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The following provides chemical information from either the chem_wiki or atsdr_tsca_ld50_a data sets from the chem.databases
R package created by the author for matching chemical substances in the commodities list from the United States Environmental Protection Agency (US EPA) East Palestine, Ohio Norfolk Southern Train 32N Cargo List. The cargo list is one of the data sets in this iemiscdata
R package also created by the author.
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install.load::load_package("iemiscdata", "chem.databases", "data.table", "mgsub", "stringi", "pander") # load needed packages using the load_package function from the install.load package (it is assumed that you have already installed these packages) data(norfolk_southern_epoh) # from iemiscdata package data(chem_wiki) # from chem.databases package data(atsdr_tsca_ld50_a) # from chem.databases package train_commodities <- unique(norfolk_southern_epoh$Commodity) pander(train_commodities) # remove items that are not needed train_commodities <- train_commodities[-c(3, 9, 16:20, 23:26)] # remove words that are not needed train_commodities <- mgsub(train_commodities, c(", STABILIZED", "COMBUSTIBLE LIQ., NOS", "LUBE OIL", "WAX"), rep("", 4), fixed = TRUE) # remove parentheses that are not needed train_commodities <- mgsub(train_commodities, c("\\(", "\\)"), rep("", 2), fixed = FALSE) # remove the beginning and trailing white space train_commodities <- stri_trim_both(train_commodities) # transform the text to sentence case train_commodities <- stri_trans_totitle(train_commodities, type = "sentence") # correct the spelling as needed train_commodities[4] <- "Dipropylene glycol" train_commodities[9] <- "Polyvinyl chloride" train_commodities[13] <- "Butyl acrylate" pander(train_commodities) # the exact matched chemical names pander(chem_wiki[`Substance Name` %in% train_commodities]) pander(atsdr_tsca_ld50_a[`Registry Name` %in% train_commodities]) # the matched names train_match1 <- chem_wiki[`Substance Name` %in% train_commodities] train_match2 <- atsdr_tsca_ld50_a[`Registry Name` %in% train_commodities] train_matchs1 <- train_match1$"Substance Name" train_matchs2 <- train_match2$"Registry Name" train_match <- unique(c(train_matchs1, train_matchs2)) pander(train_match) train_match_cas1 <- train_match1$CAS train_match_cas2 <- train_match2$CAS train_match_cas <- unique(c(train_match_cas1, train_match_cas2)) pander(train_match_cas) # remove the matched names train_commodities2 <- train_commodities[-which(train_commodities %in% train_match)] # these are the chemical substances without an exact match pander(train_commodities2)
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