Session 1: Do's and Don'ts of Data Visualization. Presenter: Randy Olson, Ph.D.

Transcription

1 Session 1: Do's and Don'ts of Data Visualization Presenter: Randy Olson, Ph.D.

2 DO S AND DON TS OF DATA VISUALIZATION Dr. Randy Olson randalolson.com

3 ABOUT ME AI researcher B.S. in CS, Uni. Central Florida (2010) Ph.D. in CS / EEBB, Michigan State Uni. (2015) Postdoc, Computational Genetics Penn Data tinkerer Data blog: randalolson.com/blog/ Community leader: reddit.com/r/dataisbeautiful

4 DATA VIZ TURNS TABLES OF NUMBERS

5 INTO MEANINGFUL INSIGHT

6 DATA VIZ CAN ALSO MISLEAD

7 LET S COVER SOME GUIDELINES FOR DATA VISUALIZATION

8 COLOR IN DATA VISUALIZATION

9 COLOR SHOULD ADD INFORMATION Google tutorial searches Python JavaScript R 0% 5% 10% 15% Python JavaScript R Google tutorial searches 0% 5% 10%

10 TYPES OF COLOR SCHEMES Qualitative Sequential Diverging

11 CONSIDER COLOR BLINDNESS

12 CONSIDER COLOR BLINDNESS ColorOracle.org

13 REQUIREMENTS OF A COLOR SCHEME A color scheme should: Add information Encode the data well Accommodate color blindness Print well both color and B&W

14 LIBRARIES WITH GOOD COLOR SCHEMES Seaborn (Python) matplotlib s style library ggplot2 (R) D3.js (JavaScript) or head to ColorBrewer2.org

15 COMMON PITFALLS IN DATA VISUALIZATION

16 UNLABELED CHARTS Most common mistake Can make your visualization useless 12% 10% 8% 6% 4% 2% 0%

17 % Google searches UNLABELED CHARTS Most common mistake Can make your visualization useless Python JavaScript R 12% 10% 8% 6% 4% 2% 0%

18 USING 3-DIMENSIONAL CHARTS

19 % Google searches % Google searches USING 3-DIMENSIONAL CHARTS 10% Python JavaScript R Python JavaScript R 10% 5% 5% 0% %

20 FRIENDS DON T LET FRIENDS USE 3-DIMENSIONAL CHARTS

21 PARTS DON T SUM TO A MEANINGFUL WHOLE % of users who use >2 programming languages R 41% Python 75% JavaScript 58%

22 PARTS DON T SUM TO A MEANINGFUL WHOLE % of users who use >2 programming languages R JavaScript Python Note: Fake data 0% 10% 20% 30% 40% 50% 60% 70% 80%

23 TOO MANY CATEGORIES Google tutorial searches Visual Ruby Basic VBAPerl Matlab Swift Objective-C R Java lua Javascript C PHP C++ C# Python

24 TOO MANY CATEGORIES Google tutorial searches Java Other PHP Python

25 BAR CHARTS NOT STARTING AT ZERO 7.40% 7.20% 7.00% 6.80% 6.60% 6.40% Google tutorial searches for JavaScript % 6.00% 4.00% 2.00% 0.00% Google tutorial searches for JavaScript

26 FAILING TO NORMALIZE DATA Orlando 1000s of Python users in Orlando % of Python users in NYC NYC Chicago Chicago Note: Fake data % 2% 4% 6% 8%

27 CHARTJUNK Extraneous labels Pointless images Excessive color Fancy animations

28 CHARTJUNK Extraneous labels Pointless images Excessive color Fancy animations

29 MORE EXAMPLES viz.wtf Reddit.com/r/DataIsUgly Makeover Monday vizwiz.blogspot.com/p/makeover-monday-challenges.html

30 GOOD PRACTICES IN DATA VISUALIZATION

31 StackOverflow Rank WHEN APPROPRIATE, SHOW THE DATA Programming language popularity GitHub Rank

32 StackOverflow Rank WHEN APPROPRIATE, SHOW THE DATA Programming language popularity GitHub rank

33 CLARIFY CORRELATION!= CAUSATION

34 LABEL OBJECTS AND DATA DIRECTLY Google tutorial searches Python JavaScript R 10% 5% 0%

35 LABEL OBJECTS AND DATA DIRECTLY Google tutorial searches 12% 10% 8% 6% 4% 2% 0% Python JavaScript R

36 LABEL OBJECTS AND DATA DIRECTLY Google tutorial searches Python JavaScript R 0% 2% 4% 6% 8% 10% 12%

37 LABEL OBJECTS AND DATA DIRECTLY Google tutorial searches Python 10.4% JavaScript 7% R 2.6%

38 LABEL OBJECTS AND DATA DIRECTLY Google tutorial searches Python 10% JavaScript 7% R 3%

39 MAKE SURE YOUR VISUALIZATION STANDS BY ITSELF Could someone interpret the chart by itself? Make sure chart alone can tell your story

40 INCLUDE THE UNDERLYING DATA When possible Public data is better Store in a public repository GitHub.com figshare.com Link to the data repository in your visualization

41 DESCRIBE YOUR METHODS IN DETAIL From raw data to the chart What data? Manipulations? Omissions? Or just provide the code! GitHub.com Link to the methods in your visualization

42 IF YOU TORTURE THE DATA LONG ENOUGH, IT WILL CONFESS TO ANYTHING. How to Lie With Statistics (1954)

43 IF IT CAN T BE REPRODUCED, THEN IT SHOULDN T BE TRUSTED

44 THESE ARE GUIDELINES, NOT RULES

45 THESE ARE GUIDELINES, NOT RULES BUT IT S HELPFUL TO KNOW RULES BEFORE YOU LEARN TO BREAK THEM

46

47 THANK YOU!