The document discusses the importance of data visualization, advocating for the use of graphs over tables to better understand data through visual means like color, size, and position. It outlines different types of graphs suitable for various data types, such as density plots for continuous variables and bar charts for categorical ones, and emphasizes the need for clear visual representation to highlight important trends and relationships. Additionally, it provides a checklist for effective graphing practices in data analysis.

1. Data Visualization
April 3, 2015
• When you should graph
• What you should graph
• Given some data, how would you graph it

2. When should you graph your data?
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Always
Don’t just make graphs for client reports -- graph your data for
yourself, so you understand it.
If you use a table in a report, see if you can make it into a graph.

3. Why graphs?
Because of the environment that humans evolved in, we are much
better at getting info from color, size, shape, and position than from
reading text.
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Find the dangerous creatures!

4. Why graphs work
• Color
•Size
• Shape
• Position
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5. Why else do people like graphs?
People like cool-looking stuff.
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Not cool Cool

6. What are we currently doing?
• Making lots of tables
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Group Mean 25% 50% 75%
Bananas 11.3 2.7 4.6 23.1
Kittens 4.0 0.9 3.6 7.5
Phones -3.1 -11.0 -2.9 2.2
Variable Parameter
Estimate
Cuteness 0.6***
Ability to Fly 1.4***
Deadliness 11.2***
Telepathy -9.8***
Big Ears -17.3***

7. What is wrong with tables?
Tables give only a partial picture – means only tell us so much.
Figuring out what’s bigger, and by how much, requires more work.
The information is not necessarily in any order, so we need to read
all the numbers.
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8. What kinds of graphs should you make?
• The distribution, instead of
giving just mean, median, etc.
• The relationship between two
variables – the conditional
distribution
• Graph estimation results’ point
estimates and confidence
intervals
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9. What to expect out of this presentation
1. Discussion of the type of graph (e.g. distributions)
2. How the type of graph applies to continuous vs. categorical data
3. Extensions (e.g. graphing more than one at a time)
What not to expect: how to do these in any particular software.
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10. Distributions
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11. Distributions – Continuous variables
Make density plots/histograms for continuous variables. These give
much more information than means, medians, etc.
Two distributions with the same mean, but which are dramatically different.
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12. Density vs. histogram
A density plot is basically a smoothed histogram.
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13. Distributions – Categorical variables
Make bar charts for categorical variables.
Tip: if your categories don’t have any inherent order, order them
from largest to smallest.
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14. Compare distributions using color
Suppose we want to compare the distribution of income among
different occupations. Plot all the distributions, distinguished by
color, and use transparency to make them all visible simultaneously.
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15. Highlighting important facts
Add vertical lines to highlight the means.
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16. Relationships
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17. Relationships between variables
If we’re asking, for example, what GDP growth looks like at different
levels of government spending, we can show this using a
scatterplot.
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18. How to show trends
We can highlight the trend using scatterplot smoothing, which
adapts the shape of the trend line to the data.
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19. How to show multiple groups
We can see if the relationship differs among groups by giving each
group a color.
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20. Another use for colors
Suppose we want to come up with rules to identify people’s favorite
food based on population density and elevation (bear with me)
Can we see this on a graph?
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21. Graphing relationships with categorical data
With categorical data, you typically can’t use scatterplots because
points fall right on top of each other (‘overplotting’).
However! We can use jittering to move the plotted points slightly.
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Without jittering With jittering

22. Graphing relationships with categorical data
The next step beyond jittering is to use a boxplot, which shows
– The mean,
– 25th and 75th percentiles,
– 1.5 times the inter-quartile range (IQR)
– outliers (plotted as points)
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mean
75th pctile
mean + 1.5 *IQR
outlier

23. Looping back
A boxplot isn’t, after all, all that different from the multi-colored
density plot we showed earlier. Which is better depends on what
you’re trying to show.
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24. Use log scale if your data spans a wide range
Let’s say you have a large
range of values, but most of
your data is concentrated to
one part of the range.
It’s easier to see what’s
going when we use log
scale.
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25. Estimation results
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26. Graphing estimation results
We make a lot of regression tables, but we can make them easier to
understand by putting them into graphs.
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27. ggplot(df, aes(population_density, elevation, color = favorite_food)) +
geom_point()
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dataset x variable y variable
make scatterplot
color variable
All graphs made in R and ggplot2

28. Data Visualization Checklist
• Always graph
• Use color, size, shape, and position
• Three important types of graph:
– Distribution
– Relationship
– Estimation results
• Highlight important facts
• Make it cool-looking
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