It has little communication value except to say “Look how cool I am!” At least all the data is present, so a meticulous reader can get what information he needs. I hope this isn’t what CHANCE is looking for.
If you need that much detail, you probably need the table of numbers.Īfter doing all that, I found Burtin’s original visualization via a NY Times article. I tried a few ways to do that with labels and lines, but the graph just became too messy. A drawback of this graph is that the points are not labeled or connected. Similarly, neomycin is best for gram negative bacteria and streptomycin is best if gram staining is unknown. The graph shows that penicillin is best for gram positive bacteria since all purple circles are below 1µg/ml for penicillin. With that in mind, my visualization shows the MIC for each antibiotic with the best dose for each scenario called out. It can’t show the 3D the alignment of the three clusters, but you can get a hint of it in the neomycin versus penicillin panel.įor my contest entry, I decided to go with the perspective of a 1950s doctor, with the idea that a doctor treating a patient doesn’t know usually know what bacteria is causing the infection and may or may not have the results of a gram staining. Here’s a view looking straight down the line.Ī scatter plot matrix shows all the 2D relationships better and is better for static presentation. If you do rotate it, you can see that three of the clusters appear roughly in a straight line, so maybe there are really two different kinds instead of four. Here, the data markers correspond to the clusters.ģD doesn’t work too well in static 2D media like this one since you need to be able to rotate it to see the structure. Since there are only three antibiotics, we can view the data as a 3D scatter plot. The bacteria that are clustered close together might suggest a commonality for future research. It’s like the heat map above, but similar bacteria are grouped together (and the color scale is slightly different). Here’s a heat map and dendrogram resulting from a cluster analysis.
Next, we might think from a researcher/statistician perspective and try to cluster the bacteria that react similarly to the antibiotics. I don’t see much advantage over the table of numbers, except to quickly find extreme values or certain other patterns that the colors help with. If an antibiotic culture grows exponentially, then the log of the concentration is the time to grow it.Įxploring the data a little bit, the simplest visualization is a heat map, where every number is represented by a swatch of color. Besides nicely spreading out the data values, the log transformation may have a physical interpretation. The MIC values vary widely from 0.001 to 1000, and I applied a logarithm transform for analysis, either on the data or on the graph. Lower is better, indicating less antibiotic is needed to treat the bacteria. The data shows Minimum Inhibitory Concentration (MIC, presumably in µg/ml) for each antibiotic and bacteria combination. My second question is, “Best for whom?” Which illustration is best depends on the audience, which in this case might be doctors, researchers, statisticians or the general public among others. With only five variables and sixteen observations, my first question is, “What’s wrong with just using a table?” The table in the contest description is even nicely laid out. Designer Will Burtin used this data set for a 1950s visualization. One still mst think what variables are most important and so select the properties for representation (see Cleveland & McGill, 1985).CHANCE magazine is running a contest to create the best illustration for a data set of the effectiveness of three antibiotics on sixteen strains of bacteria. So one can fit up to 6 variables in a "simple" 2D plot that is by far easier to read than that piece of chart junk above. Far from it! They should be eye-candy, too - but fulfilling the above mentioned conditions!Ī standard way to show this kind of multivariate data is a bubble plot, where you have two axes (X/Y), the size of the symbols, the color or hue of the symbols, the brightness or saturation of the symbols, and the kind of symbols (circles, squares. I am not saying that scientific charts/plots/diagrams should be ugly.
The greatest number of ideas in the shortest time with the least ink in the smallest space. Good graphs communicate complex ideas with clarity, precision and efficiency, and Such charts as the one shown are definitely NOT for scientific purposes.