R Tutorial: Plotting multivariate data

Want to learn more? Take the full course at https://learn.datacamp.com/courses/multivariate-probability-distributions-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Visualizing multivariate data is essential to do before performing any multivariate analysis. In this video, we will discuss some widely used plotting techniques for multivariate data in R. We will start with the basic R plotting techniques, and then move on to the lattice library, which provides a formula interface for plotting multivariate data. We will also discuss specific plotting tools from GGPlot, and some 3D plotting techniques. To plot the pairwise scatter plot of all the numeric variables in the dataset, we can use the pairs() plot, which produces a matrix of plots, where the ith jth entry is the scatter plot of variables i and j. Note that if you have a lot of variables it might not be a good idea to use a pairs() plot, since the individual plots might be very small. It is often useful to color plots by specific variables. For the Iris dataset, we can color the scatterplots by species. In the pairs() command, we provide the extra argument col equals iris underscore raw dollar sign Species to color the points by species. Now we will draw a similar plot for all the four variables with the splom() function. Using the formula interface, the first argument specifies the variable you want to plot, the second argument, col, colors the point according to the species, and the third argument, pch, changes the symbol type. There is an entire DataCamp course on data visualization in R with lattice, which will help you learn more about the full potential of the lattice package. ggplot2 allows us to use a single line of code to automatically subset multivariate data, calculate statistics on the subsets, and provide plots specific to each subset, coloring them appropriately. We will first discuss the ggpairs() function, which i