Rex | Your RegEx companion | Regex library

Create a CTE that returns for each Block_id the step of the first John.
Then join the table to the CTE:

One method uses an updatable CTE:

You can chain a .reduce after d3.groups in order that the nested array is recast into a nested object.

You can initialize the reduce with {} so it returns an object. curr[0] for each array returned from d3.groups will be the animal. curr[1] for each array returned from d3.groups will be the array of the status and the original array of items grouped per the animal/ status logic.

See below:

I believe you need.

You could use CTE's. Easy to read, but this could get pretty long if having multiple pet names. Not sure how you want to handle occasions where someone has more than one pet of same species, so that would impact this answer, too.

One issue (which causes others) is that you're hiding the type breed by also having a field of the same name in the same scope.

That's a very rare problem to have, because the naming conventions of Java usually prevent this kind of clash: Types (classes, interfaces, enums, annotations) are usually written in CamelCase whereas field names start with a lower case letter (fieldName). While this is not technically a "rule" that the compiler enforces, following this makes your code much more readable to others and also avoids the follow-up problem of hiding the type. Also note that constant fields.

I also made two changes that are good ideas but not really related to your issue:

• constant values (i.e. most static final fields an enum constants) use ALL_UPPER casing, so I also changed your Breed values
• I've moved the nested type definition to the top of your Dog class so as not to hide it within all the instance fields. This is just to keep those things that logically belong together close to each other.

Try glue package.

• One more thing, either use " inside ' ' or ' inside "", but don't mix these.
• Use either
  • df[df$ID=="1234","Name"] bought the expensive product df[df$ID=="1234","price"]
  • OR
  • df[df$ID=='1234','Name] bought the expensive product df[df$ID=='1234','price']
  • but don't use
  • df[df$ID=="1234",'Name'] bought the expensive product df[df$ID=="1234",'price']

You could filter the object with a check of the properties.

Mmm, enumerate should do the trick.