Using R to process CSV to evaluate if ((ColA != ColB) with consideration for ColC -

i'm trying achieve simple string comparison across 2 columns. sample of (mocked up) data:

emplid,from_deptcode,fromdept,to_deptcode,to_dept,transactiontypecode,transactiontype,effectivedate,changetype 0239583290,21,sales,43,customerservice,10,promotion,12/12/2012 1230495829,21,sales,21,sales,10,promotion,9/1/2013 4059503918,93,operations,93,operations,10,demotion,11/18/2014 3040593021,19,headquarters,23,international,11,reorg,12/13/2011 7029406920,15,marketing,84,development,19,reassignment,01/05/2010 2039052819,19,headquarters,19,headquarters,10,promotion,4/15/2015 

the logic want use is:

if from_deptcode = to_deptcode        changetype="no change"  elseif from_deptcode != to_deptcode , transactiontype = "reorg"        changetype="reorg" else changetype="transfer" 

so output like:

emplid,from_deptcode,fromdept,to_deptcode,to_dept,transactiontypecode,transactiontype,effectivedate,changetype 0239583290,21,sales,43,customerservice,10,promotion,12/12/2012,transfer 1230495829,21,sales,21,sales,10,promotion,9/1/2013,no change 4059503918,93,operations,93,operations,10,demotion,11/18/2014,no change 3040593021,19,headquarters,23,international,11,reorg,12/13/2011,reorg 7029406920,15,marketing,84,development,19,reassignment,01/05/2010,transfer 2039052819,19,headquarters,19,headquarters,10,promotion,4/15/2015,no change 

here's know far:

transfers <- read.csv(file="transfers.csv", head=true,     sep=",",colclasses=c(na,na,na,na,na,na,na,"date",na)) 

at point, would, assume, implement logic:

if from_deptcode = to_deptcode        changetype="no change"  elseif from_deptcode != to_deptcode , transactiontype = "reorg"        changetype="reorg" else changetype="transfer" 

i assume here i'd write out new csv write.csv(transfers, file = "transfersprocessed.csv", row.names = false)

any advice on getting rest of way there?

update:

per answer @josilber, ran following code:

transfers <- read.csv(file="transfers.csv", head=true, sep=",", colclasses=c(na,na,na,na,na,na,na,"date",na))  dat$changetype <- ifelse(dat$from_deptcode == dat$to_deptcode, "no change",ifelse(dat$transactiontype == "reorg", "reorg", "transfer"))  view(transfers) 

on following data:

emplid,from_deptcode,fromdept,to_deptcode,to_dept,transactiontypecode,transactiontype,effectivedate,changetype 0239583290,21,sales,43,customerservice,10,promotion,12/12/2012 1230495829,21,sales,21,sales,10,promotion,9/1/2013 4059503918,93,operations,93,operations,10,demotion,11/18/2014 3040593021,19,headquarters,23,international,11,reorg,12/13/2011 7029406920,15,marketing,84,development,19,reassignment,01/05/2010 2039052819,19,headquarters,19,headquarters,10,promotion,4/15/2015 

and changetype variable remained "na".

is nested ifelse statement syntax correct? idea why changetype isn't working?

you can nested ifelse statement:

dat$changetype <- ifelse(dat$from_deptcode == dat$to_deptcode, "no change",                          ifelse(dat$transactiontype == "reorg", "reorg", "transfer")) dat #       emplid from_deptcode     fromdept to_deptcode         to_dept transactiontypecode # 1  239583290            21        sales          43 customerservice                  10 # 2 1230495829            21        sales          21           sales                  10 # 3 4059503918            93   operations          93      operations                  10 # 4 3040593021            19 headquarters          23   international                  11 # 5 7029406920            15    marketing          84     development                  19 # 6 2039052819            19 headquarters          19    headquarters                  10 #   transactiontype effectivedate changetype # 1       promotion    12/12/2012   transfer # 2       promotion      9/1/2013  no change # 3        demotion    11/18/2014  no change # 4           reorg    12/13/2011      reorg # 5    reassignment    01/05/2010   transfer # 6       promotion     4/15/2015  no change 

the ifelse passed vector of true/false values first argument, using second argument true cases , using third argument false cases. false cases want run ifelse, why logic nested here.

note large data frames deal quicker looping through data , doing nested if statement 1 row @ time.