Here's the answer to my question that I came up with after working with all
of your suggestions. It's not elegant by any means but gets the job done:
MERGE_PViol.Detail.Per.Case <-
read.csv("~/FOIA_FLSA/MERGE_PViol.Detail.Per.Case_for_rtf10.csv",
stringsAsFactors=TRUE)
# select only certain columns from original dataset
PViol.Type.Per.Case <- MERGE_PViol.Detail.Per.Case[,c("CaseID",
"Primary.Viol.Type")]
# create a dataframe with all the primary violation types possible
Primary.Viol.Type <- c("BW.BackWages",
"LD.Liquid_Damages",
"MW.Minimum_Wage",
"OT.Overtime",
"RK.Records_FLSA",
"V.Poster_Other",
"AS.Age",
"BW.WHMIS_BackWages",
"HS.Hours",
"OA.HazOccupationAg",
"ON.HazOccupationNonAg",
"R3.Reg3AgeOccupation",
"RK.Records_CL",
"V.Other")
CaseID <- c('1','2','3','4','5','6','7','8','9','10','11','12','13','14')
PViol.Type.df <- data.frame(CaseID, Primary.Viol.Type)
# merge original data with primary viol type template dataframe
df<- merge(PViol.Type.Per.Case, PViol.Type.df, all=TRUE)
library(reshape2)
df1<-dcast(df, df[,1] ~ df[,2])
# delete rows with template CaseID's;
# replace strings with "1"
colnames(df1)[1] <- "CaseID"
remove <- c('1','2','3','4','5','6','7','8','9','10','11','12','13','14')
rownames(df1) <- df1$CaseID
df2 <- df1[!df1$CaseID %in% remove, ]
df2[!is.na(df2)] <- 1
df2$CaseID <- rownames(df2)
--
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