Hi Dennis Yes you are completely right. Well, we use to say "Quick work is wretched work" and this is perfect example.
Regards Petr > Hi Petr: > > You might want to double check your post. The OP wanted to remove > cases where *all* the variables in a row were NA. complete.cases() > eliminates rows that have *any* NA values. That's why Michael coded it > the way he did. > > Example: > > dff <- data.frame(x1 = c(1, 2, 3, NA, 4, NA), > x2 = c(NA, 2, 3, NA, 4, 5), > x3 = c(1, 2, 3, NA, 4, NA)) > dff > dff[complete.cases(dff),] > dff[!(rowSums(is.na(dff)) == NCOL(dff)), ] > > > Cheers, > Dennis > > 2011/11/7 Petr PIKAL <petr.pi...@precheza.cz>: > >> > >> Perhaps something like this will work. > >> > >> df[!(rowSums(is.na(df))==NCOL(df)),] > > > > > > Or > > > > df[complete.cases(df),] > > > > Regards > > Petr > > > > > >> > >> Michael > >> > >> On Fri, Nov 4, 2011 at 9:27 AM, Jose Iparraguirre > >> <jose.iparragui...@ageuk.org.uk> wrote: > >> > Hi, > >> > > >> > Imagine I have the following data frame: > >> > > >> >> a <- c(1,NA,3) > >> >> b <- c(2,NA,NA) > >> >> c <- data.frame(cbind(a,b)) > >> >> c > >> > a b > >> > 1 1 2 > >> > 2 NA NA > >> > 3 3 NA > >> > > >> > I want to delete the second row. If I use na.omit, that would also > >> affect the third row. I tried to use a loop and an ifelse clause with > >> is.na to get R identify that row in which all records are missing, as > >> opposed to the first row in which no records are missing or the third > > one, > >> in which only one record is missing. How can I get R identify the row in > > > >> which all records are missing? 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