Top Reasons For Why Should You Use R for Data Science

R is becoming on of the most popular language in the world . Due to increasing the need of big data. R comes into the popularity index among students. Basically R is used for statistical computations, data analysis and Graphical representation of data. R is facing a huge competition from Python. R programming plays an essential role in Data Science with the emergence of Data Science. As it has several tools to perform statistical computation, data analysis, data processing, data transferring, and so on.

Data Science

Data Science is a multifaceted field which uses different scientific tools, methods, process, systems, and algorithms to draw out deep understanding and knowledge from both structured and unstructured data. It is primarily related to big data, data mining, and deep learning.

What is R Programming

R is a language created by Ross Jhaka and Robert Gentleman in 1990. The ‘R’ name is derived from the initials of both of their names. It is generally used by Data analysts for statistical computation of data, data analysis, and graphical representation of data. The primary usage of the R language is in Data Science.

Using R for Data Science

Data Science has become the takeaway field in today’s world that’s why the need to analyze and construct the insights from the data has emerged. Thus, the R language provides a rigorous environment to process the data and to draw interpretations thereto. R contains several branches like astronomy, biology, etc. At present, R is used for both academic and industrial purposes. 

R is an advanced language used in Data Science as it can perform complex statistical computations. It can also be used to perform operations on arrays, vectors, and matrices, etc. Since it displays the data through graphical representation therefore it makes the data intractable for the users. 

The foremost part of Data Science is a data extraction and allows interface R code with its database management system. Moreover, R consists of ample options for advanced data analytics namely, machine learning, algorithms, etc. It also has many packages to perform image processing.

Data Wrangling

Data wrangling is the process to structure the unstructured data for further analysis. This process takes a lot of time in data science. The data is collected from various sources.

Therefore each source has its own way to present the data.So it becoming hard to manipulate the data and it takes lots of time. But with the use of R language the data wrangling process become easier.

Data wrangling is a time-consuming process of cleaning composite and messy data sets to easy access and construction. Since it is a very prominent and time-consuming process in data science therefore R plays a key role in performing Data Wrangling speedily and easily as R has an intensive library of tools for manipulation and wrangling of data sets.

Here the the reason why it is easy to manipulate and wrangle data using. The following tools in R make this process easy:-

  • Deplyr
  • data.table Package
  • readr Package

Data Visualization

Data visualization is the process to visualize the data in graphical form. This helps in analyzing the data through angle that are not clear in unorganized data. R comes with the large number of tools for data visualization, analysis, and representation.

When the data is represented in the graphical form then it is much easier to analyze data from different perspectives. R programming consists of several tools for data visualization, analysis, and representation. GGPLOT2 and GGEDIT are the most important standard plotting packages in R. Where GGPLOT2 performs data visualization and GGEDIT fills the gap between making a plot and getting the entire trouble-making plot as correct.

Specificity

R is not common as other programming languages. R is especially designed for statistical and data reconfiguration. The library of R is especially designed to make data analysis easier, more detailed and approachable.

R libraries enable each and every new statistical methods. Therefore R becomes the perfect choice for data analysis and projection.

The best part of R language is that it holds a large community where each and every aspirant help each other to solve complex problem with R language.

The main objective of R libraries is to make data analysis easier, more perspective, and enhanced. Since all the new statistical methods are first enabled upon R libraries that is why it is always preferred for Data science. All the members of the R community are always active, knowledgeable, and highly supportive that’s why it has emerged as the first choice of data science projects.

Machine Learning

Data science is all about the prediction. That is why the data scientist need to build up an algorithm that can make prediction. For this  R provides large numbers of tools to developers to train and evaluate an algorithm and predict future events.

As data science, analysts may be required to train algorithms and to automate them accordingly and to make future predictions. And R helps the programmers to use ample tools to train and develop the algorithms and to make future predictions thus, R makes it easy for Data Scientists to learn a branch of data science ( that is machine learning ) easy and fast.

  •  MICE
  • rpart & PARTY
  • CARET
  • randomFOREST

Availability

R is an open source programming language. Therefore it is free to use and implement in the data science project. It is a better and cheaper option to develop large projects.

There are lots of free resources available online for R languages. any beginner can learn R programming language with the help of community members of R.

Even an company can hire the R developer through the community that makes it cost effective data science programming language.

Accessibility

R language is open-source so anyone can use this programming for data science easily. Thus it is a very cost-effective and efficient tool to perform data analysis and data disfiguration irrespective of the size of the project. Since it is easily accessible by everyone at minimal charges therefore it has emerged as the perfect choice to begin learning the R language for Data Science.

Code without complier

Since R is an interpreted language therefore anyone can learn this language for free and anyone can run code without complier. R draws analyzes and develops the code easily and promptly.

Statistical calculations without Vectors 

R is a vector language in which anyone can add functions in the single vector without putting in a loop that is why R is very strong and prompt as compared to other languages. 

Trendy 

As the R language is easy to access for free so everyone has started to learn R programming. Thus, it has become prevalent in both academia and industries with the emergence of Data Science. 

Web Application 

With the help of R, one can build his aesthetic web applications as by using R shiny Package one can build interactive dashboards directly from the console of R IDE. 

Conclusion

The programming R has also developed and emerged with the rapid growth of data science. Data science is a branch whereby numerous statistical tools and methods are used for data analysis and data interpretation that’s why R is widely used in the same.

Because R language is easy to access for free and it is machine learning and there are several reasons as discussed above to use R in Data Science. Thus we can conclude that in today’s world Data Science and R programming go hand in hand. This is beneficial for both the company and for the developer. If you are already doing data science course then you can R programming assignment help to score higher grade.

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