Tag: Data Visualization

Data Visualization 101: The Most Important Rule for Developing a Graph

I suspect everyone has seen a bad graph, a mess of bars, lines, pie slices, or what have you that you dreaded having to look at. Maybe you have even made one, which you look at today and wonder what on earth you were thinking.

These graphs violate the most basic graph-making rule in data visualization:

A graph is like a sentence, expressing one idea.

This rule applies to all uses of graphs, whether you are a data scientist, data analyst, statistician, or just making graphs for your friends for fun.

In grade school, your grammar teachers likely explained that a sentence, at its most basic, expresses on thought or idea. Graphs are visual sentences: they should state one and only one thought or idea about the data.

When you look at a graph, you should be able to say, in one sentence, what the graph is saying: such as “Group A is greater than Group B,” or “Y at first improved but is now declining.” If you cannot, then you have yourself a run-on graph.

For example, the above graph is trying to say too many statements: trying to depict the immigration patterns of twenty-two different countries over the course of nearly a century. There are likely useful statements in this data, but the representation as one graph prevents a viewer/reader from being able to easily decipher them.

Likewise, this graph shows way too many lens sizes to meaningfully express a single, coherent idea, leaving the reader/viewer struggling to determine which fields to focus on.

Potential Objection #1: But I have more to say about the data than a single statement.

 Great! Then provide more than one graph. Say everything you need to say about the data; just use one graph for each of your statements.

            Don’t fall into the One-Graph-to-Rule-Them-All Fallacy: trying to use one graph to express all your statements about the data that ends up a visual mess of incomprehensibility. Create multiple easy-to-read graphs where each graph demonstrates one of your points at a time. Condensing everything into one graph just prevents your viewers from determining what you have to say at all.

Bar Chart, Chart, Statistics, Analytics, Data Analytics
One-Graph-to-Rule-Them-All Fallacy: Trying to use one graph to express all your thoughts about the data that ends up a visual mess of incomprehensibility
Statistics, Graph, Chart, Data, Information, Growth
Instead, use one graph for each of your points

Potential Objection #2: I want the viewers to interpret the findings for themselves, not just impart my own ideas/conclusions.

Fair point. When presenting/communicating data, there is a time for showing your own insights and a time to open-endedly display the information for your viewers/readers to interpret for themselves. Graphs are tools for the former, and for the latter, use tables. Tables, among other potential uses, convey a wide scope of information for the reader/viewer to interpret on their own.

Remember that first example above about U.S. immigration from various parts of Europe? A table (see below) would convey that information much more easily and allow readers to track whatever places, patterns, or questions they would to learn about. Are you in a situation where you would like to report a large amount of information that your readers can use for their own purposes? Then tables are a much better starting point than graphs.

 Some situations require that I lean towards sharing my insights/analysis and others towards encouraging my readers/viewers to form their own conclusions, but since most situations require a combination of the two, I generally combine graphs and tables. I try, when I can, to put smaller tables in the document or slides themselves and, when I cannot, include full tables in an Appendix.

Potential Objection #3: My main idea/point has multiple subpoints.

            Many sentences have multiple subpoints needed to express the single idea as well, which does not prevent the sentence structure from meaningfully capturing those ideas. The fancy grammar word for such a subpoint is a claus. Even though some sentences are simple and straightforward with only one subject and predicate, many (like this very sentence) require multiple sets of subjects and predicates to express its thought.

            Likewise, some graphical ideas require multiple subordinate or compounded subpoints, and there are types of graphs that allow this. Consider Joint Plots, like the one below. To present the relationships and combined distribution between the two variables adequately, they also display each variable’s individual distributions above and to the right. That way, the viewer can see how both distributions might be influencing the combined distribution. Thus, it displays each variable’s distribution on the side like a subordinate clause.

The darker colors in this graph signify a higher density of data points, showing the combined joint distribution of the variables.

These are advanced graphs to make, since like with multi-part sentences, one must present the subpoints carefully to make clear what the main point is. Multi-part sentences, likewise, require carefulness in how to organize multiple clauses cohesively. I intend to write a post later describing how to develop these multi-part graphs in more detail.

The general rule still applies for these more complicated graphs:

Can you summarize what the graph is saying in one coherent sentence?

If you cannot, do not use/show that graph. Our brains are very good at intuiting whether a sentence carries one thought, so use this to determine whether your graph is effective.

Photo/Graph credit #1: kreatikar at https://pixabay.com/illustrations/statistics-graph-chart-data-3411473/

Photo/Graph credit #2: Linux Screenshots at https://www.flickr.com/photos/xmodulo/23635690633/

Photo/Graph credit #3: Andrew Guyton at https://www.flickr.com/photos/disavian/4435971394/

Photo/Graph credit #4: TymonOziemblewski at https://pixabay.com/illustrations/bar-chart-chart-statistics-1264756/

Photo/Graph credit #5 (the first graph again): kreatikar at https://pixabay.com/illustrations/statistics-graph-chart-data-3411473/

Photo/Graph credit #6: Michael Waskom provides a helpful tutorial that formed the inspiration behind the random graph I created.

Data Visualization 102: The Most Important Rules for Making Data Tables

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.