人们使用什么技巧来管理交互式R会话的可用内存?我使用下面的函数[基于Petr Pikal和David Hinds在2004年发布的r-help列表]来列出(和/或排序)最大的对象，并偶尔rm()其中一些对象。但到目前为止最有效的解决办法是……在64位Linux下运行，有充足的内存。
大家还有什么想分享的妙招吗?请每人寄一份。
# improved list of objects
.ls.objects <- function (pos = 1, pattern, order.by,
decreasing=FALSE, head=FALSE, n=5) {
napply <- function(names, fn) sapply(names, function(x)
fn(get(x, pos = pos)))
names <- ls(pos = pos, pattern = pattern)
obj.class <- napply(names, function(x) as.character(class(x))[1])
obj.mode <- napply(names, mode)
obj.type <- ifelse(is.na(obj.class), obj.mode, obj.class)
obj.size <- napply(names, object.size)
obj.dim <- t(napply(names, function(x)
as.numeric(dim(x))[1:2]))
vec <- is.na(obj.dim)[, 1] & (obj.type != "function")
obj.dim[vec, 1] <- napply(names, length)[vec]
out <- data.frame(obj.type, obj.size, obj.dim)
names(out) <- c("Type", "Size", "Rows", "Columns")
if (!missing(order.by))
out <- out[order(out[[order.by]], decreasing=decreasing), ]
if (head)
out <- head(out, n)
out
}
# shorthand
lsos <- function(..., n=10) {
.ls.objects(..., order.by="Size", decreasing=TRUE, head=TRUE, n=n)
}
在将数据框架传递给回归函数的data=参数时，我积极地使用子集参数，只选择所需的变量。如果我忘记向公式和select=向量添加变量，确实会导致一些错误，但由于减少了对象的复制，它仍然节省了大量时间，并显著减少了内存占用。假设我有400万条记录和110个变量(我确实有)。例子:
# library(rms); library(Hmisc) for the cph,and rcs functions
Mayo.PrCr.rbc.mdl <-
cph(formula = Surv(surv.yr, death) ~ age + Sex + nsmkr + rcs(Mayo, 4) +
rcs(PrCr.rat, 3) + rbc.cat * Sex,
data = subset(set1HLI, gdlab2 & HIVfinal == "Negative",
select = c("surv.yr", "death", "PrCr.rat", "Mayo",
"age", "Sex", "nsmkr", "rbc.cat")
) )
通过设置上下文和策略:gdlab2变量是一个逻辑向量，它是为一组实验室测试的所有正常或几乎正常值的数据集中的主题构建的，而HIVfinal是一个字符向量，总结了艾滋病毒的初步和确认测试。
For both speed and memory purposes, when building a large data frame via some complex series of steps, I'll periodically flush it (the in-progress data set being built) to disk, appending to anything that came before, and then restart it. This way the intermediate steps are only working on smallish data frames (which is good as, e.g., rbind slows down considerably with larger objects). The entire data set can be read back in at the end of the process, when all the intermediate objects have been removed.
dfinal <- NULL
first <- TRUE
tempfile <- "dfinal_temp.csv"
for( i in bigloop ) {
if( !i %% 10000 ) {
print( i, "; flushing to disk..." )
write.table( dfinal, file=tempfile, append=!first, col.names=first )
first <- FALSE
dfinal <- NULL # nuke it
}
# ... complex operations here that add data to 'dfinal' data frame
}
print( "Loop done; flushing to disk and re-reading entire data set..." )
write.table( dfinal, file=tempfile, append=TRUE, col.names=FALSE )
dfinal <- read.table( tempfile )
