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Simple Missing cases Function
7 messages · Tim Elwell-Sutton, PIKAL Petr, Philipp Pagel +1 more
Hi Hi try colSums(is.na(data.m)) It is not in data frame but you can easily transform it if you want. Regards Petr r-help-bounces at r-project.org napsal dne 19.04.2011 09:29:08:
Dear all I have written a function to perform a very simple but useful task which
I
do regularly. It is designed to show how many values are missing from
each
variable in a data.frame. In its current form it works but is slow
because I
have used several loops to achieve this simple task.
Can anyone see a more efficient way to get the same results? Or is there
existing function which does this?
Thanks for your help
Tim
Function:
miss <- function (data)
{
miss.list <- list(NA)
for (i in 1:length(data)) {
miss.list[[i]] <- table(is.na(data[i]))
}
for (i in 1:length(miss.list)) {
if (length(miss.list[[i]]) == 2) {
miss.list[[i]] <- miss.list[[i]][2]
}
}
for (i in 1:length(miss.list)) {
if (names(miss.list[[i]]) == "FALSE") {
miss.list[[i]] <- 0
}
}
data.frame(names(data), as.numeric(miss.list))
}
Example:
data(ToothGrowth)
data.m <- ToothGrowth
data.m$supp[sample(1:nrow(data.m), size=25)] <- NA
miss(data.m)
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On Tue, Apr 19, 2011 at 03:29:08PM +0800, Tim Elwell-Sutton wrote:
Dear all I have written a function to perform a very simple but useful task which I do regularly. It is designed to show how many values are missing from each variable in a data.frame. In its current form it works but is slow because I have used several loops to achieve this simple task.
Why not use summary?
foo <- data.frame(a=c(1,3,4,NA), b=c(NA,4,NA,8), c=factor(c('A', NA, 'A', 'B')))
summary(foo)
a b c Min. :1.000 Min. :4 A :2 1st Qu.:2.000 1st Qu.:5 B :1 Median :3.000 Median :6 NA's:1 Mean :2.667 Mean :6 3rd Qu.:3.500 3rd Qu.:7 Max. :4.000 Max. :8 NA's :1.000 NA's :2 cu Philipp
Dr. Philipp Pagel Lehrstuhl f?r Genomorientierte Bioinformatik Technische Universit?t M?nchen Wissenschaftszentrum Weihenstephan Maximus-von-Imhof-Forum 3 85354 Freising, Germany http://webclu.bio.wzw.tum.de/~pagel/
On Tue, Apr 19, 2011 at 03:29:08PM +0800, Tim Elwell-Sutton wrote:
Dear all I have written a function to perform a very simple but useful task which I do regularly. It is designed to show how many values are missing from each variable in a data.frame. In its current form it works but is slow because I have used several loops to achieve this simple task.
Oh - and in case you ONLY wnt the number of NAs in each column this
should be pretty efficient:
lapply(foo, function(x){sum(is.na(x))})
cu
Philipp
Dr. Philipp Pagel Lehrstuhl f?r Genomorientierte Bioinformatik Technische Universit?t M?nchen Wissenschaftszentrum Weihenstephan Maximus-von-Imhof-Forum 3 85354 Freising, Germany http://webclu.bio.wzw.tum.de/~pagel/
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Dear Petr Thanks so much. That is a LOT more efficient. Tim -----Original Message----- From: Petr PIKAL [mailto:petr.pikal at precheza.cz] Sent: Tuesday, April 19, 2011 3:37 PM To: tesutton Cc: r-help at r-project.org Subject: Odp: [R] Simple Missing cases Function Hi Hi try colSums(is.na(data.m)) It is not in data frame but you can easily transform it if you want. Regards Petr r-help-bounces at r-project.org napsal dne 19.04.2011 09:29:08:
Dear all I have written a function to perform a very simple but useful task which
I
do regularly. It is designed to show how many values are missing from
each
variable in a data.frame. In its current form it works but is slow
because I
have used several loops to achieve this simple task.
Can anyone see a more efficient way to get the same results? Or is there
existing function which does this?
Thanks for your help
Tim
Function:
miss <- function (data)
{
miss.list <- list(NA)
for (i in 1:length(data)) {
miss.list[[i]] <- table(is.na(data[i]))
}
for (i in 1:length(miss.list)) {
if (length(miss.list[[i]]) == 2) {
miss.list[[i]] <- miss.list[[i]][2]
}
}
for (i in 1:length(miss.list)) {
if (names(miss.list[[i]]) == "FALSE") {
miss.list[[i]] <- 0
}
}
data.frame(names(data), as.numeric(miss.list))
}
Example:
data(ToothGrowth)
data.m <- ToothGrowth
data.m$supp[sample(1:nrow(data.m), size=25)] <- NA
miss(data.m)
[[alternative HTML version deleted]]
______________________________________________ R-help at 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.
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