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。
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# 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)
}
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 )
我非常喜欢Dirk开发的改进的对象函数。不过，大多数时候，一个包含对象名称和大小的更基本的输出对我来说就足够了。这是一个具有类似目标的简单函数。内存使用可以按字母顺序或大小排序，可以限制为一定数量的对象，并且可以按升序或降序排序。此外，我经常处理1GB以上的数据，因此该函数相应地改变单位。
showMemoryUse <- function(sort="size", decreasing=FALSE, limit) {
objectList <- ls(parent.frame())
oneKB <- 1024
oneMB <- 1048576
oneGB <- 1073741824
memoryUse <- sapply(objectList, function(x) as.numeric(object.size(eval(parse(text=x)))))
memListing <- sapply(memoryUse, function(size) {
if (size >= oneGB) return(paste(round(size/oneGB,2), "GB"))
else if (size >= oneMB) return(paste(round(size/oneMB,2), "MB"))
else if (size >= oneKB) return(paste(round(size/oneKB,2), "kB"))
else return(paste(size, "bytes"))
})
memListing <- data.frame(objectName=names(memListing),memorySize=memListing,row.names=NULL)
if (sort=="alphabetical") memListing <- memListing[order(memListing$objectName,decreasing=decreasing),]
else memListing <- memListing[order(memoryUse,decreasing=decreasing),] #will run if sort not specified or "size"
if(!missing(limit)) memListing <- memListing[1:limit,]
print(memListing, row.names=FALSE)
return(invisible(memListing))
}
下面是一些输出示例:
> showMemoryUse(decreasing=TRUE, limit=5)
objectName memorySize
coherData 713.75 MB
spec.pgram_mine 149.63 kB
stoch.reg 145.88 kB
describeBy 82.5 kB
lmBandpass 68.41 kB
