Tidyverse Skills for Data Science in R by unknow

Author:unknow
Language: eng
Format: epub
Publisher: leanpub.com
Published: 2021-02-02T00:00:00+00:00

Explanatory Plots

These are data displays that aim to communicate insights to others. These are plots that you spend a lot of time making sure they’re easily interpretable by an audience. General characteristics of explanatory plots:

They take a while to make.

There are only a few of these for each project.

You’ve spent a lot of time making sure the colors, labels, and sizes are all perfect for your needs.

Here we see an improvement upon the exploratory plot we looked at previously. Here, the axis labels are more descriptive. All of the text is larger. The legend has been moved onto the plot. The points on the plot are larger. And, there is a title. All of these changes help to improve the plot, making it an explanatory plot that would be presentation-ready.

Explanatory Plots

Explanatory plots are made after you’ve done an analysis and once you really understand the data you have. The goal of these plots is to communicate your findings clearly to others. To do so, you want to make sure these plots are made carefully - the axis labels should all be clear, the labels should all be large enough to read, the colors should all be carefully chosen, etc.. As this takes times and because you do not want to overwhelm your audience, you only want to have a few of these for each project. We often refer to these as “publication ready” plots. These are the plots that would make it into an article at the New York Times or in your presentation to your bosses.

Other Explanatory Plotting Examples:

How the Recession Shaped the Economy (NYT)

2018 Flue Season (FiveThirtyEight)

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