这并没有增加上面的内容，而是以我喜欢的简单和大量注释的风格编写的。它生成一个对象大小排序表，但没有上面例子中给出的一些细节:

#Find the objects       
MemoryObjects = ls()    
#Create an array
MemoryAssessmentTable=array(NA,dim=c(length(MemoryObjects),2))
#Name the columns
colnames(MemoryAssessmentTable)=c("object","bytes")
#Define the first column as the objects
MemoryAssessmentTable[,1]=MemoryObjects
#Define a function to determine size        
MemoryAssessmentFunction=function(x){object.size(get(x))}
#Apply the function to the objects
MemoryAssessmentTable[,2]=t(t(sapply(MemoryAssessmentTable[,1],MemoryAssessmentFunction)))
#Produce a table with the largest objects first
noquote(MemoryAssessmentTable[rev(order(as.numeric(MemoryAssessmentTable[,2]))),])

确保在可重复的脚本中记录您的工作。不时地重新打开R，然后source()您的脚本。您将清除不再使用的任何东西，作为一个额外的好处，您将测试您的代码。

如果您正在Linux上工作，希望使用多个进程，并且只需要对一个或多个大对象执行读取操作，请使用makeForkCluster而不是makePSOCKcluster。这也节省了将大对象发送给其他进程的时间。

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 )

gData包中的llfunction也可以显示每个对象的内存使用情况。

gdata::ll(unit='MB')

I'm fortunate and my large data sets are saved by the instrument in "chunks" (subsets) of roughly 100 MB (32bit binary). Thus I can do pre-processing steps (deleting uninformative parts, downsampling) sequentially before fusing the data set. Calling gc () "by hand" can help if the size of the data get close to available memory. Sometimes a different algorithm needs much less memory. Sometimes there's a trade off between vectorization and memory use. compare: split & lapply vs. a for loop. For the sake of fast & easy data analysis, I often work first with a small random subset (sample ()) of the data. Once the data analysis script/.Rnw is finished data analysis code and the complete data go to the calculation server for over night / over weekend / ... calculation.