When it comes to choosing libraries and packages for Data Science, Python is the first name that comes to mind. However, there’s another language that has become a favourite staple for the Data Science community – the R programming language. Learn how important Python & R for data science community.

R is a programming language, one of the top in-demand languages to learn in 2020. Since it was designed with a focus on statistical computing, its interface and structure are highly suited for statistical and scientific computing tasks. The reason behind R’s increasing popularity is that it has an easy-to-understand syntax and it comes equipped with the fantastic RStudio tool and numerous R packages. These R packages for Data Science can be used to perform various Data Science (ML) tasks, including data manipulation, data visualization, model building, and much more.

Without further ado, let’s take a look at some of the best R packages for Data Science!

Best R Libraries for Data Science

1. Dplyr

Dplyr is an R library that is best suited for data manipulation. It incorporates five functions that allow you to solve some of the most common data manipulation challenges. These five functions are:

• mutate() – It is used to add new variables that are functions of existing variables
• select() – It is used to choose variables according to their names.
• filter()- It is used to pick cases based on their values.
• summarise() – It is used for reducing multiple values into a single summary.
• arrange() – It is used for changing the order/sequence of the rows

These five functions are all you need to perform a bulk of data manipulation tasks. With Dplyr, you can use the same R code to work with local data frames and also with remote database tables.

2. ggplot2

ggplot2 is an R tool designed explicitly to create graphics by implementing the standards of The Grammar of Graphics. With ggplot2, you can produce high-quality graphical visualizations by expressing relationships between the data attributes and their graphical representation.

All you need to do is feed the data into the ggplot2 system and command it how to make variables to aesthetics and what graphical primitives to use – ggplot2 will take care of everything else.

 While the tool comes loaded with a host of intuitive functions and is relatively easy to use, you can always resort to the RStudio community and Stack Overflow to seek help for any ggplot2 issues and problems. Learn more about data visualization in R Programming language.

3. Esquisse

Esquisse is another excellent data visualization tool in R. It is probably the most simple and straightforward visualization tool that brings one of the best features of Tableau to R – the famous drag and drop!

Esquisse is built on top of the ggplot2 system. So, you can easily explore the data in the Esquisse environment by generating ggplot2 graphs. Plus, you can launch the Esquisse add-in function via the RStudio menu. With ggplot2, creating plots is way easier since you don’t need to write elaborate code. You can create any visualization patterns, from bar graphs and curves, to scatter plots and histograms, and also export the graph or retrieve the code generating the graph.

4. MLR

If you are looking for an R tool for Machine Learning tasks, MLR is just the tool you need. This R package was explicitly built for Machine Learning. Hence, it includes almost all essential machine learning algorithms you need for performing a wide range of ML tasks. 

The MLR framework offers supervised methods like classification, regression, and survival analysis, along with their corresponding evaluation and optimization methods, as well as unsupervised methods like clustering. Its structure is such that you can both extend it yourself or deviate from the implemented convenience methods and construct your own complex experiments or algorithms.

5. Shiny

If collaboration is what you desire, Shiny is the R package for you. Shiny brings together the computational power of R and the interactivity of the modern web. The best part – Shiny apps are easy to write and develop as you do not require any special web development skills.

Shiny lets you interact and communicate with your team on the same platform for greater transparency and collaboration. It is the perfect tool for building interactive web apps straight from R. You can either host standalone apps on a webpage, or you can embed them in R Markdown documents. Not just that, Shiny also lets you build interactive dashboards. It is packed with a wide range of built-in input widgets. Once your Shiny apps are created, you can extend them using htmlwidgets, CSS themes, and JavaScript actions.

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6. Lubridate

Lubridate is an incredible data-wrangling R library. The primary aim of this particular package is to make dealing with date-times and time-spans fast and easy. It has a consistent and memorable syntax that makes working with dates super fast and efficient. Anything that has to do wit data arithmetic, you can easily accomplish that with Lubridate. 

Lubridate allows for easy and fast parsing of date-times and offers simple functions to get and set components of a date-time such as year(), month(), day(), hour(), minute() and second(). Lubridate can also expand the type of mathematical operations that you can perform with date-time objects by introducing three new time span classes:

• Durations – It measures the exact amount of time between two points
• Periods – It can accurately track clock times despite leap years, leap seconds, and daylight savings time
• Intervals – It is a protean summary of the time information between two points.

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7. RCrawler 

RCrawler is an R library primarily used for domain-based web crawling and content scraping. It can crawl, parse, store pages, extract contents, and produce data that can be directly implemented for web content mining applications. One thing to keep in mind while using this tool is that since the process of a crawling operation is performed by several concurrent processes or nodes in parallel, it is better to use the 64bit version of R. 

 With Rcrawler, you can study the website structure by building a network representation of a site’s internal and external hyperlinks (nodes & edges).

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Conclusion

These are 7 exceptional R libraries for Data Science. However, there are many, many other R libraries that serve other Data Science purposes including Plotly, Rcharts, Rbokeh, Rvest, RMySQL, StringR, Broom, SnowballC, Swirl, and DataScienceR, to name a few. 

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