Data Visualization Packages for R you should check out in 2022
In the following, we have listed exciting libraries in R for data visualization that you should check out.
As part of the robust tidiyverse ecosystem, ggplot2 offers the most feature-rich and versatile graph creation package based on the grammar of graphics framework. This principle divides graphs into components such as datasets, scales, levels, etc. The user combines these components to describe and create any diagram in a declarative fashion.
This approach has become so widely accepted that it has effectively replaced R’s native diagrams and added several extensions, some of which we will mention here.
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With ggplot2- and leaflet, we have already introduced two practical tools. With simplevis, the aim is to facilitate visualization with less brainpower by providing a package of wrapper functions of these two packages.
gganimate is an extension of ggplot, which makes it possible to create diagrams with beautiful animations in R.
The grammar principle is extended by a series of additional components that the data scientist can add to the ggplot object to represent how it should change over time.
In keeping with the philosophy of ggplot2 (and the grammar of the graphics), ggvis makes it possible to create interactive graphics that the user can test either in Rstudio or in the browser using the shiny infrastructure.
Enables the creation of interactive 3D graphics in R based on OpenGL or WebGL in the backend.
Inspired by Peter Shirley’s three books on ray tracing, this open-source R package allows you to do the same thing when creating 3D scenes in R.
With Plotly, the focus is on interactive, publication-quality diagrams. You will find examples for creating line charts, scatter plots, area plots, etc.
With a focus on multivariate data, Lattice is a powerful and equally elegant high-level data visualization system inspired by Trellis graphics. While sufficient for typical graphing needs, Lattice is flexible enough to meet most non-standard requirements.