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I've been working on a few projects that have required me to do a lot of list subsetting and while profiling code I realised that the object[["nameHere"]] approach to subsetting lists was usually faster than the object$nameHere approach.
As an example if we create a list with named components:
a.long.list <- as.list(rep(1:1000)) names(a.long.list) <- paste0("something",1:1000) Why is this:
system.time ( for (i in 1:10000) { a.long.list[["something997"]] } ) user system elapsed 0.15 0.00 0.16 faster than this:
system.time ( for (i in 1:10000) { a.long.list$something997 } ) user system elapsed 0.23 0.00 0.23 My question is simply whether this behaviour is true universally and I should avoid the $ subset wherever possible or does the most efficient choice depend on some other factors?
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Lists in R can be subsetted using all three of the operators mentioned above, and all three are used for different purposes. The [[ operator can be used to extract single elements from a list. Here we extract the first element of the list.
Method 2: Subsetting in R Using [[ ]] Operator [[ ]] operator is used for subsetting of list-objects. This operator is the same as [ ] operator but the only difference is that [[ ]] selects only one element whereas [ ] operator can select more than 1 element in a single command.
We can use the [[index]] function to select an element in a list. The value inside the double square bracket represents the position of the item in a list we want to extract.
Function [[ first goes through all elements trying for exact match, then tries to do partial match. The $ function tries both exact and partial match on each element in turn. If you execute:
system.time ( for (i in 1:10000) { a.long.list[["something9973", exact=FALSE]] } ) i.e., you are running a partial match where there is no exact match, you will find that $ is in fact ever so slightly faster.
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