Historically, large scale computing and interactivity have been at odds. This is a particularly sore spot for data analytics applications, which are typically interactive in nature. To help address this problem, we introduce a new client/server framework for the R language. This framework allows the R programmer to remotely control anywhere from one to thousands of batch servers running as cooperating instances of R. And all of this is done from the user's local R session. Additionally, no specialized software environment is needed; the framework is a series of R packages, available from CRAN. The communication between client and server(s) is handled by the well-known ZeroMQ library. To handle computations, we use the established pbdR packages for large scale distributed computing. These packages utilize HPC standards like MPI and ScaLAPACK to handle complex, tightly-coupled computations on large datasets. In this paper, we outline the client/server architecture, discuss the pros and cons to this approach, and provide several example workflows which bring
