“Internal Validity”: A blog about stuff
I continue my series on constructing a presentation using R Markdown with a new addition (Part 3) on font colors. We use colors to highlight text to enhance or draw attention to it. In this article, I provide code on how one can do this in R Markdown’s presentation using revealjs.
Part 3 of this series is available on my RPubs site.
Labeling objects (data points, categories, axes, etc.) in your data visualizations is an important part of telling a good story. Without proper labels the figures in your presentation will leave out important elements of the narrative. Labels provide information about the data points or the categories in the figure. We normally use labels to provide information about the axes of the figure (e.g., horizontal and vertical). This is crucial because it tells our audience what the data visualization is measuring. But labels can also be used to provide a richer and informative description of your data visualization that enhances the narrative of your data-driven story.
Take a look at the two figures below. Which one tells you a better story?
The obvious answer is the right figure because it contains labels for the lines that reflect the sales of hardware and software products between 2010 and 2019. We easily see the sales growth from 2010 to 2019 because the labels identify these two products. Additionally, the labels are color coordinated with the line colors so that these are explicitly clear what lines the labels represent. Without these labels, we would have no idea what the lines represent.
Take a look at the next set of figures, what’s different about them? Are they better than the figures above?
The figure on the left removes the Y-axis and tells us that the growth of hardware sales was greater than software. However, we don’t know the magnitude of the difference in the sales. The figure on the right is more efficient in presenting the hardware and software sales because it includes the values from 2010 to 2019. In other words, the right figure removes the unnecessary values from the X-axis and provides the values that are relevant, in particular, those from 2010 and 2019. (This is a return to Tufte’s principle of the data-ink ratio where we want to maximize the information the ink provides in terms of the data.)
According to the Web Content Accessibility Guidelines (WCAG), the minimum contrast ratio between the text and background is 4.5:1. This meets Section 508 requirements from the Rehabilitation Act (29 U.S.C. 794d) that was amended by the Workforce Rehabilitation Act of 1973, which requires that all electronic content purchased by any Federal Agency be accessible to people with disabilities. These requirements are in place to assist those who have difficulties seeing the full color spectrum.
Take a look at the following figures below. Which one has a better contrast ratio?
The left figure has a contrast ratio of 2.35:1, which is below Section 508 requirements. The right figure has a contrast ratio of 7.36:1, which is above Section 508 requirements. It’s clear that the data labels are much easier to see in the right figure compared to the left figure. Having a good contrast ratio is critical to telling your narrative with data, but it is also a considerable advantage when presenting using slides where colors can be washed out by different projectors or bright rooms. Make sure to use high contrast ratio to have your data be more effective for your audience. (Note: For large-scale text (≥ 18 point font or ≥ 14 bold point font), you can use a contrast ratio of 3:1.)
You can check the contrast ratio using online tools such as the one here (developed by WebAIM). However, you will need to get the hex triplet color number from your data visualization. The hex triplet is a six-digit hexadecimal code used for web-based design and reflects the 24-bit RGB color spectrum.
To get the hex triplet color number from your data visualization in Excel (we are using Excel as an example, but this can work with other products that use a color palette), go to color format window and select the “More Colors…” option.
Use the eye dropper to select the color from your file (e.g., Excel, Word). The hex triplet color number will automatically populate in the “Hex Color #” field. Use this on the following website to determine the contrast ratio. (Remember, you want to have a contrast ratio of ≥ 4.5:1.)
Labels should be consistent throughout your data visualization. If you decide to use Arial font in your labels, make sure that you consistently use them for the same label type.
Compare the two figures below. The figure in the bottom panel uses different fonts for the data labels, but the figure in the top panel has a font that is consistent. Having different fonts can be distracting, so it’s best to be consistent with the font (and size) that you use in your data visualizations.
Another point about consistency is the case rule for labels. Normal sentence case is the preferred method for providing labels according to the US Data Visualization Standards. However, I believe you are the best judge for when to use sentence case or other case rules for your data visualizations.
Compare the two figures below. The left figure has a legend that uses a sentence case where each word is capitalized (e.g., “Prevalence Of Deaths in 2015”). The right figure’s legend uses a normal sentence case (e.g., “Prevalence of deaths in 2015”). Which is better?
For me, having each word capitalized looks awkward (see below). I prefer to use a legend with a normal sentence case, but you may choose to use something different. I encourage you to experiment and find the right rules for your specific scenarios.
The top panel has the sentence case where all the words are capitalized. The bottom panel has normal sentence case.
Including data labels can enhance your data visualizations and strengthen your narrative. But you need to make sure that you are consistent and apply high contrast to be effective with your presentation. In this article, we introduce the importance of using the correct contrast ratios according to the WCAG and standardizing your font style. However, it is also important to incorporate your own creativity into your data visualization. Some rules should be broken in order to improv the narrative. So, be adventurous!
WebAIM is a site that provides a contrast ratio tool, that checks the contrast ratio for your projects. WebAIM a non-profit organization that is based at the Center for Persons with Disabilities in the University of Utah. Their mission is to “…empower organizations to make their web content accessible to people with disabilities.”
The US Data Visualization Standards (DVS) is a great site for rules that the US Government uses for their data visualizations and web tools.
Web Content Accessibility Guidelines are a great resource for learning more about standardizing your data visualization. Although the WCAG was meant for web content and design, it can be generalized to your presentations, publications, and other data visualization tools.
