Here is another way based on pasting ids as hinted below:
a <- data.frame(id=c(c("A1","A2","A3","A4","A5"),
c("A3","A2","A3","A4","A5")),
loc=c("B1","B2","B3","B4","B5"),
clm=c(rep(("General"),6),rep("Life",4)))
a$uid <- paste(a$id, ".", a$loc, sep="")
out <- tapply( a$clm, a$uid, paste ) # can also add collapse=","
$A1.B1
[1] "General"
$A2.B2
[1] "General" "Life"
$A3.B1
[1] "General"
$A3.B3
[1] "General" "Life"
$A4.B4
[1] "General" "Life"
$A5.B5
[1] "General" "Life"
Then here are those with single policies.
> out[ which( sapply(out, length) == 1 ) ]
$A1.B1
[1] "General"
$A3.B1
[1] "General"
David Winsemius wrote:
On Oct 28, 2009, at 9:30 PM, Steven Kang wrote:
Dear R users,
Basically, from the following arbitrary data set:
a <-
data
.frame
(id
=
c
(c
("A1
","A2
","A3
","A4
","A5
"),c
("A3
","A2
","A3
","A4","A5")),loc=c("B1","B2","B3","B4","B5"),clm=c(rep(("General"),
6),rep("Life",4)))
a
id loc clm
1 A1 B1 General
2 A2 B2 General
3 A3 B3 General
4 A4 B4 General
5 A5 B5 General
6 A3 B1 General
7 A2 B2 Life
8 A3 B3 Life
9 A4 B4 Life
10 A5 B5 Life
I desire removing records (highlighted records above) with identical
values
in each fields ("id" & "loc") but with different value of "clm" (i.e
according to category)
Take a look at this merge operation on separate rows of "a".
> merge( a[a$clm=="Life", ], a[a$clm=="General", ] , by=c("id",
"loc"), all=T)
id loc clm.x clm.y
1 A1 B1 <NA> General
2 A2 B2 Life General
3 A3 B1 <NA> General
4 A3 B3 Life General
5 A4 B4 Life General
6 A5 B5 Life General
Assignment of that object and selection with is.na should complete the
process.
> a2m <- merge( a[a$clm=="Life", ], a[a$clm=="General", ] ,
by=c("id", "loc"), all=T)
> a2m[ is.na(a2m$clm.x) | is.na(a2m$clm.y), ]
id loc clm.x clm.y
1 A1 B1 <NA> General
3 A3 B1 <NA> General
Alternate methods might include paste-ing id to loc and removing
duplicates.
i.e
categ <- table(a$id,a$clm)
categ
General Life
A1 1 0
A2 1 1
A3 2 1
A4 1 1
A5 1 1
The desired output is
id loc clm
1 A1 B1 General
6 A3 B1 General
Because the data set I am working on is quite big (~ 800,000 x 20)
with majority of the fields values being long strings, looping
turned out to
be very inefficient in comapring individual rows..
Are there any alternative efficient methods in implementing this
problem?
Steven
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