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MarioA
New Member

Simple Like for like analysis

Hello Guys,

 

I am faced with a classic like for like problem. 

 

I have a set of stores data, and would like to create a column where I can flag which store is "like for like", which one has been closed, which one has been opened from one year to another. See below example.

 

In my simple mind it is something like: IF StoreID is found in both years then Like for Like; if StoreID is found only in 2021 is Closed; if StoreID only in 2022 is new.

 

Only thing... I cannot translate this in DAX. 

 

Thanks a lot for your help!

 

Mario

 

YearStoreIDSalesStatus
2021Store_110Like for like
2022Store_110Like for like
2022Store_210Open
2021Store_310Closed

 

 

 

 

1 ACCEPTED SOLUTION
mangaus1111
Solution Sage
Solution Sage

Hi @MarioA ,

try this calculated column

Column = 
VAR MaxYear = MAX('Facts9'[Year])
RETURN
SWITCH(TRUE(),
       COUNTROWS(
               FILTER(
                   'Facts9',
                   'Facts9'[StoreID] = EARLIER('Facts9'[StoreID])
               )) > 1, "Like for like",
       'Facts9'[Year] = MaxYear,"Open",
       'Facts9'[Year] < MaxYear,"Close"      
)

If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.

View solution in original post

3 REPLIES 3
mangaus1111
Solution Sage
Solution Sage

Hi @MarioA ,

try this calculated column

Column = 
VAR MaxYear = MAX('Facts9'[Year])
RETURN
SWITCH(TRUE(),
       COUNTROWS(
               FILTER(
                   'Facts9',
                   'Facts9'[StoreID] = EARLIER('Facts9'[StoreID])
               )) > 1, "Like for like",
       'Facts9'[Year] = MaxYear,"Open",
       'Facts9'[Year] < MaxYear,"Close"      
)

If this post helps, then please consider Accept it as the solution to help the other members find it more quickly.

mangaus1111
Solution Sage
Solution Sage

Hi @MarioA ,

do you need a measure or a calculated column?

One of the two would work - I am also open to suggestion on wether to utilise one or the other! Thanks a lot for your reply! 

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