Merge pull request #346 from USEPA/release_v1.7.2

Release v1.7.2

Author: bl-young

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: useeior
 Type: Package
 Title: USEEIO R modeling software
-Version: 1.7.1
-Date: 2025-4-28
+Version: 1.7.2
+Date: 2025-7-28
 Authors@R: c(
   person("Ben","Young", email="[email protected]", role="aut"),
   person("Jorge","Vendries", email="[email protected]", role="aut"),

R/CalculationFunctions.R

@@ -726,6 +726,6 @@ calculateGhoshB <- function(model, use_domestic_requirements = FALSE) {
 #' @param GhoshB Ghosh matrix B
 #' @return G, Ghosh matrix G
 calculateGhoshG <- function(GhoshB) {
-  G <- useeior:::calculateLeontiefInverse(GhoshB)
+  G <- calculateLeontiefInverse(GhoshB)
   return(G)
 }

R/DataDocumentation.R

@@ -817,8 +817,8 @@
 #' Detail Margins (Before Redef) table for 2017 (2017 schema)
 #' @format A dataframe with 58051 obs. and 9 variables:
 #' \describe{
-#'  \item{NIPACode}{text code}
-#'  \item{MarginsCategory}{text category name, like 'Therapeutic medical equipment'}
+#'  \item{IndustryCode}{text code}
+#'  \item{IndustryDescription}{text category name, like 'Therapeutic medical equipment'}
 #'  \item{CommodityCode}{BEA_2017_Detail_Code}
 #'  \item{CommodityDescription}{BEA_2017_Detail_Name}
 #'  \item{ProducersValue}{USD2017}
@@ -833,8 +833,8 @@
 #' Detail Margins (Before Redef) table for 2012 (2017 schema)
 #' @format A dataframe with 58032 obs. and 9 variables:
 #' \describe{
-#'  \item{NIPACode}{text code}
-#'  \item{MarginsCategory}{text category name, like 'Therapeutic medical equipment'}
+#'  \item{IndustryCode}{text code}
+#'  \item{IndustryDescription}{text category name, like 'Therapeutic medical equipment'}
 #'  \item{CommodityCode}{BEA_2017_Detail_Code}
 #'  \item{CommodityDescription}{BEA_2017_Detail_Name}
 #'  \item{ProducersValue}{USD2017}

R/DisaggregateFunctions.R

@@ -132,6 +132,8 @@ disaggregateSetup <- function (model, configpaths = NULL, setupType = "Disaggreg
                                             sep = ",", header = TRUE,
                                             stringsAsFactors = FALSE,
                                             check.names = FALSE)
+    colnames(spec$NAICSSectorCW)[which(
+      names(spec$NAICSSectorCW) %in% c("NAICS_2012_Code", "NAICS_2017_Code"))] <- "NAICS_Code"
 
     newNames <- unique(data.frame("SectorCode" = spec$NAICSSectorCW$USEEIO_Code,
                                   "SectorName" = spec$NAICSSectorCW$USEEIO_Name,
@@ -647,9 +649,9 @@ disaggregateSatelliteSubsetByRatio <- function(sattable, disagg, allocating_sect
 disaggregateSatelliteTable <- function (disagg, tbs, sat_spec) {
   sattable <- tbs
   # identify NAICS that require further disaggregation
-  naics <- disagg$NAICSSectorCW[c('NAICS_2012_Code','USEEIO_Code')]
-  codes <- unique(naics[duplicated(naics$NAICS_2012_Code),]$NAICS_2012_Code)
-  naics <- naics[which(naics$NAICS_2012_Code %in% codes),]
+  naics <- disagg$NAICSSectorCW[c('NAICS_Code','USEEIO_Code')]
+  codes <- unique(naics[duplicated(naics$NAICS_Code),]$NAICS_Code)
+  naics <- naics[which(naics$NAICS_Code %in% codes),]
 
   original_code <- gsub("/.*", "", disagg$OriginalSectorCode)
   codes <- c(original_code, codes)
@@ -1030,13 +1032,13 @@ disaggregateMasterCrosswalk <- function (model, disagg){
   # used to determine the length of the sector codes. E.g., detail would be 6, while summary would generally
   # be 3 though variable, and sector would be variable
   secLength <- regexpr(pattern ='/',disagg$OriginalSectorCode) - 1
-  cw <- disagg$NAICSSectorCW[, c('NAICS_2012_Code','USEEIO_Code')]
+  cw <- disagg$NAICSSectorCW[, c('NAICS_Code','USEEIO_Code')]
   # For all rows in the USEEIO_Code column, remove all characters after (and including) "/"
   cw$USEEIO_Code <- sub("/.*","",cw$USEEIO_Code)
 
   # Update original sector codes with disaggregated sector codes in the relevant column (i.e. cwColIndex)
   # where rows have an exact match for the disaggregated codes in the NAICS column
-  new_cw <-merge(new_cw, cw, by.x=c("NAICS"), by.y=c("NAICS_2012_Code"), all=T)
+  new_cw <-merge(new_cw, cw, by.x=c("NAICS"), by.y=c("NAICS_Code"), all=T)
 
   new_cw$USEEIO <- ifelse(is.na(new_cw$USEEIO_Code), new_cw$USEEIO, new_cw$USEEIO_Code)
 
@@ -1053,10 +1055,10 @@ disaggregateMasterCrosswalk <- function (model, disagg){
       rowComparisons[1:length(disagg$NewSectorCodes)] <- TRUE
     } else {
       #compare the value in the first column (NAICS) to the NAICS values in the disaggCrosswalk. Result is a string with TRUE where first column is a substring of values in disaggCrosswalk
-      rowComparisons <- grepl(crosswalkRow$NAICS[1], disagg$NAICSSectorCW$NAICS_2012_Code) 
+      rowComparisons <- grepl(crosswalkRow$NAICS[1], disagg$NAICSSectorCW$NAICS_Code) 
     }
 
-    rowReplacements <- disagg$NAICSSectorCW$NAICS_2012_Code[rowComparisons] #Get the NAICS sector codes in the disagg crosswalk that are a match for the NAICS substring in the master crosswalk 
+    rowReplacements <- disagg$NAICSSectorCW$NAICS_Code[rowComparisons] #Get the NAICS sector codes in the disagg crosswalk that are a match for the NAICS substring in the master crosswalk 
     rowReplacements <- sub("/.*","",disagg$NAICSSectorCW$USEEIO_Code[rowComparisons]) #Get the disaggregated sector codes that are mapped to the matches of the NAICS substring
     rowReplacements <- unique(rowReplacements) #reduce the list to the unique number of disaggregated sectors that the row comparisons map to
 

R/IOFunctions.R

@@ -199,6 +199,7 @@ generateDomesticUse <- function(Use, Import, model) {
   # needs to be subtracted from the original Import matrix
   if (model$specs$BasePriceType == "BAS") {
     # Find "MDTY - import duties" in Supply table
+    schema <- getSchemaCode(model$specs)
     Supply <- get(paste(na.omit(c(model$specs$BaseIOLevel, "Supply", model$specs$IOYear, schema)),
                         collapse = "_")) * 1E6
     ImportDuty <- Supply[rownames(Import), "MDTY"]
@@ -257,10 +258,10 @@ convertUsefromPURtoBAS <- function(UseSUT_PUR, specs, io_codes) {
   # Load UsePRO and UsePUR under Make-Use framework
   Redef <- ifelse(specs$BasewithRedefinitions, "AfterRedef", "BeforeRedef")
   schema <- getSchemaCode(specs)
-  UsePUR <- get(paste(na.omit(c(specs$BaseIOLevel, "Use", specs$IOYear, "PUR", Redef, specs)), collapse = "_"))
-  UsePRO <- get(paste(na.omit(c(specs$BaseIOLevel, "Use", specs$IOYear, "PRO", Redef, specs)), collapse = "_"))
+  UsePUR <- get(paste(na.omit(c(specs$BaseIOLevel, "Use", specs$IOYear, "PUR", Redef, schema)), collapse = "_"))
+  UsePRO <- get(paste(na.omit(c(specs$BaseIOLevel, "Use", specs$IOYear, "PRO", Redef, schema)), collapse = "_"))
   # Load Supply table
-  Supply <- get(paste(na.omit(c(specs$BaseIOLevel, "Supply", specs$IOYear, specs)), collapse = "_"))
+  Supply <- get(paste(na.omit(c(specs$BaseIOLevel, "Supply", specs$IOYear, schema)), collapse = "_"))
 
   # Convert from PUR to PRO by removing margins obtained from Supply table
   rows <- io_codes$Commodities

R/LoadMargins.R

@@ -17,7 +17,9 @@ getMarginsTable <- function (model) {
   # Change in inventory has negative margins for positive change, which does not accurately portray actual margins either.
   purchaser_removal <- sapply(list("Export", "Import", "ChangeInventories"), getVectorOfCodes,
                               ioschema = model$specs$BaseIOSchema, iolevel = "Detail")
-  MarginsTable <- MarginsTable[!MarginsTable$NIPACode%in%purchaser_removal,]
+  # column names from Margins Tables from 2012 schema were not fixed
+  col <- ifelse("NIPACode" %in% names(MarginsTable), "NIPACode", "IndustryCode")
+  MarginsTable <- MarginsTable[!MarginsTable[[col]] %in% purchaser_removal,]
   # Remove Scrap, Used and secondhand goods, and Non-comparable imports, and Rest of world adjustment commodities
   commodity_removal <- sapply(list("Scrap", "UsedGoods", "NonComparableImport", "RoWAdjustment"), getVectorOfCodes,
                               ioschema = model$specs$BaseIOSchema, iolevel = model$specs$BaseIOLevel)

R/WIOFunctions.R

@@ -741,7 +741,7 @@ addSectorsToWIOCW <- function (model, spec){
   WIONAICSSectorCW[,1] <- "WIO"
   WIONAICSSectorCW$Tag <- "USEEIO sectors as WIO Treatment sectors"
   WIONAICSSectorCW$Type <- "N/A"
-  WIONAICSSectorCW$NAICS_2012_Code <- "N/A"
+  WIONAICSSectorCW$NAICS_Code <- "N/A"
   WIONAICSSectorCW$Subcategory <- "N/A"
   WIONAICSSectorCW$Description <- "N/A"
 

data-raw/BEAData.R

@@ -1089,7 +1089,7 @@ getBEADetailMarginsBeforeRedef <- function(year, schema_year = NULL) {
   # Trim table, assign column names
   Margins <- Margins[!is.na(Margins[, 4]), ][-1, ]
   rownames(Margins) <- NULL
-  colnames(Margins) <- c("NIPACode", "MarginsCategory",
+  colnames(Margins) <- c("IndustryCode", "IndustryDescription",
                          "CommodityCode", "CommodityDescription",
                          "ProducersValue", "Transportation",
                          "Wholesale", "Retail", "PurchasersValue")