In a previous post about data visualization in data science and statistics, I discussed what I consider the single most important rule of graphing data. In this post, I am following up to discuss the most important rules for making data tables. I will focus on data tables in reporting/communicating findings to others, as opposed to the many other uses of tables in data science say to store, organize, and mine data.
To summarize, graphs are like sentences, conveying one clear thought to the viewer/reader. Tables, on the other hand, can function more like paragraphs, conveying multiple sentences or thoughts to get an overall idea. Unlike graphs, which often provide one thought, tables can be more exploratory, providing information for the viewer/reader to analyze and draw his or her own conclusions from.
Table Rule #1: Don’t be afraid to provide as much or as little information as you need.
Paragraphs can use multiple sentences to convey a series of thoughts/statements, and tables are no different. One can convey multiple pieces of information that viewers/readers can look through and analyze at his or her own leisure, using the data to answer their own questions, so feel free to take up the space as you need. Several page long tables are fair game and, in many cases, absolutely necessary (although often end up in appendices for readers/viewers needing a more in-depth take).
In my previous data visualization post, I gave this bar chart as an example of trying to say too many statements for a graph:
This is a paragraphs-worth of information, and a table would represent it much better.[i] In a table, the reader/viewer can explore the table values by country and year themselves and answer whatever questions he or she might have. For example, if someone wanted to analyze how a specific country changed overtime, he or she could do so easily with a table, and/or if he or want to analyze compare the immigration ratios between countries of a specific decade, that is possible as well. In the graph above, each country’s subsegment starts in a different place vertically for each decade column, making it hard to compare the sizes visually, and since each decade has dozens of values, that the latter analysis is visually difficult to decipher as well.
But, at the same time, do not be afraid to convey a sentence- or graphs-worth of data into a table, especially when such data is central for what you are saying. Sometimes writers include one-sentence paragraphs when that single thought is crucial, and likewise, a single statement table can have a similar effect. For example, writing a table for a single variable does helps convey that that variable is important:
| Gender | Some Crucial Result |
| Male | 36% |
| Female | 84% |
Now, sometimes in these single statement instances, you might want to use a graph instead of a table (or both), which I discuss in more detail in Rule #3.
Table Rule #2: Keep columns consistent for easy scanning.
I have found that when viewers/readers scan tables, they generally subconsciously assume that all variables in a column are the same: same units and type of value. Changing values of a column between rows can throw off your viewer/reader when he or she looks at it. For example, consider this made-up study data:
| Control Group (n = 100) | Experimental Group (n = 100) | |
| Mean Age | 45 | 44 |
| Median Age | 43 | 42 |
| Male No. (%) | 45 (45%) | 36 (36%) |
| Female No. (%) | 55 (55%) | 64 (64%) |
In this table, the rows each mean different values and/or units. So, for example, going down the control column, the first column is mean age measured in years. The second column switches to median age, a different type of value than mean (although the same unit of years). The final two rows convey the number and percentages of males and females of each: both a different type of value and a different unit (number and percent unlike years). This can be jarring for viewers/readers who often expect columns to be of the same values and units and naturally compare them as if they are similar types of values.
I would recommend transposing it like this, so that the columns represent the similar variables and the rows the two groups:
| Mean Age | Median Age (IQR) | Male No. (%) | Female No. (%) | |
| Control Group (n = 100) | 45 | 43 (25, 65) | 45 (45%) | 55 (55%) |
| Experimental Group (n = 100) | 44 | 42 (27, 63) | 36 (36%) | 64 (64%) |
Table Rule #3: Don’t be afraid to also use a graph to convey magnitude, proportion, or scale
A table like the gender table in Rule #1 conveys pertinent information numerically, but numbers themselves do not visually show the difference between the values.
| Gender | Some Crucial Result |
| Male | 36% |
| Female | 84% |
Graphs excel at visually depicting the magnitude, proportion, and/or scale of data, so, if in this example, it is important to convey how much greater the “Some Crucial Result” is for females than males, then a basic bar graph allows the reader/viewer to see that the percent is more than double for the females than for the males.
Now, to convey this visual clarity, the graph loses the ability to precisely relate the exact numbers. For example, looking at only this graph, a reader/viewer might be unsure whether the males are at 36%, 37%, or 38%. People have developed many graphing strategies to deal with this (ranging from making the grid lines sharper, writing the exact numbers on top of, next to, or around the segment, among others), but combining the graph and table in instances where one both needs to convey the exact numbers and to convey a sense of their magnitude, proportion, or scale can also work well:
Finally, given that tables can convey multiple statements, feel free to use several graphs to depict the magnitude, proportion, or scale of one table. Do not try to overload a multi-statement table into a single, incomprehensible graph. Break down each statement you are trying to relate with that table and depict each separately in a single graph.
Conclusion
If graphs are sentences, then tables can function more like paragraphs, conveying a large amount of information that make more than one thought or statement. This gives space for your reader/viewer to explore the data and interpret it on their own to answer whatever questions they have.
Photo/Table credit #1: Mika Baumeister at https://unsplash.com/photos/Wpnoqo2plFA
Photo/Table credit #2: Linux Screenshots at https://www.flickr.com/photos/xmodulo/23635690633/
[i] Unfortunately, I do not have the data myself that this chart uses, or I would make a table for it to show what I mean.