What are R's memory constraints?
As Konstantin has pointed, R performs all its computation in the system's memory i.e. RAM. Hence, RAM capacity is a very important constraint for computation intensive operations in R. Overcoming this constraint, data is being stored these days in HDFS systems, where data isn't loaded onto memory and program is run instead, program goes to the data and performs the operations, thus overcoming the memory constraints.  RHadoop (https://github.com/RevolutionAnalytics/RHadoop/wiki) is the connector you are looking for.

Coming to the impact on algorithms which are computation intensive, Random Forests/Decision Trees/Ensemble methods on a considerable amount of data (minimum 50,000 observations in my experience) take up a lot of memory and are considerably slow. To speed up the process, parallelization is the way to go and parallelization is inherently available in Hadoop! That's where, Hadoop is really efficient.

So, if you are going for ensemble methods which are compute intensive and are slow, you would want to try out on the HDFS system which gives a considerable performance improvement.