Pros and Cons of R Programming Language – Unveil the Essential Aspects!

In this article, we will discuss the various pros and cons of the R language. We will first go through the various advantages that the R language provides to us and then we will have a look at some of the negatives of this language i.e disadvantages of R.

As we all know, the R language has a unique place in the programming world, which shows its importance. But it is a universal fact that each technology utters some positive as well as negative aspects. The same thing applies to R programming. There are some pros and cons of R programming, which proves the fact to be right.

Before exploring the advantages and disadvantages of R, you must know Why Learn R Programming

Pros and Cons of R Programming Language

R is one of the most popular languages for statistical modeling and analysis. But like every other programming language, R has its own set of benefits and limitations.

In this article, we will discuss the weighing of the pros and cons of R programming against each other. R is a continuously evolving language. This means that many of the cons will gradually fade away with the future updates of R.

Advantages of R Programming

Various benefits of R language are mentioned below, which will help you to grasp the concept:

1. Open Source

R is an open-source programming language. This means that anyone can work with R without any need for a license or a fee. Furthermore, you can contribute towards the development of R by customizing its packages, developing new ones and resolving issues.

2. Exemplary Support for Data Wrangling

R provides exemplary support for data wrangling. The packages like dplyr, readr are capable of transforming messy data into a structured form.

3. The Array of Packages

R has a vast array of packages. With over 10,000 packages in the CRAN repository, the number is constantly growing. These packages appeal to all the areas of industry.

4. Quality Plotting and Graphing

R facilitates quality plotting and graphing. The popular libraries like ggplot2 and plotly advocate for aesthetic and visually appealing graphs that set R apart from other programming languages.

5. Highly Compatible

R is highly compatible and can be paired with many other programming languages like C, C++, Java, and Python. It can also be integrated with technologies like Hadoop and various other database management systems as well.

6. Platform Independent

R is a platform-independent language. It is a cross-platform programming language, meaning that it can be run quite easily on Windows, Linux, and Mac.

7. Seamless Database Integration

R seamlessly integrates with various databases like MySQL, PostgreSQL, and MongoDB. This empowers users to directly query, manipulate, and analyze data stored in databases, eliminating manual data extraction. By leveraging R’s database integration, users efficiently handle large-scale data sets, conduct complex analyses, and derive actionable insights.

8. Dynamic Data Visualization

R excels in dynamic data visualization, enabling users to create immersive visualizations that aid exploration and interpretation of complex data sets. With advanced graphical libraries like Plotly and Leaflet, R facilitates the development of interactive plots, maps, and dashboards for real-time data analysis. These capabilities foster collaboration and engagement by allowing stakeholders to explore and uncover hidden insights. By harnessing interactive visualization, R enables users to drive data-driven decision-making and effectively communicate findings to diverse audiences.

9. Eye-Catching Reports

With packages like Shiny and Markdown, reporting the results of an analysis is extremely easy with R. You can make reports with the data, plots and R scripts embedded in them. You can even make interactive web apps that allow the user to play with the results and the data.

10. Machine Learning Operations

R provides various facilities for carrying out machine learning operations like classification, regression and also provides features for developing artificial neural networks.

11. Statistics

R is prominently known as the lingua franca of statistics. This is the main reason as to why R is dominant among other programming languages for developing statistical tools.

12. Continuously Growing

R is a constantly evolving programming language. It is a state of the art technology that provides updates whenever any new feature is added.

If you want to learn R from scratch then just have a look at 100+ Free R tutorials that DataFlair offers to make you R expert.

Disadvantages of R Programming

1. Weak Origin

R shares its origin with a much older programming language “S”. This means that it’s base package does not have support for dynamic or 3D graphics. With common packages of R like Ggplot2 and Plotly, it is possible to create dynamic, 3D as well as animated graphics.

2. Data Handling

In R, the physical memory stores the objects. This is in contrast to other languages like Python. Furthermore, R utilizes more memory as compared with Python. Also, R requires the entire data in one single place, that is, in the memory. Therefore, it is not an ideal option when dealing with Big Data. However, with data management packages and integration with Hadoop possible, this is easily covered.

3. Basic Security

R lacks basic security. This feature is an essential part of most programming languages like Python. Because of this, there are several restrictions with R as it cannot be embedded into a web-application.

4. Complicated Language

R is not an easy language to learn. It has a steep learning curve. Due to this, people who do not have prior programming experience may find it difficult to learn R.

5. Memory-Intensive Data Handling

In R, objects are stored in physical memory, unlike languages like Python. R also tends to utilize more memory than Python and requires all data to be loaded into memory at once, making it less suitable for Big Data applications. However, with data management packages and integration with Hadoop, this limitation can be mitigated.

6. Security Concerns

R lacks basic security features, which are standard in languages like Python. This limitation restricts its usage in environments where robust security measures are required, such as embedding R into web applications.

7. Lesser Speed

R packages and the R programming language is much slower than other languages like MATLAB and Python.

8. Spread Across various Packages

The algorithms in R are spread across different packages. Programmers without prior knowledge of packages may find it difficult to implement algorithms.

Summary

In this article, we went through the essential pros and cons of R programming. We got to know the positive aspects of R Language which place us a step ahead towards generating our interest in learning R. We also inferred many of its weaknesses but, most of them are under the correction phase through several upgrades and further development. Therefore, many of the limitations will be eradicated in the future.

Now, its time to learn about the Applications of R and find out which top companies are using R.

Any doubts? Ask our DataFlair experts, they will assist you in the best possible way.

Keep Learning.