扫描错误（文件，什么，nmax，sep，dec，quote，skip，nlines，na.strings，：第1行没有2个元素

2 回答

• 7

  看起来你只需要跳过几行 . 我使用 readLines(textConnection(examdata)) 来确定实际数据表的开始位置 . 原来它从第32行开始 . 因此，我们可以使用 read.csv 中的 skip 参数跳过前31行 . 我使用了 strip.white 参数，因为表中似乎有一些错误的空格 .

  (df <- read.csv(text = examdata, skip = 31L, strip.white = TRUE))
  #                          Type Cash Check Credit Debit Electronic Other Total
  # 1 Average Number of Purchases 23.7   3.9   10.1  14.4        4.4   2.3  58.7
  # 2   Average Transaction Value  $21  $168    $56   $44       $216   $69   $59
  # 3      Value of Payments in %   14    19     16    18         27     5   100
  

  由于'll probably want those numbers to be numeric, you'll需要删除 $ 符号并将列转换为数字，因此您可以将它们用于以后可能执行的任何计算 .

  df[-1] <- lapply(df[-1], function(x) as.numeric(sub("[$]", "", x)))
  df
  #                          Type Cash Check Credit Debit Electronic Other Total
  # 1 Average Number of Purchases 23.7   3.9   10.1  14.4        4.4   2.3  58.7
  # 2   Average Transaction Value 21.0 168.0   56.0  44.0      216.0  69.0  59.0
  # 3      Value of Payments in % 14.0  19.0   16.0  18.0       27.0   5.0 100.0
  

  现在除了第一列之外的所有列都是数字 .

  回复于 2024-05-04T16:58:07+08:00
• 0

  read.table 和 read.csv 会将URL作为路径并为您处理连接，因此您不需要 RCurl ：

  read.csv("https://raw.githubusercontent.com/jrwolf/IT497/master/spendingdata.txt", 
           skip = 31)
  
  ##                          Type Cash Check Credit Debit Electronic Other Total
  ## 1 Average Number of Purchases 23.7   3.9   10.1  14.4        4.4   2.3  58.7
  ## 2   Average Transaction Value  $21  $168    $56   $44       $216   $69   $59
  

  此外，如果您使用 readr::read_csv ，您可以告诉它将列解析为数字，在读取时删除 $ 个字符：

  library(readr)
  
  read_csv("https://raw.githubusercontent.com/jrwolf/IT497/master/spendingdata.txt", 
           skip = 31, 
           col_types = cols(Type = 'c', .default = 'n'))    # c = character, n = number
  
  ## # A tibble: 2 × 8
  ##                          Type  Cash Check Credit Debit Electronic Other Total
  ##                         <chr> <dbl> <dbl>  <dbl> <dbl>      <dbl> <dbl> <dbl>
  ## 1 Average Number of Purchases  23.7   3.9   10.1  14.4        4.4   2.3  58.7
  ## 2   Average Transaction Value  21.0 168.0   56.0  44.0      216.0  69.0  59.0
  

  回复于 2024-05-04T16:58:07+08:00