2. Let’s explore the humble pie chart…
Party Percentage
E 38%
D 25%
C 20%
B 15%
A 2%
Break the whole into parts.
3. Let’s explore the humble pie chart…
Party Percentage
E 38%
D 25%
C 20%
B 15%
A 2%
Break the whole into parts.
Data: One dimensional Visual Encoding: Area
4. New Terms
• Dimension: Columns by which you group data.
• Facts: Numbers that you can count, sum, average, etc.
• Examples:
• Seat count by party
• Seat count by party and state
• Visual Encoding: Area, Position, Colour, Length, Thickness, etc.
9. One-dimensional Charts …
What is wrong here?
Problems:
• Colour communicates no data
• 3D communicates no data
Source: thehindu.com
10. One-dimensional Charts …
Source: thehindu.com
#2 - Your goal is to communicate data. Wrong use of visual encoding confuses.
Problems:
• Colour communicates no data
• 3D communicates no data
12. One-dimensional Charts …
What is wrong here?
Problems:
• Colour
• Too many values. Too
cluttered.
Source: firstpost.com
13. One-dimensional Charts …
Problems:
• Colour
• Too many values. Too
cluttered.
#3 - AREA encoding is useful for only few values after which it is unreadable.
Source: firstpost.com
18. Grouped One-dimensional Charts
Group various bubbles by colours
Party Alliance Percentage
A NDA 38%
B NDA 25%
C NDA 20%
D UPA 15%
E Others 2%
#4 - You can always fit in an extra dimension (GROUP) in charts using colour.
19. New Data Set
One dimensional:
Seat count by party
Grouped One dimensional:
Seat count by party grouped by alliance
Two dimensional:
Which party won in which year
22. Two-dimensional Charts…
Scatter Line Area
Bar Column Spider
All these charts require the same data.#5 - Number of dimensions in data determines which chart to use
23. New Data Set
One dimensional:
Seat count by party
Grouped One dimensional:
Seat count by party grouped by alliance
Two dimensional:
Which party won in which constituency
Weighted Two dimensional:
Which party won in which constituency by what vote margin
25. Weighted Two-dimensional Charts …
Let’s add weight to it, hence now we have three data points
X axis Y axis Weight
A Z 40
B Y 20
C X 1
D V 300
E W 60
25Visual encoding: Position, Length, Area
26. Weighted Two-dimensional Charts …
Weighted Scatter Circle Comparison
All these charts require the same data.#6 - You can always fit in an extra fact (WEIGHT) in charts using size.
27. New Data Set
One dimensional:
Seat count by party
Grouped One dimensional:
Seat count by party grouped by alliance
Two dimensional:
Which party won in which constituency
Weighted Two dimensional:
Which party won in which constituency by what vote margin
Grouped Weighted Two dimensional:
Which party won in which constituency by what vote margin grouped by alliance
29. Multi-series Two-dimensional Charts …
RangeGanttMulti-series Line
Group Column Stack Column Group Stack Column
Stack Area Stack Percentage Area
Add more dimensions in creative ways.
30. Multi-series Two-dimensional Charts …
What is right and wrong here?
Source: livemint.com
Is the equities rally percolating into the broader market?
31. Multi-series Two-dimensional Charts …
What is right and wrong here?
Source: livemint.com
Is the equities rally percolating into the broader market?
Bad parts:
• BSE Small-cap lines is not
visible and that’s the story.
32. Multi-series Two-dimensional Charts …
What is right and wrong here?
Good parts:
• Y axis from 97 instead of 0
Source: livemint.com
Is the equities rally percolating into the broader market?
Bad parts:
• BSE Small-cap lines is not
visible and that’s the story.
#7 - Purpose of line chart is to show trend. Focus on it.
34. Multi-series Two-dimensional Charts …
What is wrong here?
Source: livemint.com
Problems:
• Cannot find the IMF line.
Does IMF wear rose-tinted glasses?
35. Multi-series Two-dimensional Charts …
What is wrong here?
Source: livemint.com
Does IMF wear rose-tinted glasses?
Problems:
• Cannot find the IMF line.
#8 - Highlight the story for the user. Use color to highlight, not confuse.
36. New Data Set
All the data we encountered so far was RDBMS i.e. could fit in a SpreadSheet.
(rows and columns).
Sometimes data is more complex. It can have“relationships”.
Types of relationships:
• Hierarchy / Tree
• Multi-level relationships
48. The same data can be visualised in many (MANY!)
ways. Without exploring the data, you will end up
visualising all your data in pies, lines and bars.
Most Imp. Lesson
49. We are at @pykih
Fun fact: The word pykih came to us in a
CAPTCHA. That’s the day we decided that till we
do good work it does not matter what we are
called.