Visual Resources Association Annual Conference March 27-30, 2018, Philadelphia Session: DIY: Crafting Your Space, Mission, and Situation Presenter: Alison Blaine, Digital Technologies Development Librarian, North Carolina State University

1. Making data visual
DIY guide to getting started with data
visualization
Alison Blaine
NCSU Libraries

2. who am i?
data & visualization librarian @
ncsu libraries

3. I help people make their data visual

4. high school humanities instructor
languages & global area studies
|
[Love of teaching & learning new things]
|
information science master’s degree
academic librarian/technologist/statistical
computing/coding

5. genomics
engineering
forestry
epidemiology
literature
100s of other subjects...

6. develop and teach
workshops
go.ncsu.edu/vizmaterials

7. consultations

8. data viz,
text mining,
data harvesting,
data cleaning,
statistical software,
network graphing,
machine learning

9. self-taught,
imposter syndrome, etc.
my approach to learning is to teach.

10. data art-related projects

11. code+art visualization contest (‘16-17)

12. code+art visualization contest (‘16-17)
lib.ncsu.edu/codeart

13. Creative coding and webVR workshops

14. CLOUDS:
Interactive generative art installation

15. Augmented Reality Art Exhibit
Student-created 3d
digital models
Published as
Snapchat “lenses”
with Snapchat Lens
studio

16. DIY data visualization

17. data
information with a format and structure, stored for reuse,
sharing and/or transfer.

18. formats
TXT, CSV, TSV, XLS, DOC, PDF, JSON, png, mp4, etc
structures
text, table, key/value, unstructured, image, clip, etc
storage
text files, spreadsheets, databases, emails, audio files, etc

19. data visualization
graphical representation of data

21. graphical symbols
points, lines, bars, bubbles, boxes, circles, etc
coordinate systems
cartesian, polar, etc
aesthetic mappings
color
fill
texture
size
length
shape

22. goal setting
Do you want to…
make graphs for a presentation or publication?
just explore your own data?
make an interactive web visualization?
make data art?
teach a workshop?

23. resource locating
free in-person workshops
online tutorials and free books
open source software to use

24. learning about the field
free online conference talks (Tableau conference, OpenVis)
local meetups
Twitter #dataviz

25. here are some tips
for getting started
making visualizations

26. 1.
start with the data

27. be excited about the data.

28. Helpful metaphors?
A dataset is a mysterious forest.
A dataset is a room with many doors.
A dataset is a mountain with many faces.

29. Walk into the forest
without set expectations.
But have a plan.

30. Plan to spend a lot of time getting your
data clean and tidy for visualization.

31. get your data clean
no errors, misspellings or inconsistencies
no non-data rows or text besides single column headers
Raleigh
Raleigh, NC
Raleigh N.C

32. Is it clean?

33. Remove extra rows/text

34. not done yet...

35. get your data tidy

36. “There are three interrelated rules which make a
dataset tidy:
1. Each variable must have its own column.
2. Each observation must have its own row.
3. Each value must have its own cell.”
-Hadley Wickham, R for Data Science

37. This is NOT tidy data.
Source: Hadley Wickham (2014) “Tidy Data.” Journal of Statistical Software. Vol. 59, Issue 10.

38. how to assess if your data
is tidy

39. “The first step is always to figure out what the variables and
observations are. Sometimes this is easy; other times you’ll
need to consult with the people who originally generated the
data. The second step is to resolve one of two common
problems:
1. One variable might be spread across multiple columns.
2. One observation might be scattered across multiple rows.”
-Hadley Wickham, R for Data Science

40. What are the variables in this data?
Variables are the things with values that change.
Source: Hadley Wickham (2014) “Tidy Data.” Journal of Statistical Software. Vol. 59, Issue 10.

41. Person
Treatment
Result
Source: Hadley Wickham (2014) “Tidy Data.” Journal of Statistical Software. Vol. 59, Issue 10, pg. 3.
Variables are the things with values that change.

42. Each variable is in its own column.
Now it’s tidy!

43. How tidy is this data?

44. Does each variable have its own column?
Does each observation have its own row?
Does each value have its own cell?

45. Does each variable have its own column? NO
Does each observation have its own row? YES
Does each value have its own cell? YES

46. Tidy data version
Year is a variable
Individual years are values of that variable
Same with Land Percentage
I used Tableau
Public’s pivot
feature to
reshape the
data.

47. How I did it using Tableau

48. X =Year
Y = weighted avg. of
Land Percentage

49. What tools are good for tidying data?
Tableau Public
OpenRefine
R (tidyverse library)
Python (pandas library)

50. 2.
Plan to spend a lot of time in the “play
and exploration” stage. Don’t rush this.

51. “Quick and dirty” graph creation stage

52. Jacques
Bertin’s
Reorderable
Matrix

53. find relationships between variables, patterns,
outliers, correlations, interesting things, errors,
etc.
find the story you ultimately want to tell.

54. For this stage, use whatever tool you
want to use for rapid prototyping
RawGraphs - rawgraphs.io
Tableau - public.tableau.com
Google Sheets charts?

55. when it comes to presenting your
data, regardless of subject

56. the unifying factor

57. is finding
and
communicating
a good story.

58. “Visualizations act as a campfire around
which we gather to tell stories.”
Al Shalloway

59. “data-driven decision making”

60. datasets themselves rarely
drive decisions.

61. the stories that get told
about the data
that gets collected
drive the decisions

62. datasets don’t go viral,
but the visual story of
the data can

63. examples of good visual storytelling
with data viz

64. NYTimes Interactive graphic, March 2013

65. NYTimes Interactive graphic, March 2018

66. NYTimes Interactive graphic, March 2018

67. Tweet by @randal_olson, March 19 2018

68. 3.
choose graphical symbols and colors
that are appropriate for telling the visual
story you want to tell

69. flowingdata.com, 2014

71. Data
Visualisation
Catalogue

73. Abela’s (2009) Chart Chooser

74. data art

75. P5.js https://p5.js.org
https://alpha.editor.p5js.org/

77. Resources
flowingdata.com – info on every aspect of the data visualization process
Tidy data, by Hadley Wickham - https://vita.had.co.nz/papers/tidy-data.pdf
R for Data Science, by Hadley Wickham - http://r4ds.had.co.nz/tidy-data.html
Tableau How-to Videos - https://public.tableau.com/en-us/s/resources
“Which Chart or Graph is Right for You?” – Tableau whitepaper
https://www.tableau.com/learn/whitepapers/which-chart-or-graph-is-right-for-you

78. Thanks!