df2 <- df2[!is.na(df2),] isn't doing what you want it to do because
df2 is a data.frame and not a vector

to solve your problem, review

http://stackoverflow.com/questions/4862178/r-remove-rows-with-nas-in-data-frame








On Thu, Dec 13, 2012 at 3:20 AM, <raphael.felber@art.admin.ch> wrote:

> Good morning!
>
> I have the following data frame (df):
>
>     X.outer  Y.outer   X.PAD1   Y.PAD1   X.PAD2 Y.PAD2   X.PAD3 Y.PAD3
> X.PAD4 Y.PAD4
> 73 574690.0 179740.0 574690.2 179740.0 574618.3 179650 574729.2 179674
> 574747.1 179598
> 74 574680.6 179737.0 574693.4 179740.0 574719.0 179688 574831.8 179699
> 574724.9 179673
> 75 574671.0 179734.0 574696.2 179740.0 574719.0 179688 574807.8 179787
> 574729.2 179674
> 76 574663.6 179736.0 574699.1 179734.0 574723.5 179678 574703.4 179760
> 574831.8 179699
> 77 574649.9 179734.0 574704.7 179724.0 574724.9 179673 574702.4 179755
> 574852.3 179626
> 78 574647.3 179742.0 574706.9 179719.0 574747.1 179598 574702.0 179754
> 574747.1 179598
> 79 574633.6 179739.0 574711.4 179710.0 574641.8 179570 574698.0 179747
>   NA     NA
> 80 574634.9 179732.0 574716.6 179698.0 574639.6 179573 574700.2 179738
>   NA     NA
> 81 574616.5 179728.6 574716.7 179695.0 574618.3 179650 574704.4 179729
>   NA     NA
> 82 574615.4 179731.0 574718.2 179690.0       NA     NA 574708.1 179724
>   NA     NA
> 83 574614.4 179733.6 574719.1 179688.0       NA     NA 574709.3 179720
>   NA     NA
> ...
>
> 44 574702.0 179754.0       NA       NA       NA     NA       NA     NA
>   NA     NA
>
> 45 574695.1 179751.0       NA       NA       NA     NA       NA     NA
>   NA     NA
>
> 46 574694.4 179752.0       NA       NA       NA     NA       NA     NA
>   NA     NA
>
> Which I subset to
>
> df2 <- df[,c("X.PAD2","Y.PAD2")]
>
> df2
>
>      X.PAD2 Y.PAD2
>
> 73 574618.3 179650
>
> 74 574719.0 179688
>
> 75 574719.0 179688
>
> 76 574723.5 179678
>
> 77 574724.9 179673
>
> 78 574747.1 179598
>
> 79 574641.8 179570
>
> 80 574639.6 179573
>
> 81 574618.3 179650
>
> 82       NA     NA
>
> 83       NA     NA
>
> ...
>
> 44       NA     NA
>
> 45       NA     NA
>
> 46       NA     NA
>
>
>
>
>
> followed by removing the NA's using
>
>
>
> df2 <- df2[!is.na(df2),]
>
>
>
> If I now call df2, I get:
>
>
>
>        X.PAD2 Y.PAD2
>
> 73   574618.3 179650
>
> 74   574719.0 179688
>
> 75   574719.0 179688
>
> 76   574723.5 179678
>
> 77   574724.9 179673
>
> 78   574747.1 179598
>
> 79   574641.8 179570
>
> 80   574639.6 179573
>
> 81   574618.3 179650
>
> NA         NA     NA
>
> NA.1       NA     NA
>
> NA.2       NA     NA
>
> NA.3       NA     NA
>
> NA.4       NA     NA
>
> NA.5       NA     NA
>
> NA.6       NA     NA
>
> NA.7       NA     NA
>
> NA.8       NA     NA
>
>
>
> It seems there are still NA's in my data frame. How can I get rid of them?
> What is the meaning of the rows numbered NA, NA.1 and so on?
>
>
>
> Thanks for any hints.
>
>
>
> Best regards
>
>
>
> Raphael Felber
>
>
>         [[alternative HTML version deleted]]
>
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>

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