Our Statistics Group is evaluating the use of R for the elaboration of some index.
We have some datasets sas (120 Mb) and we would like to evaluate performance in the elaborations of mean, percentile, Gini index of a population and of a survey sample.
I need to open "a dataset". Currently I've understood that I've to follow a code sequence like this:
alfa <- {a moltiplicator} memory.limit(alfa*round(memory.limit()/1048576.0, 2)) library(foreign) hereis <- read.xport("C:/R/ { my exported file sas }")
The dimension of { my exported file sas } is 120 mega Is correct to allocate all the file in memory in a variable ( hereis ) ? With an alfa ( the moltiplicator ) of 2 , I have the following errors: Error: cannot allocate vector of size 214 Kb In addition: Warning message: Reached total allocation of 446Mb: see help(memory.size) How can I solve this problem? Is R-language able to manage data of 100-150 Mb ? And in which conditions?
I'm looking for informations about the use of R and some specific problems. I'm looking for example (Code of) of complex program in R. The program I must build could be described by the following steps: 1) open a dataset sas of 120 mega 2) merge it with a weighting universe little dataset 3) calculate a survey index 4) store it in a file I'm looking also for some developers/users in R language.
Thank you for any advice. Yours faithfully
Diego Moretti
-- ============================================================ Diego Moretti ([EMAIL PROTECTED]) Italian National Statistical Institute (ISTAT)
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