#5 DataBeersBCN -"Dos and Don'ts of Data Viz"
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This document provides dos and don'ts for data visualization. It discusses how to properly scale and represent proportions in charts. Common misleading techniques are shown such as rescaling axes, omitting the y-axis origin, using different scales for the same axis, and including meaningless or invented data. The document advocates showing only relevant information without crowding plots. It also notes that the visualization should fit the intended audience and goal. While rules can be broken, the overall message is that visualizations should accurately and honestly portray the data.
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#5 DataBeersBCN -"Dos and Don'ts of Data Viz"
- 1. DOS AND DON’TS OF DATAVIZ ATALEOFPIES,DECEPTIONANDMINDTRICKS IÑAKIPUIGDOLLERS SABIN Data Scientist
- 2. DON’T RESCALE PROPORTIONS! x1.75 times bigger Source: http://cadenaser.com/ 15.1 + 70.7 + 15.2 = 101%
- 3. DO KEEP THE PROPORTIONSAS THEYARE
- 4. YET ANOTHER EXAMPLE … ? Source: Twitter, @ppmadrid
- 5. NOW IN A PROPORTIONAL SCALE PSOE PARTIDO POPULAR NúmerodeParados
- 6. DON’T OMIT THE ORIGIN OF THE Y-AXIS Where is the Axis?? 94 is not 0 Source: http://blog.rtve.es/ http://mediamatters.org/
- 7. DO SHOW THE Y-AXIS FROM THE ORIGIN MillionDollars 50.66% 49.07%
- 8. THIS ALSO HAPPENS IN SCIENTIFIC PAPERS This is a big difference, isn’t it? According to the paper, this should be 1.82 The value of Y (Rape Myth Acceptance) varies between 1 and 5 There are values placed in the wrong position Source: Fox, Jesse; Bailenson, Jeremy N.; Tricase, Liz (2013). "The embodiment of sexualized virtual selves: The Proteus effect and experiences of self-objectification via avatars". Computers in Human Behavior 29 (3): 930–938
- 9. THE REALITY IS SOMETHING DIFFERENT Face It was not that different in the end… Remember: The value of Y (Rape Myth Acceptance) varies between 1 and 5
- 10. DON’T USE INVENTED OR TAILOR-MADE SCALES How can this be a line? Source: http://mediamatters.org/
- 11. DO PLOT DATAAS IT IS
- 12. DON’T USE DIFFERENT SCALES FOR THE SAME AXIS Left Y-Axis (representing the non-smokers) starts at 2 Right Y-Axis (representing the smokers) starts at 3 Source: H. Wainer, Visual Revelations, Graphical Tales of Fate and Deceptions from Napoleon Bonaparte to Ross Perot Disclaimer! This Graph is from a tobacco company
- 13. DO USE THE SAME SCALE TO MAKE DATA COMPARABLE
- 14. DON’T SHOW MEANINGLESS NUMBERS DON’T USE PIE CHARTS 193% ??? That’s a big pie! Source: http://mediamatters.org/ DON’T USE 3D Perspective makes percentages look different Source: http://imgarcade.com/1/misleading-circle-graphs/
- 15. SOME THINGS WE LEARNED AT SCHIBSTED ■Know your audience and adapt the visualization to them ■The title matters, it has to be attractive but not distracting ■Select the most suitable plot, there is no one-plot-fit-all ■Show only relevant information, crowded visualizations are misleading ■Sometimes you can break the rules…
- 16. DO CHOOSE A VISUALIZATION FITTING YOUR AUDIENCE Percentage of Sellers per segment Slack channels sharing users
- 17. DON’T USE CROWDED PLOTS WITH MISLEADING INFORMATION ■Too many elements ■The colours are meaningless ■The axes are misleading (not showing the origin)
- 18. DO SHOW ONLY WHAT IS IMPORTANT ■Axes starting at 0 ■Only the necessary elements GOAL Show the correlation of the data points
- 19. … A DIFFERENTAPPROACH ■We don’t care about the value it’s OK to break the axis rule!! ■The colours have a meaning GOAL Show the distribution and density of the data points
- 20. WE ARE LOOKING FOR TALENT! inaki.puigdollers@schibsted.com Thanks, questions? Data Scientist – Schibsted Product & Technology
Editor's Notes
- -A picture tells a thousand words -Goal: share examples of visualizations showing distorted information and how can this be addressed
- -Common practise to fool people’s mind is rescaling porportions -Even though you show the numbers, if the plot is not proportional contradictory information -A picture tells a thousand words
- -Here you see how different the plot looks when the proportions are as they should -However this particular example can be just an error, just not intentional. But what about this one?
- -Spatial perception is a very important component of image processing in human’s brain -This is why mass media abuses this kind of blatant distortions to communicate somehow biased message
- -Again, if we do the exercise of re-plotting the data in a fairer way we see that reality is something different to what they try to show -So the blue line is flatter than the one they presented originally, take your own conclusions…
- -Another technique to show distorted data is omitting the Y-axis. -Messes up with spatial perception again -Comparing is very difficult
- -But if we re-plot it truth comes to surface again… -And that incredibly huge difference betwwen both candidates is gone -And the federal wellfare received in US hasn’t grown as much neither...
- -No surprise media uses this -We all knew that TV and newspapers provided biased information -Is more strange is to see this in science
- -Some scientific studies use distortion techniques as well to “enhance” their message -But if we see how it really looks like this is what we have: the difference between conditions is not that big -Is it science a matter of believe in the end?
- -Another great example from Fox news: created a linear growth of the job loss by QUARTER out of the blue
- -This is how it really looks, not only the values are not linear, but the periods are not quarters but random months across 3 different years!
- -Another good deceiving technique is to use double axis in the same plot -It can be good: enhanced readability, but if the axis are not the same you can create effects like the one from this tobacco company showing that smoking is not affecting with death rate, only the age matters
- -However if we re-plot it correctly we see a complete different story -No Surprise it comes from a tobacco company, right?
- -And then we have the pie charts. -Should I use them? I ‘ll try to avoid them -If you insist -remember simple rule: pie charts show parts of a whole so make them sum up to 100% and no more - avoid perspective games
- -Things we learned at Schisted, I’m going to talk about a couple of them -One of the most delicate points: choosing which visualization to use -Know audience beforehand
- -Not everybody understands reality the same way, while a DS may feel comfortable with a network plot, BP tend to prefer bar plots or waterfall plots -In addition, There is no one-plot-fit-all solution
- -Once you have decided which way to go you have to be careful with the number elements you add to the plot. By elements I mean : colours, size of the points, width of the bars, regression lines,… amogn others -Crowded plots are, more often than not, misleading and distracting audience's attention from what is important.
- -My suggestion: do not add irrelevant elements, every single element you have in the plot has to be meaningful by itself. -Here, for instance we have a clear goal, so we sticked to it and showed only elements that helped us to explain that message
- -If your goal is different, so is your plot -All in all, I would say that the golden rule in data visualization is two folded to communicate a message (this is your goal) based on some observed data (which you have to respect)
- -If your goal is different, so is your plot -All in all, I would say that the golden rule in data visualization is two folded to communicate a message (this is your goal) based on some observed data (which you have to respect)