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)
}
使用环境而不是列表来处理占用大量工作内存的对象集合。
原因是:每当列表结构的一个元素被修改时，整个列表都会被临时复制。如果列表的存储需求大约是可用工作内存的一半，这就会成为一个问题，因为这时必须将数据交换到慢速硬盘上。另一方面，环境不受这种行为的影响，它们可以类似于列表。
这里有一个例子:
get.data <- function(x)
{
# get some data based on x
return(paste("data from",x))
}
collect.data <- function(i,x,env)
{
# get some data
data <- get.data(x[[i]])
# store data into environment
element.name <- paste("V",i,sep="")
env[[element.name]] <- data
return(NULL)
}
better.list <- new.env()
filenames <- c("file1","file2","file3")
lapply(seq_along(filenames),collect.data,x=filenames,env=better.list)
# read/write access
print(better.list[["V1"]])
better.list[["V2"]] <- "testdata"
# number of list elements
length(ls(better.list))
结合结构，如大。矩阵或数据。表允许修改其内容的地方，非常有效的内存使用可以实现。
只有4GB的内存(运行Windows 10，所以大约是2或更现实的1GB)，我必须非常小心地分配。
我使用数据。几乎只有桌子。
'fread'函数允许您在导入时按字段名划分信息子集;只导入开始时实际需要的字段。如果使用base R read，则在导入后立即将伪列空。
正如42-所建议的，只要有可能，我将在导入信息后立即在列中进行子集。
我经常从环境中rm()对象，一旦他们不再需要，例如在使用他们子集其他东西后的下一行，并调用gc()。
'fread'和'fwrite'从数据。表的读写速度可以非常快。
As kpierce8 suggests, I almost always fwrite everything out of the environment and fread it back in, even with thousand / hundreds of thousands of tiny files to get through. This not only keeps the environment 'clean' and keeps the memory allocation low but, possibly due to the severe lack of RAM available, R has a propensity for frequently crashing on my computer; really frequently. Having the information backed up on the drive itself as the code progresses through various stages means I don't have to start right from the beginning if it crashes.
As of 2017, I think the fastest SSDs are running around a few GB per second through the M2 port. I have a really basic 50GB Kingston V300 (550MB/s) SSD that I use as my primary disk (has Windows and R on it). I keep all the bulk information on a cheap 500GB WD platter. I move the data sets to the SSD when I start working on them. This, combined with 'fread'ing and 'fwrite'ing everything has been working out great. I've tried using 'ff' but prefer the former. 4K read/write speeds can create issues with this though; backing up a quarter of a million 1k files (250MBs worth) from the SSD to the platter can take hours. As far as I'm aware, there isn't any R package available yet that can automatically optimise the 'chunkification' process; e.g. look at how much RAM a user has, test the read/write speeds of the RAM / all the drives connected and then suggest an optimal 'chunkification' protocol. This could produce some significant workflow improvements / resource optimisations; e.g. split it to ... MB for the ram -> split it to ... MB for the SSD -> split it to ... MB on the platter -> split it to ... MB on the tape. It could sample data sets beforehand to give it a more realistic gauge stick to work from.
A lot of the problems I've worked on in R involve forming combination and permutation pairs, triples etc, which only makes having limited RAM more of a limitation as they will often at least exponentially expand at some point. This has made me focus a lot of attention on the quality as opposed to quantity of information going into them to begin with, rather than trying to clean it up afterwards, and on the sequence of operations in preparing the information to begin with (starting with the simplest operation and increasing the complexity); e.g. subset, then merge / join, then form combinations / permutations etc.
There do seem to be some benefits to using base R read and write in some instances. For instance, the error detection within 'fread' is so good it can be difficult trying to get really messy information into R to begin with to clean it up. Base R also seems to be a lot easier if you're using Linux. Base R seems to work fine in Linux, Windows 10 uses ~20GB of disc space whereas Ubuntu only needs a few GB, the RAM needed with Ubuntu is slightly lower. But I've noticed large quantities of warnings and errors when installing third party packages in (L)Ubuntu. I wouldn't recommend drifting too far away from (L)Ubuntu or other stock distributions with Linux as you can loose so much overall compatibility it renders the process almost pointless (I think 'unity' is due to be cancelled in Ubuntu as of 2017). I realise this won't go down well with some Linux users but some of the custom distributions are borderline pointless beyond novelty (I've spent years using Linux alone).
希望其中一些能帮助到其他人。
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 )
