# [R] Subsetting problem data, 2

arun smartpink111 at yahoo.com
Fri Jul 20 13:50:46 CEST 2012

Hi,

Just a doubt regarding the dataset.

Suppose, I include two more patients F and G with different missing values as in this new dataset and run the code.
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
B  1  0.4  NA  NA  NA  NA
B  2  0.4  NA  NA  NA  NA
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
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
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
F  1  0.2  NA   0.2 0.5 0.1
F  2  0.5  NA   0.4 NA   0.3
F  3  0.6  NA   NA  0.3  0.2
G  1  0.2   0.5  NA  0.5  0.2
G  3  0.4   0.3  0.4 NA  0.3
G  4  0.6   0.2  0.2  0.4 NA

nms <- names(dat1)[grep("^V[1-9]$", names(dat1))] dd <- split(dat1, dat1$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])
Patient Cycle  V1  V2  V3  V4  V5
A.1        A     1 0.4 0.1 0.5 1.5  NA
A.2        A     2 0.3 0.2 0.5 1.6  NA
A.3        A     3 0.3  NA 0.6 1.7  NA
A.4        A     4 0.4  NA 0.4 1.8  NA
A.5        A     5 0.5 0.2 0.5 1.5  NA
C.8        C     1 0.9 0.9 0.9  NA  NA
C.9        C     3 0.3 0.5 0.6  NA  NA
C.10       C     4  NA  NA  NA  NA  NA
C.11       C     5 0.4  NA  NA  NA  NA
E.16       E     1 0.1  NA  NA  NA  NA
E.17       E     2 0.5 0.3  NA  NA  NA
E.18       E     3 0.4 0.3  NA  NA  NA
F.19       F     1 0.2  NA 0.2 0.5 0.1
F.20       F     2 0.5  NA 0.4  NA 0.3
F.21       F     3 0.6  NA  NA 0.3 0.2
G.22       G     1 0.2 0.5  NA 0.5 0.2
G.23       G     3 0.4 0.3 0.4  NA 0.3
G.24       G     4 0.6 0.2 0.2 0.4  NA

Then, patients F and G are included in the list.  But, according to your initial statement, V1 and V2 are the most important variables.  If B is not included in the list because B has missing values for both cycles of B, then do you know think F or G should be included in the list.  Only difference is that F and G have missing values in other variables which do not behave consistently.  Do you have situations like that?

A.K.

----- Original Message -----
From: Lib Gray <libgray3827 at gmail.com>
Cc: r-help <r-help at r-project.org>
Sent: Thursday, July 19, 2012 8:17 PM
Subject: Re: [R] Subsetting problem data, 2

I'm still getting the message (if this is what you were suggesting I try).
The data set I'm using has many more columns other than these variables;
could that be a problem? I didn't think it would affect it.

>pattern <- "L[1-8][12]"
> nms<-names(data)[grep(vars,names(data))]
Warning message:
In grep(vars, names(data)) :
argument 'pattern' has length > 1 and only the first element will be used
>

On Thu, Jul 19, 2012 at 6:55 PM, Rui Barradas <ruipbarradas at sapo.pt> wrote:

> Hello,
>
> Sorry, forgot about that. It's trickier to write code without a dataset to
> test it.
>
> Try
>
> pattern <- "L[1-8][12]"
>
> and after the grep print nms to see if it's right.
>
>
> Em 20-07-2012 00:33, Lib Gray escreveu:
>
>> I'm getting this error message:
>>
>> nms<-names(data)[grep(vars,**names(data))]
>> Warning message:
>> In grep(vars, names(data)) :
>>    argument 'pattern' has length > 1 and only the first element will be
>> used
>>
>> Is there a way around this?
>>
>>
>> On Thu, Jul 19, 2012 at 6:17 PM, Rui Barradas <ruipbarradas at sapo.pt>
>> wrote:
>>
>>  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.
>>>
>>>
>>> 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,
>>>>>
>>>>>
>>>>> 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!
>>>>>>
>>>>>>           [[alternative HTML version deleted]]
>>>>>>
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>>>>>>
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>>>>>> >
>>>>>>
>>>>>> and provide commented, minimal, self-contained, reproducible code.
>>>>>>
>>>>>>
>>>>>>
>

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