Untested code below
----- Original message -----
From: ramoss <ramine.mossad...@finra.org>
To: r-help@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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