small code fixes to work with latest releases of data.table and resha…

…pe packages in R 4
CMAP-REPOS · Mar 5, 2021 · 7f0817c · 7f0817c
1 parent 60c5c90
commit 7f0817c
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Showing 4 changed files with 5 additions and 5 deletions.
diff --git a/lib/scripts/firm_sim_enumerate.R b/lib/scripts/firm_sim_enumerate.R
@@ -68,7 +68,7 @@ firm_synthesis_enumerate <- function(cbp, c_n6_n6io_sctg, EmpBounds, cbp_ag = NU
   FirmsDomestic[, c("n2","n4") := .(substr(Industry_NAICS6_CBP, 1, 2), substr(Industry_NAICS6_CBP, 1, 4))]
 
   # Melt to create separate rows for each firm size category
-  FirmsDomestic <- melt(FirmsDomestic,
+  FirmsDomestic <- melt.data.table(FirmsDomestic,
                         measure.vars = paste0("e",1:8),
                         variable.name ="esizecat",
                         value.name = "est")

diff --git a/lib/scripts/firm_sim_input_output.R b/lib/scripts/firm_sim_input_output.R
@@ -4,7 +4,7 @@ firm_synthesis_input_output <- function(io, c_n6_n6io_sctg){
   # Clean and format the input output data
   setnames(io, names(io),sub("X", "", names(io)))
 
-  io <- melt(io,
+  io <- melt.data.table(io,
              id.vars = "Industry_NAICS6_MakeUse",
              variable.name = "Industry_NAICS6_Use",
              value.name = "ProVal")

diff --git a/lib/scripts/firm_sim_mesozones.R b/lib/scripts/firm_sim_mesozones.R
@@ -11,7 +11,7 @@ firm_synthesis_mesozones <- function(Firms){
   FirmsCMAP[n2 %in% c("S0"), n2 := "92"]
 
   # Convert the ranking table to long format
-  mzemp <- melt(mzemp,
+  mzemp <- melt.data.table(mzemp,
                 id.vars = c("COUNTY", "MESOZONE"),
                 variable.name = "n2",
                 value.name = "EmpRank")

diff --git a/lib/scripts/sc_sim_distchannel.R b/lib/scripts/sc_sim_distchannel.R
@@ -268,7 +268,7 @@ predict_logit <- function (df, mod, cal = NULL, calcats = NULL, iter = 1) {
       sim <- sim/colSums(sim)
       if (length(unique(calcats$CHOICE)) < length(unique(calcats$CHID))) {
         sim <- cbind(calcats, sim)
-        sim <- melt(sim, id.vars = c("CHOICE", "CHID"), 
+        sim <- melt.data.table(sim, id.vars = c("CHOICE", "CHID"), 
                     variable.name = "CATEGORY")
         sim <- sim[, list(MODEL = sum(value)), by = list(CHOICE, 
                                                          CATEGORY)]
@@ -283,7 +283,7 @@ predict_logit <- function (df, mod, cal = NULL, calcats = NULL, iter = 1) {
         caldat <- caldat[, list(TARGET = sum(TARGET)), 
                          by = list(CATEGORY, CHID)]
         sim <- data.table(CHID = 1:nrow(sim), sim)
-        sim <- melt(sim, id.vars = c("CHID"), variable.name = "CATEGORY")
+        sim <- melt.data.table(sim, id.vars = c("CHID"), variable.name = "CATEGORY")
         sim <- merge(sim, caldat, c("CATEGORY", "CHID"))
         sim[, `:=`(ascadj, log(TARGET/value))]
         adj <- sim[, list(CATEGORY, CHID, ascadj)]