2.1.1 Designing good graphs
Graphs are only as good as they are clear and understandable. A checklist of best practice to ensure a graph is conveying your message most effectively is given in Table 5 below:
| 1 | Decide on type of graph e.g. histogram, bar chart, box-and-whiskers plot, line graph, etc. |
| 2 | Keep it simple while providing all the information needed to convey the key messages |
| 3 | Avoid using styles that distract from the key messages, such as too many colours, symbols and 3D images |
| 4 | Where possible, include error bars or confidence intervals to represent uncertainty in the data |
| 5 | Label the x-axis (horizontal) and y-axis (vertical) and include the units of measurement. The x-axis usually represents the independent (or exposure) variable (e.g. time, group), and the y-axis plots the dependent (or outcome) variable, such as measures of frequency (e.g. proportion resistant, number of diseased animals) |
| 6 | The scale for each axis must be appropriate for the data being presented and evenly spaced |
| 7 | The title should be clear and concise. It should describe the what, where and when of the data in the table |
| 8 | Use a legend/key to describe the various colouring, shading or line styles (solid, dashed) used in the graph to distinguish different variables. The legend should be placed in an unused space next to the graph |
| 9 | Identify missing or unknown data in a footnote below the graph |
| 10 | Explain any codes, acronyms, abbreviations or symbols in a footnote |
| 11 | Note the source of the data in a footnote if the data is from secondary or tertiary sources |
In the following sections, you will learn about common graph types: histograms, boxplots, scatter plots, bar charts and line graphs. Not all of these graphs will be appropriate for the data and message that you want to convey. Understanding the strengths and limitations of each graph will help guide your decision on which graph to choose.
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2.1 Graphs of ‘AMR data’
