[R] Merging data in R compared to SAS

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Untested code below

----- Original message -----
From: ramoss <ramine.mossadegh at finra.org>
To: r-help at r-project.org
Subject: [R] Merging data in R  compared to SAS
Date: Wed, 22 Aug 2012 07:59:04 -0700 (PDT)

Hello,

I am a SAS user new to R.  What is the R equivalent to following SAS
statements:

1) data all;
    merge test1(in=a)
              test2(in=b)
;
by account_id;
if a;
run;

a <- transform ( a, inA = TRUE)
b <- transform ( b, inB = TRUE)

all <- subset (  merge ( a, b, by = "account_id"), subset = inA )
# The merge will produce NAs where there was no match. Recode them to simplify tests for step 3:
transform ( all, inA = ifelse ( is.na( inA ), FALSE, inA) ), inB = ifelse ( is.na ( inB ), FALSE, inB ) )


2) proc sort data=all nodupkey;
 by account_id;
 run;

# You do not need the sort in R
allUniqueAccount <- subset ( all, !duplicated ( account_id) ) # You are sure dropping these is ok without inspection?

3)  data all test1onnly test2only;
    merge test1(in=a)
               test2(in=b)
  by account_id;
  if a and b then output all;
  else if a and not b the output test1only;
 else if b and not a then output test2only;
run;

all_AandB <- subset ( all,  inA  &  inB )
test1only <- subset ( all,  inA  & !inB )
test2only <- subset ( all, !inA &   inB )

Thanks in advance



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