Reputation: 1275
I want to use na.omit (data) for the following example dataset, but on a condition so as to remove rows with NAs only when they are present in lets say "more than 30%" of the columns.
data:
C1 C2 C3 C4 C5
Gene1 0.07 NA 0.05 0.07 0.07
Gene2 0.2 0.18 0.16 0.15 0.15
Gene3 NA 0.93 0.9 NA 0.92
Gene4 0.32 0.05 0.12 0.13 0.05
Gene5 0.44 0.53 0.46 0.03 0.47
Gene6 NA 0.34 NA 0.8 NA
Gene7 0.49 0.55 0.67 0.49 0.89
Gene8 0.25 NA 0.49 NA NA
Gene9 0.1 0.1 0.05 NA 0.09
So the resulting file should be as follows:
C1 C2 C3 C4 C5
Gene1 0.07 NA 0.05 0.07 0.07
Gene2 0.2 0.18 0.16 0.15 0.15
Gene4 0.32 0.05 0.12 0.13 0.05
Gene5 0.44 0.53 0.46 0.03 0.47
Gene7 0.49 0.55 0.67 0.49 0.89
Gene9 0.1 0.1 0.05 NA 0.09
Thanks for the help!
Upvotes: 10
Views: 1779
Reputation: 4921
And here another option using apply
dat[apply(dat,1,function(x){sum(is.na(x))/dim(dat)[2]})<0.3,]
#C1 C2 C3 C4 C5
#Gene1 0.07 NA 0.05 0.07 0.07
#Gene2 0.20 0.18 0.16 0.15 0.15
#Gene4 0.32 0.05 0.12 0.13 0.05
#Gene5 0.44 0.53 0.46 0.03 0.47
#Gene7 0.49 0.55 0.67 0.49 0.89
#Gene9 0.10 0.10 0.05 NA 0.09
Upvotes: 4
Reputation: 887223
Here is another version with Reduce
df1[!Reduce(`+`, lapply(df1, is.na)) > ncol(df1)*0.3,]
# C1 C2 C3 C4 C5
#Gene1 0.07 NA 0.05 0.07 0.07
#Gene2 0.20 0.18 0.16 0.15 0.15
#Gene4 0.32 0.05 0.12 0.13 0.05
#Gene5 0.44 0.53 0.46 0.03 0.47
#Gene7 0.49 0.55 0.67 0.49 0.89
#Gene9 0.10 0.10 0.05 NA 0.09
Upvotes: 4
Reputation: 10131
You can subset based on the row sums of NA values:
test[!rowSums(is.na(test)) > ncol(test)*.3,]
C1 C2 C3 C4 C5
Gene1 0.07 NA 0.05 0.07 0.07
Gene2 0.20 0.18 0.16 0.15 0.15
Gene4 0.32 0.05 0.12 0.13 0.05
Gene5 0.44 0.53 0.46 0.03 0.47
Gene7 0.49 0.55 0.67 0.49 0.89
Gene9 0.10 0.10 0.05 NA 0.09
Upvotes: 8