Automatically create a range map for aquatics based on selected HUCs about regional_sdm HOT 8 CLOSED

Done. Removed nm_StudyAreaExtent from the aquatic branch, as its no longer needed.

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Is this necessary to save it, if it's only used in the map(?) Seems it could be generated on the fly just in step 5.

Terrestrial already has methods in helper/crop_rast_mask.r, to pull from a huc10 shapefile:

db <- dbConnect(SQLite(),dbname=nm_db_file)
SQLquery <- paste0("SELECT huc10_id from lkpRange
                   inner join lkpSpecies on lkpRange.EGT_ID = lkpSpecies.EGT_ID
                   where lkpSpecies.sp_code = '", model_species, "';")
hucList <- dbGetQuery(db, statement = SQLquery)$huc10_id
dbDisconnect(db)
rm(db)

# now get that info spatially
nm_range <- nm_HUC_file
qry <- paste("SELECT * from HUC10 where HUC10 IN ('", paste(hucList, collapse = "', '"), "')", sep = "")
hucRange <- st_zm(st_read(nm_range, query = qry))

One option to have consistent aquatic/terrestrial methods here would be to calculate and write the huc10s values (that make up the aquatic range) to lkpRange.

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I did not know about that happening in Terrestrial. I was saving it, as I thought it could be useful as a standalone product. I had thought of just making it a temporary file.

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Sounds good, saving a shapefile probably is a good idea.

We could also adjust terrestrial methods to pull from the huc12 table you generated, if there are not concerns about differences between versions as discussed yesterday in #62.

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In order to make a layer of HUC10s that match the aquatic ranges for the model review tool, I've been trying to dissolve the by the HUC10 column using sf without success. @tghoward and @dnbucklin, do you have any experience with this?

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If I'm understanding correctly I think dplyr is the way to go for something like this:

library(sf)
library(dplyr)

huc <- st_read("hucs.shp")
names(huc)

# use your huc10 field in group_by, geometry field in st_union
huc10 <- huc %>% group_by(HUC_10) %>% summarize(geometry = st_union(geometry))

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Thanks @dnbucklin! I had most of it together, but I was trying to use a different group_by approach. This works a lot better and cleaner than my draft code.

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Note that dplyr has to loaded before sf for this to work.

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