R Data Visualization
In R, we can create visually appealing data visualizations by writing few lines of code. For this purpose, we use the diverse functionalities of R. Data visualization is an efficient technique for gaining insight about data through a visual medium. With the help of visualization techniques, a human can easily obtain information about hidden patterns in data that might be neglected.
By using the data visualization technique, we can work with large datasets to efficiently obtain key insights about it.
R Visualization Packages
R provides a series of packages for data visualization. These packages are as follows:
1) plotly
The plotly package provides online interactive and quality graphs. This package extends upon the JavaScript library ?plotly.js.
2) ggplot2
R allows us to create graphics declaratively. R provides the ggplot package for this purpose. This package is famous for its elegant and quality graphs, which sets it apart from other visualization packages.
3) tidyquant
The tidyquant is a financial package that is used for carrying out quantitative financial analysis. This package adds under tidyverse universe as a financial package that is used for importing, analyzing, and visualizing the data.
4) taucharts
Data plays an important role in taucharts. The library provides a declarative interface for rapid mapping of data fields to visual properties.
5) ggiraph
It is a tool that allows us to create dynamic ggplot graphs. This package allows us to add tooltips, JavaScript actions, and animations to the graphics.
6) geofacets
This package provides geofaceting functionality for ‘ggplot2’. Geofaceting arranges a sequence of plots for different geographical entities into a grid that preserves some of the geographical orientation.
7) googleVis
googleVis provides an interface between R and Google’s charts tools. With the help of this package, we can create web pages with interactive charts based on R data frames.
8) RColorBrewer
This package provides color schemes for maps and other graphics, which are designed by Cynthia Brewer.
9) dygraphs
The dygraphs package is an R interface to the dygraphs JavaScript charting library. It provides rich features for charting time-series data in R.
10) shiny
R allows us to develop interactive and aesthetically pleasing web apps by providing a shiny package. This package provides various extensions with HTML widgets, CSS, and JavaScript.