PLOTCON NYC: Get Your Point Across: The Art of Choosing the Right Visualization for your Data
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Why does one decide to visualize data? And once they have decided to visualize their data, how do they know the best way to tell their story? To answer these questions, this talk will focus on the iterative design process, visualization fundamentals, and storytelling techniques. We will then anchor these principles in effective visualization examples. Of particular importance is the ability and willingness to refine and redefine your objectives as you determine the right visualization for you (or your client/audience). This talk will walk through a Datascope client case study in order to convey the importance of a flexible and collaborative process when approaching data visualization problems in order to deliver the best end result. The audience will walk away armed with helpful visualization techniques and an understanding of the iterative design process.
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PLOTCON NYC: Get Your Point Across: The Art of Choosing the Right Visualization for your Data
- 1. get your point across for PLOTCON • Nov 16, 2016 the art of choosing the right visualization for your data Mollie Pettit @MollzMP Jess Freaner @jessfreaner
- 2. - iteration in action: a case study agenda - iterative design process - storytelling techniques - visualization fundamentals - why visualize data?
- 4. explore data Same mean for x and y Same variance for x and y Same correlation Same best fit line
- 5. explore data
- 6. tell a story Mona Chalabi mona_chalabi
- 8. tradeoffs Randall Munroe - XKCD @xkcd
- 10. maps Randall Munroe - XKCD @xkcd
- 11. maps proxies for population density! Randall Munroe - XKCD @xkcd
- 12. 5 years of drought John Nelson @John_M_Nelson
- 13. pie charts Randall Munroe - XKCD @xkcd
- 14. pie charts Randall Munroe - XKCD @xkcd
- 15. bar graphs Randall Munroe - XKCD; @xkcd
- 16. line charts Yes you are! And you’re sitting there! Hi, Kitty! Randall Munroe - XKCD @xkcd
- 19. does it add anything? is it necessary? will it be missed? revise
- 20. feedback Randall Munroe - XKCD @xkcd
- 22. 5 years of drought
- 23. compare all medalists through time men’s 100-meter sprint Kevin Quealy and Graham Roberts @KevinQ, @Grahaphics
- 24. simple choices
- 25. complex choice Kevin Quealy and Graham Roberts @KevinQ, @Grahaphics
- 26. complex choice Kevin Quealy and Graham Roberts @KevinQ, @Grahaphics
- 27. complex choice Kevin Quealy and Graham Roberts @KevinQ, @Grahaphics
- 28. with complex stories… Nate Cohn and Amanda Cox @Nate_Cohn, @amandacox
- 29. introduce idea Nate Cohn and Amanda Cox @Nate_Cohn, @amandacox
- 30. Nate Cohn and Amanda Cox @Nate_Cohn, @amandacox how do you do this?!
- 32. anticipate refining your solution generate ideas build prototype iterative problem solving evaluation
- 33. anticipate refining your solution generate ideas build prototype iterative problem solving evaluation
- 34. anticipate refining your solution generate ideas build prototype iterative problem solving evaluation
- 35. anticipate refining your solution generate ideas build prototype iterative problem solving evaluation
- 36. anticipate refining your solution generate ideas build prototype iterative problem solving evaluation
- 37. anticipate refining your solution generate ideas build prototype iterative problem solving evaluation
- 38. anticipate refining your solution generate ideas build prototype iterative problem solving evaluation
- 39. anticipate refining your solution generate ideas build prototype iterative problem solving evaluation
- 40. a walk through the process
- 42. backstory
- 44. I think you learn about computer safety. :) When you are a genis at electronics :) Using code and fixing and making computers. :)
- 45. science on a computer programming / coding how computers work how to use computers studying computers a website, program, or game typing / testing using computers to solve problems engineering someone good at computers internet safety experiments / research / modeling I like it! class / learning / lessons making apps, games, or websites I don’t know what’s inside a computer
- 46. brainstorm
- 47. bar charts
- 48. bar charts
- 49. bar charts
- 50. bar charts
- 51. “ribbon” bar
- 54. cohort view
- 55. cohort view
- 56. keywords/phrases in free text word cloud
- 57. keywords/phrases in free text clusters
- 58. punctuation
- 59. emoticons
- 62. the verdict?
- 63. cohort view
- 67. like how u can control the angry birds like u make:) Learning how computers work.:) blurb list
- 68. like how u can control the angry birds like u make stuff move :) Learning how computers work. :) side blurbs
- 69. side blurbs like how u can control the angry birds like u make stuff move :)
- 70. Learning how computers work. like how u can control the angry birds like u make stuff move crowd blurbs
- 74. takeaways - fundamentals / tradeoffs - know when to follow / break them - use context, audience, purpose to guide you
- 75. - fundamentals / tradeoffs - iterate and be flexible takeaways
- 76. - fundamentals / tradeoffs - iterate and be flexible - use context, audience, purpose to guide you takeaways
- 77. Mollie Pettit @MollzMP Jess Freaner @jessfreaner Thanks! @DsAtweet get your point across the art of choosing the right visualization for your data
Editor's Notes
- Quote: The power of the unaided mind is highly overrated… Without external aids, memory, thought, and reasoning are all constrained. But human intelligence is highly flexible and adaptive, superb at inventing procedures and objects that overcome its own limits. The real powers come from devising external aids that enhance cognitive abilities. How have we increased memory, thought, and reasoning? By the invention of external aids: It is things that make us smart. — Don Norman Things That Make us Smart: Defending Human Attributes in the Age of the Machine
- There are two reasons … One you might most often overlook. Taking a look at patterns visually will help you have a better understanding of a data set
- There are two reasons … One you might most often overlook. Taking a look at patterns visually will help you have a better understanding of a data set
- When you do exploratory analysis, it’s like hunting for pearls in oysters. We might have to open 100 oysters (test 100 different hypotheses or look at the data in 100 different ways) to find perhaps two pearls.
- helps to get to more interesting questions. Great thing about this kind of data - takes very little effort. You can do quick and dirty. For your eyes only. Not worried about fancy titles or pretty colors. Glean insight.
- Mona Chalabi's handdrawn data sketches
- When you do exploratory analysis, it’s like hunting for pearls in oysters. We might have to open 100 oysters (test 100 different hypotheses or look at the data in 100 different ways) to find perhaps two pearls. When communicating analysis to your audience, you will likely have a specific story you want to tell - probably about those two pearls Too often, people simply present the data - all 100 oysters - which makes the audience reopen all of the oysters all over again! It is often best to concentrate on the pearls, the information your audience needs to know to understand your story / narrative
- When you do exploratory analysis, it’s like hunting for pearls in oysters. We might have to open 100 oysters (test 100 different hypotheses or look at the data in 100 different ways) to find perhaps two pearls. When communicating analysis to your audience, you will likely have a specific story you want to tell - probably about those two pearls Too often, people simply present the data - all 100 oysters - which makes the audience reopen all of the oysters all over again! It is often best to concentrate on the pearls, the information your audience needs to know to understand your story / narrative
- When you do exploratory analysis, it’s like hunting for pearls in oysters. We might have to open 100 oysters (test 100 different hypotheses or look at the data in 100 different ways) to find perhaps two pearls. When communicating analysis to your audience, you will likely have a specific story you want to tell - probably about those two pearls Too often, people simply present the data - all 100 oysters - which makes the audience reopen all of the oysters all over again! It is often best to concentrate on the pearls, the information your audience needs to know to understand your story / narrative
- but when there’s an audience sometimes there are better choices particularly for a given context, especially when you consider the end purpose of the visualizations, end users / audience the same exact choices in different situations can make a visualization successful or completely miss the mark
- but when there’s an audience sometimes there are better choices particularly for a given context, especially when you consider the end purpose of the visualizations, end users / audience the same exact choices in different situations can make a visualization successful or completely miss the mark
- due to all the geocoded data available, quite popular to put data visualizations on maps. but something to be careful of is that often times these end up being proxies for pop. density
- for instance, consider what our stick figure friend in pointing out here: Our site’s users subscribers to Martha Stewart Living Consumers of furry pornagraphy These all just show that more of each group of people exist in more populous regions. :facepalm:
- use maps only when geographic data is relevant John Nelson @John_M_Nelson
- Discuss what human eyes can detect. What they’re good at, what they’re not. The one single thing pie charts are good at is when you're comparing 2-3 different data points with very different amounts of information. The human eye isn’t good at scribing quantitative value to two-dimensional space. Said more simply: pie charts are hard for people to read. When pieces are close in size, it’s difficult (or likely impossible) to tell which is bigger. When they aren’t close in size, the best you can do is determine that one is bigger than the other, but you can’t judge by how much.
- These pie charts, however, are perfect examples of good pie charts. Because they are only comparing two items within the pie chart, you can gather a lot of information. Leonardo - more than half know him as Renaissance artist Michelangelo - less than half Donatello - less than a quarter Raphael - just over half
- Bar and column graphs are great representations of categorical data, in which you can count the number of different categories. Bar charts are sometimes avoided because they are common. Mistake! One of the strong points of bar charts is that they are common, requiring less of a learning curve for their audience. Rather than needing to use brain power to understand how to read the graph, your audience spends it figuring out what information to take away from visual. Bar charts are easy for our eyes to read and understand. Our eyes compare the end points of the bars, so it is easy to see quickly which categories is the biggest, which is smallest, and also the incremental differences between categories Because of how our eyes compare the relative end points of the bars, it is important that they have a zero baseline to prevent giving false visual impressions. sometimes the simplest viz is best conveys 1 (or few) message(s) easy to digest but even with the seemingly simple…. there are choices
- Bar and column graphs are great representations of categorical data, in which you can count the number of different categories. Bar charts are sometimes avoided because they are common. Mistake! One of the strong points of bar charts is that they are common, requiring less of a learning curve for their audience. Rather than needing to use brain power to understand how to read the graph, your audience spends it figuring out what information to take away from visual. Bar charts are easy for our eyes to read and understand. Our eyes compare the end points of the bars, so it is easy to see quickly which categories is the biggest, which is smallest, and also the incremental differences between categories Because of how our eyes compare the relative end points of the bars, it is important that they have a zero baseline to prevent giving false visual impressions. sometimes the simplest viz is best conveys 1 (or few) message(s) easy to digest but even with the seemingly simple…. there are choices
- Bar graphs and line graphs can seem nearly interchangeable but generally, line graphs work best for continuous data, whereas bar and column graphs work best for categorical data. Continuous data is quantitative, you cannot count the number of different values. This includes data like sales, height, profit, time, etc. (Although, time can be looked at as categorical data, as in the last example where the bars were clumped into decades) Examples: Price in dinner bill compared to tip, tuition cost at a university over time, etc.
- It may be easy to discount tables, but tables still have their place. Tables are great for communicating to a mixed audience whose members will each look for their particular row of interest
- Joey Cherdarchuk
- depending on what you want to highlight or represent, the same data can manifest in radically different ways.
- to illustrate the power and importance of consider what story you’re telling we’ll revisit that earlier drought visualization. same data can be presented in different ways depending on purpose
- This map uses the EXACT same data as the other map (and by the same person so the way that they handled, cleaned and analyzed is also the same), just this one is stress where droughts were RARE. in both cases, these are relatively simple stories that you can quickly absorb (at least on a high level — drought happening in these areas and not in these)
- This map uses the EXACT same data as the other map (and by the same person so the way that they handled, cleaned and analyzed is also the same), just this one is stress where droughts were RARE. in both cases, these are relatively simple stories that you can quickly absorb (at least on a high level — drought happening in these areas and not in these)
- with more complex stories, you need to provide much more information / context before thrusting everything upon your audience
- some folks that are particularly great at this are at the NY Times’s Upshot so we’ll use Amanda Cox’s story / visualization about the voting habits of Americans to help illustrate an effective way to do this. @Nate_Cohn, @amandacox
- with more complex stories, you need to provide much more information / context before thrusting everything upon your audience
- ensure results are communicable avoid solving the wrong problem encourages more / better data
- ensure results are communicable avoid solving the wrong problem encourages more / better data
- ensure results are communicable avoid solving the wrong problem encourages more / better data
- ensure results are communicable avoid solving the wrong problem encourages more / better data
- Right now, only 25% of the K-12 schools in the U.S. offer computer science with programming and coding, and only 28 states allow those courses to count towards high school graduation requirements, according to the White House. Recognizing Chicago's success, the White House launched a national CS4All initiative, which aims to provide funding for states and school districts with the goal of making sure every K-12 student has access to computer science curriculum.
- Just this year, the Chicago Public Board of Education announced that computer science courses would become a graduation requirement for all high school students. The Chicago Public School district is working with Code.org and other organizations to further develop a CS education curriculum to implement across all its high schools. In doing so, Chicago Public Schools became the first school district in the country to elevate computer science as a core requirement for high school, separate from math and science. "Making sure that our students are exposed to STEM and computer science opportunities early on is critical in building a pipeline to both college to career," said Mayor Emanuel. "Requiring computer science as a core requirement will ensure that our graduates are proficient in the language of the 21st century so that they can compete for the jobs of the future."
- bar charts very good for easy comparison scales well no granularity no concept of the size of the study no individuality to students - all clumped together
- bar charts very good for easy comparison scales well no granularity no concept of the size of the study no individuality to students - all clumped together
- bar charts very good for easy comparison scales well no granularity no concept of the size of the study no individuality to students - all clumped together
- make comparisons very easy scale well
- show relative sizes in a compact amount of space
- harder to gauge relative sizes, but these are fun subcategories can be patterns inside the bubble emphasize the number of objects more than bar chart- distributed in 2d
- - emphasize the fragmentedness relative size can be determined but comparisons are harder
- other extractable data could also do this for emotion words, like exciting, cool, fun as well as proper nouns such as code.org, google, and angry birds
- - more visually compelling - emphasizes the size of the study and *students* rather than aggregates - Gives personality and individuality to each person
- - more visually compelling - emphasizes the size of the study and *students* rather than aggregates - Gives personality and individuality to each person
- blurb list: quick to implement, shows everything at once
- slide blurbs: show more sentences, gives user control to browse or just watch them cycle through, more compact
- slide blurbs: show more sentences, gives user control to browse or just watch them cycle through, more compact
- crowd blurbs: cute, humanizes the dots, not as much control for the user
- crowd blurbs: cute, humanizes the dots, not as much control for the user
- learn rules of thumb and best practices but don’t be constrained by them know them, to make informed decisions for when to follow and when to break use context, audience, purpose to guide you
- learn rules of thumb and best practices but don’t be constrained by them know them, to make informed decisions for when to follow and when to break use context, audience, purpose to guide you
- learn rules of thumb and best practices but don’t be constrained by them know them, to make informed decisions for when to follow and when to break use context, audience, purpose to guide you