Note: Posting will be slow in the next few weeks due to holidays.
Vado in vacanza a Roma presto. Se abita alla citta', mi manda una email per favore. Forse ci possiamo incontrare.
Reader Daniel L. submitted this chart a while back, and it's an instructive one.
This Chicago Tribune chart accompanies an analysis by Greentopia of the "green-ness" of 10 oil companies. The company concocted a ranking based on the composite of 6 factors (e.g. emissions, efficiency).
The bar charts have a couple of unusual, but unconvincing, features:
1. Typically, in a bar chart, longer is better but when bars are used to depict ranks, shorter is better. In charting, it is usually safer to satisfy expectations, or risk being misinterpreted.
2. For rank n, the length of the bar is (n+2). The chart designer just decided that the piece including the actual rank should be thrice the size of the other highlighted pieces. There are a total of 12 strips in each bar.
Why is it a bad thing to add 2 units to every bar? Consider rank 1 vs rank 3. If n = n, then the ratio of bar lengths is 3 to 1. If n = n+2, then ratio is 5 to 3, and not the same! Thus, once the extra unit is added to each bar, the comparative lengths mean something different.
One thing is left unexplained: how is the overall ranking derived from the factor ranking? Hess, with three #1 ranks, would seem to be in contention for overall #1.
Daniel thinks a bumps chart would work nicely, and it certainly would for any kind of ranking data. A slight variation of the Tribune chart would also work nicely... think of the bar as consisting of ten little lights. For each rank, the appropriate light is switched on. (This is in essence a dot plot.)
In both these variations, the charts are self-sufficient -- there is no need to print all the ranks on the chart as shown above.
Daniel also commented that it is difficult to incorporate the stock price data onto a bumps chart. Why bother? What is the point of including the stock price data anyway? If it is included, readers have to be given tools to interpret such data, in particular, some explanation ought to be provided for large jumps or slips. In addition, the scales should be tailored to allow comparisons of relative value, rather than sticking to equal scales for each stock (Dona Wong covers this topic well in her book.)