Efficient way of subsetting a dataframe in R using multiple conditions

Question

Is there a clearer and more efficient way to subset a dataframe in R using multiple conditions? Here is my simplified example. Columns containing triplicates (v1,v2,v3 and v4,v5,v6) can contain max one 0 value within triplicate per row, otherwise should be excluded:

v1  v2  v3  v4  v5  v6
1   0   3   0   0   2
1   1   1   1   2   0
0   0   0   1   1   0
0   0   0   0   0   0

Here is my simple way of approaching the problem.

data_short<-subset(data, (((v1 != 0 & v2 !=0) | (v1 != 0 & v3 !=0) | (v2 != 0 & v3 !=0)) & ((v4 != 0 & v5 !=0) | (v4 != 0 & v6 !=0) | (v5 != 0 & v6 !=0)))

v1  v2  v3  v4  v5  v6
1   1   1   1   2   0

df[rowSums(df[,1:3]==0)<=1 & rowSums(df[,4:6]==0)<=1,]

HubertL
– HubertL

2016-11-22 23:42:20 +00:00
Commented Nov 22, 2016 at 23:42 — HubertL
– HubertL, Commented Nov 22, 2016 at 23:42

HubertL · Accepted Answer · 2016-11-22 23:45:14Z

5

You can use rowSums to count the number of time the data is 0 in any 3 first and 3 last columns:

df <- read.table(text="v1  v2  v3  v4  v5  v6
1   0   3   0   0   2
1   1   1   1   2   0
0   0   0   1   1   0
0   0   0   0   0   0", header=TRUE)

df[rowSums(df[,1:3]==0)<=1 & rowSums(df[,4:6]==0)<=1,]

  v1 v2 v3 v4 v5 v6
2  1  1  1  1  2  0

answered Nov 22, 2016 at 23:45

HubertL

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1 Comment

user2904120 Over a year ago

Thanks, it does the job!

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