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.
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
