Although we cannot control the world, we can help clean it up. A “good” plot is significantly easier to digitize than a “poor” plot, so before we start digitizing data it will be beneficial to discuss some of the aspects of a “good” plot. The attributes of a “good” plot described are presented in MAE 6420 Experimental Methods in Mechanical Engineering taught by Dr. Smith here at Utah State University.
A graph should be used when it will convey information and portray significant features more efficiently than words or tabulations.
1. Require minimal effort from the reader in understanding and interpreting the information it conveys.
2. The axes should have clear labels that name the quantity plotted, its units, and its symbol.
3. Axes should be clearly numbered and should have tick marks for significant numerical divisions. Typically, ticks should appear in increments of 1, 2, or 5 units. Not every tick need be numbered. Too many will clutter the axis.
4. Use scientific notation to avoid placing too many digits on the graph.
5. When plotting on logarithm axes, place major ticks at powers of 10 and minor ticks at 10, 20, 50, 100, 200, etc.
6. Axes should usually include 0.
7. The choice in scales and proportions should be commensurate with the relative importance of the variations shown in the results. Use a square axis as a starting point.
8. Use symbols, Not dots, for data points. Open symbols should be used before closed.
9. Either place error bars on the plot that indicate uncertainty or use symbols that are the size of the uncertainty.
10. When several curves appear on the same plot, use different line styles to distinguish them. Avoid using colors.
11. Minimize lettering on graphs.
12. Labels on the axes and curves should be oriented to be read from the bottom or from the right. Avoid forcing the reader to rotate the figure to read it.
13. The Graph should have a descriptive but concise title.
14. Software defaults are seldom what you want!
Bottom Line – You want to communicate information to your reader. The burden to get your point across falls to you. The chances of successfully communicating your point are improved considerably when you make it east on the reader. Never think of your plot as pretty graphics. If that is all it is, you should remove it.
· Grid lines: Grid lines are wonderful if you intend your reader to read the data with their naked eye. However, in today’s digital world, assume that your reader will first look for trends in your data, and if they want values from the plot they will digitize it. Grid lines make a plot harder to digitize.
· Too many labels, not enough ticks: Some plotting software will allow you to have so many labels that axis values begin to overlap and become unreadable. However make sure you have enough major and minor grid points that your data can be interpreted. Other schools of thinking will interpret this to mean only say something in your figure once. For example Dr. Carl Wood used to say “Figures should stand alone.” This means that a plot and its caption should be self explanatory. The figure should not need additional explanation in the body of a paper so that it can be correctly interpreted. By this thinking, you should always have a caption with a figure that will be in a presentation or paper, removing the need for a title.
· Meaningless color: Often your work will be printed on a black and white printer or a black and white copy machine. This makes the use of color lines and markers pointless.
· Lines connecting data points: If lines connect your data point, it is often assumed that you know something about the data between data points. This can be misleading, so lines between data points should be used with caution.
· Multiple plots to make a comparison in which the scale of the plots is changed: If you need multiple plots to compare trends between data from different sources (ex. Different numerical methods) plots all plots on the same scale (generally the y-axis). Assume the reader won’t notice a change in scale.
· Using the Software Defaults: It is the software companies job to create a working plotting package that will generally be very generic. It is your responsibility to make the plot readable, and how you want. You should never use the excuse “I didn’t know how to change that parameter of the plot” for a bad plot.
Example of a good plot:
Excel – General Purpose plotting package with spreadsheet.
KaleidaGraph – Plotting package with spreadsheet. KaleidaGraph has significantly more options than excel when it comes to curve fitting, and data manipulation.
Techplot – Plotting package that allows for plot animation and other manipulation. Techplot also allows for the use of symbolic equations to be plotted.
Paraview – Open source software similar to Techplot.
Matlab – Numerical engine with built in plotting package.
Gnuplot – Free general plotting package which supports 3-D surface plots, and curve fitting
PlotMTV – Archaic plotting software
Xmgrace – 2-D only plotting package which supports data manipulation, Unix and Cygwin only.
Matplotlib – Python package which allows similar plotting features as Matlab
Visit – General field plotting package, harder to use but very powerful.
Gmsh – Mesh generator with post processing capabilities.