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Make your own hotly criticized circle graph! The recent critical post on R-bloggers has got me wanting to make my own bubble graphs despite the popular criticism of the graph. After some attempts I have produced something that looks similar but needs some more work.
I would like to say briefly that R-blogger post by PirateGrunt while interesting an reasonable seemed to miss the point. The graphic was not support to convey massive amounts of information but simply to make obvious the scope and degree of how the current ebola epidemic compares with other epidemics.
There are many good reasons to critique this graph from an epidemiological perspective (forthcoming), yet from a graphing perspective I think this graph format is extremely efficient at conveying memorable information while the graphs assembled by PirateGrunt were more detailed they were also entirely forgettable unfortunately.
Here are a couple of examples. The first is with meaningless simulated data demonstrating how large differences in scale may appear. The second is actually using HIV prevalence data to give a graphical representation of how such rates have changed over the years. You can fine the code as well as the data on github.
Table 1: Demonstrates how scale can be communicated by size of circle
Table 2: Demonstrates the relative stability of AIDS in terms of people ill. Top number is year while lower number is millions of cases.
I would like to say briefly that R-blogger post by PirateGrunt while interesting an reasonable seemed to miss the point. The graphic was not support to convey massive amounts of information but simply to make obvious the scope and degree of how the current ebola epidemic compares with other epidemics.
There are many good reasons to critique this graph from an epidemiological perspective (forthcoming), yet from a graphing perspective I think this graph format is extremely efficient at conveying memorable information while the graphs assembled by PirateGrunt were more detailed they were also entirely forgettable unfortunately.
Here are a couple of examples. The first is with meaningless simulated data demonstrating how large differences in scale may appear. The second is actually using HIV prevalence data to give a graphical representation of how such rates have changed over the years. You can fine the code as well as the data on github.
Table 2: Demonstrates the relative stability of AIDS in terms of people ill. Top number is year while lower number is millions of cases.
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