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Second, I encourage you to use data to add annotations and marks to your graphs (especially in a tool like Excel).

As Amanda Cox from the New York Times once famously said, “The annotation layer is the most important thing we do…otherwise it’s a case of here it is, you go figure it out.” The DI Recipiency Rate and Median Household Income are Negatively Correlated.” Annotation can help educate the reader about both how to read the graph and the content you are trying to deliver. The DI Recipiency Rate and Median Household Income” you can say something like “Figure 1. You can start by creating active titles–for example, instead of “Figure 1. First, to remind you that annotation is really really important in data visualization and to encourage you to annotate your visualizations when you can.In this post, I’m going to walk you through the steps of how I made these graphs, and in doing so, I have three goals in mind: The amount of manual work to update the chart with new data is relatively minor. Especially because I was repeating this chart with different data series, I wanted to make it as easy as possible to update with new data and labels therefore, I didn’t draw any lines or text boxes. I created these graphs in Excel (I also created some interactive versions using HighCharts, which you can see at the landing page for the original paper) and plotted everything with data. I also added some very simple annotations along the x- and y-axes to help the reader better and more quickly understand how the plots work. For each variable, I visualize the relationship in a scatterplot and in each, I show the values for all states, highlight those in New England, the average across all states, and the “best fit” line. In that paper, I showed the relationship between the DI recipiency rate-defined as the number of people who receive DI for reasons of mental disorders divided by the population age 18 to 65-and a variety of economic, demographic, health, and policy variables. This group of DI beneficiaries now constitutes the largest group of recipients in the program, and they are an especially large group in the New England states. I recently published a paper on the share of people who receive benefits through the Social Security Disability Insurance (DI) program for reasons of mental disorders, such as mood disorders and schizophrenia.