What’s next after you postdoc?
Part 2 of “Pie or no Pie”.
In my last blog I discussed why pie charts are hard to read and therefore better to be avoided. Today, I offer a real life example and answer the question of all scientists in training: What’s next after my postdoc? And I have the answer! (at least for those of you working at the Max Planck in Dresden!
According to the numbers collected in the fifteen years since the institute was founded most of you, as you suspected, will not become professors, but most, 74%, will remain closely connected to academic science, by being a staff scientist, on a second postdoc or entering the administration. If you came to MPI to go into industry, bad luck!, your chances are low, as only 11% end up in Pharma (maybe because the tech industry in Dresden is not very strong yet?) Many that work in science-related business become editors or consultants. You don’t fall into any category? Me neither, and we are in the category “other”, which really is a miscellaneous category of people on parental leave or unemployed, working at a bank or freelancing.
So let’s think of how to present the data best. The default to show percentages of a whole is often the pie chart. But – we immediately see a problem: we would like to show the three large categories, academia, science-related businesses, and “others”, but we also want to split up each category into its subgroups, and there are 12 of them! This means, the pie chart has way too many categories to really comprehend them. And, to help the reader, each subcategory therefore must be labeled, resulting in a pie chart completely cluttered with category names and data labels, none of them nicely aligned.
Let’s try the column or bar chart. From last week you might remember that it is easier to use horizontal bar if you have long category names: this gives you plenty of space for the labels, which really is a plus in this case!
Then, when using a bar chart we add another layer of information, and this for instance can be done using color. To visually group subcategories into the large categories, we use three distinct colors for ‘academia’, ‘science-related’, and ‘other’. Need help on choosing color? Colorbrewer is a fantastic resource! (For our plot: we have three data classes and they are all qualitatively different). Now, with one glance we can see the large categories and all subcategories! In addition, I have added a little more text to indicate the name and overall percentage of the three large categories.
One reason people love pie charts is that they visually present parts of a whole (although our eyes more often than not struggle to make that out!). To allow the audience to clearly make out parts of a whole, we can use a little trick and extent the bars to 100% (or here 50%) and fill the bar with color according to its percentage. I personally think there are too many categories and that the empty bars create lot of lines clutter on the right hand side. Another possibility is to show stacked bars, but one looks a bit lonely. I’d use this to compare for example the data per year.
Finally, here is a wonderful compilation of atrocious pie charts, and I hope you NEVER use one again.