- making good graphics for publication
- basic principles of graphing
- building blocks of graphs
- examples of common types of graph
Visualising data
Peter Geelan-Small – Stats Central, UNSW
13/06/2019
What this talk is about
What this talk is not about
- animations
- 3-D graphics
- web-based graphics
- interactive graphics
- how to make graphs in particular software packages (examples here are produced in R because you can make great graphs with it!
1 )
1: https://www.r-project.org
Statistical graphs
“… statistical graphics are instruments to help people reason about quantitative information”
2: Tufte, E. R. 2001. 'The Visual Display of Quantitative Information', p. 91
Basic elements of a plot
- graph panel
- scales, labels, tick marks
- plotting symbols, line types, colour
- reference lines
- keys
- captions
Maximise the “data-ink ratio”
- use ink for data, not unnecessary decoration
3: Tufte, E. R. 2001. 'The Visual Display of Quantitative Information', p. 96
Basic guidelines of graphing data
Make the data stand out
- let the data fill the graph panel
- don’t force axes to start at zero if data values are not close to zero (could use “break” marks to show axis does not start at zero)
- label the axes clearly
- use symbols, lines, colours etc. that highlight the data
- use reference lines or grids with care
- put legends outside the graph panel
- put notes in the caption or text
- make sure the graph remains clear if made smaller (e.g. for publication)
Basic guidelines of graphing data
Make the data stand out continued
Colour - symbols and lines
- some people are colourblind
- hard to see differences especially among reds, oranges, yellows and greens
- there are different types of colourblindness
- don’t use red and green together
- check out ColorBrewer for colour palettes (in R, RColorBrewer package)
Avoid “chartjunk”
4: Tufte, E. R. 2001. 'The Visual Display of Quantitative Information', p. 107
Basic guidelines of graphing data
Make sure the graph is properly understood
- Put all necessary details about the graph in the caption
- describe everything that is graphed
- clearly explain any error bars
- note important features in the data
- describe the conclusions that are made from the data shown
Variation
Two areas for showing variation:
- empirical variation of data - exploring data
- variation of statistics - exploring data and model output
Empirical variation - discrete data
Data: A sample of three types of gastropod shell was scored as occupied by a hermit crab or empty
Question: Do hermit crabs prefer a certain shell type?
| Species | Occupied | Empty |
|---|---|---|
| Austrocochlea | 47 | 42 |
| Bembicium | 10 | 41 |
| Cerithiidae | 125 | 49 |
5: Glover, T. & Mitchell, K. 2015. 'An Introduction to Biostatistics.' Waveland Press, p. 369.
Comparing variation - discrete data
Mosaic plot
Data set - Sleep in mammals
Data: Measurements of brain and body weight, life span, gestation time, time sleeping, predation and danger indices for 62 species of mammals
Question: What is the best prediction model for total sleep time?
6: http://www.statsci.org/data/general/sleep.html
Empirical variation - Box plots
Empirical variation - Scatter plot matrix
Empirical variation - Scatter plot matrix
Log-transform positively skewed data to clarify relationships
Empirical variation - Groups of data and trends
- Overplotting?
- Symbols
- Colours
Empirical variation - Groups of data and trends
Jitter points to avoid overplotting - open circles remain distinct
Add local trend line (loess)
Empirical variation - Groups of data and trends
Solid circles for display on screen?
Empirical variation - Groups of data and trends
What does this graph tell you?
Data set - sport, gender and iron
Data: Measurements on 102 male and 100 female athletes collected at the Australian Institute of Sport
Question: What variables are important in explaining ferritin concentration?
| Sport | Hematocrit |
| Gender - female, male | Hemoglobin |
| Height (cm) | Plasma ferritin concentration |
| Weight (kg) | Body mass index |
| Lean body mass | Sum of skin folds |
| Red cell count | Percent body fat |
| White cell count |
7: http://www.statsci.org/data/oz/ais.html
Empirical variation - comparing distributions
Empirical variation in data
Empirical variation - comparing distributions
Axis labels could be more informative?
Empirical variation - comparing distributions
(A dot plot may even be clearer)
Empirical variation - comparing distributions
Pie charts
- No scale
- Difficult to infer percentages based on sizes of angles
Empirical variation - comparing distributions
3-D pie charts
- Aargh! Worse.
- “Chartjunk”
Empirical variation - comparing distributions
Clustered histograms
Empirical variation - comparing distributions
Clustered density plots
Empirical variation - comparing distributions
Clustered box plots
Empirical variation - comparing distributions
Violin plots
Variation of statistics
A useful picture of variation in means?
Don’t start scale at zero if data values are not close to zero.
Variation of statistics
What do the error bars show?
Variation of statistics
Describe what the error bars show!
Variation of statistics
Much clearer!
“Chartjunk” again
Display data with fitted model - modelling scale
Variation of fitted values
Back to the sleeping mammals data
Display data with fitted model - natural scale
Summary
Think about what the purpose of the graph is
Put the components of the graph together to make its message clear
Look at the outcome - show it to a colleague
Do it all again … and again …
Creating effective graphs is an iterative process
Happy graphing!
Resources
Cleveland, William S. 1994. The Elements of Graphing Data. AT&T Laboratories: Murray Hill, USA. 2nd edition.
Chen, Chun-houh et al. (eds) 2008. Handbook of Data Visualization. Springer: Berlin, Germany.
Edward Tufte’s principles for visualising data - one of many websites
Colour palettes and specifications: ColorBrewer (Also R package: RColorBrewer)
Also for R users: Hadley Wickham’s ggplot2 package
Another R package: pals