How to delete only those rows in a dataframe in which all records are missing

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.pikal at 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.Iparraguirre at 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 

which all records are missing? Or, how can I get R delete/omit only 

row?

Thanks in advance,

Jos?


Jos? Iparraguirre
Chief Economist
Age UK

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