在R中选择性读取txt文件

Question

I'm looking for an easy fix to read a txt file that looks like this when opened in excel:

IDmaster    By_uspto    App_date    Grant_date  Applicant   Cited   
2   1   19671106    19700707    Motorola Inc    1052446 
2   1   19740909    19751028    Gen Motors Corp 1062884 
2   1   19800331    19820817    Amp Incorporated    1082369 
2   1   19910515    19940719    Dell Usa L.P.   389546  
2   1   19940210    19950912    Schueman Transfer    Inc.   1164239
2   1   19940217    19950912    Spacelabs Medical    Inc.   1164336

EDIT: Opening the txt file in notepad looks like this (with commas). The last two rows exhibit the problem.

IDmaster,By_uspto,App_date,Grant_date,Applicant,Cited
2,1,19671106,19700707,Motorola Inc,1052446
2,1,19740909,19751028,Gen Motors Corp,1062884
2,1,19800331,19820817,Amp Incorporated,1082369
2,1,19910515,19940719,Dell Usa L.P.,389546
2,1,19940210,19950912,Schueman Transfer, Inc.,1164239
2,1,19940217,19950912,Spacelabs Medical, Inc.,1164336

The problem is that some of the Applicant names contain commas so that they are read as if they belong in a different column, which they actually don't.

Is there a simple way to a) "teach" R to keep string variables together, regardless of commas in between b) read in the first 4 columns, and then add an extra column for everything behind the last comma?

Given the length of the data I can't open it entirely in excel which would be otherwise a simple alternative.

Answer 1

If your example is written in a "Test.csv" file, try with:

read.csv(text=gsub(', ', ' ', paste0(readLines("Test.csv"),collapse="\n")),
         quote="'",
         stringsAsFactors=FALSE)

It returns:

#   IDmaster By_uspto App_date Grant_date              Applicant   Cited
# 1        2        1 19671106   19700707           Motorola Inc 1052446
# 2        2        1 19740909   19751028        Gen Motors Corp 1062884
# 3        2        1 19800331   19820817       Amp Incorporated 1082369
# 4        2        1 19910515   19940719          Dell Usa L.P.  389546
# 5        2        1 19940210   19950912 Schueman Transfer Inc. 1164239
# 6        2        1 19940217   19950912 Spacelabs Medical Inc. 1164336

Answer 2

This provides a very silly workaround but it does the trick for me (because I don't really care about the Applicant names atm. However, I'm hoping for a better solution.

Step 1: Open the .txt file in notepad, and add five column names V1, V2, V3, V4, V5 (to be sure to capture names with multiple commas).

bc <- read.table("data.txt", header = T, na.strings = T, fill = T, sep = ",", stringsAsFactors = F)

library(data.table)

sapply(bc, class)
unique(bc$V5) # only NA so can be deleted
setDT(bc)
bc <- bc[,1:10, with = F]
bc$Cited <- as.numeric(bc$Cited)
  bc$Cited[is.na(bc$Cited)] <- 0
  bc$V1 <- as.numeric(bc$V1)
  bc$V2 <- as.numeric(bc$V2)
  bc$V3 <- as.numeric(bc$V3)
  bc$V4 <- as.numeric(bc$V4)

  bc$V1[is.na(bc$V1)] <- 0
  bc$V2[is.na(bc$V2)] <- 0
  bc$V3[is.na(bc$V3)] <- 0
  bc$V4[is.na(bc$V4)] <- 0

head(bc, 10)
bc$Cited <- with(bc, Cited + V1 + V2 + V3 + V4)

It's a silly patch but it does the trick in this particular context