这是几年后的一次更新

这个答案是旧的，而R已经继续了。调整阅读。表跑得快一点没有什么好处。你的选择是:

Using vroom from the tidyverse package vroom for importing data from csv/tab-delimited files directly into an R tibble. See Hector's answer. Using fread in data.table for importing data from csv/tab-delimited files directly into R. See mnel's answer. Using read_table in readr (on CRAN from April 2015). This works much like fread above. The readme in the link explains the difference between the two functions (readr currently claims to be "1.5-2x slower" than data.table::fread). read.csv.raw from iotools provides a third option for quickly reading CSV files. Trying to store as much data as you can in databases rather than flat files. (As well as being a better permanent storage medium, data is passed to and from R in a binary format, which is faster.) read.csv.sql in the sqldf package, as described in JD Long's answer, imports data into a temporary SQLite database and then reads it into R. See also: the RODBC package, and the reverse depends section of the DBI package page. MonetDB.R gives you a data type that pretends to be a data frame but is really a MonetDB underneath, increasing performance. Import data with its monetdb.read.csv function. dplyr allows you to work directly with data stored in several types of database. Storing data in binary formats can also be useful for improving performance. Use saveRDS/readRDS (see below), the h5 or rhdf5 packages for HDF5 format, or write_fst/read_fst from the fst package.

原来的答案

无论你是否使用read，都有一些简单的事情可以尝试。表或扫描。

设置nrows=数据中的记录数量(扫描中的nmax)。 确保你的评论。Char =""关闭注释的解释。 使用read.table中的colClasses显式定义每列的类。 设置多。line=FALSE也可以提高扫描的性能。

如果这些都不起作用，那么使用一个概要包来确定哪些行减慢了速度。也许你可以写一个删减版的read。表基于结果。

另一种方法是在将数据读入R之前过滤数据。

或者，如果问题是必须定期读入，那么使用这些方法一次性读入数据，然后使用save saveRDS将数据帧保存为二进制blob，然后下次使用load readRDS可以更快地检索它。

Often times I think it is just good practice to keep larger databases inside a database (e.g. Postgres). I don't use anything too much larger than (nrow * ncol) ncell = 10M, which is pretty small; but I often find I want R to create and hold memory intensive graphs only while I query from multiple databases. In the future of 32 GB laptops, some of these types of memory problems will disappear. But the allure of using a database to hold the data and then using R's memory for the resulting query results and graphs still may be useful. Some advantages are:

(1)数据一直加载在数据库中。当重新打开笔记本电脑时，只需在pgadmin中重新连接所需的数据库。

(2) R的确可以比SQL做更多漂亮的统计和绘图操作。但是我认为SQL比R更适合于查询大量的数据。

# Looking at Voter/Registrant Age by Decade

library(RPostgreSQL);library(lattice)

con <- dbConnect(PostgreSQL(), user= "postgres", password="password",
                 port="2345", host="localhost", dbname="WC2014_08_01_2014")

Decade_BD_1980_42 <- dbGetQuery(con,"Select PrecinctID,Count(PrecinctID),extract(DECADE from Birthdate) from voterdb where extract(DECADE from Birthdate)::numeric > 198 and PrecinctID in (Select * from LD42) Group By PrecinctID,date_part Order by Count DESC;")

Decade_RD_1980_42 <- dbGetQuery(con,"Select PrecinctID,Count(PrecinctID),extract(DECADE from RegistrationDate) from voterdb where extract(DECADE from RegistrationDate)::numeric > 198 and PrecinctID in (Select * from LD42) Group By PrecinctID,date_part Order by Count DESC;")

with(Decade_BD_1980_42,(barchart(~count | as.factor(precinctid))));
mtext("42LD Birthdays later than 1980 by Precinct",side=1,line=0)

with(Decade_RD_1980_42,(barchart(~count | as.factor(precinctid))));
mtext("42LD Registration Dates later than 1980 by Precinct",side=1,line=0)

这是几年后的一次更新

这个答案是旧的，而R已经继续了。调整阅读。表跑得快一点没有什么好处。你的选择是:

Using vroom from the tidyverse package vroom for importing data from csv/tab-delimited files directly into an R tibble. See Hector's answer. Using fread in data.table for importing data from csv/tab-delimited files directly into R. See mnel's answer. Using read_table in readr (on CRAN from April 2015). This works much like fread above. The readme in the link explains the difference between the two functions (readr currently claims to be "1.5-2x slower" than data.table::fread). read.csv.raw from iotools provides a third option for quickly reading CSV files. Trying to store as much data as you can in databases rather than flat files. (As well as being a better permanent storage medium, data is passed to and from R in a binary format, which is faster.) read.csv.sql in the sqldf package, as described in JD Long's answer, imports data into a temporary SQLite database and then reads it into R. See also: the RODBC package, and the reverse depends section of the DBI package page. MonetDB.R gives you a data type that pretends to be a data frame but is really a MonetDB underneath, increasing performance. Import data with its monetdb.read.csv function. dplyr allows you to work directly with data stored in several types of database. Storing data in binary formats can also be useful for improving performance. Use saveRDS/readRDS (see below), the h5 or rhdf5 packages for HDF5 format, or write_fst/read_fst from the fst package.

原来的答案

无论你是否使用read，都有一些简单的事情可以尝试。表或扫描。

设置nrows=数据中的记录数量(扫描中的nmax)。 确保你的评论。Char =""关闭注释的解释。 使用read.table中的colClasses显式定义每列的类。 设置多。line=FALSE也可以提高扫描的性能。

如果这些都不起作用，那么使用一个概要包来确定哪些行减慢了速度。也许你可以写一个删减版的read。表基于结果。

另一种方法是在将数据读入R之前过滤数据。

或者，如果问题是必须定期读入，那么使用这些方法一次性读入数据，然后使用save saveRDS将数据帧保存为二进制blob，然后下次使用load readRDS可以更快地检索它。

下面是一个使用fread from data的例子。表1.8.7

这些例子来自我的windows XP Core 2 duo E8400上的帮助页面。

library(data.table)
# Demo speedup
n=1e6
DT = data.table( a=sample(1:1000,n,replace=TRUE),
                 b=sample(1:1000,n,replace=TRUE),
                 c=rnorm(n),
                 d=sample(c("foo","bar","baz","qux","quux"),n,replace=TRUE),
                 e=rnorm(n),
                 f=sample(1:1000,n,replace=TRUE) )
DT[2,b:=NA_integer_]
DT[4,c:=NA_real_]
DT[3,d:=NA_character_]
DT[5,d:=""]
DT[2,e:=+Inf]
DT[3,e:=-Inf]

标准read.table

write.table(DT,"test.csv",sep=",",row.names=FALSE,quote=FALSE)
cat("File size (MB):",round(file.info("test.csv")$size/1024^2),"\n")    
## File size (MB): 51 

system.time(DF1 <- read.csv("test.csv",stringsAsFactors=FALSE))        
##    user  system elapsed 
##   24.71    0.15   25.42
# second run will be faster
system.time(DF1 <- read.csv("test.csv",stringsAsFactors=FALSE))        
##    user  system elapsed 
##   17.85    0.07   17.98

优化read.table

system.time(DF2 <- read.table("test.csv",header=TRUE,sep=",",quote="",  
                          stringsAsFactors=FALSE,comment.char="",nrows=n,                   
                          colClasses=c("integer","integer","numeric",                        
                                       "character","numeric","integer")))


##    user  system elapsed 
##   10.20    0.03   10.32

从文件中读

require(data.table)
system.time(DT <- fread("test.csv"))                                  
 ##    user  system elapsed 
##    3.12    0.01    3.22

sqldf

require(sqldf)

system.time(SQLDF <- read.csv.sql("test.csv",dbname=NULL))             

##    user  system elapsed 
##   12.49    0.09   12.69

# sqldf as on SO

f <- file("test.csv")
system.time(SQLf <- sqldf("select * from f", dbname = tempfile(), file.format = list(header = T, row.names = F)))

##    user  system elapsed 
##   10.21    0.47   10.73

Ff / FFDF

 require(ff)

 system.time(FFDF <- read.csv.ffdf(file="test.csv",nrows=n))   
 ##    user  system elapsed 
 ##   10.85    0.10   10.99

总而言之:

##    user  system elapsed  Method
##   24.71    0.15   25.42  read.csv (first time)
##   17.85    0.07   17.98  read.csv (second time)
##   10.20    0.03   10.32  Optimized read.table
##    3.12    0.01    3.22  fread
##   12.49    0.09   12.69  sqldf
##   10.21    0.47   10.73  sqldf on SO
##   10.85    0.10   10.99  ffdf

这个问题之前在R-Help上被问到过，所以值得回顾一下。

一个建议是使用readChar()，然后用strsplit()和substr()对结果进行字符串操作。您可以看到readChar所涉及的逻辑比read.table要少得多。

我不知道这里内存是否是一个问题，但您可能还想看看HadoopStreaming包。它使用Hadoop，这是一个MapReduce框架，设计用于处理大型数据集。为此，您将使用hsTableReader函数。这是一个例子(但是学习Hadoop有一个学习曲线):

str <- "key1\t3.9\nkey1\t8.9\nkey1\t1.2\nkey1\t3.9\nkey1\t8.9\nkey1\t1.2\nkey2\t9.9\nkey2\"
cat(str)
cols = list(key='',val=0)
con <- textConnection(str, open = "r")
hsTableReader(con,cols,chunkSize=6,FUN=print,ignoreKey=TRUE)
close(con)

这里的基本思想是将数据导入分解成块。您甚至可以使用一个并行框架(例如snow)，并通过分割文件来并行运行数据导入，但对于大型数据集来说，这是没有帮助的，因为您将遇到内存限制，这就是为什么map-reduce是一种更好的方法。

另一种选择是使用vroom包。现在在CRAN。 Vroom不加载整个文件，它索引每条记录所在的位置，并在稍后使用它时读取。

只按使用付费。

请参阅vroom介绍，开始使用vroom和vroom基准。

基本的概述是，对一个大文件的初始读取将会快得多，而对数据的后续修改可能会稍微慢一些。所以根据你的用途，这可能是最好的选择。

查看下面vroom基准测试的简化示例，关键部分是超快的读取时间，但稍微播种操作，如聚合等。

package                 read    print   sample   filter  aggregate   total
read.delim              1m      21.5s   1ms      315ms   764ms       1m 22.6s
readr                   33.1s   90ms    2ms      202ms   825ms       34.2s
data.table              15.7s   13ms    1ms      129ms   394ms       16.3s
vroom (altrep) dplyr    1.7s    89ms    1.7s     1.3s    1.9s        6.7s