“The ubiquity of visual metaphors in describing cognitive processes hints at a nexus of relationships between what we see and what we think” - Mackinlay & Card (1999)
country year cases population Afghanistan 1999 745 19987071 Afghanistan 2000 2666 20595360 Brazil 1999 37737 172006362 Brazil 2000 80488 174504898 China 1999 212258 1272915272 China 2000 213766 1280428583
country year cases population Afghanistan 1999 745 19987071 Afghanistan 2000 2666 20595360 Brazil 1999 37737 172006362 Brazil 2000 80488 174504898 China 1999 212258 1272915272 China 2000 213766 1280428583 country year cases population Afghanistan 1999 745 19987071 Afghanistan 2000 2666 20595360 Brazil 1999 37737 172006362 Brazil 2000 80488 174504898 China 1999 212258 1272915272 China 2000 213766 1280428583 Variables
country year cases population Afghanistan 1999 745 19987071 Afghanistan 2000 2666 20595360 Brazil 1999 37737 172006362 Brazil 2000 80488 174504898 China 1999 212258 1272915272 China 2000 213766 1280428583 country year cases population Afghanistan 1999 745 19987071 Afghanistan 2000 2666 20595360 Brazil 1999 37737 172006362 Brazil 2000 80488 174504898 China 1999 212258 1272915272 China 2000 213766 1280428583 Variables country year cases population Afghanistan 1999 745 19987071 Afghanistan 2000 2666 20595360 Brazil 1999 37737 172006362 Brazil 2000 80488 174504898 China 1999 212258 1272915272 China 2000 213766 1280428583 Observations
country year key value Afghanistan 1999 cases 745 Afghanistan 1999 population 19987071 Afghanistan 2000 cases 2666 Afghanistan 2000 population 20595360 Brazil 1999 cases 37737 Brazil 1999 population 172006362 Brazil 2000 cases 80488 Brazil 2000 population 174504898 China 1999 cases 212258 China 1999 population 1272915272 China 2000 cases 213766 China 2000 population 1280428583
country year key value Afghanistan 1999 cases 745 Afghanistan 1999 population 19987071 Afghanistan 2000 cases 2666 Afghanistan 2000 population 20595360 Brazil 1999 cases 37737 Brazil 1999 population 172006362 Brazil 2000 cases 80488 Brazil 2000 population 174504898 China 1999 cases 212258 China 1999 population 1272915272 China 2000 cases 213766 China 2000 population 1280428583 Variables country year key value Afghanistan 1999 cases 745 Afghanistan 1999 population 19987071 Afghanistan 2000 cases 2666 Afghanistan 2000 population 20595360 Brazil 1999 cases 37737 Brazil 1999 population 172006362 Brazil 2000 cases 80488 Brazil 2000 population 174504898 China 1999 cases 212258 China 1999 population 1272915272 China 2000 cases 213766 China 2000 population 1280428583
country year key value Afghanistan 1999 cases 745 Afghanistan 1999 population 19987071 Afghanistan 2000 cases 2666 Afghanistan 2000 population 20595360 Brazil 1999 cases 37737 Brazil 1999 population 172006362 Brazil 2000 cases 80488 Brazil 2000 population 174504898 China 1999 cases 212258 China 1999 population 1272915272 China 2000 cases 213766 China 2000 population 1280428583 Variables country year key value Afghanistan 1999 cases 745 Afghanistan 1999 population 19987071 Afghanistan 2000 cases 2666 Afghanistan 2000 population 20595360 Brazil 1999 cases 37737 Brazil 1999 population 172006362 Brazil 2000 cases 80488 Brazil 2000 population 174504898 China 1999 cases 212258 China 1999 population 1272915272 China 2000 cases 213766 China 2000 population 1280428583 country year key value Afghanistan 1999 cases 745 Afghanistan 1999 population 19987071 Afghanistan 2000 cases 2666 Afghanistan 2000 population 20595360 Brazil 1999 cases 37737 Brazil 1999 population 172006362 Brazil 2000 cases 80488 Brazil 2000 population 174504898 China 1999 cases 212258 China 1999 population 1272915272 China 2000 cases 213766 China 2000 population 1280428583 Observations
country year key value Afghanistan 1999 cases 745 Afghanistan 1999 population 19987071 Afghanistan 2000 cases 2666 Afghanistan 2000 population 20595360 Brazil 1999 cases 37737 Brazil 1999 population 172006362 Brazil 2000 cases 80488 Brazil 2000 population 174504898 China 1999 cases 212258 China 1999 population 1272915272 China 2000 cases 213766 China 2000 population 1280428583
country year cases population Afghanistan 1999 745 19987071 Afghanistan 2000 2666 20595360 Brazil 1999 37737 172006362 Brazil 2000 80488 174504898 China 1999 212258 1272915272 China 2000 213766 1280428583 country year key value Afghanistan 1999 cases 745 Afghanistan 1999 population 19987071 Afghanistan 2000 cases 2666 Afghanistan 2000 population 20595360 Brazil 1999 cases 37737 Brazil 1999 population 172006362 Brazil 2000 cases 80488 Brazil 2000 population 174504898 China 1999 cases 212258 China 1999 population 1272915272 China 2000 cases 213766 China 2000 population 1280428583 We want to gather the values corresponding to each key.
country year cases population Afghanistan 1999 745 19987071 Afghanistan 2000 2666 20595360 Brazil 1999 37737 172006362 Brazil 2000 80488 174504898 China 1999 212258 1272915272 China 2000 213766 1280428583 country year key value Afghanistan 1999 cases 745 Afghanistan 1999 population 19987071 Afghanistan 2000 cases 2666 Afghanistan 2000 population 20595360 Brazil 1999 cases 37737 Brazil 1999 population 172006362 Brazil 2000 cases 80488 Brazil 2000 population 174504898 China 1999 cases 212258 China 1999 population 1272915272 China 2000 cases 213766 China 2000 population 1280428583 Tidy We want to gather the values corresponding to each key.
country year cases Afghanistan 1999 745 Afghanistan 2000 2666 Brazil 1999 37737 Brazil 2000 80488 China 1999 212258 China 2000 213766 country 1999 2000 Afghanistan 745 2666 Brazil 37737 80488 China 212258 213766 We want to spread the values to the corresponding keys.
country year cases Afghanistan 1999 745 Afghanistan 2000 2666 Brazil 1999 37737 Brazil 2000 80488 China 1999 212258 China 2000 213766 country 1999 2000 Afghanistan 745 2666 Brazil 37737 80488 China 212258 213766 Tidy We want to spread the values to the corresponding keys.
Tidy Data country year cases population Afghanistan 1999 745 19987071 Afghanistan 2000 2666 20595360 Brazil 1999 37737 172006362 Brazil 2000 80488 174504898 China 1999 212258 1272915272 China 2000 213766 1280428583 country year cases population Afghanistan 1999 745 19987071 Afghanistan 2000 2666 20595360 Brazil 1999 37737 172006362 Brazil 2000 80488 174504898 China 1999 212258 1272915272 China 2000 213766 1280428583 Variables country year cases population Afghanistan 1999 745 19987071 Afghanistan 2000 2666 20595360 Brazil 1999 37737 172006362 Brazil 2000 80488 174504898 China 1999 212258 1272915272 China 2000 213766 1280428583 Observations Values
Position N O Q Size N O Q Color Value N O Q Texture N O Color Hue N Angle N Shape N Nominal Ordinal Quantitative Note: Q⊂O⊂N Bertin’s Levels of Organization
Grammar of Graphics 1. Data 2. Transformations 3. Marks 4. Encoding - mapping from fields to mark properties 5. Scale - functions that map data to visual scales 6. Guides - visualizations of scales (axes, legends, etc.)
Edward Tufte “Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.” ― Edward R. Tufte, The Visual Display of Quantitative Information
Edward Tufte “Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.” ― Edward R. Tufte, The Visual Display of Quantitative Information
Edward Tufte “Graphical excellence is that which gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.” ― Edward R. Tufte, The Visual Display of Quantitative Information (within reason!)
Small Multiples “At the heart of quantitative reasoning is a single question: Compared to what? Small multiple designs answer directly by visually enforcing comparisons of changes, of the differences among objects, of the scope of alternatives.” - Edward Tufte, Envisioning Information, p. 67
Ten Simple Rules for Better Figures By Nicolas P. Rougier 1. Know your audience 2. Identify your message 3. Adapt the figure to the support medium 4. Captions are not optional 5. Do not trust the defaults 6. Use color effectively 7. Do not mislead the reader 8. Avoid “Chartjunk” 9. Message trumps beauty 10. Get the right tool
1. Know your audience 2. Identify your message 3. Adapt the figure to the support medium 4. Captions are not optional 5. Do not trust the defaults 6. Use color effectively 7. Do not mislead the reader 8. Avoid “Chartjunk” 9. Message trumps beauty 10. Get the right tool Ten Simple Rules for Better Figures By Nicolas P. Rougier
1. Know your audience 2. Identify your message 3. Adapt the figure to the support medium 4. Captions are not optional 5. Do not trust the defaults 6. Use color effectively 7. Do not mislead the reader 8. Avoid “Chartjunk” 9. Message trumps beauty 10. Get the right tool Ten Simple Rules for Better Figures By Nicolas P. Rougier
1. Know your audience 2. Identify your message 3. Adapt the figure to the support medium 4. Captions are not optional 5. Do not trust the defaults 6. Use color effectively 7. Do not mislead the reader 8. Avoid “Chartjunk” 9. Message trumps beauty 10. Get the right tool Ten Simple Rules for Better Figures By Nicolas P. Rougier
1. Know your audience 2. Identify your message 3. Adapt the figure to the support medium 4. Captions are not optional 5. Do not trust the defaults 6. Use color effectively 7. Do not mislead the reader 8. Avoid “Chartjunk” 9. Message trumps beauty 10. Get the right tool Ten Simple Rules for Better Figures By Nicolas P. Rougier
eitanlees
cumclavis
koic
motherhoodinadhd
oripsolob
cm_manaus
florets1
dahatake
ashitanoterakoya
vtryo
signer
olanetsoft
bernhardsvt
3n
moore
rmw
gr2m
bermonpainter
sstephenson
bkeepers
tanoku
kneath
speakerdeck
mraible
danielanewman