将txt文件合并为R上的数据文件

Question

我有一个带有100,000+行数据的txt文件。我想把它变成一个数据，但不需要每一行数据。数据输入的示例如下所示：

FN Clarivate Analytics Web of Science
VR 1.0
PT J
AU Yang, Qiang
   Liu, Yang
   Chen, Tianjian
   Tong, Yongxin
TI Federated Machine Learning: Concept and Applications
SO ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
VL 10
IS 2
AR 12
DI 10.1145/3298981
DT Article
PD FEB 2019
PY 2019
AB Today's artificial intelligence still faces two major challenges (...) etc. 

我只想要开始TI，AU，PD，AB的行，并将它们提取到相应的命名列中。这就是我所得到的，我真的在挣扎！

read.table("groupprojectdatabase.txt", header = FALSE, sep = ",", quote = "",
           dec = ".", numerals = c("allow.loss"),
           row.names = c("TI", "AU", "PB","AB"), col.names = c('title_col','author_col','date_col','summary_col'), as.is = !stringsAsFactors,
           na.strings = "NA", colClasses = NA, nrows = -1,
           skip = 0, check.names = TRUE, fill = FALSE,
           strip.white = FALSE, blank.lines.skip = TRUE,
           comment.char = "#",
           allowEscapes = FALSE, flush = FALSE,
           stringsAsFactors = FALSE,
           fileEncoding = "", encoding = "unknown", text, skipNul = FALSE)

任何帮助都将是非常感谢的，即使这是我需要查找的功能，或者我在正确的轨道上。我在想，sep =命令是相关的，但我想不出怎么告诉它跳过除了TI、AU、PB和AB行之外的所有东西

特别是，我不知道如何编程R来处理整个句子作为变量，而不是每个单词等等。

Error in scan(file = file, what = what, sep = sep, quote = quote, dec = dec,  : 
  line 1 did not have 4 elements

Answer 1

我已经根据你上面的数据制作了一个文件test.txt。在使用read.table遇到一些问题之后，我从tidyverse切换到了read::read_delim。

这将逐行读取文件。然后用第一个whitespace分隔这一行，即前两个字母之后。

因为有4行(AU前两个字母)属于一起，下面代码的最后一部分将这些行合并在一起。

library(tidyverse)

df <- read_delim("path_to_your/test.txt", delim = ";", col_names = TRUE)

ddf <- df |> 
  separate(`FN Clarivate Analytics Web of Science`, 
           into = c("first", "rest"), 
           sep = " ", extra = 'merge') |> 
  mutate(first = ifelse(first == "", NA, first)) |> 
  fill(first) |> 
  group_by(first) |> 
  mutate(rest = paste0(rest, collapse = "")) |> 
  distinct(first, .keep_all = T)
  
ddf |> 
  filter(first %in% c('TI', 'AU', 'PD', 'AB'))

#> # A tibble: 4 × 2
#> # Groups:   first [4]
#>   first rest                                                            
#>   <chr> <chr>                                                           
#> 1 AU    Yang, Qiang  Liu, Yang  Chen, Tianjian  Tong, Yongxin           
#> 2 TI    Federated Machine Learning: Concept and Applications            
#> 3 PD    FEB 2019                                                        
#> 4 AB    Today's artificial intelligence still faces two major challenges