vectorize combining two matrices in R - r

Question

I have a data frame A as follows (the numbers are totally made up)
ID statistic p.value
1 4 .1
2 5 .3
3 3 .4
4 2 .4
5 1 .5
6 7 .8
and data frame B as follows:
ID Info1 Info2 ....
4 A1 B1
5 A2 B2
2 A3 ..
3 A4
1 A5
6 A6
7 A7
9 A8
8 A9
How would I cbind data frame A to data frame B in the correct order without a loop. I know I need to do something like:
cbind(A, B[something in here, ]) but how do I get the ordering? do I do a which statement? something else?

Answer 1

Too long for a comment.
So if I understand you correctly (from your question and all the comments), A and B are extremely large data frames. A has an ID column, and B has the IDs in the row names.
You should definitely use data.tables for this. Assuming you are pulling in the data from some kind of text file, read up on fread(...) in the data.table package. This will read the file directly into a data.table. fread(...) is extremely fast: 10 - 100 times faster than read.table(...) or read.csv(...) for large datasets.
Below is a comparison of the data frame approach with merge(...) and the data.table join approach.
data.frame approach
N <- 1e7 # 10 million rows; big enough??
set.seed(1) # for reproducible example
A <- data.frame(ID=1:N,statistic=sample(1:10,N,replace=T),pvalue=runif(N),stringsAsFactors=F)
B <- data.frame(info1=sample(LETTERS,N,replace=T),info2=sample(letters,N,replace=T),stringsAsFactors=F)
rownames(B) <- sample(1:N,N) # row names in randon order in B
system.time({
# this does the work...
B$ID <- as.integer(rownames(B))
result <- merge(B,A,by="ID")
})
# user system elapsed
# 285.75 3.15 289.33
data.table approach
set.seed(1)
A <- data.frame(ID=1:N,statistic=sample(1:10,N,replace=T),pvalue=runif(N),stringsAsFactors=F)
B <- data.frame(info1=sample(LETTERS,N,replace=T),info2=sample(letters,N,replace=T),stringsAsFactors=F)
rownames(B) <- sample(1:N,N)
library(data.table)
system.time({
# this does the work...
IDs <- as.integer(rownames(B))
setDT(A)
setDT(B)
B[,ID:=IDs]
setkey(A,ID)
setkey(B,ID)
B[A,c("statistic","pvalue"):=list(statistic,pvalue=pvalue)]
})
# user system elapsed
# 122.46 0.40 122.87
So the data.table approach is twice as fast in this example. But most of the time is spent converting the rownames to a column, so if you can read them into a column to begin with, and especially if you can read the data directly into data.tables using fread(...), this will be much faster.