Handle a string return from R to Tableau and SPLIT it

Question

I connect Tableau to R and execute an R function for recommending products. When R ends, the return value is a string which will have all products details, like below:

ID|Existing_Prod|Recommended_Prod\nC001|NA|PROD008\nC002|PROD003|NA\nF003|NA|PROD_ABC\nF004|NA|PROD_ABC1\nC005|PROD_ABC2|NA\nC005|PRODABC3|PRODABC4

(Each line separated by \n indicating end of line)

On Tableau, I display the calculated field which is as below:

ID|Existing_Prod|Recommended_Prod
C001|NA|PROD008
C002|PROD003|NA
F003|NA|PROD_ABC
F004|NA|PROD_ABC1
C005|PROD_ABC2|NA
C005|PRODABC3|PRODABC4

Above data reaches Tableau through a calculated field as a single string which I want to split based on pipeline ('|'). Now, I need to split this into three columns, separated by the pipeline.

I used Split function on the calculated field :

SPLIT([R_Calculated_Field],'|',1)
SPLIT([R_Calculated_Field],'|',2)
SPLIT([R_Calculated_Field],'|',3)

But the error says "SPLIT function cannot be applied on Table calculations", which is self explanatory. Are there any alternatives to solve this ?? I googled to check for best practices to handle integration between R and Tableau and all I could find was simple kmeans clustering codes.

Answer 1

Make sure you understand how partitioning and addressing work for table calcs. Table calcs pass vectors of arguments to the R script, and receive a single vector in response. The cardinality of those vectors depends on the partitioning of the table calc. You can view that by editing the table calc, clicking specific dimensions. The fields that are not checked determine the partitioning - and thus the cardinality of the arguments you send and receive from R

This means it might be tricky to map your problem onto this infrastructure. Not necessarily impossible. It was designed to send a series of vector arguments with one cell per partitioning dimension, say, Manufacturer and get back one vector with one result per Manufacturer (or whatever combination of fields partition your data for the table calc). Sounds like you are expecting an arbitrary length list of recommendations. It shouldn’t be too hard to have your R script turn the string into a vector before returning, but the size of the vector has to make sense.

As an example of an approach that fits this model more easily, say you had a Tableau view that had one row per Product (and you had N products) - and some other aggregated measure fields in the view per Product. (In Tableau speak, the view’s level of detail is at the Product level.)

It would be straightforward to pass those measures as a series of argument vectors to R - each vector having N values, and then have R return a vector of reals of length N where the value returned at each location was a recommender score for the product at that position. (Which is why the ordering aka addressing of the vectors also matters)

Then you could filter out low scoring products from the view and visually distinguish highly recommended products.

So the first step to understanding R integration is to understand how table calcs operate with partitioning and addressing and to think in terms of vectors of fixed lengths passed in both directions.

If this model doesn’t support your use case well, you might be able to do something useful with URL actions or the JavaScript API.