You can follow my tutorial to reduce the Gap Width between the columns. Then, we widened the columns so that the numeric labels were legible. We decided to label each column with the specific number of people who had the disease. In a moment, we’re going to add values to each of the columns, which means we won’t need an overly-labeled scale. We continued decluttering the vertical scale by only labeling the starting and ending points (just 0 and 50). We re-sized the vertical scale (from 0 to 50 instead of from 0 to 60). We re-sized the graph a bit to fill the slide. And, the title makes the axes obvious-the axes literally show new diagnoses by age and sex. I rarely remove axis titles from written products like reports and handouts, but in this case, the speaker would physically be present to explain which variables were on each axis. They had designed a stacked column chart with one column per age bracket.įor starters, we removed the axis titles. The “before” version of the public health agency’s graph looked like this. We listed the age ranges down the first column and then recorded the number of males and females in the other columns.Įven if you don’t work in public health, you probably have similar datasets-an ordinal variable (like age ranges) along with a categorical variable (like sex).

The Dataset: An Ordinal Variable and a Categorical Variable

In other words, there were just two simple variables: age and sex. I recently worked with a state public health agency that wanted to depict how many males and females were diagnosed with a disease and the age at which they were diagnosed.

How to make a visualizer