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Data analysis and visualisation: Visualisation Tips

Figures are an essential part of scholarly publications. To ensure that your figures communicate your research effectively, there are a few considerations that you should keep in mind.

Your figures will form an integral part of your publication - people often read an abstract and then look at figures to get an idea of the main points of the paper, and to decide if the paper is worth reading further. Therefore your figures need to be carefully chosen to make sure that they tell as much of your story for you as possible.

Plan out your figures as part of the process of drafting an outline for your manuscript so that your figures and text complement each other without repeating information. Selecting effective figures can help to engage reader interest and increase comprehension (and therefore hopefully your citations!). It can also help to decrease word counts, as lengthy text explanations can be replaced with succinct figures. What makes a figure effective will vary between fields and disciplines, but overviews, trends, and spatial data are all good candidates for information that might be better displayed in a figure rather than described in the text.

As you plan your manuscript, you should have some idea of the publication you intend to submit to. The author guidelines on a publication's website will often contain information on requirements for figures, such as the number of figures allowed, minimum font size, required file formats and resolution, or the column width that the figure will need to fit into. Guidelines vary between publishers, so always make sure you know your publisher's requirements, and follow them throughout the process of creating your figures.

Plan for your figures to fit either a single column width or across the full page, and then make sure to create them at that size. A common mistake is to draft figures very large, and then shrink them down to fit the allotted space. This frequently results in features of your visualisation becoming minuscule and illegible at the new size, requiring you to redraw your figure and re-size all annotations.

All of the information necessary to understand your figure should be contained within it. Figures may end up being reproduced or presented separately from manuscript text, for instance if they are used in a presentation, in which case any explanation or context provided by the text would be lost. To make a figure self-contained ensure that:

  • all necessary labels and annotations are present
  • a detailed (but not too lengthy) figure caption is present
  • any acronyms used on the figure are explained in the caption, even if they were previously defined in the text
  • a legend is included, if necessary

Even though many journals can be accessed online and people tend to obtain electronic copies of articles, figures in papers stand a high likelihood of being reproduced in black and white at some point. This may be because the print version of a journal is only black and white, or charges extra for colour images, or could occur when readers print a hard copy of a paper. If colour was used to convey information in these cases, the resulting black and white figure could become useless. It's a good idea either to create greyscale versions of your figures for the print versions of journals, or, even better, to choose a colour palette that can be printed in black and white such that the colours are reproduced as distinguishable shades of grey. ColorBrewer is a good resource to help you create suitable colour palettes. Doing this will also help to ensure that your figures are accessible to people with vision impairments, such as colour blindness.

Make it easy to see

In a large presentation venue some members of your audience may be quite far away, and projectors may introduce some fuzziness into figures and text that looked crisp on a computer monitor. As your visualisation will likely be the primary way that you convey information on a given slide, it's important to ensure that all audience members can easily see it. To maximize the readability of your visualisation you should:

  • make the visualisation as large as possible, it should be the focal point of the slide
  • keep explanatory text on the slide to a minimum - you'll be talking the audience through the figure, so you don't need to write it out as well!
  • pare down your visualisation to only essential points, at a distance any extra clutter and busyness will combine to make the visualisation indecipherable
  • keep essential labels, like (meaningful) axis labels
  • use large fonts for any text elements
  • keep visualisations that need to be compared on the same slide, as it's very difficult to compare images between slides. Make sure that baselines and scales are the same between visualisations that are being compared
  • use a single slide for a single visualisation if you're not comparing them, so that you can make each visualisation as large as possible
  • use high contrast colours, e.g. black on a white background rather than something like yellow on a white background
  • consider any visual impairments, such as colour blindness, that your audience might have, and choose your colour palette accordingly

Static and dynamic visualisations

In presentations you have the option of using dynamic visualisations, such as animations or interactive visualisations, as well as static figures, offering further options for telling your data's story. Animations can be a useful way of showing how data change, or for flying-through a three dimensional visualisation. When using an animation in a presentation, in addition to the points above, ensure that:

  • the frame rate is appropriate - you don't want your visualisation to whiz past too quickly for you to point out pertinent details, but neither do you want to have long gaps between interesting the features of your visualisation
  • you can pause and restart your visualisation to point out features or important things to watch for. If your animation is relatively short, you can instead have it play on a continuous loop and draw viewers' attention to different important details on each play-through, however this is not appropriate for longer animations
  • your animation plays correctly on the computer you'll use when presenting. Animations in presentations are notoriously finicky. Embedding your visualisation as an animated gif is a safe way of ensuring that it'll play correctly, however it can result in very large file sizes, and will likely be unsuitable for longer animations

Pick an appropriate chart for your data

There are many different kinds of charts out there, some very familiar from everyday use, while others can be highly unusual, complex, or rarely seen. It's important to use a chart that's appropriate to the data you have, and that allows you to explore the information you're interested in. Check out the Key tools links on the right side of this page to help select the best visualisation for your purpose.

Common chart types and tips

  • Use for data that can be placed into categories
  • Always use zero as the baseline that bars start from
  • Sort your bars into a meaningful order; if your categories have inherent order (e.g. age groups) then you should use that order to sort, however if your categories are unordered (e.g. ethnicities) then generally sorting by either ascending or descending bar heights will be the most appropriate
  • Use spaces between your bars, but don't space them farther apart than two thirds of the width of a bar

  • Use for data that is divided into range bins
  • Always use zero as the baseline that bars start from
  • Your data is continuous, so do not use spaces between your bars

  • Use to display data that represent portions of a whole
  • Pie charts are useful to provide an overview of the relative proportions of a small number of categories
  • Use a bar chart rather than a pie chart if you:
    • have more than five or six categories;
    • have segments of similar size;
    • want to compare multiple charts; or
    • want people to pay attention to the sizes of the categories rather than just get an overall impression of relative importance
  • Do not use exploded pie charts

  • Use to display continuous quantitative data that changes over some interval, such as time
  • Useful to display the tend of a data series, or to compare different trends by plotting multiple lines on the same graph
  • Make sure you have enough data points to make the trend of the line meaningful - the trend of only two or three points is not useful
  • If your data are all in a limited range well above zero you can start plotting y-axis values just below the lowest y-value of your data. You should indicate this with a zigzag line at the bottom of the y-axis. Be aware that plotting this way will increase the apparent variation within your data, so if you are interested in absolute trends rather than the variation within, or between trends, then you should start your y-axis from zero.
  • Values on the y-axis can be positive or negative, with negative values being plotted below the x-axis

  • Use for data where you wish to investigate the relationship (if any) between two continuous, numerical variables
  • Colour or symbol shape can be used to indicate a third, often categorical variable of the data
  • If a lot of your points are overlapping, it can be useful to plot your points as unfilled outlines so that you can see all of the points in the cluster, rather than just an undifferentiated blob
  • A line of best fit, also called trend line or regression line, can be added to demonstrate the proposed underlying mathematical relationship between the variables, however it is important not to over-fit your data - you want to be representing the data, not the errors that are inherent in measurements!
  • Always remember that a correlation between two variables does not necessarily mean that they are causally related!

  • Use when you want to want to compare the relative sizes of some variable of your data
  • Comparing areas accurately is difficult, so these plots are most useful to communicate an overview of relative sizes, rather than as part of a rigorous analysis
  • Ensure that you plot you shapes so that the area of the shape scales with the variable, rather than a linear quantity of the shape, such as radius (for circles) or side length (squares, triangles, etc.). For example, the area of a circle is proportional to the square of its radius, so if you plot circles with the radius corresponding to the data value, you will end up greatly exaggerating the differences between your data values
  • Proportional area plots can be combined with scatterplots to show the relationships between three different variables - these are often called bubble charts or plots

  • Use either of these chart types for hierarchical data
  • For treemaps, plot the area of the nested rectangles to be proportional to a specified variable of the data

Other chart tips

You need to make sure that you include enough annotations for your chart to be understood: x- and y-axis labels indicating units, a legend, labels for highlighting important points or trends in the data can be essential to ensure that your chart succeeds in conveying the intended information. However, you should make sure that you remove any non-essential annotations that might be cluttering up your chart. Generally remove gridlines, any background fill, extra tick marks along the axes, busy pattern fills, any colour that doesn't represent information, and any extraneous text. It will be up to you to decide what is necessary to keep to make your chart usable, but keep in mind that having less detail to sort through means that your viewers will quickly see and focus in on the main messages of your visualisation.

Keep in mind that if you try to put too much on one chart then the relationships that you want to display can become lost. For instance, plotting a large number of lines on a line graph can mean that the trends of all lines become obscured and no information can be taken away from the resulting visualisation. In this case, it would be better to plot small subsets of the data on multiple charts, and compare the charts to each other. Using dual y-axes can also make your chart difficult to interpret, and potentially even imply relationships between variables that are merely artefacts of how the variables were plotted. Any time you feel the data are overwhelming the viewer, assess whether it would be possible to use multiple plots to make relationships and trends clearer. Note that this is not a licence to throw away data that doesn't fit your message! You always need to make sure that you are accurately representing your data, or the whole point of creating a visualisation is lost.

If you intend to compare multiple plots you must ensure that they have been plotted the same way, are the same size, have same scale axes, and employ the same conventions to enable your viewers to draw accurate comparisons between them. They should also be plotted next to each other, rather than on separate pages or slides.

If you use colour in your charts, make sure that a difference in colour provides the viewer with useful information, as extraneous colour changes can be distracting and make it more difficult to compare the data represented. For instance, don't colour all of your bars in a chart a different colour; the different categories are already indicated by the fact that they are separate bars! Either use the same colour for all of your bars, or colour them based on another variable, or to highlight a particular bar that you want to draw attention to. Remember to think about how colour in your chart will affect its usability for people with colour blindness or if the chart is printed in black and white. Use resources like ColorBrewer to choose colour palettes that are colour blind and black and white friendly.

Maps are used in a wide range of fields to display data with a geographic component. Population characteristics, land use, or geologic data could all be visualised on maps. Creating map visualisations requires that a few unique considerations be taken into account.



If possible, maps should be oriented with north pointing to the top of the visualisation. In some cases it may be necessary to create a map in a different orientation, for instance in order to fit a map on a page. In these cases it's essential to include an arrow indicating the direction of north on the map. It's good practice to include a north arrow on all maps, even those with north oriented to the top, as your viewers can then be certain of the map's orientation.


Ease of reuse

Future users of your map will potentially want to incorporate your data into their own maps. To make it easy for them to georeference and reuse your map, you should make sure that you include the map projection used (e.g. Mercator, Orthographic, Mollewide, etc.), as well as several latitude and longitude markers. Having only one latitude and longitude tie point is not enough to make your map reusable!



It's good practice to include a scale on all maps, although this may not be necessary for maps that are at a continent scale or larger, depending on the purpose of the map. Map scales can be written as ratios, e.g. 1:10,000 where 1 cm on the map would be 100 m, or as graphical scales. Using a graphical scale is recommended as it will remain accurate even if your map is resized, whereas a written scale will become completely meaningless and even misleading if someone reproduces your map at a different size to the original.



All maps must have legends; you need to give people the information necessary to interpret the visualisation! If your field has any symbols or patterns that are conventionally used to represent specific mapped features, then make sure to employ those conventions.


Map patterns and fills

Patterns and fills are commonly used to present regional data on maps. If possible, try to limit the number of different patterns used on a map, as they increase visual clutter. Numerous different patterns on a single map will compete for attention and decrease the readability of a map. Using different colours for different categories, or different shades of the same colour for different ranges of the data can help keep your map easy to interpret. However, be aware that using colour to convey data can make your map unusable for people with vision impairments such as colour blindness, and try to pick a colour palette that is colour blind accessible using tools such as ColorBrewer.

The most important part of creating a three dimensional (3D) visualisation is making sure that the data and visualisation type are suitable for displaying in 3D. Incorporating 3D elements in a visualisation purely for aesthetic appeal usually results in a less effective visualisation. While there will be exceptions, it is generally only appropriate to employ a 3D visualisation when:

  • your visualisation is dynamic and allows people to view it from multiple angles. A single angle view of 3D data can present a very misleading picture of the data, however allowing viewers to fly-through in an animation, or change the view in an interactive visualisation can allow for in-depth exploration of the data
  • it is important to convey the 3D spatial relationships in you data

Do not add 3D effects to 2D visualisations, such as bar charts or pie charts! The 3D effects will distort your data and make it difficult for viewers to interpret, resulting in misleading or useless visualisations.

Key tools

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