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smartinsightsfromdata avatar smartinsightsfromdata commented on August 18, 2024

Sadly there isn't an overarching solution to this (you can consult the list of Frequently Asked Questions in the pivottable github page in Nicolas Krutchen pivottable page here )

I can suggest the following trick though:

  • Identify sub-categories / sub-classes in the data dimensions that are "too large". If there in't any natural sub-division, you could use any relevant sub-class (e.g. letters of alphabet if the category as a whole is character based or a factor.
  • Select first the appropriate subdivision for your analysis.

I know that the above "trick" will not work in every case, but it could help to address some of the most common cases.

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nickknauer1 avatar nickknauer1 commented on August 18, 2024

I actually found a solution to this. Below is the stackoverflow answer:

http://stackoverflow.com/questions/34793135/rpivottable-being-able-to-list-more-than-allowable-amount/34895345#34895345

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smartinsightsfromdata avatar smartinsightsfromdata commented on August 18, 2024

@nickknauer1

Thanks and congrats! I don't know of this possibility myself.

I post it here the solution for future reference:

tmp <- rbind(iris, iris)
# Here you have limit on num column:
rpivotTable(cbind(tmp, num = 1:dim(tmp)[1]))
# Here you don't:
rpivotTable(cbind(tmp, num = 1:dim(tmp)[1]), menuLimit = 500)

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