Efficient alternatives to merge for larger data.frames R

Question

I am looking for an efficient (both computer resource wise and learning/implementation wise) method to merge two larger (size>1 million / 300 KB RData file) data frames.

"merge" in base R and "join" in plyr appear to use up all my memory effectively crashing my system.

Example
load test data frame

and try

test.merged<-merge(test, test) 

or

test.merged<-join(test, test, type="all")   

-

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The following post provides a list of merge and alternatives:
How to join (merge) data frames (inner, outer, left, right)?

The following allows object size inspection:
https://heuristically.wordpress.com/2010/01/04/r-memory-usage-statistics-variable/

Data produced by anonym

Answer 1

Here are some timings for the data.table vs. data.frame methods.
Using data.table is very much faster. Regarding memory, I can informally report that the two methods are very similar (within 20%) in RAM use.

library(data.table)  set.seed(1234) n = 1e6  data_frame_1 = data.frame(id=paste("id_", 1:n, sep=""),                           factor1=sample(c("A", "B", "C"), n, replace=TRUE)) data_frame_2 = data.frame(id=sample(data_frame_1$id),                           value1=rnorm(n))  data_table_1 = data.table(data_frame_1, key="id") data_table_2 = data.table(data_frame_2, key="id")  system.time(df.merged <- merge(data_frame_1, data_frame_2)) #   user  system elapsed  # 17.983   0.189  18.063    system.time(dt.merged <- merge(data_table_1, data_table_2)) #   user  system elapsed  #  0.729   0.099   0.821