{
message("This code was generated from R by makeParallel version 0.2.1 at 2019-10-02 17:36:03")
{
}
library(parallel)
assignments = c(1, 2, 1)
nWorkers = 2
cls = makeCluster(nWorkers)
c.data.frame = rbind
clusterExport(cls, character(0))
clusterExport(cls, c("assignments", "c.data.frame"))
parLapply(cls, seq(nWorkers), function(i) assign("workerID", i, globalenv()))
clusterEvalQ(cls, {
assignments = which(assignments == workerID)
NULL
})
}
{
clusterEvalQ(cls, {
read_args = c("d1.csv", "d2.csv", "d3.csv")
read_args = read_args[assignments]
chunks = lapply(read_args, function(fname) {
command = paste("cut -d , -f 2,4", fname)
read.table(pipe(command), header = FALSE, sep = ",", col.names = c("b", "d"), colClasses = c("numeric", "integer"))
})
x = do.call(rbind, chunks)
NULL
})
}
{
collected = clusterEvalQ(cls, {
list(x = x)
})
vars_to_collect = names(collected[[1]])
for (i in seq_along(vars_to_collect)) {
varname = vars_to_collect[i]
chunks = lapply(collected, `[[`, i)
value = do.call(c, chunks)
assign(varname, value)
}
}
b = as.Date(x[, "b"], origin = "2010-01-01")
d = as.Date(x[, "d"], origin = "2010-01-01")
rb = range(b)
rd = range(d)
print(rb)
print(rd)
stopCluster(cls)
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