📊Graphical Representation: Graphics We Live By (Part XI: Comparisons Between Data Series)

Graphical Representation Series

Over the past 10-20 years it became so easy to create data visualizations just by dropping some of the data available into a tool like Excel and providing a visual depiction of it with just a few clicks. In many cases, the first draft, typically provided by default in the tool used, doesn't even need further work as the objective was reached, while in others the creator must have a minimum skillset for making the visualization useful, appealing, or whatever quality is a final requirement for the work in scope. However, the audience might judge the visualization(s) from different perspectives, and there can be a broad audience with different skills in reading, evaluating and understanding the work.

There are many depictions on the web resembling the one below, taken from a LinkedIn post:

Example Chart - Boing vs. Airbus

Even if the visualization is not perfect, it does a fair job in representing the data. Improvements can be made in the areas of labels, the title and positioning of elements, and the color palette used. At least these were the improvements made in the original post. It must be differentiated also between the environment in which the charts are made available, the print format having different characteristics than the ones in business setups. Unfortunately, the requirements of the two are widely confused, probably also because of the overlapping of the mediums used. 

Probably, it's a good idea to always start with the row data (or summaries of it) when the result consists of only a few data points that can be easily displayed in a table like the one below (the feature to round the decimals for integer values should be available soon in Power BI):

Summary Table

Of course, one can calculate more meaningful values like percentages from the total, standard deviations and other values that offer more perspectives into the data. Even if the values adequately reflect the reality, the reader can but wonder about the local and global minimal/maximal values, without talking much about the meaning of data points, which is easily identifiable in a chart. At least in the case of small data sets, using a table in combination with a chart can provide a more complete perspective and different ways of analyzing the data, especially when the navigation is interactive. 

Column and bar charts do a fair job in comparing values over time, though they do use a lot of ink in the process (see D). While they make it easy to compare neighboring values, the rectangles used tend to occupy a lot of space when they are made too wide or too high to cover the empty space within the display (e.g. when just a few values are displayed, space being wasted in the process). As the main downside, it takes a lot of scanning until the reader identifies the overall trends, and the further away the bars are from each other, the more difficult it becomes to do comparisons. 

In theory, line charts are more efficient in representing the above data points, because the marks are usually small and the line thin enough to provide a better data-ink ratio, while one can see a lot at a glance. In Power BI the creator can use different types of interpolation: linear (A), step (B) or smooth (C). In many cases, it might be a good idea to use a linear interpolation, though when there are no or minimal overlapping, it might be worthwhile to explore the other types if interpolation too (and further request feedback from the users):

Linear, Step and Smooth Line Charts

The nearness of values from different series can raise difficulties in identifying adequately the points, respectively delimiting the lines (see B).When the density of values allows it, it makes sense also to include the averages for each data series to reflect the distance between the two data sets. Unfortunately, the chart can get crowded if further data series or summaries are added to the cart(s). 

If the column chart (E) is close to the redesigned chart provided in the original redesign, the other alternatives can provide upon case more value. Stacked column charts (D) allow also to compare the overall quantity by month, area charts (F) tend to use even more color than needed, while water charts (G) allow to compare the difference between data points per time unit. Tornado charts (H) are a variation of bar charts, allowing easier comparing of the size of the bars, while ribbon charts (I) show well the stacking values. 

Alternatives to Line Charts

One should consider changing the subtitle(s) slightly to reflect the chart type when the patterns shown imply a shift in attention or meaning. Upon case, more that one of the above charts can be used within the same report when two or more perspectives are important. Using a complementary perspective can facilitate data's understanding or of identifying certain patterns that aren't easily identifiable otherwise. 

In general, the graphics creators try to use various representational means of facilitating a data set's understanding, though seldom only two series or a small subset of dimensions provide a complete description. The value of data comes when multiple perspectives are combined. Frankly, the same can be said about the above data series. Yes, there are important differences between the two series, though how do the numbers compare when one looks at the bigger picture, especially when broken down on element types (e.g. airplane size). How about plan vs. actual values, how long does it take more for production or other processes? It's one of a visualization's goals to improve the questions posed, but how efficient are visualizations that barely scratch the surface?

In what concerns the code, the following scripts can be used to prepare the data:

-- Power Query script (Boeing vs Airbus)
= let
    Source = let
    Source = #table({"Sorting", "Month Name", "Serial Date", "Boeing Deliveries", "Airbus Deliveries"},
    {
        {1, "Oct", #date(2023, 10, 31), 30, 50},
        {2, "Nov", #date(2023, 11, 30), 40, 40},
        {3, "Dec", #date(2023, 12, 31), 40, 110},
        {4, "Jan", #date(2024, 1, 31), 20, 30},
        {5, "Feb", #date(2024, 2, 29), 30, 40},  // Leap year adjustment
        {6, "Mar", #date(2024, 3, 31), 30, 60},
        {7, "Apr", #date(2024, 4, 30), 40, 60},
        {8, "May", #date(2024, 5, 31), 40, 50},
        {9, "Jun", #date(2024, 6, 30), 50, 80},
        {10, "Jul", #date(2024, 7, 31), 40, 90},
        {11, "Aug", #date(2024, 8, 31), 40, 50},
        {12, "Sep", #date(2024, 9, 30), 30, 50}
    }
    ),
    #"Changed Types" = Table.TransformColumnTypes(Source, {{"Sorting", Int64.Type}, {"Serial Date", type date}, {"Boeing Deliveries", Int64.Type}, {"Airbus Deliveries", Int64.Type}})
in
    #"Changed Types"
in
    Source

It can be useful to create the labels for the charts dynamically:

-- DAX code for labels
MaxDate = Format(Max('Boeing vs Airbus'[Serial Date]),"MMM-YYYY")
MinDate = FORMAT (Min('Boeing vs Airbus'[Serial Date]),"MMM-YYYY")
MinMaxDate = [MinDate] & " to " & [MaxDate]
Title Boing Airbus = "Boing and Airbus Deliveries " & [MinMaxDate]

Happy coding!

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