the list I'm dealing with is the follow-up of an lapply() not a "native"
data structure I've been set up for storing data originally;
the list it's a data structure I have to manage as a consequence of my
previous operations, something like:
path<-"./"
files <-list.files(path, pattern=".csv")
mylist<-lapply(files, function(files)
na.omit(read.csv(paste0(path,"/",files)), header=TRUE))
anyway, thank you for the good hint: is.null seems promising
cheers
m
Il 03/10/2013 16:55, Bert Gunter ha scritto:
Have you read An Introduction to R (ships with R) or another of the
many excellent R tutorials on the web? I ask, because you do not
appear to be using a sensible data structure. As your list appears to
be of a single type (probably numeric, maybe integer), it would be
preferable to use a vector, like this:
y <- c(45, NA, 18, NA, 99)
(The NULLS must be converted to NA's to "hold" their places).
There would then seem to be little need for the data frame structure,
as it tends to slow things down in R. But if you insist,
which(is.na(y))
will give you the indices of the NA's.
See also: ?is.na ?is.null.
Cheers,
Bert
On Thu, Oct 3, 2013 at 7:41 AM, Massimo Bressan<mbres...@arpa.veneto.it> wrote:
#let's suppose I have a list like this
mytest<-list(45, NULL, 18, NULL, 99)
#to note that this is just an amended example because in fact
#I'm dealing with a long list (more than 400 elements)
#with no evident pattern of the NULL values
#I want to end up with a data frame like the following
data.frame(i=c(1,3,5), n=c(45,18,99))
#i.e. a data frame storing in
#column i the number of corresponding element list
#column n the unique component of that element
#I've been trying with
do.call(rbind, mytest)
#or
do.call(rbind.data.frame, mytest)
#but this approach is not properly achieving the desired result
#now I'm in trouble on how to store each element number of the list in
the first column data frame
#any help for this?
#thanks
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