On 04/26/2014 12:42 PM, Jennifer Sabatier wrote:
So, I know that's a confusing Subject header.
Here's similar data:
tmp<- data.frame(matrix(
c(rbinom(1000, 1, .03),
array(1:127, c(1000,1)),
array(format(seq(ISOdate(1990,1,1), by='month',
length=56), format='%d.%m.%Y'), c(1000,1))),
ncol=3))
tmp<- tmp[with(tmp, order(X2, X3)), ]
table(tmp$X1)
X1 is the variable of interest - disease status. It's a survival-type of
variable, where you are 0 until you become 1.
X2 is the person ID variable.
X3 is the clinic date (here it's monthly, just for example...but in my real
data it's a bit more complicated - definitely not equally spaced nor the
same number of visits to the clinic per ID.).
Some people stay X1 = 0 for all clinic visits. Only a small proportion
become X1=1.
However, the data has errors I need to clean off. Once someone becomes
X1=1 they should have no more rows in the dataset. These are data entry
errors.
In my data I have people who continue to have rows in the data. Sometimes
the rows show X1=0 and sometimes X1=1. Sometimes there's just one more row
and sometimes there are many more rows.
How can I go through, find the first X1 = 1, and then delete any rows after
that, for each value of X2?
Thanks!
Jen
Hi Jen,
This might do what you want:
tmp$X3<-as.Date(tmp$X3,"%d.%m.%Y")
tmp<-tmp[order(tmp$X2,tmp$X3),]
first<-TRUE
for(patno in unique(tmp$X2)) {
cat(patno,"\n")
tmpbit<-tmp[tmp$X2 == patno,]
firstone<-which(tmpbit$X1 == 1)[1]
cat(firstone,"\n")
if(is.na(firstone)) firstone<-dim(tmpbit)[1]
newtmpbit<-tmpbit[1:firstone,]
if(first) {
newtmp<-newtmpbit
first<-FALSE
}
else newtmp<-rbind(newtmp,newtmpbit)
}
Jim
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