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
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?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 spector at 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]] > >
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]]
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
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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