The first segment of the line shows that between 20, academic performance increased (the line moves to the right) but so did the number of behavioral referrals (the line goes up). But it’s storytelling powers are pretty impressive. I added in the years as markers along the line so the chronology is clear.Īt first, this graph is not the most intuitive to read. The line connecting the dots in the scatterplot suggests change over time. Yet this data also runs over time – which makes a connected scatterplot a sweet, elegant answer. Two continuous variables are usually graphed as a scatterplot. That should ring a bell from grad school days. If you think about it, what’s really happening with two y-axes in the same graph is that we are trying to show a relationship between two continuous variables. This alternative also lets me move the axes titles to a subtitle location, getting rid of the need to read lengthy text vertically. Both graphs are not as wide as a default graph, so I can fit them into the same amount of space as the original graph with two y-axes. I inserted a textbox that spans across both graphs for the title. We still see that academics are increasing while behavior is decreasing, without the complicating intersection we had when they were on top of each other. Just nestle them up next to each other like best friends. Perhaps even easier than making one graph with two y-axes would be to make two separate graphs. “I need to fit it all into one graph.”Īs in, there’s only room on the slide for one graph, huh buddy? That’s an unfortunate parameter to work within but both of the alternatives here would take up the same amount of space. By putting these two variables into the same graph, it implies more relationship between the two things being graphed than actually exists in real life. Like “oh I wonder what happened that made behavior suddenly drop below academics.” But these two things aren’t on the same scale, so the point at which they cross is meaningless. People see that intersection point and think it means something but it really doesn’t. Your heart is in the right place, darling, but putting them in the same graph introduces confusion points, like where the academic and behavior lines cross in the graph above. Why do people insist on using a second y-axis? I suspect the answer is because they are just easy to make but I often hear reasoning like: “I want people to see the relationship between these two things.” Which line goes with which dataset? At least here I have color coded the line to its axis, a smart improvement on what I typically see. Using two y-axes is not a great idea because it gets confusing, fast. Graphs like this appear in every industry, everywhere I consult all around the globe. Two Alternatives to Using a Second Y-AxisĪlmost as often as I see a pie chart with a hundred tiny slivers, I see line graphs using two y-axes.
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