Match attains wqp water types

[hillarymarler]

Pull Request Checklist (convert PR to draft if in progress)

Adds a new function to add/crosswalk ATTAINS.WaterType to a TADA df based on TADA.MonitoringLocationTypeName.. This function will help facilitate the creation of AUs from WQP Monitoring Locations for new ATTAINS tribes.

I will add this function to a mod 2 vignette when additional functions to support this workflow are developed.

Required

• Update your branch from the latest develop and resolve any merge conflicts

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Best practices

• Include a summary of the changes made and relevant context/motivation

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Conditional

• If there is a bot spelling comment, run spelling::spell_check_package() locally and fix any misspellings; add approved project terms to WORDLIST with spelling::update_wordlist()

• If tests fail suggesting internal reference files need a refresh, run .TADA_UpdateRefFiles() and .TADA_UpdateExampleData() locally via MaintenanceScheduled.R or trigger the Component File Update GitHub Action

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[github-actions]

Spelling check failed. Details:

Spelling check failed. Found 2 potential misspelling(s). Note: Run spelling::spell_check_package() locally and fix any misspellings; add approved project terms to WORDLIST with spelling::update_wordlist().

  WORD                   FOUND IN
ATTAINSWaterTypeFlag   TADA_CrosswalkATTAINSWaterTypes.Rd:27,32
crosswalking           TADA_CrosswalkATTAINSWaterTypes.Rd:31

[github-actions]

Spelling check failed. Details:

Spelling check failed. Found 2 potential misspelling(s). Note: Run spelling::spell_check_package() locally and fix any misspellings; add approved project terms to WORDLIST with spelling::update_wordlist().

  WORD                   FOUND IN
ATTAINSWaterTypeFlag   TADA_CrosswalkATTAINSWaterTypes.Rd:27,32
crosswalking           TADA_CrosswalkATTAINSWaterTypes.Rd:31

[github-actions]

coverage-report

File	Coverage		Missing
All files	58%	✅
R/ATTAINSCrosswalks.R	48%	✅	32-891 1116-1120 1125-1130 1135-1138 1143-1174 1180-1183 1189-1192 1206-1209 1220-1235 1281-1284 1528-1529 1536-1537 1542-1543 1570-1577 1708 2031-2034 2039 2068-2072 2084-2087 2095 2104-2111 2126-2135 2140-2148 2154-2177 2197-2212 2217-2220 2225-2256 2269-2272 2344-2348 2443-2452 2668-2669 2738 2909-2915 3135-3740 3918-3921 3929-3934 3940-3960 3968-3972 3985-3989 4020-4129 4181-4336 4341-4344 4405-4406 4442 4518-4524 4656-4657 4780-4799
R/RequiredCols.R	30%	✅	406-649

Minimum allowed coverage is 20%

Generated by 🐒 cobertura-action against d93c152

[wokenny13]

Not sure if this updated workflow makes sense, if a user wants to now use ML as the AU, they can proceed with creating the AUML ref table as follows:

#' # filter to Blackfeet
#' Tribal_modified2 <- Tribal_modified |>
#' dplyr::filter(OrganizationIdentifier == "BLCKFEET")
#'
#' # append additional columns to the data frame to allow for creating the AUML crosswalk
#' Tribal_reviewUpdate2 <- TADA_CrosswalkATTAINSWaterTypes(Tribal_modified2,
#' org_id = "BLCKFEET",
#' review_all = TRUE,
#' review_action = "update",
#' create_AUMLRef = TRUE)
#'
#' Tribal_AUMLRef <- TADA_UpdateATTAINSAUMLCrosswalk(
#' org_id = "BLCKFEET",
#' crosswalk = Tribal_reviewUpdate2,
#' api_key = .setEQKey())

[hillarymarler]

Not sure if this updated workflow makes sense, if a user wants to now use ML as the AU, they can proceed with creating the AUML ref table as follows:

#' # filter to Blackfeet #' Tribal_modified2 <- Tribal_modified |> #' dplyr::filter(OrganizationIdentifier == "BLCKFEET") #' #' # append additional columns to the data frame to allow for creating the AUML crosswalk #' Tribal_reviewUpdate2 <- TADA_CrosswalkATTAINSWaterTypes(Tribal_modified2, #' org_id = "BLCKFEET", #' review_all = TRUE, #' review_action = "update", #' create_AUMLRef = TRUE) #' #' Tribal_AUMLRef <- TADA_UpdateATTAINSAUMLCrosswalk( #' org_id = "BLCKFEET", #' crosswalk = Tribal_reviewUpdate2, #' api_key = .setEQKey())

What piece of it needs to be modified do you think?

I believe Jesse has a naming convention established for the point AUs based on MLs, so I would like to get that logic from him and create another function which would generate the AU identifiers. Then from there would create the batch upload file with the new AU Ids, Water types, and MLs.

[wokenny13]

I made some push with updates on how I envisioned the workflow might work (though very open to modifying it). Handling of the ATTAINS batch upload vs TADA workflow might need to be considered as well as how we handle assignment of org_id if there exists multiple orgs like in the Tribal example data frame. I have an open issue #932 that could potentially address the org_id crosswalk.

[wokenny13]

Example Workflow to get AUMLRef and UsesAURef:

MT_ExData <- Data_MT_AUMLRef$TADA_with_ATTAINS |>
sf::st_drop_geometry()

# add ATTAINS.WaterType only for rows without values in that column
MT_addMissing <- TADA_CrosswalkATTAINSWaterTypes(MT_ExData)

# add ATTAINS.WaterType for all rows (replace existing matches)
MT_replaceAll <- TADA_CrosswalkATTAINSWaterTypes(MT_ExData, replace_all = TRUE)

# add ATTAINS.WaterType to TADA df without ATTAINS.WaterType column
Tribal_addAll <- TADA_CrosswalkATTAINSWaterTypes(Data_TribalNations_Harmonized)

# modify tribal example data to include an ATTAINS.WaterType not allowed by ATTAINS
Tribal_modified <- Tribal_addAll |>
dplyr::mutate(ATTAINS.WaterType =
ifelse(TADA.MonitoringLocationIdentifier %in%
 c("REDLAKE_WQX-GREE-REDLAKE",
   "UTEMTN-COTTONWOOD WASH SPRING",
   "BLCKFEET-00000054",
   "BLCKFEET-00000056"
 ), "INVALID WATER TYPE",
 ATTAINS.WaterType))

 # add ATTAINS.WaterType for any rows where it is missing, review all ATTAINS.WaterType
 # values and update any that are not allowed
 Tribal_reviewUpdate <- TADA_CrosswalkATTAINSWaterTypes(Tribal_modified,
 review_all = TRUE,
 review_action = "update")

 # filter to Blackfeet
 Tribal_modified2 <- Tribal_modified |>
 dplyr::filter(OrganizationIdentifier == "BLCKFEET")

 # append additional columns to the data frame to allow for creating the AUML crosswalk
 Tribal_reviewUpdate2 <- TADA_CrosswalkATTAINSWaterTypes(Tribal_modified2,
 org_id = "BLCKFEET",
 review_all = TRUE,
 review_action = "update",
 create_AUMLRef = TRUE)

 Tribal_AUMLRef <- TADA_UpdateATTAINSAUMLCrosswalk(
 org_id = "BLCKFEET",
 crosswalk = Tribal_reviewUpdate2,
 api_key = .setEQKey())

# First creates the uses water type using org_id = NULL, next determine which of these uses are appropriate
# Note: can we decide and assign which epa304a recommended uses may be applicable from ATTAINS here?
usesWater_filtered <- TADA_AssignUsesToWaterType(
Tribal_reviewUpdate2, org_id = NULL, AUMLRef = Tribal_AUMLRef) |> dplyr::filter(ATTAINS.UseName %in% c("Aquatic Life Support", "Primary Human Contact", "Secondary Human Contact", "Primary Contact Recreation")) |> dplyr::mutate(ATTAINS.OrganizationIdentifier = "BLCKFEET"

# from the waterUseRef, we will assign all unique uses to each ML/AU
usesAU_BLCKFEET <- TADA_AssignUsesToAU(Tribal_reviewUpdate2, org_id = "BLCKFEET", AUMLRef = Tribal_AUMLRef, waterUseRef = usesWater_filtered)
)```