Section 4.5 Plot Generation Process
Having discussed some general guidelines, there are a number of questions you should ask yourself before making a plot. These have been nicely laid out in a blog post from the wonderful team at Chartable, Datawrapper’s blog and we will summarize them here. The post argues that there are three main questions you should ask any time you create a visual display of your data. We will discuss these three questions below
Subsection 4.5.1 What’s your point?
Whenever you have data you’re trying to plot, think about what you’re actually trying to show. Once you’ve taken a look at your data, a good title for the plot can be helpful. Your title should tell viewers what they’ll see when they look at the plot.
Subsection 4.5.2 How can you emphasize your point in your chart?
We talked about it in the last lesson, but an incredibly important decision is choosing an appropriate chart for the type of data you have. In the next section of this lesson, we’ll discuss what type of data are appropriate for each type of plot in R; however, for now, we’ll just focus on an iPhone data example. With this example, we’ll discuss that you can emphasize your point by:
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Adding data
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Highlighting data with color
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Annotating your plot
Subsubsection 4.5.2.1 Adding data
In any plot that makes a specific claim, it usually important to show additional data as a reference for comparison. For example, if you were making a plot of that suggests that the iPhone has been Apple’s most successful product, it would be helpful for the plot to compare iPhone sales with other Apple products, say, the iPad or the iPod. By adding data about other Apple products over time, we can visualize just how successful the iPhone has been compared to other products.
Subsubsection 4.5.2.2 Highlighting data with color
Colors help direct viewers’ eyes to the most important parts of the figure. Colors tell your readers where to focus their attention. Grays help to tell viewers where to focus less of their attention, while other colors help to highlight the point your trying to make.
Subsubsection 4.5.2.3 Annotate your plot
By highlighting parts of your plot with arrows or text on your plot, you can further draw viewers’ attention to certain part of the plot. These are often details that are unnecessary in exploratory plots, where the goal is just to better understand the data, but are very helpful in explanatory plots, when you’re trying to draw conclusions from the plot.
Subsection 4.5.3 What Does Your Final Chart Show?
A plot title should first tell viewers what they would see in the plot. The second step is to show them with the plot. The third step is to make it extra clear to viewers what they should be seeing with descriptions, annotations, and legends. You explain to viewers what they should be seeing in the plot and the source of your data. Again, these are important pieces of creating a complete explanatory plot, but are not all necessary when making exploratory plots.
Subsubsection 4.5.3.1 Write precise descriptions
Whether it’s a figure legend at the bottom of your plot, a subtitle explaining what data are plotted, or clear axes labels, text describing clearly what’s going on in your plot is important. Be sure that viewers are able to easily determine what each line or point on a plot represents.
Subsubsection 4.5.3.2 Add a source
When finalizing an explanatory plot, be sure to source your data. It’s always best for readers to know where you obtained your data and what data are being used to create your plot. Transparency is important.
