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Since the way SPSS uses memory is fundamentally different from how R uses it, there really isn't a close equivalent between rm and SPSS memory management mechanisms. SPSS does not keep datasets in memory in most cases - which is why it can process files of unlimited size. When you close an SPSS dataset, all its associated metadata - which is in memory, is removed. DATASET CLOSE ALL closes all open datasets, but there can still be an unnamed dataset remaining. To really remove everything, you would write dataset close all. new file. because a dataset cannot remain open if another one is opened unless it has a dataset name. You might also be interested to know that you can run R code from within SPSS via BEGIN PROGRAM R. END PROGRAM. SPSS provides apis for reading the active SPSS data, creating SPSS pivot tables, creating new SPSS datasets etc. You can even use the SPSS Custom Dialog Builder to create a dialog box interface for your R program. In addition, there is a mechanism for building SPSS extension commands that are actually implemented in R or Python. All this apparatus is free once you have the basic SPSS Statistics. So it is easy to use SPSS to provide a nice user interface and nice output for an R program. You can download the R Essentials and a good number of R extensions for SPSS from the SPSS Community website at www.ibm.com/developerworks/spssdevcentral. All free, but registration is required. p.s. rm(ls()) is useful in some situations - it is often used with R code within SPSS, because the state of the R workspace is retained between R programs within the same SPSS session. Regards, Jon Peck
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1. POclear memory in SPSS, like what is done in R
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