Top Reasons to choose R Programming for Data Science Projects

Introduction

With the increase in data, there is a rise in demand for data scientists. There is a situation now seeking tools in advanced data analytics that help in developing insight from the data. R is popular and is a prominent tool for Data Science preferred by the statisticians and data scientists. We analyze the Top Reasons to choose R Programming for Data Science Projects in statistical computing and graphic techniques.  There are around 10,000 packages in the CRAN repository that have an appeal in many statistical applications. All the users have to gain proper knowledge of statistics to get the advantage of R.

1. It provides an intensive environment

The first reason to Choose R for Data Science is that it is a programming language that provides an intensive environment for analyzing, processing, and visualizing information. It is effective in designing statistical models to solve complex problems in the domains of astronomy, and biology, and is used in a few industries also.

2. Support in operations

R is used to do complex statistical modeling. It provides support in operations related to arrays, matrices, and vectors. Additionally, its graphical libraries allow the users to describe aesthetic graphs. It also allows the users to develop web-applications through R Shiny, for embedding visualizations in the web-pages providing a top-level of interaction for the users. For data extraction there is an interface through an R code with database management systems. It provides many options of advanced data analytics also. There are prediction models, as well as machine learning algorithms and packages for image processing.

3. Many Impressive features

Additionally, it is dynamic in nature that is available under the GNU GPL v2 license allowing a free use. It allows free downloading and modification of the code and development of own libraries. It is complete language. It is backed by technically sound professionals with an aim to enhance it continuously. There is an active community support making its learning simple for all categories of users. In order to manage data science projects it is better to become familiar with programming concepts and R programming language.

4. It is Specific:

R is special for statistical analysis and data reconfiguration. Moreover, its libraries have a focus on making data analysis easy, approachable and detailed. All new statistical methods are initially enabled through R libraries and R is the right option in data analysis and projection. Furthermore, its Members are active and supportive, besides possessing extensive statistical knowledge.

5. Impressive statistics

Rexer Analytics of the US offering business analytics and marketing research services after surveying data scientists showed in a 2015 survey of a 76% growth in R usage.  In 2016 as per the data science salary survey of O’Reilly, R enjoyed the second ranking in programming languages for data science. In the KDnuggets Analytics software survey poll, R was at the top rank securing 49% vote. Thus it is an ideal choice for boosting a career in analytics, and is worth the focus.

6. Facilitates Machine Learning:

Programmers also may need to train the algorithm and opt for automation and learning capabilities in order to make predictions. R is ideal for developers to train and evaluate algorithms thereby making predicting future events easy. R makes machine learning easier and approachable. There is a long list of R packages facilitating machine learning like  MICE,  part & PARTY  CARET  random forest serving in a big way for the user.

7-A powerful infrastructure

R helps to perform analytical operations through the support libraries making cleaning, organizing, analyzing and visualizing data  creating the predictive models. R has the capacity to implement top algorithms, like the TensorFlow deep learning packages, ML package H20, and XGBoost. This will lead to an added advantage for the user and improve his/her potential in a smart manner.

 8-Inherent smartness

It is a developer-friendly language allowing for changes and updates. Another feature is that the Learning is easy and convenient adding to its huge appeal. R also provides many boot camps and workshops to provide support to the users.

9 Extensive Community support of data scientists and statisticians 

There is an explosion in the field of data science and as per the statistics quoted above, R has become a fast-growing language in the world. That means it is easy to find answers in it for the vexing questions.  There is community guidance also. It is backed by technically sound professionals with an aim to enhance it continuously. There is active community support making its learning simple for all categories of users. In order to manage data science projects, it is better to become familiar with programming concepts and R programming language.

Conclusion

After knowing these top Reasons to choose R Programming for Data Science Projects, we can say that is a good choice to gain mastery and the learner can expect to rise in the competitive field.