这是一个基本的R方式。

mydata[affectedColumns] <- lapply(mydata[affectedColumns], \(x){
  for(nm in names(replace)) x <- sub(nm, replace[nm], x)
  as.numeric(x)
})

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您可以使用readr包中的parse_number转换为数字，同时删除大于/小于符号。

library(readr)

df <- data.frame("name1" = c("1,5", "> 1,5", "<1,6"), 
                 "name2" = c("1,5", "1,5", "1,5"), 
                 "name3" = c("1,0", "1", "1"),
                 "name4" = c(1.5, 1, 0.5)
                 )

affectedColumns <- c("name1", "name2", "name3")

new_df <- mutate(df, across(affectedColumns, .fns = ~parse_number(.x, locale = locale(decimal_mark = ","))))

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以下是dplyr解决方案：

library(dplyr)
mydata %>%
  # Step 1: remove < and >:
  mutate(across(c(everything()), 
                ~ sub("\\s?(>|<)", "", .))) %>%
  # Step 2: replace dot by comma:
  mutate(across(c(everything()), 
                ~ sub("\\.", ",", .))) 
  col1   col2
1  1,2 12,701
2    3  55,77
3    5   5000

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编辑：

下面是一个使用setNames和stringr的解决方案：
首先定义新值和旧值的集合（确保转义regex元字符，如.）：

replacements <- setNames(c("", "", ","),     # new values
                         c("<", ">", "\\.")) # old values

型
或者，更经济地说：

replacements <- setNames(c("", ","),      # new values
                         c("<|>", "\\.")) # old values

型
现在使用str_replace_all一次性实现这些更改：

library(stringr)
mydata %>%
  mutate(across(c(col1:col2), 
                ~ str_replace_all(., replacements)))

型
玩具数据：

mydata <- data.frame(
  col1 = c("1.2", "3", "<5"),
  col2 = c(">12.701", "55,77", "< 5000")
)

考虑mutate、across和case_when函数的组合，形成dplyr包。您可以在这里找到它们：https://dplyr.tidyverse.org/reference/across.html和这里：https://dplyr.tidyverse.org/reference/case_when.html或给予一个最小的可重复的例子。
最好的，M。

structure(list(D = c(12327, 12328, 12329, 12330, 12331, 12333, 
12334, 12335, 12336, 12337, 12338, 12339, 12340, 12343, 12345, 
12348, 12349, 12350, 12351, 12352), E = c(12310, 12310, 12326, 
12326, 12315, 12326, 0, 12324, 12324, 12334, 12334, 0, 12339, 
0, 0, 12345, 12345, 0, 12343, 12343), Basiswert = c("AUDCAD", 
"AUDCAD", "USDJPY", "USDJPY", "USDCAD", "USDJPY", "USDCHF", "USDCHF", 
"USDCHF", "USDCHF", "USDCHF", "USDCAD", NA, "USDCAD", "CADJPY", 
"CADJPY", "CADJPY", "USDCHF", "USDCAD", "USDCAD"), Einstieg = c(NA, 
0.89262, NA, 139.192, NA, NA, 0.9052, NA, 0.90834, NA, 0.90816, 
NA, NA, 1.362, 103.188, NA, 102.886, 0.9051, NA, 1.36504), Profit = c(33, 
NA, 34, NA, 68, 68, NA, 33, NA, 33, NA, NA, NA, NA, NA, 34, NA, 
NA, 33, NA), SL = c(NA, NA, NA, NA, NA, NA, 0.91134, NA, NA, 
NA, NA, NA, NA, 1.3684, 102.545, NA, NA, 0.91138, NA, NA), TP = c(NA, 
NA, NA, NA, NA, NA, 0.89325, NA, NA, NA, NA, NA, NA, 1.3504, 
104.35, NA, NA, 0.8933, NA, NA), Trader = c(NA, NA, NA, NA, NA, 
NA, "Trade by Jason\" ", NA, NA, NA, NA, NA, NA, "Trade by Jason\" ", 
"Trade by Jason\" ", NA, NA, "Trade by Jason\" ", NA, NA)), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -20L), groups = structure(list(
    E = c(0, 12310, 12315, 12324, 12326, 12334, 12339, 12343, 
    12345), .rows = structure(list(c(7L, 12L, 14L, 15L, 18L), 
        1:2, 5L, 8:9, c(3L, 4L, 6L), 10:11, 13L, 19:20, 16:17), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -9L), .drop = TRUE))

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非常感谢您的努力和解决方案。然而，我没有对整个数据集进行工作。请参阅上面的示例。