Top Reasons to Choose R Programming For Your Next Project
Ross Ihaka and Robert Gentleman created R programming in the 90s and from then on the programmers have made the best use of it as an efficient statistical platform in data cleaning, data handling, analysis and representational. Down to this day, programmers see immense value in R programming when working with statistical computation, graphically representing data, and data analysis. When launched, the R programming wasn’t as popular as in our days. Nowadays, R programming is a preferred option for data science projects.
Let us check out some of the top reasons why choose R programming for your next project. What are some of the pros and are there any cons that you need to beware of. Let’s dive in.
R Programming is Open Source
Digital transformation is an expensive affair, and cost is a major consideration in several projects. R programming comes as good news because it is an open source language. So anyone can work with R programming without the need to pay any fees or procure a license. Programmers also get an opportunity to contribute within the community by resolving existing issues, customizing packages, and also by developing new ones.
R Programming has Great Support in Data Wrangling
When it comes to transforming one raw format of data to another, programmers rely on R. The process is known as data wrangling and is mostly used when working on analytics. The raw data is messy and is not of any value unless transformed into intelligible data. This is when programs such as readr and dplyr come in handy to give a proper structure to the data. With years of updates and modification, r programming has had several new features added. As a result, many programmers prefer this language when there is a need for data wrangling.
Wide Range of Packages
The CRAN repository has over 10,000 packages and this number keeps growing with time. With a wide range of packages there is a lot of variety for the programmers to choose from. The packages come in handy for a wide area within the industry. The packages contain packaged data and compiled code specific to the process being worked on, and this further eases the work of programmers.
Compatible Program
There are a wide variety of programs being used in the industry and some of the other languages are python, java, C, C++. R is highly compatible with these languages and adds immense value to the programmers when working on projects that need compatibility within these languages. It also comes in handy when working with data management systems such as Hadoop and others. A high level of compatibility is a boon for programmers in our times as an organization and industry-wide digital transformation requires transparent and compatible programs.
Exceptional Reports
When working with r programming, you can use packages like markdown and shiny, and these make it extremely easy to report analysis. You can easily embed the r scripts and make reports on plots or data depending upon the need. It is crucial that web apps be interactive to retain user attention, and you can easily achieve this with R. You can create apps that are interactive wherein users can play with the results of the data. With exceptional reports, it becomes easy for programmers to create customized solutions for clients.
Ease of Using in ML Projects
Machine learning is futuristic and several organizations look for reliable solutions to further navigate their digital efforts. R programming has various facilities to enable efficient machine learning solutions such as regression and classification. You can also use the customized solutions to use in networks that are artificial intelligence neutral.
While there are many pros of using r programming, we can still look at it with an unbiased glass.
Some of the Worrying Factors
R programming is famous for several tangible benefits and variety for programmers. However, there are a few shortcomings in memory management and security as well. There is still some more room for improvement in R language when it comes to speed, memory management, and efficiency. When moving in from some of the other languages to r programming, some of the programmers may find it quirky. When working with large sets of data sheets, the programmers may find it challenging due to the design of the program.
Security was not built in with the language when released and it has been one of the issues why it is almost impossible to use R as a backend server to do calculations. However, now with the introduction of cloud servers such as Amazon Web Services and its virtual containers availability, the security concerns are handled well. Although there have been several new developments in this field, it is good to check how r programming fits into your project needs.
Help from Programmers
When identifying whether to go for r programming or any other language for your next project, why not take help from the professionals. Professionals with years of experience in programming will be able to identify areas that you may have overlooked. They may also be able to suggest using two or a mix of programming languages on a particular project.
When it comes to keeping up with the speed at which digital transformation is going, it is best to get an opinion on what is best today and will stay relevant in the future. Trust us…R programming is a language that has been there for years and we look forward to executing many more exciting projects using this language.
Codetru at the Digital Front
We have been in the mobile and web app development for a long time and have a good understanding of how technology can be effectively used to scale a business. We have an exceptional team working on ground breaking solutions using state of the art technology for clients from across the globe. If you are planning to use r programming with your next project, we will be happy to help you implement the best solution.
FAQs About R Programming
1. What are the main advantages of R Programming?
The advantages of R Programming are its open-source nature, extensive package ecosystem, and strong data contention. It is widely used for statistical analysis, data visualization and machine learning, making it a favorite of data scientists and researchers.
2. How does R Programming compare to other languages like Python?
When comparing R Programming vs Python, R excels in statistical analysis and visualization, while Python is known for its versatility and general purpose programming. Both languages are highly compatible, often used together in data science projects to utilize their capabilities.
3. Can R programming handle big data?
R programming with big data can be difficult due to memory management issues. However, R integrates well with big data technologies like Hadoop and Spark, and packages like dplyr and data.table can improve performance with large datasets.
4. What types of applications are most useful from R programming?
Projects that benefit from R Programming typically involve mathematical calculations, data analysis, and visualization. It is particularly useful in areas such as bioinformatics, economics and social sciences, which require complex data analysis and interpretation.
5. How can I improve security when using R programming?
Security improvements in an R system can be achieved by using secure cloud services such as AWS and implementing data binding and compliance best practices. Although R was not originally designed with security in mind, however, modern tools and services help minimize these concerns effectively.
6. What tools improve reporting in R programming?
Tools for Reporting in R Programming include packages like Markdown and Shiny, which facilitate creating interactive web applications and comprehensive reports. These tools help in embedding R scripts, generating plots, and presenting data in a user-friendly format, enhancing the overall data analysis experience.
