Hi,
You could use either:
?na.omit() #the option was already suggested
#or
df2[complete.cases(df2),]
#In this case, this should also work
sapply(df2,function(x) x[!is.na(x)])
#or
apply(df2,2,function(x) x[!is.na(x)]) #If the NAs are not in the same rows,
then the ouptut will be a list with the list elements differ in length.
A.K.
----- Original Message -----
From: "raphael.fel...@art.admin.ch" <raphael.fel...@art.admin.ch>
To: r-help@r-project.org
Cc:
Sent: Thursday, December 13, 2012 3:20 AM
Subject: [R] remove NA in df results in NA, NA.1 ... rows
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
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and provide commented, minimal, self-contained, reproducible code.
