Populate data frame for meta-analysis
Hi Jerry,
Try this:
jl.df<-read.table(text="ID,score,success,failure
study1,1,35,85
study1,2,46,83
study2,1,78,246
study2,2,45,96",
sep=",",
header=TRUE)
nrows<-dim(jl.df)[1]
jlexp.df<-data.frame()
for(row in 1:nrows) {
success_rows<-data.frame(ID=rep(jl.df$ID[row],jl.df$success[row]),
score=rep(jl.df$score[row],jl.df$success[row]),
outcome=rep("success",jl.df$success[row]))
failure_rows<-data.frame(ID=rep(jl.df$ID[row],jl.df$failure[row]),
score=rep(jl.df$score[row],jl.df$failure[row]),
outcome=rep("failure",jl.df$failure[row]))
jlexp.df<-rbind(jlexp.df,success_rows,failure_rows)
}
Jim
On Tue, Jul 28, 2015 at 2:06 PM, Jerry <josephlockhart at hotmail.com> wrote:
I am trying to model an existing meta-analysis to examine alternative hypotheses (e.g., doing a random-effects analysis), as well as re-sampling techniques. There are over 2,000 subjects, but the data is fairly simple: a binary outcome, success or failure, linked with a score (0-10) on a structured assessment. I have the frequencies of success or failure for each score, nested within each study. I am looking for a easier way to create the dataset rather than keying it in, or using the rep function multiple times. I would like each row to look something like this: Study_ID, Test_Result[0-10], Outcome[0 or 1] For example, let's say I just had two studies and two test levels (1 or 2): study 1 has 35 successes, and 85 failures for score of "1"; for a score of "2," 46 successes and 83 failures. In study 2, for a score of "1" there are 78 successes, 246 failures; for a score of "2," 45 successes and 96 failures. Using just the frequencies provided, how could I most easily create a data frame with the several hundred lines of data? Thanks in advance, -- View this message in context: http://r.789695.n4.nabble.com/Populate-data-frame-for-meta-analysis-tp4710450.html Sent from the R help mailing list archive at Nabble.com.
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