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  • 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.


CARET is the abbreviation for Classification And Regression Training, the caret library provides several functions to optimize the process of model training for tricky regression and classification problems. caret comes with several additional tools and functions for tasks like data splitting, variable importance estimation, feature selection, pre-processing, and many more. With caret, you can also measure the performance of the models, and even fine-tune the model behavior by using various parameters like tuneLength or tuneGrid according to your requirements. The package itself is easy to use and only loads the necessary components as it goes.


Lattice is another elegant yet powerful data visualization library focussed on multivariate data. What makes this library special, is that apart from handling the regular visualizations, lattice also comes prepared with support for nonstandard situations and requirements. Due to being the practical implementation of Trellis graphics for R, it allows you to create Trellis graphs and even offers options to tune the graphs according to your requirements. lattice comes with R by default, but there’s an advanced version of lattice called latticeExtra, which might come in handy in case you want to extend the core features provided by the lattice.


R is an excellent programming language for Data Science, but there are certain areas where R may feel incomplete. One such area is the handling of date and time. For anyone extensively working with date and time in R, may find it’s built-in capabilities cumbersome.

To overcome this, we have a handy package called lubridate. The package not only handles the standard date and time in R, but also offers additional enhancements such as time periods, daylight savings times, leap days, supports various time zones, fast time parsing, and many helper functions.


Tidyr is one of the core packages in the Tidyverse ecosystem, and as the name suggests, it is used to tidy up messy data. Now, if you’re wondering what tidy data is, let me clear it for you. A tidy data indicates that every column is variable, each row is an observation, and each cell is a singular value. According to tidyr, tidy data is a way of storing the data that is to be used throughout the tidyverse and can help you save time and be more productive with your analysis.


The Machine Learning in R(mlr), is a library that was released in 2013 and was updated to mlr3 with newer techniques, a better architecture, and core design in 2019. As of now, the library provides a framework to address several classifications, regression, support vector machines, and many other Machine Learning activities. mlr3 is targeted towards Machine Learning practitioners and researchers to facilitate the benchmarking and deployment of various Machine Learning algorithms without much hassle. For those looking to extend and even combine the existing learners and fine-tune the best technique for a task, will find mlr3 to be a perfect option.


ggplot2 is among the top R libraries for data visualization and is actively being used by thousands of users around the world to create compelling charts, graphs, and plots. The reason behind this popularity is ggplot2 was created to simplify the visualization process by taking minimal input from the developer, such as the data to visualize, the style, and the primitives to use while leaving the rest onto the library.The result is a graph that effortlessly presents complex statistics for instant visualizations.

9. Esquisse

Esquisse is not a library per se, but an addin for the powerful data visualization library ggplot2. You might be wondering why would you need this with ggplot2, let me clear it for you. ggplot2 is already smart enough, but if you need an additional layer of intuitiveness for your visualizations, esquisse is the right way to go. esquisse allows you to simply drag and drop the required data, choose the desired customization options, and there you have it, a tailored plot built within a short period and ready to export to your application of choice.

Learn more about data science and R in HURU Schools' Data Science Program

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