• Benadette Wambui

Useful Libraries in the R Programming Language

In addition to providing robust statistical computing, R offers a huge collection, over 16 thousand to be exact, of highly resourceful libraries, catering to the needs of Data Scientists, Data Miners, and Statisticians alike. Further, in this article, we will shed some light on a handful of top R libraries for Data Science.

R is extremely popular among Data Miners and Statisticians, and part of the reason is the extensive range of libraries that comes with R. These tools and functions can simplify statistical tasks to a great extent, making tasks such as data manipulation, visualization, web crawling, Machine Learning and more, a breeze. Some of the libraries include the following.


With Rcrawler’s powerful web crawling, data scraping, and data mining capabilities, you can not only crawl through websites and scrape data, but also analyze the network structure of any website, including its internal and external hyperlinks. In case you’re wondering why not use rvest, the Rcrawler package is a step up from rvest as it goes through all the pages on a website and extracts the data, which can be extremely helpful while trying to gather all the information from one source and in one go.


The DT package acts as a wrapper of the JavaScript library called DataTables, for R. DT allows you to transform the data in your R matrix into an interactive table on your HTML page, which facilitates easy searching, sorting, and filtering of data. The package works by letting the main function, the datatable function, create an HTML widget for the R objects. DT allows further fine-tuning via the “options” arguments and even some additional customizability to your tables, all of this without going deep into the coding.