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R Programming for Data Science

Last Updated : 11 Mar, 2024
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ThisR Programming Language is an open-source programming language that is widely used as a statistical software and data analysis tool. R is an important tool for Data Science. It is highly popular and is the first choice of many statisticians and data scientists. But what makes R so popular? Why and How to Use R for Data Science? 

Data Science in R Programming Language

Data Science has emerged as the most popular field of the 21st century. This is because there is a pressing need to analyze and construct insights from the data. Industries transform raw data into furnished data products. In order to do so, it requires several important tools to churn the raw data. R is one of the programming languages that provide an intensive environment for you to research, process, transform, and visualize information. 

Difference between R Programming and Python Programming

Feature  Python
Introduction R is a language and environment for statistical programming which includes statistical computing and graphics. Python is a general-purpose programming language for data analysis and scientific computing
Objective It has many features which are useful for statistical analysis and representation. It can be used to develop GUI applications and web applications  as well as with embedded systems
Workability It has many easy-to-use packages for performing tasks It can easily perform matrix computation as well as optimization
Integrated development environment Various popular R IDEs are Rstudio, RKward, R commander, etc. Various popular Python IDEs are Spyder, Eclipse+Pydev, Atom, etc.
Libraries and packages There are many packages and libraries like ggplot2, caret, etc. Some essential packages and libraries are Pandas, Numpy, Scipy, etc.
Scope   It is mainly used for complex data analysis in data science. It takes a more streamlined approach for data science projects.

Features of  R – Data Science

Some of the important features of R for data science applications are: 

  • R provides extensive support for statistical modeling.
  • R is a suitable tool for various data science applications because it provides aesthetic visualization tools.
  • R is heavily utilized in data science applications for ETL (Extract, Transform, Load). It provides an interface for many databases like SQL and even spreadsheets.
  • R also provides various important packages for data wrangling.
  • With R, data scientists can apply machine learning algorithms to gain insights about future events.
  • One of the important features of R is to interface with NoSQL databases and analyze unstructured data.

Most common R Libraries in Data Science

  • Dplyr: For performing data wrangling and data analysis, we use the dplyr package. We use this package for facilitating various functions for the Data frame in R. Dplyr is actually built around these 5 functions. You can work with local data frames as well as with remote database tables. You might need to: 
    Select certain columns of data. 
    Filter your data to select specific rows. 
    Arrange the rows of your data in order. 
    Mutate your data frame to contain new columns. 
    Summarize chunks of your data in some way.
  • Ggplot2: R is most famous for its visualization library ggplot2. It provides an aesthetic set of graphics that are also interactive. The ggplot2 library implements a “grammar of graphics” (Wilkinson, 2005). This approach gives us a coherent way to produce visualizations by expressing relationships between the attributes of data and their graphical representation.
  • Esquisse: This package has brought the most important feature of Tableau to R. Just drag and drop, and get your visualization done in minutes. This is actually an enhancement to ggplot2. It allows us to draw bar graphs, curves, scatter plots, and histograms, then export the graph or retrieve the code generating the graph.
  • Tidyr: Tidyr is a package that we use for tidying or cleaning the data. We consider this data to be tidy when each variable represents a column and each row represents an observation.
  • Shiny: This is a very well-known package in R. When you want to share your stuff with people around you and make it easier for them to know and explore it visually, you can use Shiny. It’s a Data Scientist’s best friend.
  • Caret: Caret stands for classification and regression training. Using this function, you can model complex regression and classification problems.
  • E1071: The E1071 package has wide use for implementing clustering, Fourier Transform, Naive Bayes, SVM, and other types of miscellaneous functions.
  • Mlr: This package is absolutely incredible in performing machine learning tasks. It almost has all the important and useful algorithms for performing machine learning tasks. It can also be termed as the extensible framework for classification, regression, clustering, multi-classification, and survival analysis.

Other worth mentioning R libraries: 

Applications of R for Data Science

Top Companies that Use R for Data Science: 

  • Google: At Google, R is a popular choice for performing many analytical operations. The Google Flu Trends project makes use of R to analyze trends and patterns in searches associated with flu.
  • Facebook makes heavy use of R for social network analytics. It uses R for gaining insights about the behavior of the users and establishes relationships between them.
  • IBM: IBM is one of the major investors in R. It recently joined the R consortium. IBM also utilizes R for developing various analytical solutions. It has used R in IBM Watson – an open computing platform.
  • Uber: Uber makes use of the R package shiny for accessing its charting components. Shiny is an interactive web application that’s built with R for embedding interactive visual graphics.

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