如果您不关心保留因子，并希望将其应用到任何可以转换为数字的列，我使用下面的脚本。 如果df是您的原始数据框架，您可以使用下面的脚本。

df[] <- lapply(df, as.character)
df <- data.frame(lapply(df, function(x) ifelse(!is.na(as.numeric(x)), as.numeric(x),  x)))

顺便说一句，我参考了谢恩和乔兰的解决方案

虽然其他人已经很好地讨论了这个话题，但我想补充一个额外的快速思考/提示。可以使用regexp提前检查字符是否可能仅由数字组成。

for(i in seq_along(names(df)){
     potential_numcol[i] <- all(!grepl("[a-zA-Z]",d[,i]))
}
# and now just convert only the numeric ones
d <- sapply(d[,potential_numcol],as.numeric)

想要了解更多复杂的正则表达式，以及为什么要学习/体验它们的力量，请访问这个非常好的网站:http://regexr.com/

使用type.convert()和rapply()的通用方式:

convert_types <- function(x) {
    stopifnot(is.list(x))
    x[] <- rapply(x, utils::type.convert, classes = "character",
                  how = "replace", as.is = TRUE)
    return(x)
}
d <- data.frame(char = letters[1:5], 
                fake_char = as.character(1:5), 
                fac = factor(1:5), 
                char_fac = factor(letters[1:5]), 
                num = 1:5, stringsAsFactors = FALSE)
sapply(d, class)
#>        char   fake_char         fac    char_fac         num 
#> "character" "character"    "factor"    "factor"   "integer"
sapply(convert_types(d), class)
#>        char   fake_char         fac    char_fac         num 
#> "character"   "integer"    "factor"    "factor"   "integer"

虽然你的问题严格是关于数字的，但在开始r时，有许多转换是难以理解的。我将致力于解决帮助的方法。这个问题和这个问题类似。

在R中，类型转换可能是一种痛苦，因为(1)因子不能直接转换为数字，它们需要首先转换为字符类，(2)日期是一种特殊情况，通常需要单独处理，(3)跨数据帧列的循环可能很棘手。幸运的是，“潮流宇宙”已经解决了大部分问题。

This solution uses mutate_each() to apply a function to all columns in a data frame. In this case, we want to apply the type.convert() function, which converts strings to numeric where it can. Because R loves factors (not sure why) character columns that should stay character get changed to factor. To fix this, the mutate_if() function is used to detect columns that are factors and change to character. Last, I wanted to show how lubridate can be used to change a timestamp in character class to date-time because this is also often a sticking block for beginners.

library(tidyverse) 
library(lubridate)

# Recreate data that needs converted to numeric, date-time, etc
data_df
#> # A tibble: 5 × 9
#>             TIMESTAMP SYMBOL    EX  PRICE  SIZE  COND   BID BIDSIZ   OFR
#>                 <chr>  <chr> <chr>  <chr> <chr> <chr> <chr>  <chr> <chr>
#> 1 2012-05-04 09:30:00    BAC     T 7.8900 38538     F  7.89    523  7.90
#> 2 2012-05-04 09:30:01    BAC     Z 7.8850   288     @  7.88  61033  7.90
#> 3 2012-05-04 09:30:03    BAC     X 7.8900  1000     @  7.88   1974  7.89
#> 4 2012-05-04 09:30:07    BAC     T 7.8900 19052     F  7.88   1058  7.89
#> 5 2012-05-04 09:30:08    BAC     Y 7.8900 85053     F  7.88 108101  7.90

# Converting columns to numeric using "tidyverse"
data_df %>%
    mutate_all(type.convert) %>%
    mutate_if(is.factor, as.character) %>%
    mutate(TIMESTAMP = as_datetime(TIMESTAMP, tz = Sys.timezone()))
#> # A tibble: 5 × 9
#>             TIMESTAMP SYMBOL    EX PRICE  SIZE  COND   BID BIDSIZ   OFR
#>                <dttm>  <chr> <chr> <dbl> <int> <chr> <dbl>  <int> <dbl>
#> 1 2012-05-04 09:30:00    BAC     T 7.890 38538     F  7.89    523  7.90
#> 2 2012-05-04 09:30:01    BAC     Z 7.885   288     @  7.88  61033  7.90
#> 3 2012-05-04 09:30:03    BAC     X 7.890  1000     @  7.88   1974  7.89
#> 4 2012-05-04 09:30:07    BAC     T 7.890 19052     F  7.88   1058  7.89
#> 5 2012-05-04 09:30:08    BAC     Y 7.890 85053     F  7.88 108101  7.90

要将字符转换为数字，您必须通过应用将其转换为因数

BankFinal1 <- transform(BankLoan,   LoanApproval=as.factor(LoanApproval))
BankFinal1 <- transform(BankFinal1, LoanApp=as.factor(LoanApproval))

您必须用相同的数据创建两列，因为一列不能转换为数字。如果你做一次转换，它会给出如下错误

transform(BankData, LoanApp=as.numeric(LoanApproval))

警告信息: 在eval(替代(列表 (...)), `_ 数据”,parent.frame ()): 胁迫引入的NAs

所以，在做了两列相同的数据应用后

BankFinal1 <- transform(BankFinal1, LoanApp      = as.numeric(LoanApp), 
                                    LoanApproval = as.numeric(LoanApproval))

它将成功地将字符转换为数字

Df列出你的数据框架。X是要转换的df的一列

as.numeric(factor(df$x))