R is an open-sourced, well developed and simple programming language for statistical computing, data analysis, etc. R has inbuilt visualisation libraries that help to present & visualize data in a brief manner with the help of ggplot2, lattice, high charter, leaflets etc. ggplot2 makes it easier to build custom plots & help in producing multi-layered graphics. Lattice help in visualizing multi-varied data while high charters is a visualisation library in JavaScript. Data visualisation in R helps to build maps, 3D plots with the help of RGL and to understand the sequence of events.

R is an amazing platform for data analysis, capable of creating almost any type of graph. R is a language and environment for statistical computing and graphics. R is also extremely flexible and easy to use when it comes to creating visualisations. One of its capabilities of R is to produce good quality plots with minimum code.

Data visualization is an art of how to turn numbers into useful knowledge. R Programming lets you learn this art by offering a set of inbuilt functions and libraries to build visualizations and present data.

R supports four different graphics systems: base graphics, grid graphics, lattice graphics, and ggplot2. Base graphics is the default graphics system in R, the easiest of the four systems to learn to use, and provides a wide variety of useful tools, especially for exploratory graphics where we wish to learn what is in an unfamiliar dataset.

In this Data Visualization in R course by Uplatz, you will be able to learn the basics and introduction of fundamentals of how R uses an inbuilt visualization library to read multi-varied data, time-series data sets, maps etc. and makes it easier for the business to extract valuable information.

This course provides a comprehensive introduction on how to plot data with R’s default graphics system, base graphics.

After an introduction to base graphics, we look at a number of R plotting examples, from simple graphs such as scatterplots to plotting correlation matrices. The course finishes with exercises in plot customization. This includes using R plot colors effectively and creating and saving complex plots in R.
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