{"id":73627,"date":"2019-12-11T09:47:04","date_gmt":"2019-12-11T04:17:04","guid":{"rendered":"https:\/\/techvidvan.com\/tutorials\/?p=73627"},"modified":"2019-12-11T09:47:04","modified_gmt":"2019-12-11T04:17:04","slug":"pros-and-cons-of-r","status":"publish","type":"post","link":"https:\/\/techvidvan.com\/tutorials\/pros-and-cons-of-r\/","title":{"rendered":"Should you start learning R? Weigh the Pros and Cons of R programming"},"content":{"rendered":"

Though R serves as the best statistical programming language of the world, you need to consider the pros and cons of R before start working with R.<\/strong><\/p>\n

R is helpful at every step of the data analysis process from gathering and cleaning data to analyzing it and reporting the conclusions.<\/p>\n

With machines generating significantly more data, data analysis languages can only be expected to grow<\/strong>.<\/p>\n

But is, however, not as straight up as most people may think (or rather as I used to think).<\/p>\n

Though R is a top practiced<\/strong> programming language to analyze and visualize data with having many advantages, it has its own set of drawbacks.<\/p>\n

For this reason, I would love to share with you the pros and cons of R programming that everyone especially the newbie<\/strong> programmer needs to know.<\/p>\n

First, we will look at the advantages of R to understand the benefits of using it.<\/p>\n

Then, we will look at the disadvantages of R to learn about R\u2019s shortcomings.<\/p>\n

Pros and Cons of R programming<\/h2>\n

So without further ado let\u2019s jump into the pros and cons of R programming!<\/p>\n

\"pros<\/a><\/p>\n

Advantages of R programming<\/h3>\n

R success revolves around several advantages it provides for beginners<\/strong> and experts<\/strong> alike. Here are the powerful advantages of R programming:<\/p>\n

1. Excellent for Statistical Computing and Analysis<\/h4>\n

R is a statistical language created by statisticians. Thus, it excels in statistical computation<\/span>. R is the most used <\/strong>programming language for developing statistical tools.<\/p>\n

2. Open-source<\/h4>\n

R is an open-source programming language. Anyone can work with R without any license or fee. Due to this, R has a huge community<\/strong> that contributes to its environment.<\/p>\n

3. A Large Variety of Libraries<\/h4>\n

R\u2019s massive community<\/strong> support has resulted in a very large collection of libraries. R is famous for its graphical libraries These libraries support and enhance the R development environment. R has libraries with a huge variety of applications.<\/p>\n

4. Cross-platform Support<\/h4>\n

\"Cross<\/a><\/p>\n

R is machine-independent<\/strong>. It supports the cross-platform operation. Thus, it is usable on many different operating systems.<\/p>\n

5. Supports various Data Types<\/h4>\n

R can perform operations on vectors, arrays, matrices, and various other data objects of varying sizes.<\/p>\n

6. Can do Data Cleansing, Data Wrangling, and Web Scraping<\/h4>\n

R can collect data from the internet through web scraping and other means. It can also perform data cleansing. Data cleansing is the process of detecting<\/strong> and removing\/correcting<\/strong> inaccurate or corrupt records. R is also useful for data wrangling which is the process of converting raw data into the desired format for easier consumption.<\/p>\n

7. Powerful Graphics<\/h4>\n

R has extensive libraries that can produce production quality graphs<\/strong> and visualizations. These graphics can be of static as well as dynamic nature.<\/p>\n

8. Highly Active Community<\/h4>\n

The R community is very active. There are users from all around the world<\/strong> to help and support you. Many latest ideas and technology appear in the R community.<\/p>\n

9. Parallel and Distributed Computing<\/h4>\n

Using libraries like ddR<\/strong> or multiDplyr<\/strong>, R can process large data sets using parallel or distributed computing.<\/p>\n

10. Doesn\u2019t need a Compiler<\/h4>\n

R is an interpreted language<\/strong>. This means that it does not need a compiler to turn the code into an executable program. Instead, R interprets the provided code into lower-level calls and pre-compiled code.<\/p>\n

11. Compatible with other Programming Languages<\/h4>\n

R is compatible with other languages like C, C++, and FORTRAN. Other languages like .NET, Java, Python can also directly manipulate objects.<\/p>\n

12. Used in Machine learning<\/h4>\n

R can be useful for machine learning as well. Facebook does a lot of its machine learning research with R. Sentiment analysis<\/span><\/strong> and mood prediction<\/strong> are all done using R. The best use of R when it comes to machine learning is in case of exploration or when building one-off models.<\/p>\n

13. Can Interact with Databases<\/h4>\n

R contains several packages that enable it to interact with databases. Some of these packages are Roracle, Open Database Connectivity Protocol), RmySQL, etc.<\/p>\n

14. Comprehensive Environment<\/h4>\n

R has a very comprehensive development environment. It helps in statistical computing as well as software development. R is an object-oriented <\/strong>programming language. It also has a robust package called Rshiny<\/strong> which can produce full-fledged web apps. R can also be useful for developing software packages.<\/p>\n

Disadvantages of R Programming<\/h3>\n

It’s no secret that there’s also a dark side to R programming. Let\u2019s move on to the disadvantages of using R:<\/p>\n

1. Steep Learning Curve<\/h4>\n

As many have said, R makes easy things hard, and hard things easy. R\u2019s syntax is very different<\/strong> than other languages so are its data types<\/span>. The learning curve for R is pretty steep for a beginner. Though R is a bit difficult in the beginning, data science enthusiasts still prefer to learn it due to the amazing features of R<\/a>.<\/strong><\/em><\/p>\n

<\/div>\n

\"learning<\/a><\/p>\n

2. Some Packages may be of Poor Quality<\/h4>\n

CRAN<\/a> houses more than 10,000 libraries<\/strong> and packages. Some of them are redundant as well. Due to the large quality, some of the packages may be of poor quality.<\/p>\n

3. Poor Memory Management<\/h4>\n

R commands don\u2019t concern with memory management. As a result, R can take up all the available space.<\/p>\n

4. Slow Speed<\/h4>\n

The programs and functions in R are spread across different packages. This makes it slower than alternatives such as MATLAB and Python.<\/p>\n

5. Poor Security<\/h4>\n

R lacks basic security measures. So making web-apps with it is not always safe.<\/p>\n

6. No Dedicated Support Team<\/h4>\n

R has no dedicated support team to help a user with their issues and problems. But the community is quite large, so everybody helps each other out.<\/p>\n

7. Flexible Syntax<\/h4>\n

R is a flexible programming language and there are no strict guidelines to follow. You need to maintain proper coding standards to avoid messy and complicated code.<\/p>\n

Summary<\/h2>\n

In this article, we looked at some of the major pros and cons of R programming language. R has workarounds for some of its cons by using extra <\/strong>libraries<\/strong> or packages.<\/span><\/p>\n

Like any other technology, R is not without its negatives but it is safe to say that the positives outweigh<\/strong> them.<\/p>\n

But from my experience, I would recommend you to take an overview of R<\/a><\/strong><\/em> if you are yet not clear what exactly R is!<\/p>\n

Still, any confusion<\/strong> regarding R programming?<\/strong><\/p>\n

Ask TechVidvan experts<\/strong> in the comment<\/strong> section below!<\/p>\n

Keep Learning.<\/p>\n","protected":false},"excerpt":{"rendered":"

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