Data Visualization by David Kretch
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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.
Data Visualization by David Kretch
- 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? 2Data Visualization 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. 3Data Visualization Find the dangerous creatures!
- 4. Why graphs work • Color •Size • Shape • Position 4Data Visualization
- 5. Why else do people like graphs? People like cool-looking stuff. 5Data Visualization Not cool Cool
- 6. What are we currently doing? • Making lots of tables 6Data Visualization 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. 7Data Visualization
- 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 8Data Visualization
- 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. 9Data Visualization
- 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. 11Data Visualization
- 12. Density vs. histogram A density plot is basically a smoothed histogram. 12Data Visualization
- 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. 13Data Visualization
- 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. 14Data Visualization
- 15. Highlighting important facts Add vertical lines to highlight the means. 15Data Visualization
- 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. 17Data Visualization
- 18. How to show trends We can highlight the trend using scatterplot smoothing, which adapts the shape of the trend line to the data. 18Data Visualization
- 19. How to show multiple groups We can see if the relationship differs among groups by giving each group a color. 19Data Visualization
- 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? 20Data Visualization
- 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. 21Data Visualization 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) 22Data Visualization 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. 23Data Visualization
- 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. 24Data Visualization
- 26. Graphing estimation results We make a lot of regression tables, but we can make them easier to understand by putting them into graphs. 26Data Visualization
- 27. ggplot(df, aes(population_density, elevation, color = favorite_food)) + geom_point() 27Data Visualization 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 28Data Visualization