Question

我正在尝试将具有以下结构的对话导入数据框：

conversation<-data.frame(
             uniquerow=c("01/08/2015 2:49:49 pm: Person 1: Hello",
                         "01/08/2015 2:51:49 pm: Person 2: Nice to meet you",
                         "01/08/2015 2:59:19 pm: Person 1: Same here"))

这种结构可以使解析日期，时间，人物和消息变得相对容易。但是有一些情况下消息带有换行符，因此数据帧是错误结构的，如下所示：

conversation_errors<-data.frame(
                     uniquerow=c("01/08/2015 2:49:49 pm: Person 1: Hello",
                                 "01/08/2015 2:51:49 pm: Person 2: Nice to meet you",
                                 "01/08/2015 2:59:19 pm: Person 1: Same here, let me tell you a haiku: ",
                                 "lend me your arms,",
                                 "fast as thunderbolts,",
                                 "for a pillow on my journey."))

你会如何合并这些实例？有没有我不知道的包裹？

所需的功能只是识别缺失的结构并与前一行“合并”，这样我就可以得到：

conversation_fixed<-data.frame(
                    uniquerow=c("01/08/2015 2:49:49 pm: Person 1: Hello",
                                "01/08/2015 2:51:49 pm: Person 2: Nice to meet you",
                                "01/08/2015 2:59:19 pm: Person 1: Same here, let me tell you a haiku: lend me your arms, fast as thunderbolts, for a pillow on my journey."))

有什么想法吗？

Answer 1

假设您可以使用时间戳（在下面properDataRegex中表示）正确识别结构正确的行，那么这样就可以了：

mydata <- c("01/08/2015 2:49:49 pm: Person 1: Hello",
            "01/08/2015 2:51:49 pm: Person 2: Nice to meet you",
            "01/08/2015 2:59:19 pm: Person 1: Same here, let me tell you a haiku: ",
            "lend me your arms,",
            "fast as thunderbolts,",
            "for a pillow on my journey.",
            "07/07/2015 3:29:00 pm: Person 1: This is not the most efficient method",
            "but it will get the job done.")

properDataRegex <- "^\\d{2}/\\d{2}/\\d{4}\\s"
improperDataBool <- !grepl(properDataRegex, mydata)
while (sum(improperDataBool)) {
    mergeWPrevIndex <- which(c(FALSE, !improperDataBool[-length(improperDataBool)]) & 
                             improperDataBool)
    mydata[mergeWPrevIndex - 1] <- 
        paste(mydata[mergeWPrevIndex - 1], mydata[mergeWPrevIndex])
    mydata <- mydata[-mergeWPrevIndex]
    improperDataBool <- !grepl(properDataRegex, mydata)
}

mydata
## [1] "01/08/2015 2:49:49 pm: Person 1: Hello"                                                                                                    
## [2] "01/08/2015 2:51:49 pm: Person 2: Nice to meet you"                                                                                         
## [3] "01/08/2015 2:59:19 pm: Person 1: Same here, let me tell you a haiku:  lend me your arms, fast as thunderbolts, for a pillow on my journey."
## [4] "07/07/2015 3:29:00 pm: Person 1: This is not the most efficient method but it will get the job done."

在这里，mydata是一个字符向量，但当然现在可以像在问题中一样制作数据框，或使用read.table()或read.fwf()解析它

Answer 2

这是另一种方法：

read.table(text=paste(gsub("(^\\d{2}/\\d{2}/\\d{4}\\s)", "\n\\1", conversation_errors$uniquerow),
                      collapse = " "), sep = "\n", stringsAsFactors = F)[,1]

给出了：

[1] "01/08/2015 2:49:49 pm: Person 1: Hello "                                                                                                   
[2] "01/08/2015 2:51:49 pm: Person 2: Nice to meet you "                                                                                        
[3] "01/08/2015 2:59:19 pm: Person 1: Same here, let me tell you a haiku:  lend me your arms, fast as thunderbolts, for a pillow on my journey."

（感谢Ken借用的正则表达式）

通过字符串解析合并数据框行

2 个答案: