R Tutorial: Bivariate graphics

Want to learn more? Take the full course at https://learn.datacamp.com/courses/interactive-data-visualization-with-plotly-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- In this lesson, we extend your plotly toolkit to include bivariate graphics. Specifically, you will learn how to explore associations using scatterplots, stacked bar charts, and boxplots. In this lesson, we'll explore a sample of 325 wines taken from the wine quality data sets on the UCI Machine Learning Repository. Here, we have the results from both a chemical analysis and assessment of the quality. Scatterplots allow us to explore the relationship between two numeric variables, such as the residual sugar and fixed acidity in the wine. As before, we begin by piping our data set into the plot_ly() command. Next, we specify that residual sugar should be mapped to the x-axis and fixed acidity should be mapped to the y-axis. Finally, we add a markers trace to plot a point for each ordered pair. Stacked bar charts allow you to explore associations between two categorical variables. For example, we can explore how the type of wine is related to the quality label. To begin, we count the number of wines for each combination of type and quality labels. Next, we map variables to the x-axis, y-axis, and color of the segments. In this example, we map type to the x-axis, n to the y-axis, and quality label to color. We add the bars trace as before, but we have to refine the layout in order to create a stacked bar chart since the bars plot side-by-side by default. To stack the bars, we modify the layout of the bar chart by specifying barmode = "stack". The stacked bar chart of the counts we just created is useful for comparing the total number of high, medium, and low-quality wines across types. If, however, we are interested in comparing the distribution of quality between red and white wines, it may be more useful to plot prop