doesn’t have a basic security measures. Consequently, it’s not good choice for making a web-safe applications. Also, R can’t be embedded in a web browsers.
• It’s a slow. R is slower than the other programming languages like a Python or MATLAB.
• It takes up lot of memory. Memory management isn’t one of a R’s strong points. R’s data must be stored in a physical memory. However, the increasing use of a cloud-based memory may eventually make this drawback moot.
• It doesn’t have a consistent documentation/package quality. Docs and packages can be patchy and be inconsistent, or incomplete. That’s the price pay for the language that doesn’t have official, dedicated support and instead is maintained and added to by a community.

What is R Used For?

Although R is famous language used by many programmers, it is especially an effective when used for:

• Data analysis.
• Statistical inference.
• Machine learning algorithms.

R offers the wide variety of statistics-related libraries and provides the favorable environment for statistical computing and design. In addition, the R programming language gets used by a many quantitative analysts as a programming tool since it’s useful for the data importing and cleaning.As of a August 2021, R is one of top five programming languages of the year, so it’s a favorite among the data analysts and research programmers. It’s also used as fundamental tool for finance, which relies a heavily on statistical data.

The Popularity of R by Industry:

• Fintech Companies (financial services).
• Academic Research.
• Government (FDA, National Weather Service).
• Retail.
• Social Media.
• Data Journalism.
• Manufacturing.
• Healthcare.

This graph, provided by a Stackoverflow, gives a better idea of R programming language usage in a recent history. Given its strength in statistics, it’s hardly surprising that are R enjoys heavy use in a world of academia, as illustrated on the chart.If for specifics, here are ten significant companies or an organizations that use R, presented in a no particular order.

• Airbnb.
• Microsoft.
• Uber.
• Facebook.
• Ford.
• Google.
• Twitter.
• IBM.
• American Express.
• HP.

What Are the Most Popular R Packages?

R packages are explained as collections of R functions, sampled data, documentation, and compiled code. These elements are stored in the directory called “library” within R environment and are installed by a default during installation. R packages boost a R’s power by improving an existing functionalities, collecting sets of a R functions into one unit. In addition, R package is a reusable resource, which makes the programmer’s life much easier.

Conclusion:

From this article, can have a clear idea about a R tools technology. It is also proved that it is most important platform for development of new statistical methods. It stands ahead of the other data analysis tools. If data scientist then should definitely learn a R like any other language. If programming then will definitely working with a R tools technology. If want to become the master of R language then need to practice the language often and use it for a performing important task.