Re: [R] data-management: Rowwise NA
-Any- was my fix... Appreciate it.
//M
On 3. juni 2010, at 21.33, Phil Spector wrote:
> ?any
>
> Not really a reproducible answer, but I think you're looking
> for
>
> apply(tes[,sam],1,function(x)any(is.na(x)))
>
>
> - Phil Spector
>Statistical Computing Facility
>Department of Statistics
>UC Berkeley
>spec...@stat.berkeley.edu
>
>
> On Thu, 3 Jun 2010, moleps wrote:
>
>> Dear R?ers..
>>
>> In this mock dataset how can I generate a logical variable based on whether
>> just tes or tes3 are NA in each row??
>>
>> test<-sample(c("A",NA,"B"),100,replace=T)
>> test2<-sample(c("A",NA,"B"),100,replace=T)
>> test3<-sample(c("A",NA,"B"),100,replace=T)
>>
>> tes<-cbind(test,test2,test3)
>>
>> sam<-c("test","test3")
>> apply(subset(tes,select=sam),1,FUN=function(x) is.na(x))
>>
>> However this just tests whether each variable is missing or not per row. I?d
>> like an -or- function in here that would provide one true/false per row
>> based on whether test or tes3 are NA. I guess it would be easy to do it by
>> subsetting in the example but I figure there is a more elegant way of doing
>> it when -sam- contains 50 variables...
>>
>> //M
>>
>>
>>
>> [[alternative HTML version deleted]]
>>
>>
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Re: [R] data-management: Rowwise NA
you probably want to use the apply function:
d=sample(1000,500);
d[sample(500,50)]<-NA; #put 50 NAs into the data
d=data.frame(matrix(d,ncol=50));
names(d)=paste('var',1:50,sep='.')
d
apply(d,1,sum) #are any of the row values NA ?
apply(d,2,function(x)sum(is.na(x))) #how many values for each of the 50
variables are NA ?
David Freedman, CDC
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Re: [R] data-management: Rowwise NA
On Jun 3, 2010, at 2:20 PM, moleps wrote:
> Dear R´ers..
>
> In this mock dataset how can I generate a logical variable based on whether
> just tes or tes3 are NA in each row??
>
> test<-sample(c("A",NA,"B"),100,replace=T)
> test2<-sample(c("A",NA,"B"),100,replace=T)
> test3<-sample(c("A",NA,"B"),100,replace=T)
>
> tes<-cbind(test,test2,test3)
>
> sam<-c("test","test3")
> apply(subset(tes,select=sam),1,FUN=function(x) is.na(x))
>
> However this just tests whether each variable is missing or not per row. I´d
> like an -or- function in here that would provide one true/false per row based
> on whether test or tes3 are NA. I guess it would be easy to do it by
> subsetting in the example but I figure there is a more elegant way of doing
> it when -sam- contains 50 variables...
How about this:
set.seed(1)
test <- sample(c("A", NA, "B"), 100, replace = TRUE)
test2 <- sample(c("A", NA, "B"), 100, replace = TRUE)
test3 <- sample(c("A", NA, "B"), 100, replace = TRUE)
tes <- cbind(test, test2, test3)
> str(tes)
chr [1:100, 1:3] "A" NA NA "B" "A" "B" "B" NA NA ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr [1:3] "test" "test2" "test3"
> head(tes)
test test2 test3
[1,] "A" NA"A"
[2,] NA NA"A"
[3,] NA "A" NA
[4,] "B" "B" "A"
[5,] "A" NA"A"
[6,] "B" "A" NA
sam <- c("test","test3")
> rowSums(is.na(subset(tes, select = sam))) > 0
[1] FALSE TRUE TRUE FALSE FALSE TRUE TRUE TRUE TRUE FALSE FALSE
[12] FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE TRUE FALSE FALSE
[23] TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE TRUE TRUE TRUE
[34] TRUE TRUE FALSE FALSE TRUE TRUE TRUE FALSE TRUE FALSE TRUE
[45] TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE TRUE TRUE TRUE
[56] FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE FALSE TRUE FALSE
[67] TRUE FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE FALSE FALSE
[78] TRUE TRUE FALSE TRUE TRUE TRUE TRUE FALSE FALSE FALSE FALSE
[89] FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE TRUE FALSE
[100] TRUE
This avoids the looping involved in calling apply().
HTH,
Marc Schwartz
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Re: [R] data-management: Rowwise NA
Hi there,
One option would be
apply(tes, 1, function(.row) any(is.na(.row[c(1,3)])))
See ?any, ?is.na and ?apply for more information.
HTH,
Jorge
On Thu, Jun 3, 2010 at 3:20 PM, moleps <> wrote:
> Dear R´ers..
>
> In this mock dataset how can I generate a logical variable based on whether
> just tes or tes3 are NA in each row??
>
> test<-sample(c("A",NA,"B"),100,replace=T)
> test2<-sample(c("A",NA,"B"),100,replace=T)
> test3<-sample(c("A",NA,"B"),100,replace=T)
>
> tes<-cbind(test,test2,test3)
>
> sam<-c("test","test3")
> apply(subset(tes,select=sam),1,FUN=function(x) is.na(x))
>
> However this just tests whether each variable is missing or not per row.
> I´d like an -or- function in here that would provide one true/false per row
> based on whether test or tes3 are NA. I guess it would be easy to do it by
> subsetting in the example but I figure there is a more elegant way of doing
> it when -sam- contains 50 variables...
>
> //M
>
>
>
>[[alternative HTML version deleted]]
>
>
> __
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
>
[[alternative HTML version deleted]]
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and provide commented, minimal, self-contained, reproducible code.
Re: [R] data-management: Rowwise NA
?any
Not really a reproducible answer, but I think you're looking
for
apply(tes[,sam],1,function(x)any(is.na(x)))
- Phil Spector
Statistical Computing Facility
Department of Statistics
UC Berkeley
spec...@stat.berkeley.edu
On Thu, 3 Jun 2010, moleps wrote:
Dear R?ers..
In this mock dataset how can I generate a logical variable based on whether
just tes or tes3 are NA in each row??
test<-sample(c("A",NA,"B"),100,replace=T)
test2<-sample(c("A",NA,"B"),100,replace=T)
test3<-sample(c("A",NA,"B"),100,replace=T)
tes<-cbind(test,test2,test3)
sam<-c("test","test3")
apply(subset(tes,select=sam),1,FUN=function(x) is.na(x))
However this just tests whether each variable is missing or not per row. I?d
like an -or- function in here that would provide one true/false per row based
on whether test or tes3 are NA. I guess it would be easy to do it by subsetting
in the example but I figure there is a more elegant way of doing it when -sam-
contains 50 variables...
//M
[[alternative HTML version deleted]]
__
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
