I am always excited to get news of a new Visual Business Intelligence Newsletter in my Inbox, and that is what arrived at the end of last week. This time Few takes on “jittering”, and suggests an interesting alternative to adding random noise.
Here is a little python snippet to do it:
hist, bin_edges = np.histogram(x)
# make y position based on values of hist
y = 
for h_i in hist:
y += range(h_i)
plt.plot(sorted(x), y, 'o')
Here is a notebook that makes that code do something: https://gist.github.com/aflaxman/235f94f9563b1675233d6d35cd30b8c2
Also Jeff Heer and company made an interactive version in Vega: https://vega.github.io/vega/examples/wheat-plot/
D3js in any substantial way
Steve Few email list, and his example with isotype and patient risk charts
538.com viz stuff
Interesting paper: http://www.ejwagenmakers.com/inpress/HoekstraEtAlPBR.pdf
Here is the quiz they used:
I’d love to replicate for a few of the target audiences for my work.
From Wikipedia, the free encyclopedia
Bland–Altman plot example
A Bland–Altman plot (Difference plot) in analytical chemistry and biostatistics is a method of data plotting used in analyzing the agreement between two different assays. It is identical to a Tukey mean-difference plot, the name by which it is known in other fields, but was popularised in medical statistics by J. Martin Bland and Douglas G. Altman.
I helped judge a plotting contest for the Scientific Python conference last summer. Who won? I don’t know, and a short web searching binge didn’t find out. A lovely plot took 3rd place, and every entry is here (with sourcecode). Good stuff for seeing how different groups do different tricks, and for checking what still doesn’t work in mpld3.