Hi all,
I have a dataset with 40 columns and 300+ rows.
For every column (= an event name) the values are binary, meaning yes/no attendance of the event for each row.
I am interested to see if there is a correlation in attendance between the different events/columns.
I managed to visualize this with a scatterplot matrix.
I adjusted the axes for the matrix to only show values that have value 1 for both columns/events, as I am only interested in cases where both events of the scatterplot matrix were attended by a row.
My scatterplot matrix looks like below image, but a lot bigger.
Every dot is a row for which both events/columns of the scatterplot matrix have a value of 1.
But it is very difficult to count the dots in the many different possible combination of events/columns (= the individual boxes/subplots of the scatterplot matrix).
My question is: is it possible to change the dots in the individual boxes into the actual count number for each individual box?
Synthax for the scatterplot matrix looks like below
Note that for readability I dont list all the event names, and only the scalebox synthax for 2 columns/events.
This is highlighted in red text.
:
Scatterplot Matrix(
Y(
:Here a long list with the ~40 column/event names,
Matrix Format( "Lower Triangular" ),
SendToReport(
Dispatch(
{},
"140",
ScaleBox,
{Min( 0.5 ), Max( 1.5 ), Inc( 1 ), Minor Ticks( 0 )}
),
Dispatch(
{},
"139",
ScaleBox,
{Format( "Best", 15 ), Min( 0.5 ), Max( 1.5 ), Inc( 1 ),
Minor Ticks( 0 )}
),
Followed by 38 more similar code segments for the other 38 columns/events.
