What are the most effective ways to use small multiples and faceting in visualizations with R?

2 Why use small multiples and faceting?

Small multiples and faceting offer several advantages over other methods of displaying multiple variables or dimensions in a single plot. These include reducing visual noise and cognitive load by separating the data into clear and consistent segments, enabling you to see both the overall and individual patterns of the data, as well as facilitating comparison and evaluation of the groups. Furthermore, they allow you to explore and discover new insights and relationships that might be hidden or obscured in a single plot.

4 What are some best practices for small multiples and faceting?

To make the most of small multiples and faceting, you should follow some best practices that will enhance the readability and effectiveness of your visualizations. For instance, choose an appropriate layout and size for your plots, depending on the number and nature of the groups, the aspect ratio of the data, and the space available. Additionally, use informative and consistent labels for your plots, axes, and legends. Utilize the same color scheme and scale for all the plots unless there is a reason to differentiate some groups. Finally, opt for simple and clear graphics that convey the main message without distracting or misleading elements. You can use various functions such as facet_wrap(), facet_grid(), theme(), labs(), scale_*_manual(), scale_*_discrete(), geom_*, and aes() to adjust these parameters.

5 What are some examples of small multiples and faceting in R?

To illustrate the use and benefits of small multiples and faceting in R, let's look at some examples using the mtcars dataset, which contains information about 32 cars from different manufacturers. You can load the dataset and the ggplot2 package with the following code:

library(ggplot2)
data(mtcars)        

Let's say you want to explore the relationship between miles per gallon (mpg) and horsepower (hp) of the cars, and how it varies by the number of cylinders (cyl) and the type of transmission (am). You can use small multiples and faceting to create four plots, one for each combination of cyl and am, with the following code:

ggplot(mtcars, aes(x = hp, y = mpg)) +
  geom_point() +
  facet_grid(cyl ~ am)        

This will produce the following visualization:

[Insert image of the plot]

You can see that the cars with more cylinders tend to have lower mpg and higher hp, and that the cars with manual transmission tend to have higher mpg and lower hp than the cars with automatic transmission. You can also compare the plots and see how the slope and spread of the points differ by cyl and am.

You can also use small multiples and faceting to create a different view of the data, for example, by using a boxplot to show the distribution of mpg by manufacturer (carb) and am, with the following code:

ggplot(mtcars, aes(x = factor(carb), y = mpg, fill = factor(am))) +
  geom_boxplot() +
  facet_wrap(~ carb)        

This will produce the following visualization:

[Insert image of the plot]

You can see that the median and range of mpg vary by carb and am, and that some carb levels have more variation or outliers than others. You can also see how the proportion of manual and automatic transmission changes by carb.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?