问题描述
我有一个庞大的csv文件.它的大小约为9 GB.我有16 GB的RAM.我遵循了页面中的建议，并实施了这些建议以下.
I have a huge csv file. Its size is around 9 gb. I have 16 gb of ram. I followed the advises from the page and implemented them below.
If you get the error that R cannot allocate a vector of length x, close out of R and add the following line to the ``Target'' field:
--max-vsize=500M
仍然出现以下错误和警告.我应该如何将9 GB的文件读入我的R中?我有R 64位3.3.1，并且在rstudio 0.99.903中的命令下运行.我有Windows Server 2012 R2标准版，64位操作系统.
Still I am getting the error and warnings below. How should I read the file of 9 gb into my R? I have R 64 bit 3.3.1 and I am running below command in the rstudio 0.99.903. I have windows server 2012 r2 standard, 64 bit os.
> memory.limit()
[1] 16383
> answer=read.csv("C:/Users/a-vs/results_20160291.csv")
Error: cannot allocate vector of size 500.0 Mb
In addition: There were 12 warnings (use warnings() to see them)
> warnings()
Warning messages:
1: In scan(file = file, what = what, sep = sep, quote = quote, ... :
Reached total allocation of 16383Mb: see help(memory.size)
2: In scan(file = file, what = what, sep = sep, quote = quote, ... :
Reached total allocation of 16383Mb: see help(memory.size)
3: In scan(file = file, what = what, sep = sep, quote = quote, ... :
Reached total allocation of 16383Mb: see help(memory.size)
4: In scan(file = file, what = what, sep = sep, quote = quote, ... :
Reached total allocation of 16383Mb: see help(memory.size)
5: In scan(file = file, what = what, sep = sep, quote = quote, ... :
Reached total allocation of 16383Mb: see help(memory.size)
6: In scan(file = file, what = what, sep = sep, quote = quote, ... :
Reached total allocation of 16383Mb: see help(memory.size)
7: In scan(file = file, what = what, sep = sep, quote = quote, ... :
Reached total allocation of 16383Mb: see help(memory.size)
8: In scan(file = file, what = what, sep = sep, quote = quote, ... :
Reached total allocation of 16383Mb: see help(memory.size)
9: In scan(file = file, what = what, sep = sep, quote = quote, ... :
Reached total allocation of 16383Mb: see help(memory.size)
10: In scan(file = file, what = what, sep = sep, quote = quote, ... :
Reached total allocation of 16383Mb: see help(memory.size)
11: In scan(file = file, what = what, sep = sep, quote = quote, ... :
Reached total allocation of 16383Mb: see help(memory.size)
12: In scan(file = file, what = what, sep = sep, quote = quote, ... :
Reached total allocation of 16383Mb: see help(memory.size)
------------------- Update1
我第一次尝试根据建议的答案
------------------- Update1
My 1st try based upon suggested answer
> thefile=fread("C:/Users/a-vs/results_20160291.csv", header = T)
Read 44099243 rows and 36 (of 36) columns from 9.399 GB file in 00:13:34
Warning messages:
1: In fread("C:/Users/a-vsingh/results_tendo_20160201_20160215.csv", :
Reached total allocation of 16383Mb: see help(memory.size)
2: In fread("C:/Users/a-vsingh/results_tendo_20160201_20160215.csv", :
Reached total allocation of 16383Mb: see help(memory.size)
------------------- Update2
我根据建议答案的第二次尝试如下
------------------- Update2
my 2nd try based upon suggested answer is as below
thefile2 <- read.csv.ffdf(file="C:/Users/a-vs/results_20160291.csv", header=TRUE, VERBOSE=TRUE,
+ first.rows=-1, next.rows=50000, colClasses=NA)
read.table.ffdf 1..
Error: cannot allocate vector of size 125.0 Mb
In addition: There were 14 warnings (use warnings() to see them)
我怎样才能将此文件读入一个对象中，以便一次性分析所有数据
How could I read this file into a single object so that I can analyze the entire data in one go
我们买了一台昂贵的机器.它具有10个内核和256 gb的ram.那不是最有效的解决方案，但至少在不久的将来会奏效.我查看了以下答案，但我认为它们不能解决我的问题:(我感谢这些答案.我想进行市场分析，我认为没有其他方法可以将数据保存在RAM中
We bought an expensive machine. It has 10 cores and 256 gb ram. That is not the most efficient solution but it works at least in near future. I looked at below answers and I dont think they solve my problem :( I appreciate these answers. I want to perform the market basket analysis and I dont think there is no other way around rather than keeping my data in RAM
推荐答案
请确保您使用的是64位R，而不仅仅是64位Windows，以便可以将RAM分配增加到全部16 GB.
Make sure you're using 64-bit R, not just 64-bit Windows, so that you can increase your RAM allocation to all 16 GB.
此外，您可以分块读取文件:
In addition, you can read in the file in chunks:
file_in <- file("in.csv","r")
chunk_size <- 100000 # choose the best size for you
x <- readLines(file_in, n=chunk_size)
您可以使用data.table更有效地处理大型文件的读取和处理:
You can use data.table to handle reading and manipulating large files more efficiently:
require(data.table)
fread("in.csv", header = T)
如果需要，您可以使用ff来利用存储内存:
If needed, you can leverage storage memory with ff:
library("ff")
x <- read.csv.ffdf(file="file.csv", header=TRUE, VERBOSE=TRUE,
first.rows=10000, next.rows=50000, colClasses=NA)
这篇关于R阅读巨大的CSV的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！