Hello,
I guess so, and I can save you some typing.
vars <- sort(apply(expand.grid("L", 1:8, 1:2), 1, paste, collapse=""))
Then use it and see the result.
Rui Barradas
Em 20-07-2012 00:00, Lib Gray escreveu:
The variables are actually L11, L12, L21, L22, ... , L81, L82. Would just creating a vector c(L11,... ,L82) be fine? (I'm about to try it, but I wanted to check to see if that was going to be a big issue). On Thu, Jul 19, 2012 at 3:27 PM, Rui Barradas <ruipbarradas at sapo.pt> wrote:
Hello,
Try the following. The data is your example of Patient A through E, but
from the output of dput().
dat <- structure(list(Patient = structure(c(1L, 1L, 1L, 1L, 1L, 2L,
2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L), .Label = c("A",
"B", "C", "D", "E"), class = "factor"), Cycle = c(1L, 2L, 3L,
4L, 5L, 1L, 2L, 1L, 3L, 4L, 5L, 1L, 2L, 4L, 5L, 1L, 2L, 3L),
V1 = c(0.4, 0.3, 0.3, 0.4, 0.5, 0.4, 0.4, 0.9, 0.3, NA, 0.4,
0.2, 0.5, 0.6, 0.5, 0.1, 0.5, 0.4), V2 = c(0.1, 0.2, NA,
NA, 0.2, NA, NA, 0.9, 0.5, NA, NA, 0.5, 0.7, 0.4, 0.5, NA,
0.3, 0.3), V3 = c(0.5, 0.5, 0.6, 0.4, 0.5, NA, NA, 0.9, 0.6,
NA, NA, NA, NA, NA, NA, NA, NA, NA), V4 = c(1.5, 1.6, 1.7,
1.8, 1.5, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA), V5 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA)), .Names = c("Patient", "Cycle",
"V1", "V2", "V3", "V4", "V5"), class = "data.frame", row.names = c(NA,
-18L))
dat
nms <- names(dat)[grep("^V[1-9]$", names(dat))]
dd <- split(dat, dat$Patient)
fun <- function(x) any(is.na(x)) && any(!is.na(x))
ix <- sapply(dd, function(x) Reduce(`|`, lapply(x[, nms], fun)))
dd[ix]
do.call(rbind, dd[ix])
I'm assuming that the variables names are as posted, V followed by one
single digit 1-9. To keep the Patients with complete cases just negate the
index 'ix', it's a logical index.
Note also that dput() is the best way of posting a data example.
Hope this helps,
Rui Barradas
Em 19-07-2012 15:15, Lib Gray escreveu:
Hello,
I didn't give enough information when I sent an query before, so I'm
trying
again with a more detailed explanation:
In this data set, each patient has a different number of measured
variables
(they represent tumors, so some people had 2 tumors, some had 5, etc). The
problem I have is that often in later cycles for a patient, tumors that
were originally measured are now missing (or a "new" tumor showed up). We
assume there are many different reasons for why a tumor would be measured
in one cycle and not another, and so I want to subset OUT the "problem"
patients to better study these patterns.
An example:
Patient Cycle V1 V2 V3 V4 V5
A 1 0.4 0.1 0.5 1.5 NA
A 2 0.3 0.2 0.5 1.6 NA
A 3 0.3 NA 0.6 1.7 NA
A 4 0.4 NA 0.4 1.8 NA
A 5 0.5 0.2 0.5 1.5 NA
I want to keep patient A; they have 4 measured tumors, but tumor 2 is
missing data for cycles 3 and 4
B 1 0.4 NA NA NA NA
B 2 0.4 NA NA NA NA
I do not want to keep patient B; they have 1 tumor that is measure
consistently in both cycles
C 1 0.9 0.9 0.9 NA NA
C 3 0.3 0.5 0.6 NA NA
C 4 NA NA NA NA NA
C 5 0.4 NA NA NA NA
I do want to keep patient C; all their data is missing for cycle 4 and
cycle 5 only measured one tumor
D 1 0.2 0.5 NA NA NA
D 2 0.5 0.7 NA NA NA
D 4 0.6 0.4 NA NA NA
D 5 0.5 0.5 NA NA NA
I do not want patient D, their two tumors were measured each cycle
E 1 0.1 NA NA NA NA
E 2 0.5 0.3 NA NA NA
E 3 0.4 0.3 NA NA NA
I DO want patient E; they only had one tumor register in Cycle 1, but
cycles 2 and 3 had two tumors.
Thanks for any help!
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