Top Packages in R – Data Visualization

Data Science, Machine Learning is not only about building predictive or descriptive models.

It is also very important to explaining the models we have got and help people to understand data which will help them make decisions.

Data visualization is an integral part of Data Science. The goal is to present the data and communicate information clearly and efficiently to users using pictorial and graphical format.

R offers a lot packages for performing data analysis, machine learning. Currently, the CRAN package repository features 12611 available packages.

Many useful R function come with these packages, free libraries of code written by R’s active user community.

To install package in R basic command used is

install.packages("package_name")

This is the first part of our Top Packages in R Series. In this series we are going to talk about top packages in R based on different categories.

For this post we are going to talk about top packages in R for Data Visualization.  

 

Data Visualization

ggplot2

It is by far the most famous package for data visualization, creating beautiful graphics. It is based on “The Grammar of Graphics” to build layered, customizable plots.

The basic idea in ggplot2 is that you can split a chart into graphical objects and define them separately. When you put it all together, you get a complete chart.

Scatter Plot in ggplot2
Scatter Plot in ggplot2

googleVis

This package let’s you use Google Chart tools to visualize data in R. It may take a little while to load all charts, since they require internet connection.

The googleVis package provides an interface for R to use Google Charts API. Google Chart tools were known as Gapminder, the graphing software Hans Rosling made famous in his TED talk.

Motion Charts in googlevis
Motion Charts in googlevis

ggvis

It is a web based interactive graphics built with the grammar of graphics.It takes the parts of ‘ggplot2’, combines them with the reactive framework of ‘shiny’ and then draws web graphics using ‘vega’.

So underlying theory behind ggvis is similar to ggplot that leverage shiny’s infrastructure to publish interactive graphics usable from any browser.

Histogram in ggvis
Histogram in ggvis

rgl

Provides interactive 3D visualization in R using OpenGL. rgl provides medium to high level functions to create 3D interactive graphics.

3D plot in rgl
3D plot in rgl

diagrammeR 

It is used to create javascript diagrams and flowcharts using tabular data R. It helps build graph and network Visualization.

diagrammeR is based on htmlwidgets that provides a framework to bridge R and Javascript. The outputs can be easily incorporated into Shiny Web Apps and RMarkdown documents.

Graph in diagrammeR
Graph in diagrammeR

 

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