Hi Phillip,
I wanted to follow up with you regarding your earlier post. Below is a
different way to work up your data than I posted earlier.
I took the baseball data you posted, stripped out
leading-and-following blank lines, removed all trailing spaces on each
line, and removed the "R1", "R2" and "R3" column names, since they're
blank columns anyway. I then read this text file ("diamond2.txt") into
R using the read.table() call below. Note the use of the sep=" "
parameter--it is very important to include this parameter when
analyzing your dataset in R, as it is not the default setting. I was
then able to generate the "R1", "R2", "R3" columns you sought, using
apply() with anonymous functions:
> testAD <- read.table("diamond2.txt", header=T, sep=" ", na.strings="",
> fill=T, row.names=NULL, stringsAsFactors=F)
> testAD$R1=rep(NA, 14)
> testAD$R2=rep(NA, 14)
> testAD$R3=rep(NA, 14)
> testAD[ ,c(6:8)] <- apply(testAD[ ,c(3:5)], 2, FUN=function(x)
> {ifelse(test=nchar(x), yes=1, no=0)} )
> testAD[ ,c(6:8)] <- apply(testAD[ ,c(6:8)], 2, FUN=function(x)
> {ifelse(test=!is.na(x), yes=x, no=0)} )
> testAD
Row Outs RunnerFirst RunnerSecond RunnerThird R1 R2 R3
1 1 0 <NA> <NA> <NA> 0 0 0
2 2 1 <NA> <NA> <NA> 0 0 0
3 3 1 <NA> <NA> <NA> 0 0 0
4 4 1 arenn001 <NA> <NA> 1 0 0
5 5 2 arenn001 <NA> <NA> 1 0 0
6 6 0 <NA> <NA> <NA> 0 0 0
7 7 0 perad001 <NA> <NA> 1 0 0
8 8 0 polla001 perad001 <NA> 1 1 0
9 9 0 goldp001 polla001 perad001 1 1 1
10 10 0 <NA> lambj001 goldp001 0 1 1
11 11 1 <NA> lambj001 goldp001 0 1 1
12 12 2 <NA> <NA> lambj001 0 0 1
13 13 0 <NA> <NA> <NA> 0 0 0
14 14 1 <NA> <NA> <NA> 0 0 0
>
HTH,
Bill.
W. Michels, Ph.D.
On Thu, Oct 24, 2019 at 12:44 PM William Michels <w...@caa.columbia.edu> wrote:
>
> Hi Phillip,
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