This section is from
Citation: Rougier NP, Droettboom M, Bourne PE (2014) Ten Simple Rules for Better Figures. PLoS Comput Biol 10(9): e1003833. https://doi.org/10.1371/journal.pcbi.1003833
Published: September 11, 2014
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Scientific visualization is classically defined as the process of graphically displaying scientific data. However, this process is far from direct or automatic. There are so many different ways to represent the same data: scatter plots, linear plots, bar plots, and pie charts, to name just a few. Furthermore, the same data, using the same type of plot, may be perceived very differently depending on who is looking at the figure. A more accurate definition for scientific visualization would be a graphical interface between people and data. In this short article, we do not pretend to explain everything about this interface; rather, see ,  for introductory work. Instead we aim to provide a basic set of rules to improve figure design and to explain some of the common pitfalls.
Rule 1: Know Your Audience
Given the definition above, problems arise when how a visual is perceived differs significantly from the intent of the conveyer. Consequently, it is important to identify, as early as possible in the design process, the audience and the message the visual is to convey. The graphical design of the visual should be informed by this intent. If you are making a figure for yourself and your direct collaborators, you can possibly skip a number of steps in the design process, because each of you knows what the figure is about. However, if you intend to publish a figure in a scientific journal, you should make sure your figure is correct and conveys all the relevant information to a broader audience. Student audiences require special care since the goal for that situation is to explain a concept. In that case, you may have to add extra information to make sure the concept is fully understood. Finally, the general public may be the most difficult audience of all since you need to design a simple, possibly approximated, figure that reveals only the most salient part of your research (Figure 1). This has proven to be a difficult exercise .
Rule 2: Identify Your Message
A figure is meant to express an idea or introduce some facts or a result that would be too long (or nearly impossible) to explain only with words, be it for an article or during a time-limited oral presentation. In this context, it is important to clearly identify the role of the figure, i.e., what is the underlying message and how can a figure best express this message? Once clearly identified, this message will be a strong guide for the design of the figure, as shown in Figure 2. Only after identifying the message will it be worth the time to develop your figure, just as you would take the time to craft your words and sentences when writing an article only after deciding on the main points of the text. If your figure is able to convey a striking message at first glance, chances are increased that your article will draw more attention from the community.
Rule 3: Adapt the Figure to the Support Medium
A figure can be displayed on a variety of media, such as a poster, a computer monitor, a projection screen (as in an oral presentation), or a simple sheet of paper (as in a printed article). Each of these media represents different physical sizes for the figure, but more importantly, each of them also implies different ways of viewing and interacting with the figure. For example, during an oral presentation, a figure will be displayed for a limited time. Thus, the viewer must quickly understand what is displayed and what it represents while still listening to your explanation. In such a situation, the figure must be kept simple and the message must be visually salient in order to grab attention, as shown in Figure 3. It is also important to keep in mind that during oral presentations, figures will be video-projected and will be seen from a distance, and figure elements must consequently be made thicker (lines) or bigger (points, text), colors should have strong contrast, and vertical text should be avoided, etc. For a journal article, the situation is totally different, because the reader is able to view the figure as long as necessary. This means a lot of details can be added, along with complementary explanations in the caption. If we take into account the fact that more and more people now read articles on computer screens, they also have the possibility to zoom and drag the figure. Ideally, each type of support medium requires a different figure, and you should abandon the practice of extracting a figure from your article to be put, as is, in your oral presentation.
Rule 4: Captions Are Not Optional
Whether describing an experimental setup, introducing a new model, or presenting new results, you cannot explain everything within the figure itself—a figure should be accompanied by a caption. The caption explains how to read the figure and provides additional precision for what cannot be graphically represented. This can be thought of as the explanation you would give during an oral presentation, or in front of a poster, but with the difference that you must think in advance about the questions people would ask. For example, if you have a bar plot, do not expect the reader to guess the value of the different bars by just looking and measuring relative heights on the figure. If the numeric values are important, they must be provided elsewhere in your article or be written very clearly on the figure. Similarly, if there is a point of interest in the figure (critical domain, specific point, etc.), make sure it is visually distinct but do not hesitate to point it out again in the caption.
Rule 5: Do Not Trust the Defaults
Any plotting library or software comes with a set of default settings. When the end-user does not specify anything, these default settings are used to specify size, font, colors, styles, ticks, markers, etc. (Figure 4). Virtually any setting can be specified, and you can usually recognize the specific style of each software package (Matlab, Excel, Keynote, etc.) or library (LaTeX, matplotlib, gnuplot, etc.) thanks to the choice of these default settings. Since these settings are to be used for virtually any type of plot, they are not fine-tuned for a specific type of plot. In other words, they are good enough for any plot but they are best for none. All plots require at least some manual tuning of the different settings to better express the message, be it for making a precise plot more salient to a broad audience, or to choose the best colormap for the nature of the data. For example, see  for how to go from the default settings to a nicer visual in the case of the matplotlib library.
Rule 6: Use Color Effectively
Color is an important dimension in human vision and is consequently equally important in the design of a scientific figure. However, as explained by Edward Tufte , color can be either your greatest ally or your worst enemy if not used properly. If you decide to use color, you should consider which colors to use and where to use them. For example, to highlight some element of a figure, you can use color for this element while keeping other elements gray or black. This provides an enhancing effect. However, if you have no such need, you need to ask yourself, “Is there any reason this plot is blue and not black?” If you don’t know the answer, just keep it black. The same holds true for colormaps. Do not use the default colormap (e.g., jet or rainbow) unless there is an explicit reason to do so (see Figure 5 and ). Colormaps are traditionally classified into three main categories:
- Sequential: one variation of a unique color, used for quantitative data varying from low to high.
- Diverging: variation from one color to another, used to highlight deviation from a median value.
- Qualitative: rapid variation of colors, used mainly for discrete or categorical data.
Use the colormap that is the most relevant to your data. Lastly, avoid using too many similar colors since color blindness may make it difficult to discern some color differences (see  for detailed explanation).
Rule 7: Do Not Mislead the Reader
What distinguishes a scientific figure from other graphical artwork is the presence of data that needs to be shown as objectively as possible. A scientific figure is, by definition, tied to the data (be it an experimental setup, a model, or some results) and if you loosen this tie, you may unintentionally project a different message than intended. However, representing results objectively is not always straightforward. For example, a number of implicit choices made by the library or software you’re using that are meant to be accurate in most situations may also mislead the viewer under certain circumstances. If your software automatically re-scales values, you might obtain an objective representation of the data (because title, labels, and ticks indicate clearly what is actually displayed) that is nonetheless visually misleading (see bar plot in Figure 6); you have inadvertently misled your readers into visually believing something that does not exist in your data. You can also make explicit choices that are wrong by design, such as using pie charts or 3-D charts to compare quantities. These two kinds of plots are known to induce an incorrect perception of quantities and it requires some expertise to use them properly. As a rule of thumb, make sure to always use the simplest type of plots that can convey your message and make sure to use labels, ticks, title, and the full range of values when relevant. Lastly, do not hesitate to ask colleagues about their interpretation of your figures.
Rule 8: Avoid “Chartjunk”
Chartjunk refers to all the unnecessary or confusing visual elements found in a figure that do not improve the message (in the best case) or add confusion (in the worst case). For example, chartjunk may include the use of too many colors, too many labels, gratuitously colored backgrounds, useless grid lines, etc. (see left part of Figure 7). The term was first coined by Edward Tutfe in , in which he argues that any decorations that do not tell the viewer something new must be banned: “Regardless of the cause, it is all non-data-ink or redundant data-ink, and it is often chartjunk.” Thus, in order to avoid chartjunk, try to save ink, or electrons in the computing era. Stephen Few reminds us in  that graphs should ideally “represent all the data that is needed to see and understand what’s meaningful.” However, an element that could be considered chartjunk in one figure can be justified in another. For example, the use of a background color in a regular plot is generally a bad idea because it does not bring useful information. However, in the right part of Figure 7, we use a gray background box to indicate the range [−1,+1] as described in the caption. If you’re in doubt, do not hesitate to consult the excellent blog of Kaiser Fung , which explains quite clearly the concept of chartjunk through the study of many examples.
Rule 9: Message Trumps Beauty
Figures have been used in scientific literature since antiquity. Over the years, a lot of progress has been made, and each scientific domain has developed its own set of best practices. It is important to know these standards, because they facilitate a more direct comparison between models, studies, or experiments. More importantly, they can help you to spot obvious errors in your results. However, most of the time, you may need to design a brand-new figure, because there is no standard way of describing your research. In such a case, browsing the scientific literature is a good starting point. If some article displays a stunning figure to introduce results similar to yours, you might want to try to adapt the figure for your own needs (note that we did not say copy; be careful with image copyright). If you turn to the web, you have to be very careful, because the frontiers between data visualization, infographics, design, and art are becoming thinner and thinner . There exists a myriad of online graphics in which aesthetic is the first criterion and content comes in second place. Even if a lot of those graphics might be considered beautiful, most of them do not fit the scientific framework. Remember, in science, message and readability of the figure is the most important aspect while beauty is only an option, as dramatically shown in Figure 8.
1. Tufte EG (1983) The Visual Display of Quantitative Information. Cheshire, Connecticut: Graphics Press.
2. Doumont JL (2009) Trees, maps, and theorems. Brussels: Principiae.
3. Kosara R, Mackinlay J (2013) Storytelling: The next step for visualization. IEEE Comput 46: 44–50.
4. Rougier NP (2012) Scientific visualization and matplotlib tutorial. Euroscipy 2012 & 2013. Available: http://www.loria.fr/~rougier/teaching/matplotlib/matplotlib.html. Accessed 12 August 2014.
5. Borland D, Taylor RM (2007) Rainbow color map (still) considered harmful. IEEE Comput Graph Appl 27: 14–17.
6. Okabe M, Ito K (2008). Color universal design (cud) – how to make figures and presentations that are friendly to colorblind people. Available: http://jfly.iam.u-tokyo.ac.jp/color/. Accessed 12 August 2014.
7. Few S (2011) The chartjunk debate, a close examination of recent findings. Visual Business Intelligence Newsletter. Available: http://www.perceptualedge.com/articles/visual_business_intelligence/the_chartjunk_debate.pdf. Accessed 12 August 2014.
8. Fung K (2005). Junk charts: Recycling chartjunk as junk art. Available: http://junkcharts.typepad.com. Accessed 12 August 2014.
9. Borkin MA, Vo AA, Bylinskii Z, Isola P, Sunkavalli S, et al. (2013) What makes a visualization memorable? IEEE Trans Vis Comput Graph 19: 2306–2315.
10. Schumann J, Strothotte T, Raab A, Laser S (1996) Assessing the effect of non-photorealistic rendered images in cad. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; 13–18 April 1996; New York, New York, United States. CHI 96. New York: Association for Computing Machinery. pp.35–41.