Reddit reviews Visualizing Data

Excel does not make publication quality graphics. I recommend Matlab or matplotlib (python) whenever I review papers with Excel figures in them.

> How did you learn the best way to organize and present your data in your publications?

Cleveland's book is a good start. Although he is is maybe a little too austere. But in general, better to have too little ink than too much.

It's an interesting book.

R's powerful
ggplot2 graphics system has a default output
style which follows many of these principles, and it looks good.

But it's not my favourite book in this area.
My favourite would be (both)
Bill Cleveland's books

• The Elements of Graphing Data (1ed 1985, 2ed 1994)
  
  
• Visualizing Data (1993)
  
  After seeing references to Cleveland in the
  R documentation
  (for example, the
  loess
  and
  lattice
  packages),
  I read both the Cleveland books, and found them extremely interesting.
  
  There's a classic paper by Cleveland and McGill,
  "Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods"
  (you can download a PDF)
  which is also interesting. (And if you find that interesting, you would
  most likely enjoy the books mentioned above.)
  
  The Cleveland books are not widely famous like
  The Visual Display of Quantitative Information,
  but I found them more appealing in a way that's kind of
  hard to describe. But, very roughly
  
  
• Cleveland feels more like a statistician trying to create
  visualisations which are efficiently and accurately perceived.
  
  
• Tufte feels a little like a designer trying to create beautiful
  visualisations based on a kind of minimalist aesthetic. Or
  maybe like a philosopher trying to find the essence of a
  visualisation.
  
  The conclusions of the two approaches are not necessarily
  incompatible. They would certainly agree on the
  undesirability of most of the ridiculous
  stuff
  in the MS Excel plot menu. (So if Tufte stops people doing that, then the more people who read him, the better).
  
  But when there's tension between the two approaches then I'd
  choose the first (Cleveland).
  
  For example, the
  Tufte (minimalist) boxplots
  manage to represent the same information as a box plot, but with less ink.
  But they feel like they might not be as easy to read.
  (See also "W. A. Stock and J. T. Behrens. Box, line, and midgap plots: Effects of display characteristics on the accuracy and bias of estimates of whisker length. Journal of Educational Statistics, 16(1): 1–20, 1991"
  (abstract) )
  
  

Aside from Tufte, you might find Cleveland's Visualizing Data worthwhile. I'm reading Stephen Few's Now You See It: Simple Visualization Techniques for Quantitative Analysis now.

Also, try following some related blogs, like Nathan Yau's Flowing Data or Kaiser Fung's Junk Charts. You can get a sense of some appropriate and/or inappropriate ways of visualizing data from these.

Finally, once you get more familiar, get something like Murrell's R Graphics. This will help you understand the basics of the base R graphics capabilities so you can make what you want, exactly how you want. ggplot2 is awesome, too, but understanding the basics is really helpful. Hope that helps.

It's a poor representation of data. In pie charts you compare angles. Humans are poor at comparing the magnitudes of angles. Without the table, labels with the actual numbers, etc. it would be very difficult to compare the information.

For instance, it is difficult based just on the visualization if Instinct or Valor has more players. A bar, column, or dot plot will show things much better. Humans are far better at perceiving differences in length or position. That table on the right is necessary - that means the pie chart is useless.

If you are serious about designing visualizations of data, I suggest you read some books by Willilam Cleveland or Edward Tufte.

EDIT: Here is article I often share with people on this topic.