如何使用R“readLines”命令从大文件中读取选定的行并将它们写入数据框？

I am engaged in data cleaning. I have a function that identifies bad rows in a large input file (too big to read at one go, given my ram size) and returns the row numbers of the bad rows as a vector badRows. This function seems to work.

我从事数据清理工作。我有一个函数可以识别大型输入文件中的坏行（对于我的ram大小来说，太大而无法一次读取）并将坏行的行号作为向量badRows返回。这个功能似乎有效。

I am now trying to read just the bad rows into a data frame, so far unsuccessfully.

我现在正试图将坏行读入数据帧，到目前为止还没有成功。

My current approach is to use read.table on an open connection to my file, using a vector of the number of rows to skip between each row that is read. This number is zero for consecutive bad rows.

我目前的方法是在我的文件的打开连接上使用read.table，使用在读取的每一行之间跳过的行数向量。对于连续的坏行，此数字为零。

I calculate skipVec as:

我将skipVec计算为：

(badRowNumbers - c(0, badRowNumbers[1:(length(badRowNumbers-1]))-1

But for the moment I am just handing my function a skipVec vector of all zeros.

但目前我只是将我的函数交给一个全零的skipVec向量。

If my logic is correct, this should return all the rows. It does not. Instead I get an error:

如果我的逻辑是正确的，这应该返回所有行。它不是。相反，我得到一个错误：

"Error in read.table(con, skip = pass, nrow = 1, header = TRUE, sep = "") : no lines available in input"

“read.table中的错误（con，skip = pass，nrow = 1，header = TRUE，sep =”“）：输入中没有可用的行”

My current function is loosely based on a function by Miron Kursa ("mbq"), which I found here.

我目前的功能基于Miron Kursa（“mbq”）的功能，我在这里找到了它。

My question is somewhat duplicative of that one, but I assume his function works, so I have broken it somehow. I am still trying to understand the difference between opening a file and opening a connection to a file, and I suspect that the problem is there somewhere, or in my use of lapply.

我的问题有点重复，但我认为他的功能有效，所以我以某种方式打破了它。我仍然试图理解打开文件和打开文件连接之间的区别，我怀疑问题出在某处，或者我使用了lapply。

I am running R 3.0.1 under RStudio 0.97.551 on a cranky old Windows XP SP3 machine with 3gig of ram. Stone Age, I know.

我在RStudio 0.97.551下运行R 3.0.1，在一台老式的Windows XP SP3机器上运行3gig。石器时代，我知道。

Here is the code that produces the error message above:

以下是产生上述错误消息的代码：

# Make a small small test data frame, write it to a file, and read it back in 
# a row at a time.
testThis.DF <- data.frame(nnn=c(2,3,5), fff=c("aa", "bb", "cc"))  
testThis.DF 

# This function will work only if the number of bad rows is not too big for memory
write.table(testThis.DF, "testThis.DF")
con<-file("testThis.DF")
open(con)
skipVec <- c(0,0,0)
badRows.DF  <- lapply(skipVec, FUN=function(pass){
  read.table(con, skip=pass, nrow=1, header=TRUE, sep="") })
close(con)

The error occurs before the close command. If I yank the readLines command out of the lapply and the function and just stick it in by itself, I still get the same error.

在关闭命令之前发生错误。如果我将readLines命令从lapply和函数中拉出来并且只是将它自己粘在一起，我仍然会得到同样的错误。

1 个解决方案

#1

If instead of running read.table through lapply you just run the first few iterations manually, you will see what is going on:

如果不是通过lapply运行read.table而是手动运行前几次迭代，您将看到发生了什么：

> read.table(con, skip=0, nrow=1, header=TRUE, sep="")
  nnn fff
1   2  aa
> read.table(con, skip=0, nrow=1, header=TRUE, sep="")
  X2 X3 bb
1  3  5 cc

Because header = TRUE it is not one line that is read at each iteration but two, so you eventually run out of lines faster than you think, here on the third iteration:

因为header = TRUE，它不是每次迭代读取的一行而是两行，所以你最终会比你想象的更快地用完行，这里是第三次迭代：

> read.table(con, skip=0, nrow=1, header=TRUE, sep="")
Error in read.table(con, skip = 0, nrow = 1, header = TRUE, sep = "") : 
  no lines available in input

Now this might still not be a very efficient way of solving your problem, but this is how you can fix your current code:

现在，这可能仍然不是解决问题的有效方法，但这是您可以修复当前代码的方法：

write.table(testThis.DF, "testThis.DF")
con <- file("testThis.DF")
open(con)
header <- scan(con, what = character(), nlines = 1, quiet = TRUE)
skipVec <- c(0,1,0)
badRows <- lapply(skipVec, function(pass){
  line <- read.table(con, nrow = 1, header = FALSE, sep = "",
                     row.names = 1)
  if (pass) NULL else line
  })
badRows.DF <- setNames(do.call(rbind, badRows), header)
close(con)

Some clues towards higher speeds:

一些提高速度的线索：

use scan instead of read.table. Read data as character and only at the end, after you have put your data into a character matrix or data.frame, apply type.convert to each column.
使用scan而不是read.table。将数据作为字符读取，并且仅在最后，将数据放入字符矩阵或data.frame后，将type.convert应用于每列。
Instead of looping over skipVec, loop over its rle if it is much shorter. So you'll be able to read or skip chunks of lines at a time.
如果它更短，则不要在skipVec上循环，而是在其rle上循环。因此，您将能够一次读取或跳过大量的行。

#1