10 R Libraries/Packages for Data Science

Introduction

 

The R is a free, open source programming language that is greatly deployed for data manipulation, data analysis and data visualization. It is a more potent and flexible tool for statistics, graphics and statistical modelling and programming. 

 

Due to its extensive feasibility, the tool is quite prominent and used by tens of thousands of developers in order to perform complicated statistical analyses, and also can execute complex operations in fraction of time. In addition to this, the open source system embraces greater than 2000 available add-ons, and plenty of packages are welcomed almost everyday. 

 

Being a part of data science, scripting a good R programming code is challenging in order to accomplish utmost reusability and potential of data science software. These codes must be structured and dispersed in a format that is compatible with community-based parameters and also being introduced to provide an excellent user experience.

 

Till the time, various computing/programming languages have been introduced for different data science projects and tasks and have their supporters and opponents. Following the fairy note, in this blog, we will discuss R programming along with packages and libraries in R programming, an open source software.

 

 

About R packages and Libraries

 

R is an open source platform where an individual can write code and publish it in the form of a package. On the other side, a user can download and install the package and can use functionalities of R or datasets fabricated inside the package. 

 

Structured in a clearly-defined layout, packages can be defined as a broadly collection of R functions, data, and assembled codes. Installing a R package allows users to employ a standard set of commands that are not accessible in the fundamental set of R functions. 

 

The directory that compiles these numbers of packages is known as the R library, basically, by default, R language offers a standard set of packages, others can be accessed by download and installation. Even after installing, these packages can be loaded into the current session at work.

 

(Must read: Best R programming courses)

 

 

Listing R Libraries 

 

1. Ggplot2

 

The ggplot2 is a popular library used in R environment, with this a user can generate graphs from the same components (such as a dataset, a coordinate system, and many more). 

 

In R programming, ggplot is a smart library that demands very few inputs and governs a bulk of tasks on its own as simple as providing the data, instructing to map it and then let the tool to operate the rest such that a user can focus on interpreting the visualization. 

 

The library gives users a graphics language to make the intuitive and elaborated plots to represent both univariate and multivariate, numerical and categorical data. 

 

Referred to as the Grammar of Graphics, ggplot has the massive range of functionalities enabling extra monitor across basic graphics capabilities of R programming to improve generic visualizations.

 

(Also read: Types of Data Visualization Using R Programming)

 

 

2. Dplyr

 

The accumulation of data from multiple sources shows some sort of structured and unstructured format, this disarrayed data can’t be fed to ml models for visualisation and analysis right away. 

 

In general, the pool of data needs to experience assorted transformations like filtering, aggregation and characterization. For the same, dplyr explains the procedure of data transformation in the context of five functions, together terms as “verbs”, they are;

 

• select(): To select the columns to work upon

• filter(): To select rows

• arrange(): To reorder rows

• mutate(): To generate new variables from the existing ones

• summarize(): To sum up data variables in the form of a single value

 

Essentially, the fundamental R functionalities can execute these operations, dplyr has much simplified and understandable code. In addition to that, dplyr has the ability to group these functions, i.e, verbs, in a logical, user-readable sequence. 

 

Deploying in context and appropriately, the verbs are powerful commands fulfilling the various data transformation tasks. Additionally, the dbplyr (dplyr’s database backend) can be used to access data within a database. Also, the family of dplyr syntax, when coupled with SQL linters, makes writing readable and error-free SQL queries.

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Data transformation with dplyr, Source

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3. Lattice

 

Being employed as a top-notch visualization library in R programming language, Lattice is the perfect choice for working multivariate data. With its full capabilities, it is a ground implementation of Trellis graphics due to which Lattice gives additional graphing features and functions to developers, apart from what R provides. 

 

What’s more, Lattice allows to make Trellis graphs portaiting the relationships between single or multiple variables within datasets. The library is efficient to manage the user's graphical needs without much tinkering along the way, also can accommodate a few non-standard necessities by its powerful integration capabilities.

 

(Related reading: Packages in R programming)

 

 

4. Tidyverse

 

As they stated, “the tidyverse is an opinionated collection of R data science packages”, the package reflects best practices due to its intuitive and versatile grammar, design outlines and data structures. To formulate tidyverse, the designing team was instructed to follow some fundamental principles stated in The Zen of Python and The Unix philosophy converging on regularity, simplicity, conciseness, and modularity. 

 

The tool ggplot2 and dplyr are part of this package. Following composability (a system design principle operating the inter-relationships of elements), tidyverse supports packages that are comprehensive and regenerated.

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Tidyverse homepage, source: tidyverse.org

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5. Shiny

 

To design communal web applications in R, Shiny is another R package incorporating an excellent consolidation amid R and the modern web apps. Despite not having web development skills, users can conveniently design web applications using Shiny. They can lodge web applications in R documents, make standalone web applications on a webpage, or design web visualization dashboards.

 

Besides, in order to extend the major functionalities of shiny applications, an individual can add HTML widgets, CSS themes, JavaScript actions, etc. For easier learning, Shiny R studio website provides “Shiny user interface tutorial videos”. The Shiny app can also be installed on cloud or at user’s own servers having an open-source or commercial license.

 

(Recommended reading: R Programming Vector Functions)

 

6. Knitr

 

Integrating several types of R codes involving Markdown, LyX, LaTeX, AsciiDoc, HTML, and many more, knitr is a popular package for flexible report generation, for example, if a user is working in research for making report, knitr will be very supportive as automated data process from data analysis to forming a report over it.

 

The knitr also incorporates multiple features into a single package that addresses some problems with Sweave (a function in R combining R codes into LyX or LaTeX documents).   

 

7. Mlr3

 

A specifically designed R tool for machine learning problems, mlr3 facilitates to use many supervised and unsupervised machine learning models on scikit-learn such as classification, regression, support vector machine, random forest, nearest neighbour, naive bayes, decision tree, clustering, etc. 

 

The mlr3 easily connects with OpenML R package to support machine learning online, moreover users can devise their own machine learning algorithms on mlr3, also they can work in existing/established algorithms. The mlr3 is an improved version over previous mlr (now outdated by the mlr-org team).   

 

 

8. RCrawler

 

In order to analyze domain-based web crawling and content scraping, RCrawler is mainly used as an R library that can crawl, interpret, compile pages, derive contents, and generate data to be deployed for web content mining applications. 

 

Things to consider while using the tool is to use the 64bit version of R as  the crawling operation process is executed by virtue of some simultaneous processes or nodes being consistent.

 

Additionally, RCrawler provides a way to scrutinize the structure of websites through constructing a precise network representation of a websites’ internal and external hyperlinks in the form of nodes and edges respectively. 

 

 

9. Esquisse

 

The package has provided the most interesting feature of Tableau to R, it is as simple as “Just drag and drop and make visualizations available within fractions of minutes.”

 

Accounted for as the improved section of ggplot2, the addin version is permitting to have active interaction and exploration available data via visualizing it within the ggplot2 package. 

 

Number of data visualization techniques such as bar graphs, curves, scatter plots, histograms, can be used from Esquisse along with exporting graphs and retrieving codes to generate graphs.

 

 

10. Caret

 

Caret: the classification and regression training, is one of the most essential packages that offers the train function employing to evaluate the influence of the model’s parameters tuning over performance with the help of re-sampling. 

 

Caret offers different functionalities working from streamlining the model construction and inspection/evaluation process to feature selection, feature engineering and other techniques as well.

 

Most of the compact supervised machine learning problems can be solved using Caret by facilitating a uniform interface to considerable machine learning algorithms and making various tasks standardized including data splitting, data pre-processing, feature selection, estimation of important features, etc.

 

 

Conclusion

 

To conclude the discussion, R, having an astounding number of libraries, adds its capabilities to multiple data science projects and their effective solutions. In fact, R programming has turned out to be a default language for numerous colleges and universities under the statistics /mathematics department worldwide. It is among the languages that often get the very first implementation of newly constructed statistical algorithms and techniques.

 

(Suggested blog: Descriptive statistics in R)

 

Though there are various generic-intended R packages and libraries, we have listed those that can offer adequate proficiency for data manipulation, data visualization, competitive market and statistical research, and machine learning. These are the mostly used open source libraries for data science.