No guarantees on "best"... but one way using base R could be:
# Note that "CaseID" is actually not a valid PViol.Type as you had it
PViol.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" )
# explicitly specifying all levels to the factor insures a complete
# set of column outputs regardless of what is in the input
PViol.Type.Per.Case.Original <-
data.frame( CaseID
, Primary.Viol.Type=factor( Primary.Viol.Type
, levels=PViol.Type ) )
tmp <- table( PViol.Type.Per.Case.Original )
ans <- data.frame( CaseID=rownames( tmp )
, as.data.frame( ifelse( 0==tmp, NA, 1 ) )
)
On Wed, 17 Dec 2014, bcrombie wrote:
# I have a dataframe that contains 2 columns:
CaseID <- c('1015285',
'1005317',
'1012281',
'1015285',
'1015285',
'1007183',
'1008833',
'1015315',
'1015322',
'1015285')
Primary.Viol.Type <- c('AS.Age',
'HS.Hours',
'HS.Hours',
'HS.Hours',
'RK.Records_CL',
'OT.Overtime',
'OT.Overtime',
'OT.Overtime',
'V.Poster_Other',
'V.Poster_Other')
PViol.Type.Per.Case.Original <- data.frame(CaseID,Primary.Viol.Type)
# CaseID?s can be repeated because there can be up to 14 Primary.Viol.Type?s
per CaseID.
# I want to transform this dataframe into one that has 15 columns, where the
first column is CaseID, and the rest are the 14 primary viol. types. The
CaseID column will contain a list of the unique CaseID?s (no replicates) and
for each of their rows, there will be a ?1? under a column corresponding to
a primary violation type recorded for that CaseID. So, technically, there
could be zero to 14 ?1?s? in a CaseID?s row.
# For example, the row for CaseID '1015285' above would have a ?1? under
?AS.Age?, ?HS.Hours?, ?RK.Records_CL?, and ?V.Poster_Other?, but have "NA"
under the rest of the columns.
PViol.Type <- c("CaseID",
"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")
PViol.Type.Columns <- t(data.frame(PViol.Type)
# What is the best way to do this in R?
--
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