Subsetting based on values of a different data frame in R

Question

I want to subset data if every value in the row is greater than the respective row in a different data frame. I also need to skip some top rows. These previous questions did not help me, but it is related:

Subsetting a data frame based on contents of another data frame

Subset data using information from a different data frame [r]

> A
     name1 name2
cond   trt  ctrl
hour     0     3
A        1     1
B       10     1
C        1     1
D        1     1
E       10    10
> B
     name1 name2
cond   trt  ctrl
hour     0     3
A        1     1
B        1    10
C        1     1
D        1     1
E        1     1

I want this. Only rows where ALL values were greater in A than B:

     name1 name2
cond   trt  ctrl
hour     0     3
E       10    10

I've tried these 3 lines:

subset(A, TRUE, select=(A[3:7,] > B[3:7,]))
subset(A, A > B)
A[A[3:7,] > B[3:7,]]

Thanks so much. Here is the code to generate the data:

A <- structure(list(name1 = c("trt", "0", "1", "10", "1", "1", "10"
), name2 = c("ctrl", "3", "1", "1", "1", "1", "10")), .Names = c("name1", 
"name2"), row.names = c("cond", "hour", "A", "B", "C", "D", "E"
), class = "data.frame")
B <- structure(list(name1 = c("trt", "0", "1", "1", "1", "1", "1"), 
    name2 = c("ctrl", "3", "1", "10", "1", "1", "1")), .Names = c("name1", 
"name2"), row.names = c("cond", "hour", "A", "B", "C", "D", "E"
), class = "data.frame")

############# Follow-up question asked 2/28/13

Error when subsetting based on adjusted values of different data frame in R

Answer 1

N <- nrow(A)
cond <- sapply(3:N, function(i) sum(A[i,] > B[i,])==2)
rbind(A[1:2,], subset(A[3:N,], cond))

Answer 2

I think it is better to use SQL for such inter table filtering. It is clean and readable( You keep the rules logic).

 library(sqldf)
sqldf('SELECT DISTINCT A.*
        FROM A,B
        WHERE A.name1   > B.name1
        AND    A.name2  > B.name2')
  name1 name2
1   trt  ctrl
2    10    10

Answer 3

requisite data.table solution:

library(data.table)

# just to preserve the order, non-alphabetically
idsA <- factor(rownames(A), levels=rownames(A))
idsB <- factor(rownames(B), levels=rownames(B))

# convert to data.table with id
ADT <- data.table(id=idsA, A, key="id")
BDT <- data.table(id=idsB, B, key="id")

# filter as needed
ADT[BDT][name1 > name1.1 & name2 > name2.1, list(id, name1, name2)]