Refactor data standard metadata tracking

[jwildfire]

Summary

It would be decouple the data standard metadata from the setting metadata.

Current State

Data standard meta data is tracked as a set of columns in data/settingsMetadata. This is ok, but not ideal for scalability (e.g. there is a hard coded list of standards saved in generateSettings)

Future state

Data standard metadata is saved in a separate data/standardsMetadata file. The file would have a text_key column that could be used to merge with settingsMetadata as needed.

[jwildfire]

Making some (fairly minor) changes to detectStandard(). For a<-detectStandard(data=adlbc):

• the standards in a[["details"]] are now lowercase. e.g. a[["details"]][["AdAM"]] -> detectStandard[["details"]][["adam"]]
• detectStandard[["details"]][["--standardName--"]][["match"]] is lowercase. "Full" -> "full"
• a[["standard"]] is also lower case - "adam", "sdtm", "none"
• added a[["standard_percent"]]which indicates what percentage of the matching standard is met .
• a[["standard"]] is now determined algorithmically using a[["standard_percent"]]using code below. Note that ties are now broken according to column order in standardsMetadata:

  for(standard in available_standards){
    # evaluate the current standard and save the result
    standard_list[["details"]][[standard]]<-evaluateStandard(data,standard=standard, includeFields=includeFields, domain=domain)  
    
    # if the current standard is a better match, use it as the overall standard
    # if there is a tie, don't change the standard - this means the column order in standardMetadata breaks ties!
    current_percent <- standard_list[["details"]][[standard]][["match_percent"]]
    overall_percent <- standard_list[["standard_percent"]]
    if(current_percent > overall_percent){
      standard_list[["standard"]] <- standard  
      standard_list[["standard_percent"]] <- current_percent
    }
  }