Is there a way to use vector style data groupings in Beast Mode?

Kuhlywhack

I am trying to find a quicker way to use grouped data values in a column to filter an issue.  Let's say I want to say any records that are A,B, and D  should be marked "Inefficient" out of column Alpha that has values A,B,C,D,E.  In something like R I would do this by using a similar code:

 

v-Inefficient <- c{"A","B","D"}

 

 Using that logic utilize some kind of beast mode where:

 

(Case when Alpha = v-Inefficient then 'Inefficient'

else efficient

end)

Even if I could have a method of grouping to filter in the beast mode like:

(Case when Aplpha = {'A', 'B', 'D'} then 'v-Inefficient'

else 'Efficient'

End)

Is there a way to replicate this concept in a Domo Beast mode?  I have some situations where I am grouping off of larger beast modes, and then boiling down those beast mode results to isolated criteria per a filter.

KurtF

In SQL syntax we write this as follows:

 

CASE

WHEN `Alpha` IN ('A', 'B', 'D')

     THEN `v-Inefficient`

WHEN `Alpha` IN ('C', 'E')

     THEN `v-Efficient`

END

 

This should do exactly what you are looking for.

 

 

AS

You can't declare variables or nest Beast Bodes in Beast Mode, but something like a case statement using LIKE would work.  It would just get 'wordy'?

 

CASE

WHEN Alpha LIKE '%A%' OR Alpha LIKE '%B%' or Alpha LIKE '%D%' 

THEN 'v-Inefficient'

ELSE 'Efficient'

END

Kuhlywhack

Yeah, my beast modes were getting ridiculously wordy... I was also overlaying other beast modes that would have more words, so I was hoping there was a quick and dirty simple way through.  What you are showing is the only method I can find to work as well.  Thank you so much for you help and time.

AS

Nested beast modes would take care of this pretty quick, so you could reuse code and keep busy beast modes simpler. Until then, to crazy town we go.

AS

I read your message to mean the field would contain a string of comma separated values like 'A,B,D'.  In that case, the LIKE style statement would be correct.  If the column is only ever a single character, @KurtF's response is the better, simpler way.