如何使用 R 或 PowerShell 从文本文件中提取数据? [英] How to extract data from a text file using R or PowerShell?

问题描述

我有一个包含如下数据的文本文件:

I have a text file containing data like this:

This is just text
-------------------------------
Username:          SOMETHI           C:                 [Text]
Account:           DFAG              Finish time:        1-JAN-2011 00:31:58.91
Process ID:        2028aaB           Start time:        31-DEC-2010 20:27:15.30

This is just text
-------------------------------
Username:          SOMEGG            C:                 [Text]
Account:           DFAG              Finish time:        1-JAN-2011 00:31:58.91
Process ID:        20dd33DB          Start time:        12-DEC-2010 20:27:15.30

This is just text
-------------------------------
Username:          SOMEYY            C:                 [Text]
Account:           DFAG              Finish time:        1-JAN-2011 00:31:58.91
Process ID:        202223DB          Start time:        15-DEC-2010 20:27:15.30

有没有办法从这种数据中提取用户名、完成时间、开始时间?我正在寻找使用 R 或 Powershell 的起点.

Is there a way to extract Username, Finish time, Start time from this kind of data? I'm looking for some starting point usign R or Powershell.

推荐答案

R 可能不是处理文本文件的最佳工具，但您可以进行如下操作:通过将文件读取为固定宽度文件来识别两列, 通过拆分冒号上的字符串将字段与其值分开，添加一个id"列，然后将所有内容放回原处.

R may not be the best tool to process text files, but you can proceed as follows: identify the two columns by reading the file as a fixed-width file, separate the fields from their value by splitting the strings on the colons, add an "id" column, and put everything back in order.

# Read the file
d <- read.fwf("A.txt", c(37,100), stringsAsFactors=FALSE)

# Separate fields and values
d <- d[grep(":", d$V1),]
d <- cbind( 
  do.call( rbind, strsplit(d$V1, ":\\s+") ), 
  do.call( rbind, strsplit(d$V2, ":\\s+") ) 
)

# Add an id column
d <- cbind( d, cumsum( d[,1] == "Username" ) )

# Stack the left and right parts
d <- rbind( d[,c(5,1,2)], d[,c(5,3,4)] )
colnames(d) <- c("id", "field", "value")
d <- as.data.frame(d)
d$value <- gsub("\\s+$", "", d$value)

# Convert to a wide data.frame
library(reshape2)
d <- dcast( d, id ~ field )

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