# Part II: One set of data, many stories

## Or, why a dual y-axis chart is not a normalized delta chart

In my original post, One Set of Data, Many Stories, I wrote about how I found a particular dual y-axis chart misleading. The core problem was that it had two y-axis for the same metric, with a different scale for each axis.

## Isn’t it just a delta chart?

Elijah proposed that the problem isn’t about dual y-axis since I could have made it into a “a single axis chart by plotting ‘delta in mortality since 2000’.”

## Let’s take a look

In the original article, I found very similar data from the CDC and remade the chart as closely as I could. The exact data is slightly different than the original, as it’s for a 10 year age range rather than 5 year. But, it tells the same story.

## Percent change

It might be better to compare percent differences rather than absolute differences in this case. Dropping from 50 to 45 deaths per 100,000 people might be more significant than dropping from 150 to 145, it’s a larger drop as a percent of mortality rate.

## Focus on the story: Percent change since 2009

Arguably the point of the original chart was to highlight the divergence since 2009, not since 1999. Adam Pearce pointed out that this could have been achieved by using a delta chart that both pinned to the 2009 data and focused only on the data from 2009 onward. In this case, I used percent change since 2009. This makes the implicit comparison to a 2009 explicit.

## In Conclusion

1. A dual y-axis chart is only similar to a normalized delta chart if the y-axis scales are the same
2. Splitting a dual y-axis chart into two side-by-side charts doesn’t fundamentally solve the problem of having two y-axis with different scales
3. Choosing a chart form that is more appropriate to the story in the data is not just about avoiding “breaking a data visualization rule”, but can also be more effective in focussing on and telling the intended story.