get_SDA_interpretation(): add subrule ratings to "reason" field
#308
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- includes many types of "not rated" - also includes a variety of other determinations that may be of general interest to interpretation users
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Added subrule names, along with reason class and rating. Format is `{SUBRULE} "{REASON}" ({RATING}); {SUBRULE} "{REASON}" ({RATING});". It will be consistent, but a pain to parse if you really need those values. Also the ordering can be inconsistent. Could potentially add an optional argument to |
…ubrule rating values
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Added argument to So, now you can quickly obtain ready-to-use subrule ratings for arbitrary interps, which should adequately cover needs from #303 library(soilDB)
x <- get_SDA_interpretation(rulename = c("NCCPI - National Commodity Crop Productivity Index (Ver 3.0)",
"AGR - Pesticide Loss Potential-Leaching",
"ENG - Local Roads and Streets"),
method = "Dominant Component", not_rated_value = "Not rated",
mukeys = c("242963","242964","242965"), wide_reason = TRUE)
x
#> mukey cokey areasymbol musym
#> 1 242963 23671045 IL019 152A
#> 2 242964 23670915 IL019 134A
#> 3 242965 23671016 IL019 154A
#> muname compname compkind comppct_r
#> 1 Drummer silty clay loam, 0 to 2 percent slopes Drummer Series 94
#> 2 Camden silt loam, 0 to 2 percent slopes Camden Series 92
#> 3 Flanagan silt loam, 0 to 2 percent slopes Flanagan Series 95
#> majcompflag rating_NCCPINationalCommodityCropProductivityIndexVer30
#> 1 Yes 0.826
#> 2 Yes 0.917
#> 3 Yes 0.899
#> class_NCCPINationalCommodityCropProductivityIndexVer30
#> 1 High inherent productivity
#> 2 High inherent productivity
#> 3 High inherent productivity
#> reason_NCCPINationalCommodityCropProductivityIndexVer30
#> 1 Impacted soil "No limitation" (0); NCCPI - NCCPI Cotton Submodel (II) "Cotton" (0.001); NCCPI - NCCPI Small Grains Submodel (II) "Small grains" (0.687); NCCPI - NCCPI Soybeans Submodel (I) "Soybeans" (0.752); NCCPI - NCCPI Corn Submodel (I) "Corn" (0.826)
#> 2 NCCPI - NCCPI Cotton Submodel (II) "Cotton" (0.001); NCCPI - NCCPI Soybeans Submodel (I) "Soybeans" (0.777); NCCPI - NCCPI Small Grains Submodel (II) "Small grains" (0.791); NCCPI - NCCPI Corn Submodel (I) "Corn" (0.917); Impacted soil "No limitation" (0)
#> 3 Impacted soil "No limitation" (0); NCCPI - NCCPI Cotton Submodel (II) "Cotton" (0.001); NCCPI - NCCPI Small Grains Submodel (II) "Small grains" (0.734); NCCPI - NCCPI Soybeans Submodel (I) "Soybeans" (0.76); NCCPI - NCCPI Corn Submodel (I) "Corn" (0.899)
#> rating_AGRPesticideLossPotentialLeaching
#> 1 Not rated
#> 2 Not rated
#> 3 Not rated
#> class_AGRPesticideLossPotentialLeaching
#> 1 <NA>
#> 2 <NA>
#> 3 <NA>
#> reason_AGRPesticideLossPotentialLeaching rating_ENGLocalRoadsandStreets
#> 1 <NA> 1
#> 2 <NA> 1
#> 3 <NA> 1
#> class_ENGLocalRoadsandStreets
#> 1 Very limited
#> 2 Very limited
#> 3 Very limited
#> reason_ENGLocalRoadsandStreets
#> 1 Ponded > 4 hours "Ponding" (1); Wet, Ground Water Near the Surface (30 - 75cm) "Depth to saturated zone" (1); Potential Frost Action > Low "Frost action" (1); Strength (AASHTO Group Index Weighted Average (25-100cm)) "Low strength" (1); Shrink-Swell (LEP WTD_AVG 25-100cm or Bedrock) "Shrink-swell" (0.37)
#> 2 Potential Frost Action > Low "Frost action" (1); Strength (AASHTO Group Index Weighted Average (25-100cm)) "Low strength" (0.955); Shrink-Swell (LEP WTD_AVG 25-100cm or Bedrock) "Shrink-swell" (0.375)
#> 3 Strength (AASHTO Group Index Weighted Average (25-100cm)) "Low strength" (1); Shrink-Swell (LEP WTD_AVG 25-100cm or Bedrock) "Shrink-swell" (0.894); Wet, Ground Water Near the Surface (30 - 75cm) "Depth to saturated zone" (0.746); Potential Frost Action > Low "Frost action" (0.5)
#> rating_reason_NCCPINationalCommodityCropProductivityIndexVer30_Impactedsoil
#> 1 0
#> 2 0
#> 3 0
#> rating_reason_NCCPINationalCommodityCropProductivityIndexVer30_NCCPINCCPICottonSubmodelII
#> 1 0.001
#> 2 0.001
#> 3 0.001
#> rating_reason_NCCPINationalCommodityCropProductivityIndexVer30_NCCPINCCPISmallGrainsSubmodelII
#> 1 0.687
#> 2 0.791
#> 3 0.734
#> rating_reason_NCCPINationalCommodityCropProductivityIndexVer30_NCCPINCCPISoybeansSubmodelI
#> 1 0.752
#> 2 0.777
#> 3 0.76
#> rating_reason_NCCPINationalCommodityCropProductivityIndexVer30_NCCPINCCPICornSubmodelI
#> 1 0.826
#> 2 0.917
#> 3 0.899
#> rating_reason_AGRPesticideLossPotentialLeaching_Notrated
#> 1 NA
#> 2 NA
#> 3 NA
#> rating_reason_ENGLocalRoadsandStreets_Ponded4hours
#> 1 1
#> 2 <NA>
#> 3 <NA>
#> rating_reason_ENGLocalRoadsandStreets_WetGroundWaterNeartheSurface3075cm
#> 1 1
#> 2 <NA>
#> 3 0.746
#> rating_reason_ENGLocalRoadsandStreets_PotentialFrostActionLow
#> 1 1
#> 2 1
#> 3 0.5
#> rating_reason_ENGLocalRoadsandStreets_StrengthAASHTOGroupIndexWeightedAverage25100cm
#> 1 1
#> 2 0.955
#> 3 1
#> rating_reason_ENGLocalRoadsandStreets_ShrinkSwellLEPWTDAVG25100cmorBedrock
#> 1 0.37
#> 2 0.375
#> 3 0.894 |
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A final consideration: For instance, inequalities are lost. "Ponded > 4 hours" and "Ponded < 4 hours" simplify to the same name "Ponded4hours". Could add a few things like replacing ">" "<" "=" with "GT" "LT" "EQ"... It appears that collisions will be rare, and only for Texas subrules in FY24 SSURGO, but not impossible: library(soilDB)
x <- SDA_query("SELECT DISTINCT rulename FROM cointerp")[[1]]
#> single result set, returning a data.frame
y <- soilDB:::.cleanRuleColumnName(x)
length(x)
#> [1] 3237
length(unique(y))
#> [1] 3231
xx <- c(x[duplicated(y)], x[duplicated(y, fromLast = TRUE)])
sort(xx)
#> [1] "AGR - Rutting Hazard =< 10,000 Pounds per Wheel (TX)"
#> [2] "AGR - Rutting Hazard > 10,000 Pounds per Wheel (TX)"
#> [3] "CaCO3 < 40% by Wght. Av. 0-40\" (TX)"
#> [4] "CaCO3 > 40% by Wght. Av. 0-40\" (TX)"
#> [5] "Excess Humus (FB, Peat, HM/MPT Surface Layer) (TX)"
#> [6] "Excess Humus (FB/Peat/HM/MPT Surface Layer) (TX)"
#> [7] "Flooding Occasional or greater; Duration Long,Very Long (TX)"
#> [8] "Flooding Occasional or greater; Duration Long/Very Long (TX)"
#> [9] "Ponding => Frequent (TX)"
#> [10] "Ponding Frequent (TX)"
#> [11] "Soil Strength (Rutting Vehicle =< 10,000 Pounds) (TX)"
#> [12] "Soil Strength (Rutting Vehicle > 10,000 Pounds) (TX)" |
…lity information - Updates `.cleanRuleColumnName()` - note that this will add several characters to calculated column names for several `mrulename`, most of which are state-specific
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There was only one existing collision in This is the list of affected interpretations that folks will need to update column name references for:
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Adds subrule ratings to "reason" fields calculated for each
mrulenamein aget_SDA_interpretation()query.This helps get key information about subrules that are exported in cointerp with
ruledepth > 0TODO:
.interpretation_weighted_average()needs SQLite compatibleSTRING_AGG()switchorder subrule "reasons" alphabetically? or at least consistently(wontfix; can't use ORDER BY in the T-SQL subquery)Will close #303
Note the "reason" field now includes the
interphras well asinterphrcvalues for rules with ruledepth != 0