R is an open source language for data analysis and graphics. It is platform-independent and allows users to load various packages and develop their own to interpret data better. This video is packed with practical recipes, designed to provide you with all the guidance needed to get to grips with data visualization with R.
We start off with the basics of R plots and an introduction to heat maps and customizing them. After this, we gradually take you through creating interactive maps using the googleVis package. Finally, we generate chloropleth maps and contouring maps, bubble plots, and pie charts.
About the Author
Atmajit Singh Gohil works as a senior consultant at a consultancy firm in New York City. After graduating, he worked in the financial industry as a Fixed Income Analyst. He writes about data manipulation, data exploration, visualization, and basic R plotting functions.
He has a master's degree in financial economics from the State University of New York (SUNY), Buffalo. He also graduated with a Master of Arts degree in economics from the University of Pune, India. He loves to read blogs on data visualization and loves to go out on hikes in his free time.
Adding a third dimension to the existing plot helps in revealing information and portraying data from a newer angle.
Applying text to a plot is the additional functionality of the plot3D package
In this video we will generate simple 3D Pie Chart using Plotrix package.
In this video, we will plot a 3D Histogram.
In this video, we will explore about implementation of 3D contours in R.
In this video, we will learn to plot a contour map in 3D using the plot3D package in R.
In this video, we will learn to surface plots and animation in R.
When the density of data increases in a particular region of a plot, it becomes hard to read. So in this video, the sunflower plots are used as variants of scatter plots to display bivariate distribution.
The hexagon-shaped bins were introduced to plot densely packed sunflower plots.
Calendar plots have been used to display data on a daily or monthly basis, where each square represents a data point.
In this video, we will implement the alternative methods to visualize multivariate data that is by using Chernoff faces.
In this video, we will construct the coxcomb plot.
In this video, we will study the basics of creating a network plot using a random dataset.
In this video, we will use oil prices in USA as an example to construct the radial plot.
You might have seen these plots in news or journal articles and wondered how to create them quickly. This video will help you accomplish this task.
Candlestick plots are widely used to display time series data related to financial markets.
We will learn an easy way to generate an interactive version of the same plot.
The main objective of this section is to introduce the concept of decomposition.
Regression lines are a visual representation of the regression equation.
The Flowing Data website provides a very detailed description on how to read a box plot.
In the violin plot, we can observe the mean, which is displayed using white dots, and the dispersion of various variables.
R comes with some basic methods to test for normality, such as the Shapiro test.
In this video, we will utilize the density() function to generate a plot.
Correlation plots are a great tool to visualize correlation data.
In this video, we will study how to quickly generate a word cloud in R.
In this video, we will learn how to create a word cloud using an entire document.
A comparison cloud works on the same principles as a word cloud.
In this video, we will learn some important matrix functions that allow us to further conduct text analysis and also generate a correlation plot.
The main aim of this video is to introduce you to installing fonts and how to use them to label plots.
The idea behind generating an XKCD-style plot is to bring the same humor to our plot.
Animating a visualization brings a new dimension to our visualization.
One of the issues while creating presentations using PowerPoint is that we have to manually update the data, contents, and plot.
In this section, we will get the basic introduction to API and XML.
In this video, we will construct a bar plot using XML data.
The shiny package allows us to create applications in R.
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