Data Visualization - A Brief Overview

Editor's Notes

• #3: (We didn’t look at this link on Friday due to time constraints, but it’s worth checking out as a quick intro to data viz!)
• #6: If you’re dealing with a lot of numbers in a table and a graph would be too “busy”, you can use a heatmap to make patterns more apparent. Conditional formatting in Excel is a simple tool that can be used to help you see patterns in large data sets. (Ask me if you’re unfamiliar with conditional formatting!) When adding conditional formatting, think about what matters. Do you want to know which items are the highest and lowest for each group? Do you want to know which groups are the highest and lowest for each item? Sometimes you can get away with doing conditional formatting on a whole block at once (as shown here), other times you may want to do each row or each column individually. Another visualization that’s useful for exploring data is to use the pivot chart option in Excel, where you can swap quickly between groups or items to see if there’s anything interesting worth showing in an executive presentation. I generally do most of my work in Excel because graphs are easy to move over to PPT when needed, but there are better programs available (such as R) that have a steeper learning curve.
• #7: When you change slides, people read/examine graphics instead of listening. Visual cues vs auditory- visual wins. If you have a lot of info on screen, the audience isn’t going to be listening for most of the time.
• #8: While experienced data viz practitioners may not agree on the “best” type of chart for a given situation, there are usually at least some rules of thumb on what might make the list. I picked a pie chart here for a couple of reasons. For one, most data viz people hate them- the human eye is much better at judging length than angles. People tend to be pretty good at determining quarters of a pie, especially when they line up with the exact top, bottom, left, and right, but if you’re looking at the pie above, can you tell what percentage of people are white/caucasian? One thing that pie charts can do better than bars is quickly compare combined groups (that are next to each other) without doing math. For example, we can immediately see there are more white/caucasian people in this population than all other groups combined. While you can get the same data from the bar chart, you need to look at the axis and then do some quick addition. If you really need to use a pie, there are some guidelines on best practices in the appendix.
• #9: One thing to watch out for on column charts is the length of group titles. Rotating titles can make them hard to read, and if you rotate to 45 degrees, they can get cut off if you aren’t careful. Heat maps rely on color as the visual rather than length to highlight differences. (That can be problematic if you have someone who is colorblind in the audience or if people are printing the presentation in grayscale.) You may want to consider using a plain table and then only highlighting significant differences (practical or statistical significance). You can potentially use line charts in these cases, but line charts are often used to display trends, and some audiences may not want to go against that convention.
• #10: Stephen Few came up with bullet graphs, but they’ve been discussed by several other data scientists. Peltier Tech’s website has tutorials on creating them in Excel.
• #11: Stacked columns/bars can be better for comparing composition of groups, but note that it can be hard to accurately compare the portions other than the ones at the ends.
• #12: One thing to consider is whether you should leave the default axis. Consider if the viewer’s interpretation would change if I had used a 0-100% axis. Group 3 here looks like they had a significant improvement from 2014 to 2017, but how big is the group (statistical significance depends partly on group size)? Showing this graph and then saying there were no sig diffs would be confusing. Using the full axis is more important in column and bar charts, since we’re relying on length to tell the story there.
• #13: While scatterplots are good for quickly seeing patterns and outliers, it is difficult to label the legibly, making it hard to determine the exact items that are outliers. Scatterplots can also be misleading when there are a large number of data points and a limited number of values, because you can’t see how many data points are overlapping. When using bubble charts, pay attention to how the 3rd variable is used. It should determine the area of the bubble, not the diameter/radius.
• #14: Note that these are guidelines, not hard and fast rules. However, if you’re do decide to break them, you should have a good reason for doing so. -Use the full axis. Using a partial axis can make your data appear skewed. For percentages, your axis should go from 0-100%. For mean scores from Likert scale items (5 bucket, such as Strongly Disagree to Strongly Agree), the axis should go from 1 to 5. -Highlight the most important information, for example color only significant differences and leave the rest grey. -Legends & labels- Label your chart in a way that allows the chart to remain the focus. For legends, if you’re only showing one metric, it’s probably unnecessary. -Data-to-ink ratio. This goes to the point above; you need to strike a balance. Avoid “busy” graphics. People often add more elements than necessary to a chart. Charts do not need background colors, 3D, shading, etc., which can make them harder to read. You don’t need a chart title if your slide title is saying the same thing. If you label every point in a bar graph, you don’t really need to label the axis as well. Alternately, if you use axis labels and gridlines, you probably don’t need to label every point. (Note I left a lot of labels out for this presentation just so that it’s not specific to one industry.) -”Squint” test- do you still get something out of it if you squint? Cluttered slides don’t make the cut. -Sort order- is it more important for groups or specific metrics to be in the same order on every slide, or would it be more useful to sort by the value itself (e.g., most to least favorable)? -Ask for a second opinion- you may be surprised what others find confusing
• #15: Some of these aren’t data viz problems so much as presentation problems! -Too much info- are you trying to create one document to serve as both presentation and handout? Also, avoid graphs that are so crowded they’re hard to read. -Poor color choices- Data can be easier to read if you choose colors that are intuitive to the audience, such as red for “bad” results and green for “good” ones. This can vary by audience, though! Consider if you were making a presentation to Coca-Cola- red might be good and blue might be bad there! Be careful to choose colors that are easy to distinguish. For example, using both navy and black could be problematic. You should also use colors consistently throughout your presentation. -Variety- while it isn’t exciting, using the same types of graphs throughout your presentation can make them easier to interpret, because the audience doesn’t need to learn a new display on each page. However (!), don’t maintain poor choices (e.g., from previous presentations) just for consistency. -Inconsistent axes- having axes that jump around from one slide to the next can make comparisons difficult. Similarly, you should use the same size and placement of graphics. -Neglecting to label charts- this can be especially problematic in consulting industries, where you’re used to looking at the same metrics and scales. It may be implied for you, but not for your audience. -Following common conventions- For example, a line chart showing trend is expected to have the most recent data on the right, and people expect if you display something like profit, they would want to see the line go up from left to right, while if you’re showing expenses, it would be “good” for the line to go down, showing a reduction.
• #18: This bar chart is a better option for conveying the same data.
• #26: Note that this example is written up in much more detail and with more interim graphics at the link.
• #27: The line chart makes it apparent that really the only two products we should comment on vs the introduction of C are A and B. Note that you could have all grey lines in this graph- color isn’t necessary because they’re labeled well, so we can use color to highlight specific points.
• #29: Too many groups make this chart impossible to read. Note I’ve removed the legend on purpose, but there are 15 groups.
• #30: If we just focus on the target group, we’re still able to “get something” from the graphic- that the group tends to score poorly relative to others.
• #32: Note these are suggestions, not required graphs! The same website also recently added a “slide chooser”.