examdata <- RCurl::getURL("https://raw.githubusercontent.com/jrwolf/IT497/master/spendingdata.txt")
examdata2 <- read.table(textConnection(examdata), sep = ",", header = T)
扫描错误（文件、内容、nmax、sep、dec、quote、skip、nlines、 na.strings, : 第 1 行没有 2 个元素
【问题讨论】：
examdata2 <- read.table(textConnection(examdata), sep = ",", header = TRUE, skip=31, stringsAsFactors=FALSE)
标签： r
试试：
df <- read.csv("x.csv",... ,**quote = "", fill=TRUE**)
【讨论】：
read.table 和 read.csv 将使用 URL 作为路径并为您处理连接，因此您实际上并不需要 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 $59
此外，如果您使用readr::read_csv，您可以告诉它将列解析为数字，并在读取时去除$ 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
【讨论】：
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 参数来跳过前31 行。我使用了strip.white 参数，因为表格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
由于您可能希望这些数字为数字，因此您需要删除 $ 符号并将列转换为数字，以便您可以将它们用于以后可能进行的任何计算。
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。
【讨论】：