如果有数据帧，可以使用hablar::convert。语法很简单:

样本df

library(hablar)
library(dplyr)

df <- dplyr::tibble(a = as.factor(c("7", "3")),
                    b = as.factor(c("1.5", "6.3")))

解决方案

df %>% 
  convert(num(a, b))

给你:

# A tibble: 2 x 2
      a     b
  <dbl> <dbl>
1    7.  1.50
2    3.  6.30

或者如果你想让一列是整数，一列是数字:

df %>% 
  convert(int(a),
          num(b))

结果:

# A tibble: 2 x 2
      a     b
  <int> <dbl>
1     7  1.50
2     3  6.30

在游戏后期，偶然地，我发现trimws()可以将因子(3:5)转换为c(“3”，“4”，“5”)。然后可以调用as.numeric()。那就是:

as.numeric(trimws(x_factor_var))

R有许多(未记录的)便利函数用于转换因子:

as.character.factor as.data.frame.factor as.Date.factor as.list.factor as.vector.factor .．.

但令人烦恼的是，没有任何东西可以处理因子->数字转换。作为Joshua Ulrich回答的延伸，我建议通过定义你自己的惯用函数来克服这个遗漏:

as.double.factor <- function(x) {as.numeric(levels(x))[x]}

你可以把它存储在你的脚本开头，或者更好的存储在你的。rprofile文件中。

如果你有很多因子列要转换成数字，

df <- rapply(df, function(x) as.numeric(levels(x))[x], "factor", how =  "replace")

这个解决方案对于包含混合类型的data.frames是健壮的，前提是所有的因子级别都是数字。

最简单的方法是使用包varhandle中的unfactor函数，它可以接受一个因子向量，甚至一个数据帧:

unfactor(your_factor_variable)

下面这个例子可以作为一个快速的开始:

x <- rep(c("a", "b", "c"), 20)
y <- rep(c(1, 1, 0), 20)

class(x)  # -> "character"
class(y)  # -> "numeric"

x <- factor(x)
y <- factor(y)

class(x)  # -> "factor"
class(y)  # -> "factor"

library(varhandle)
x <- unfactor(x)
y <- unfactor(y)

class(x)  # -> "character"
class(y)  # -> "numeric"

你也可以在数据框架上使用它。例如虹膜数据集:

sapply(iris, class)

萼片。花萼长度。宽度花瓣。花瓣长度。宽度的物种 "数字" "数字" "数字" "因素"

# load the package
library("varhandle")
# pass the iris to unfactor
tmp_iris <- unfactor(iris)
# check the classes of the columns
sapply(tmp_iris, class)

萼片。花萼长度。宽度花瓣。花瓣长度。宽度的物种 "数字" "数字" "数字" "字符"

# check if the last column is correctly converted
tmp_iris$Species

[1] "setosa" "setosa" "setosa" "setosa" "setosa" [6] "setosa" "setosa" "setosa" "setosa" "setosa" [11] "setosa" "setosa" "setosa" "setosa" "setosa" [16] "setosa" "setosa" "setosa" "setosa" "setosa" [21] "setosa" "setosa" "setosa" "setosa" "setosa" [26] "setosa" "setosa" "setosa" "setosa" "setosa" [31] "setosa" "setosa" "setosa" "setosa" "setosa" [36] "setosa" "setosa" "setosa" "setosa" "setosa" [41] "setosa" "setosa" "setosa" "setosa" "setosa" [46] "setosa" "setosa" "setosa" "setosa" "setosa" [51] "versicolor" "versicolor" "versicolor" "versicolor" "versicolor" [56] "versicolor" "versicolor" "versicolor" "versicolor" "versicolor" [61] "versicolor" "versicolor" "versicolor" "versicolor" "versicolor" [66] "versicolor" "versicolor" "versicolor" "versicolor" "versicolor" [71] "versicolor" "versicolor" "versicolor" "versicolor" "versicolor" [76] "versicolor" "versicolor" "versicolor" "versicolor" "versicolor" [81] "versicolor" "versicolor" "versicolor" "versicolor" "versicolor" [86] "versicolor" "versicolor" "versicolor" "versicolor" "versicolor" [91] "versicolor" "versicolor" "versicolor" "versicolor" "versicolor" [96] "versicolor" "versicolor" "versicolor" "versicolor" "versicolor" [101] "virginica" "virginica" "virginica" "virginica" "virginica" [106] "virginica" "virginica" "virginica" "virginica" "virginica" [111] "virginica" "virginica" "virginica" "virginica" "virginica" [116] "virginica" "virginica" "virginica" "virginica" "virginica" [121] "virginica" "virginica" "virginica" "virginica" "virginica" [126] "virginica" "virginica" "virginica" "virginica" "virginica" [131] "virginica" "virginica" "virginica" "virginica" "virginica" [136] "virginica" "virginica" "virginica" "virginica" "virginica" [141] "virginica" "virginica" "virginica" "virginica" "virginica" [146] "virginica" "virginica" "virginica" "virginica" "virginica"

看起来解决方案作为.numeric(水平(f))[f]不再工作与R 4.0。

可选择的解决方案:

factor2number <- function(x){
    data.frame(levels(x), 1:length(levels(x)), row.names = 1)[x, 1]
}

factor2number(yourFactor)