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Transformations over multiple rows


#1

Just a thought; would it be possible to change the table window properties so that transformations icons are spread or wrapped over multiple rows at the top of the table window, rather than a single line?

I regularly work with large numbers of transformations, and it’s really tricky to find and rearrange transformations when working over a single line. The only way around it it to create a derived table, in effect starting the transformation list again, but that’s pretty clunky.

Apologies of this has already been raised before, or if there’s a good reason why this can’t be done.


#2

Thank you for the suggestion @alexlea88. Out of curiosity, would would be the max number of transformations (approximately) per table in your projects?


#3

BTW, did you know that you can rearrange transformations using buttons Move Right and Move Left on the Design toolbar?


#4

Hi Dimitry

Thanks for the reply. Hadn’t spotted the Move Left/ Right buttons, so i’ll give them a try. Cheers!

Some of the projects I have bring multiple datasets into a single table, or running multiple Table-Wide Replace in order to tidy up area names etc. In some cases, there might be a hundred transformations in a single table (I’ve been using Derive Table to in effect reset the transformation list to help make things easier). Not the most efficient way of working, but it frees up resources in more technical areas of our team.

Out of interest, is there an easier way to compile multiple workbooks into a single table using Easymorph?

Ideally, the transformation list in a table window would just wrap across multiple lines, rather than going off the end of the table and into the drop-down list. I would imagine most people would find the space better used for transformations rather than the data table - I know I just look at the first few rows of the table in order to see the effect of my transformations.

Regards

Alex


#5

Typically (but not always), if you have a hundred transformations in a single table then there is a more optimal way to design calculations. For instance, use iterations. With iterations you can transform datasets in a similar fashion one by one, and then automatically append all resulting datasets into one table.

Does it sound anywhere similar to your case?