R programming language has been one of the leading preferences of data scientists, researchers and statisticians. R is a GNU package which appeared in late 1993; It is a free software environment for statistical computing. In recent years, the popularity of R has increased greatly due to advances in the field of data analysis. While it takes great time and effort to get your tasks done in R language, you can always get R programming assignment help.
As data science is evolving day by day, it can be assumed that data science is the future of business analytics. In this competitive environment, you don’t want to lag behind your competitors and therefore no one wants to waste any time on the wrong tool. You should always know which is the best tool for this task, to figure this, here are some points which prove that R is the best programming language for data science.
1. R is data science for non-computer scientists
If you go and look for advanced data science tools, you’ll only find two options: R or Python.
Python is a programming language for software engineers with knowledge of mathematics, statistics and machine learning but it lacks library support for important topics regarding topics such as econometrics and various communication tools such as reporting.
Mostly, people interested in data science for business are from a business background and not from technology in development and programming. Teaching Python is a challenge for them and it is not coupled with the support provided by econometrics, and most of the activities in the field of business include financial affairs the connection that is in Form charts, reports or interactive applications. Obviously, support for these two is also not provided by python so we need to look at our other option which is R.
R is a statistical programming language with support libraries for ML, Stats, and Data Science. R is best suited to business data science because it lends itself perfectly to its in-depth support for subject matter packaging and its communication infrastructure. Besides this R contains support packages or financial libraries, econometrics, etc. which are widely used for business analytics, it is interactive and easy to use in comparison with the complexities of Python.
2. Learning R is easy after entering “Tidyverse”
Initially, R was considered one of the most complex languages to learn and it is assumed that it is very inconsistent, as structural and formality during that period were not of the highest priorities as they were in other programming languages. But that all changed when Tidyverse was introduced, which is a collection of packages and tools that provide a consistent structured programming interface.
With the arrival of tools like dplyr and ggplot2 the complexities of the learning curve have been reduced even further. As with the time R remained in development like any other programming interface it made more and more structured and consistent, Tidyverse became more efficient, which included support packages for manipulation, visualization, iteration, modeling, and communication, which made R all an easy language to learn.
3. R is mainly for business:
The main advantage of R compared to any other programming language is that it is able to produce business ready reports and infographics, and ML powered web applications.
RMARKDOWN is a framework for creating rebuilt reports that have come a long way in building blogs, even presentations, websites, book magazines, and much more. Not only does this tool look great, in fact, it is used by many top management companies as a way to prepare a business analysis report for their companies and even market what they achieve through this wonderful framework.
“Shiny” is a framework capable of creating interactive web applications supported by R. This is a widely used framework where almost all projects require a website where results are displayed, hence, shiny tool is a very useful tool.
4. R is #1 in the field
The term R as just being strong is actually just a lack of strength that it has. From a corporate perspective, R is basically Excel on steroids and a lot of them. R is not only powerful but it is smart and has a strong infrastructure. It implements many algorithms including High-End Machine Learning Package (H2O), TensorFlow Deep Learning Packages, xgboost top Kaggle algorithm and many more.
I objected to the infrastructure that we have talked about a lot already, but this infrastructure is the main strength of the R language as this enables the development of the application ecosystem using a more appropriate and stable hierarchical approach. It comes with libraries like “dpylr”, “tidyr”, “stringr”, “lubridate”, “forcats” and many more, making the development process easier.
Thus it would not be wrong to say that R is a universal firm.
5. Community support
For any programming language or programming interface that exceeds the needs of community support to be first class, even if it is the best product but without community support, it will most likely not be used as there will be no helping hand and there will be no analysts. Like any other language, R has great community support. It is a group of passionate technology enthusiasts who have a lot of desire to learn and provide learning, and the community is always kept in a fun environment, every question is answered peacefully and quickly, a helping hand is provided for beginners, all the things that a beginner might need are already in place and this is the coolest part Of the existence of this huge community.
All of these features make R stand out when it comes to business analytics via data science, as this technology has gotten the light in the past few years to learn this now and it could be useful for beginners and even former developers or for someone with a non-programming background.