How To: Plot Meaningful Graphs

Virtually all scientific reports and articles deal with numbers of some sort, and what better way to represent large amounts of data than on a well-made graph? Unfortunately, not all graphs are created equal; this post will introduce the basics of graph building and common pitfalls to avoid.

This post is designed for the students who are transitioning from high school to college, and will have to design and will address the basics, from formatting to plotting – so they don’t have to learn it the hard way! The programs that I use are ProFit and SigmaPlot, but the tips here will generally apply to any software out there.

Type Of Graph

The type of graph depends on the data that you have. Generally, you will get a range of values for both dependent and independent variables – in this case scatter plots are your friend. Let’s load up a sample data set:

A simple scatter graph.

Although scatter plots are the appropriate choice of graph in most situations, bar graphs can also be used to compare variables between discrete groups, while pie charts do their job to compare related variables that form part of a whole.

All Graphs Need a Title

Your graph should always have context! Whether the title of your graph is placed on top or in the figure description it has to make sense to the reader. A general rule of thumb is to name the graph:

‘[y-axis] vs [x-axis] of/for [system]’

This will give the reader ample description without cluttering the title with information. It is important to name it [y-axis] vs [x-axis] and not the other way round! This is as you are portraying y (dependent variable) as a function of x (independent variable).

Always Label

UNITS ON AXES. Another thing you might want to include is a figure legend if you have multiple plots. A neat way to include it is within the empty space on the graph – this will make everything look neater, but outside of it works just as well. Make sure it’s easy to match the plot to the legend (using colours, shapes, etc).

Axis Formatting

Once you have the texty bits under control, you have the option to include gridlines. Gridlines are obviously more important if you are trying to highlight key values within your plots.

Be careful not to go overboard; most graphing programs will let you adjust the intervals between the lines. And please please please, if you are going to use gridlines use them for BOTH x and y axes.

Another thing you may have noticed is the axis ticks, that I prefer to use facing into the graph. You may want to use outward facing ticks if you have plot points very close to the axes.

Best Fit Lines

You might be under the impression that a graph isn’t complete without a line of best fit running through as many points in the graph as possible, but this isn’t the case. Not all scatter plots require best-fit lines, more often than not a linear correlation between the variables does not exist.

This is especially true for systems that don’t follow a linear or log/exp pattern, where it is better to leave the plots on their own than try to fit them under a ridiculous 7th order polynomial expression and extrapolate the data inappropriately.

There you have it, a quick guide to good graphing practices. Here’s an example where instead of a header title, there is a description in the figure caption. This format is mostly used in papers, where it is easier to identify the graph by the figure coding.


Happy graphing! If you have any suggestions for more tutorial posts, please contact us through our email or social media links!

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