Role of R in Data Science: 3 Critical Aspects

As more and more companies are joining the Data Science crusade, the need for using Programming Languages for Data Analysis and building complex Statistical Software is escalating rapidly.

This has led to the proliferation of Programming Languages like R in Data Science, Python, etc. According to TIOBE’s index and IEEE Spectrum, R in Data Science is one of the most popular Programming Languages in the world.

This article provides a comprehensive overview of R in Data Science and how it can be useful for every Data Scientist.

What is R in Data Science?

R is a Procedural Programming Language that breaks down a task into a sequence of Stages, Processes, and Subroutines. This allows R to easily transform data into meaningful Statistics, Graphs, and develop Statistical Learning Models for predictions and inferences.

Distinctive Features of R in Data Science

Built from the ground up for Statistical Analysis, R in Data Science has become one of the favorite Programming Languages for Data Scientists.

• Quick Analysis
• Better Integrated Development Environment (IDE)
• Speed
• Range of Database Support
• Interactive Web-Based Dashboards

1) Quick Analysis

Tidyverse is used for manipulating data and plotting graphs for various Data Analyses. It has a collection of R Packages like Tidyr, Dplyr, and Ggplot2.

It also offers a consistent Structural Programming Interface for the R. Tidyverse streamlines the process of creating Data Science Applications using Packages like Forcats, Lubridate, and Stringr, making R a one-stop solution for Data Analysis.

2) Better Integrated Development Environment (IDE)

The IDE for R and RStudio is quite well-designed and supports several Scripting Languages that are widely used in the Data Science community including Python.

RStudio includes a Syntax-Highlighting Editor that helps with Code Execution, Documentation, and Data Visualization with better accessibility to graphics.

3) Speed

Since R is an Interpreted Language, Data Scientists can run code without the need for a compiler. In addition, it is a Vector Language, which means that anyone can add functions to a single Vector without creating a loop.

This, in terms of implementation, makes R in Data Science very powerful and fast in comparison to other languages.

4) Range of Database Support

As R provides an interface for Databases like SQL, it is extensively used in Data Science Applications for ETL (Extract, Transform, and Load). Packages like the RODBC Package, Open DataBase Connectivity Protocol (ODBC), and the ROracle assist Data Scientists in working with Databases effectively.

While the RODBC Package is useful for implementing ODBC Database connectivity, the ODBC Package can connect to any Database that is ODBC-compliant and has been thoroughly tested on SQL Server, PostgreSQL, and MySQL. ROracle Package supports interaction with Oracle Databases.

5) Interactive Web-Based Dashboards

R has Packages that allow Data Scientists to build Web-Based Dashboards to enhance collaboration among decision-makers. Shiny is one such tool that allows users to design Interactive Web Applications directly from R. It assists people without much technical experience to create Dashboards and share them with colleagues.

Another example is R Markdown, which is a report-building file format that can be used to create Blogs, Books, Presentations, and more. The Web Applications created using Shiny can be embedded in R Markdown too.

Benefits Of R in Data Science Over Python

Python is easier to learn as its syntax is simpler and more intuitive. In comparison, R in Data Science has a steep learning curve for beginners. Case in point, assignment in R is used by an arrow (<-), but most of the other popular Programming Languages use equal (=) for the same.

Other changes include the use of the plus symbol (+) and percentage symbol (%) in R which requires a better understanding of its own style. R in Data Science was even regarded as one of the most difficult Programming Languages to learn in its early days. R didn’t have the same structuring capabilities as its peers at the time.

However, Hadley Wickham and his team created Tidyverse to provide utilities for cleaning and working with data while dissolving the learning curve complexities associated with the Statistical Programming Language. Along with that, the better readability of R syntax and the possibility of executing Statistical tasks with lesser code made this Programming Language more popular.

R Packages

R offers a diverse range of Packages, with more than 10,000 in the CRAN Repository. The majority of these Packages are used for conducting Data Science operations.

While some of the popular ones are already mentioned above, the following list also includes some of the widely used Packages of R in Data Science:

• Ggplot2: Inspired by the Grammar of Graphics, Ggplot2 is one of the most popular R Packages that facilitate visualization in only a few lines of code. Data Scientists simply need to state Ggplot2 how to map variables to aesthetics, and what graphical primitives to use, and it takes care of all the details to plot appealing plots.
• Plotly: It is a Graphing Library used to create graphs that are interactive and can then easily be embedded in Web Applications.
• Tidyr: This R Package allows Data Scientists to clean and organize data. A data is considered tidy when each variable represents columns, each row represents an observation, and every cell is a single value. 
• Dplyr: It is the go-to Package for Data Wrangling and Manipulation. It enables several functions for the Data Frames in R like Subsetting, Summarizing, Rearranging, and Joining together data sets. 
• Caret: The Caret Package stands for Classification and Regression Training and is used for Predictive Modeling. It optimizes Data Splitting, Pre-Processing, Feature Selection, Variable Importance Estimate, and other duties, as well as providing a unified interface to multiple Machine Learning methods.
• Knitr: Mostly used for generating reports in various file formats, Knitr supports a variety of code forms like LaTeX, HTML, Markdown, LyX, AsciiDoc, and reStructuredText documents. 
• Xtable: It generates HTML or LaTeX code when Data Scientists need to paste an R project into the final document.
• Foreign: It provides functions that allow loading data files from other programs like SAS or SPSS into R.
• Data.table: This Package can handle a vast amount of data during Data Manipulation. It is the performance-optimized version of R’s data.frame with improved syntax and features for ease of use and programming speed.
• Rcpp: It is used to write R functions that call C++ code for lightning-fast speed.
• Bioconductor: It is an open-source project that hosts a wide range of tools for the analysis and comprehension of high-throughput Genomic Data.
• Parallel: It is used for running Parallel Processing in R to speed up codes or to crunch large data sets.
• Mlr: It is an incredible Software Package for executing Machine Learning tasks. It has all the key and relevant classifications such as Regression, Clustering, Multi-Classification, and Survival Analysis algorithms. For feature selection, it also includes Filter and Wrapper methods.
• RCrawler: It is a contributed R Package for domain-based Web Crawling and Content Scraping. It can Crawl, Parse, Store, and Extract material from online sites, as well as generate data that may be used directly in Web applications.

Conclusion

• Many sources claim that R in Data Science is less popular than Python. However, it is crucial to remember that Python is a general-purpose Programming Language, whereas R is specialized in Statistical Computing.

Share your experience of understanding the role of R in Data Science in the comments section below!