![]() ![]() In the middle example the cropping of the y axis makes the increase look steeper than when it is unedited. By limiting the axis, it not only reduces how much is visible by the ratio also shifts. Here are three histograms of the same data. I think all of us could find plots in that cut data to make their point look more valid than it is. ![]() Depending on what program and type of plot this could happen accidentally as well as deliberately. An axis can be cropped for the X or Y dimension (or both) to show a subset of the data. The X and Y axis are the key to understanding the scale and relationship of a plot. I will explore ways visualizations can be modified including:įor each of these there are specific situations that these techniques can also be used to help a visualization make a clearer story, so like most things in data science they are tools that can be used or abused. A change to the number of gridlines or ticks can emphasize granularity, labels can be carefully crafted to create bias, or color choices can evoke subconscious emotional reactions. ![]() ![]() Elements of a visualization can be modified in ways that can either emphasize or diminish the impact of the data. ![]()
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