I’m joining an on-going discussion chain by four data – chart blogs that I follow. Andrew Gellman started the chain with a post on The Monkey Cage about a New York Times article on data visualizations sites like Many Eyes. Andrew pointed out that the NYT example chart “.. is just horrible”. “It’s a classic example of a graph that looks cool but is just confusing.”
Kaiser Fung of JunkCharts followed up with a post on Loss Aversion raising concerns about “cramming as much data into the chart as possible“. Kaiser points out that this tendency is “..taking Tuft’s concept of maximizing data-ink ratio to the extreme.” In discussing the original NYT graph, Kaiser says “.. Every piece of data is given equal footing, which results in nothing standing out.”
Jorge Camoes, following up on Kaiser’s post, points to a Tufte corollary “..To clarify, add detail” , which supports the loss aversion tendency. Jorge shows an example chart with nine time series and asks “does it make any sense to add those nine series to a single chart?“
Andreas Lipphardt of XLCubed followed up Jorge’s question on how to best show this chart data by adding an elegant set of grouped colors.
Does Andreas’s color coding solve the readability issue? No! While it helps, it does not significantly clarify the data. We need to rethink our chart; what are we trying to show? There are three factors in this data set: year, income class and % of households in the class. What are we most interested in? Do we really need to show the data for each year, aren’t we more interested in the long term trend?
To me, the most important information is the long term shift in income distribution by income group, not the year to year changes. Lets use a dot plot and directly compare 1967 and 2005 distributions.
The dot plot clarifies the situation by showing changes in income by class for just 2 years so that we can compare changes by class. The % of households in the top 3 income classes were much higher in 2005, the $50-74,900 class stayed the same and % of households with total income less than $49,900 decreased.
In this case, changing chart type improved the chart more than enhanced color coding. We need to make sure we select the most appropriate chart before we try to optimize the chart format.
Kaiser’s Loss Aversion concerns raise an important charting prinicple, clarity in our chart purpose is critical to making an effective chart. More data or better colors won’t help a poor chart type selection.
Source data file link.