Before we get to the ten guidelines, recognize that just like in graphs and charts, there are a lot of pieces to tables. And, just like graphs and charts, we can control the look and design of all of these elements.
Rule 1. Offset the Heads from the Body
Make your column titles clear. Try using boldface type or lines to offset them from the numbers and text in the body of the table.
Rule 2. Use Subtle Dividers Rather Than Heavy Gridlines
Lighten or even remove many of the heavy borders and dividers in your tables. Every single cell border is rarely necessary.
Rule 3. Right-Align Numbers and Headers
Right-align numbers along the decimal place or comma. We might need to add zeros to maintain the alignment, but it’s worth it so the numbers are easier to read and scan.
Rule 4. Left-Align Text and Headers
Once we’ve right-aligned the numbers, we should left-align the text. Notice how much easier it is to read the country names in the far-right column than in the other two columns.
Rule 5. Select the Appropriate Level of Precision
Precision to the fifth decimal place is rarely necessary. Consider a balance between necessary precision and a clean, spare table.
Rule 6. Guide Your Reader with Space between Rows and Columns
Your use of space in and around the table can influence the order in which someone reads the data. Use spacing strategically to match the order you want your reader to take in the table.
Rule 7. Remove Unit Repetition
Our reader knows that the values in the table are dollars because we told them in the title or subtitle. Repeating the symbol throughout the table is overkill and adds clutter.
Rule 8. Highlight Outliers
Some readers will wade through all of the numbers in the table because they need specific information, but many readers likely only need the most important values.
Rule 9. Group Similar Data and Increase White Space
Reduce repetition by grouping similar data or labels. Similar to eliminating dollar signs on every number value, we can reduce some of the clutter in our tables by grouping like terms or labels.
Rule 10. Add Visualizations When Appropriate
We can make larger changes to our tables by adding small visualizations, which can make it easier to for the reader to navigate and find patterns and trends you want to highlight.
Want even more on good table design? Check out the full JBCA article (cambridge.org/core/journals/…) and my book 'Better Data Visualizations: A Guide for Researchers, Scholars, and Wonks.' | amzn.to/2zHQ4qv
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Well, it's time for me to close out my week hosting @iamscicomm. Thanks for all of your questions and comments! I had so much fun chatting with folks from different fields and walks of life!
I'll leave you with my 5 general guidelines for creating more effective dataviz.
1. Show the Data.
Your reader can only grasp your point, argument, or story if they see the data. This doesn’t mean that all the data must be shown, but it does mean that you should highlight the values that are important to your argument.
2. Reduce the Clutter.
The use of unnecessary visual elements distracts your reader from the central data and clutters the page. Reduce/eliminate heavy tick marks, gridlines, textured gradients, too much text and labels. Focus on the data.
I close out my week hosting the @iamscicomm account by sharing just a select few examples of #dataviz that don't follow the rules or templates or tried-and-true approaches. But they are beautiful and engaging and enlightening.
As you go forth and create your visualizations, continue to explore. Draw inspiration from all around you and from the amazing work these and other creators are generating.
Earlier in the week, I promised I'd put together a list of my favorite #dataviz tools. 🛠
These are by no means the only tools out there, but these are the ones I like and use regularly.
(Sigh, another thread....I know, I know....) 🧵
1. Excel. Yep, I use Excel for much of my #dataviz work. Excel is based on bars, lines, and dots, all in an X-Y space. Think of it that way, and you can create lots of stuff.
Perhaps the most common #dataviz for qualitative data is the Word Cloud. ⛅️
In a word cloud, the size of each word is adjusted according to its frequency in a passage of text.
But here's the thing: The font, alignment, and color of the words in the word cloud can affect our perception of the data. Furthermore, it's hard to see the most important *concepts* in the text.
We kick off today's subject of #dataviz for part-to-whole relationships and qualitative data with some of my favorite fun pie charts. I did not originally create these, and the original creators are lost to history.
Flow maps are another kind of way to visualize your data. Maybe the most famous flow map is this one from Charles Joseph Minard in 1869. Tufte always touts this one as being the "best statistical chart ever made".
A quick 🧵 on the Minard map.
The famous Minard map shows 6 data values in a single view: 1. Number of troops (line thickness) 2. Distance traveled (scale) 3. Temperature (line at bottom) 4. Time (line at bottom) 5. Direction of travel (color) 6. Geography (cities, etc.)
But Tufte left out the fact that the Minard Napolean map was only one panel in a full spread. It also included the lesser-known map of Hannibal’s 218 BC march through the Alps to Rome. (This image from Ecole nationale des ponts et chaussées, which I include in my book.)