我建议你在管道中更早地解决这个问题。scale()返回一个矩阵，所以你的列名中最终会有括号。修改如下所示的代码，以提取该矩阵的第一列，并保持列名不变：

library(dplyr)

iris |>
  mutate(across(where(is.numeric), \(x) scale(x)[, 1]))
#>     Sepal.Length Sepal.Width Petal.Length   Petal.Width    Species
#> 1    -0.89767388  1.01560199  -1.33575163 -1.3110521482     setosa
#> 2    -1.13920048 -0.13153881  -1.33575163 -1.3110521482     setosa
#> 3    -1.38072709  0.32731751  -1.39239929 -1.3110521482     setosa
#> 4    -1.50149039  0.09788935  -1.27910398 -1.3110521482     setosa
#> 5    -1.01843718  1.24503015  -1.33575163 -1.3110521482     setosa
#> 6    -0.53538397  1.93331463  -1.16580868 -1.0486667950     setosa
#> 7    -1.50149039  0.78617383  -1.33575163 -1.1798594716     setosa
#> 8    -1.01843718  0.78617383  -1.27910398 -1.3110521482     setosa
#> 9    -1.74301699 -0.36096697  -1.33575163 -1.3110521482     setosa
#> 10   -1.13920048  0.09788935  -1.27910398 -1.4422448248     setosa
#> 11   -0.53538397  1.47445831  -1.27910398 -1.3110521482     setosa
#> 12   -1.25996379  0.78617383  -1.22245633 -1.3110521482     setosa
#> 13   -1.25996379 -0.13153881  -1.33575163 -1.4422448248     setosa
#> 14   -1.86378030 -0.13153881  -1.50569459 -1.4422448248     setosa
#> 15   -0.05233076  2.16274279  -1.44904694 -1.3110521482     setosa
#> 16   -0.17309407  3.08045544  -1.27910398 -1.0486667950     setosa
#> 17   -0.53538397  1.93331463  -1.39239929 -1.0486667950     setosa
#> 18   -0.89767388  1.01560199  -1.33575163 -1.1798594716     setosa
#> 19   -0.17309407  1.70388647  -1.16580868 -1.1798594716     setosa
#> 20   -0.89767388  1.70388647  -1.27910398 -1.1798594716     setosa
#> 21   -0.53538397  0.78617383  -1.16580868 -1.3110521482     setosa
#> 22   -0.89767388  1.47445831  -1.27910398 -1.0486667950     setosa
#> 23   -1.50149039  1.24503015  -1.56234224 -1.3110521482     setosa
#> 24   -0.89767388  0.55674567  -1.16580868 -0.9174741184     setosa
#> 25   -1.25996379  0.78617383  -1.05251337 -1.3110521482     setosa
#> 26   -1.01843718 -0.13153881  -1.22245633 -1.3110521482     setosa
#> 27   -1.01843718  0.78617383  -1.22245633 -1.0486667950     setosa
#> 28   -0.77691058  1.01560199  -1.27910398 -1.3110521482     setosa
#> 29   -0.77691058  0.78617383  -1.33575163 -1.3110521482     setosa
#> 30   -1.38072709  0.32731751  -1.22245633 -1.3110521482     setosa
#> 31   -1.25996379  0.09788935  -1.22245633 -1.3110521482     setosa
#> 32   -0.53538397  0.78617383  -1.27910398 -1.0486667950     setosa
#> 33   -0.77691058  2.39217095  -1.27910398 -1.4422448248     setosa
#> 34   -0.41462067  2.62159911  -1.33575163 -1.3110521482     setosa
#> 35   -1.13920048  0.09788935  -1.27910398 -1.3110521482     setosa
#> 36   -1.01843718  0.32731751  -1.44904694 -1.3110521482     setosa
#> 37   -0.41462067  1.01560199  -1.39239929 -1.3110521482     setosa
#> 38   -1.13920048  1.24503015  -1.33575163 -1.4422448248     setosa
#> 39   -1.74301699 -0.13153881  -1.39239929 -1.3110521482     setosa
#> 40   -0.89767388  0.78617383  -1.27910398 -1.3110521482     setosa
#> 41   -1.01843718  1.01560199  -1.39239929 -1.1798594716     setosa
#> 42   -1.62225369 -1.73753594  -1.39239929 -1.1798594716     setosa
#> 43   -1.74301699  0.32731751  -1.39239929 -1.3110521482     setosa
#> 44   -1.01843718  1.01560199  -1.22245633 -0.7862814418     setosa
#> 45   -0.89767388  1.70388647  -1.05251337 -1.0486667950     setosa
#> 46   -1.25996379 -0.13153881  -1.33575163 -1.1798594716     setosa
#> 47   -0.89767388  1.70388647  -1.22245633 -1.3110521482     setosa
#> 48   -1.50149039  0.32731751  -1.33575163 -1.3110521482     setosa
#> 49   -0.65614727  1.47445831  -1.27910398 -1.3110521482     setosa
#> 50   -1.01843718  0.55674567  -1.33575163 -1.3110521482     setosa
#> 51    1.39682886  0.32731751   0.53362088  0.2632599711 versicolor
#> 52    0.67224905  0.32731751   0.42032558  0.3944526477 versicolor
#> 53    1.27606556  0.09788935   0.64691619  0.3944526477 versicolor
#> 54   -0.41462067 -1.73753594   0.13708732  0.1320672944 versicolor
#> 55    0.79301235 -0.59039513   0.47697323  0.3944526477 versicolor
#> 56   -0.17309407 -0.59039513   0.42032558  0.1320672944 versicolor
#> 57    0.55148575  0.55674567   0.53362088  0.5256453243 versicolor
#> 58   -1.13920048 -1.50810778  -0.25944625 -0.2615107354 versicolor
#> 59    0.91377565 -0.36096697   0.47697323  0.1320672944 versicolor
#> 60   -0.77691058 -0.81982329   0.08043967  0.2632599711 versicolor
#> 61   -1.01843718 -2.42582042  -0.14615094 -0.2615107354 versicolor
#> 62    0.06843254 -0.13153881   0.25038262  0.3944526477 versicolor
#> 63    0.18919584 -1.96696410   0.13708732 -0.2615107354 versicolor
#> 64    0.30995914 -0.36096697   0.53362088  0.2632599711 versicolor
#> 65   -0.29385737 -0.36096697  -0.08950329  0.1320672944 versicolor
#> 66    1.03453895  0.09788935   0.36367793  0.2632599711 versicolor
#> 67   -0.29385737 -0.13153881   0.42032558  0.3944526477 versicolor
#> 68   -0.05233076 -0.81982329   0.19373497 -0.2615107354 versicolor
#> 69    0.43072244 -1.96696410   0.42032558  0.3944526477 versicolor
#> 70   -0.29385737 -1.27867961   0.08043967 -0.1303180588 versicolor
#> 71    0.06843254  0.32731751   0.59026853  0.7880306775 versicolor
#> 72    0.30995914 -0.59039513   0.13708732  0.1320672944 versicolor
#> 73    0.55148575 -1.27867961   0.64691619  0.3944526477 versicolor
#> 74    0.30995914 -0.59039513   0.53362088  0.0008746178 versicolor
#> 75    0.67224905 -0.36096697   0.30703027  0.1320672944 versicolor
#> 76    0.91377565 -0.13153881   0.36367793  0.2632599711 versicolor
#> 77    1.15530226 -0.59039513   0.59026853  0.2632599711 versicolor
#> 78    1.03453895 -0.13153881   0.70356384  0.6568380009 versicolor
#> 79    0.18919584 -0.36096697   0.42032558  0.3944526477 versicolor
#> 80   -0.17309407 -1.04925145  -0.14615094 -0.2615107354 versicolor
#> 81   -0.41462067 -1.50810778   0.02379201 -0.1303180588 versicolor
#> 82   -0.41462067 -1.50810778  -0.03285564 -0.2615107354 versicolor
#> 83   -0.05233076 -0.81982329   0.08043967  0.0008746178 versicolor
#> 84    0.18919584 -0.81982329   0.76021149  0.5256453243 versicolor
#> 85   -0.53538397 -0.13153881   0.42032558  0.3944526477 versicolor
#> 86    0.18919584  0.78617383   0.42032558  0.5256453243 versicolor
#> 87    1.03453895  0.09788935   0.53362088  0.3944526477 versicolor
#> 88    0.55148575 -1.73753594   0.36367793  0.1320672944 versicolor
#> 89   -0.29385737 -0.13153881   0.19373497  0.1320672944 versicolor
#> 90   -0.41462067 -1.27867961   0.13708732  0.1320672944 versicolor
#> 91   -0.41462067 -1.04925145   0.36367793  0.0008746178 versicolor
#> 92    0.30995914 -0.13153881   0.47697323  0.2632599711 versicolor
#> 93   -0.05233076 -1.04925145   0.13708732  0.0008746178 versicolor
#> 94   -1.01843718 -1.73753594  -0.25944625 -0.2615107354 versicolor
#> 95   -0.29385737 -0.81982329   0.25038262  0.1320672944 versicolor
#> 96   -0.17309407 -0.13153881   0.25038262  0.0008746178 versicolor
#> 97   -0.17309407 -0.36096697   0.25038262  0.1320672944 versicolor
#> 98    0.43072244 -0.36096697   0.30703027  0.1320672944 versicolor
#> 99   -0.89767388 -1.27867961  -0.42938920 -0.1303180588 versicolor
#> 100  -0.17309407 -0.59039513   0.19373497  0.1320672944 versicolor
#> 101   0.55148575  0.55674567   1.27004036  1.7063794137  virginica
#> 102  -0.05233076 -0.81982329   0.76021149  0.9192233541  virginica
#> 103   1.51759216 -0.13153881   1.21339271  1.1816087073  virginica
#> 104   0.55148575 -0.36096697   1.04344975  0.7880306775  virginica
#> 105   0.79301235 -0.13153881   1.15674505  1.3128013839  virginica
#> 106   2.12140867 -0.13153881   1.60992627  1.1816087073  virginica
#> 107  -1.13920048 -1.27867961   0.42032558  0.6568380009  virginica
#> 108   1.75911877 -0.36096697   1.43998331  0.7880306775  virginica
#> 109   1.03453895 -1.27867961   1.15674505  0.7880306775  virginica
#> 110   1.63835547  1.24503015   1.32668801  1.7063794137  virginica
#> 111   0.79301235  0.32731751   0.76021149  1.0504160307  virginica
#> 112   0.67224905 -0.81982329   0.87350679  0.9192233541  virginica
#> 113   1.15530226 -0.13153881   0.98680210  1.1816087073  virginica
#> 114  -0.17309407 -1.27867961   0.70356384  1.0504160307  virginica
#> 115  -0.05233076 -0.59039513   0.76021149  1.5751867371  virginica
#> 116   0.67224905  0.32731751   0.87350679  1.4439940605  virginica
#> 117   0.79301235 -0.13153881   0.98680210  0.7880306775  virginica
#> 118   2.24217198  1.70388647   1.66657392  1.3128013839  virginica
#> 119   2.24217198 -1.04925145   1.77986923  1.4439940605  virginica
#> 120   0.18919584 -1.96696410   0.70356384  0.3944526477  virginica
#> 121   1.27606556  0.32731751   1.10009740  1.4439940605  virginica
#> 122  -0.29385737 -0.59039513   0.64691619  1.0504160307  virginica
#> 123   2.24217198 -0.59039513   1.66657392  1.0504160307  virginica
#> 124   0.55148575 -0.81982329   0.64691619  0.7880306775  virginica
#> 125   1.03453895  0.55674567   1.10009740  1.1816087073  virginica
#> 126   1.63835547  0.32731751   1.27004036  0.7880306775  virginica
#> 127   0.43072244 -0.59039513   0.59026853  0.7880306775  virginica
#> 128   0.30995914 -0.13153881   0.64691619  0.7880306775  virginica
#> 129   0.67224905 -0.59039513   1.04344975  1.1816087073  virginica
#> 130   1.63835547 -0.13153881   1.15674505  0.5256453243  virginica
#> 131   1.87988207 -0.59039513   1.32668801  0.9192233541  virginica
#> 132   2.48369858  1.70388647   1.49663097  1.0504160307  virginica
#> 133   0.67224905 -0.59039513   1.04344975  1.3128013839  virginica
#> 134   0.55148575 -0.59039513   0.76021149  0.3944526477  virginica
#> 135   0.30995914 -1.04925145   1.04344975  0.2632599711  virginica
#> 136   2.24217198 -0.13153881   1.32668801  1.4439940605  virginica
#> 137   0.55148575  0.78617383   1.04344975  1.5751867371  virginica
#> 138   0.67224905  0.09788935   0.98680210  0.7880306775  virginica
#> 139   0.18919584 -0.13153881   0.59026853  0.7880306775  virginica
#> 140   1.27606556  0.09788935   0.93015445  1.1816087073  virginica
#> 141   1.03453895  0.09788935   1.04344975  1.5751867371  virginica
#> 142   1.27606556  0.09788935   0.76021149  1.4439940605  virginica
#> 143  -0.05233076 -0.81982329   0.76021149  0.9192233541  virginica
#> 144   1.15530226  0.32731751   1.21339271  1.4439940605  virginica
#> 145   1.03453895  0.55674567   1.10009740  1.7063794137  virginica
#> 146   1.03453895 -0.13153881   0.81685914  1.4439940605  virginica
#> 147   0.55148575 -1.27867961   0.70356384  0.9192233541  virginica
#> 148   0.79301235 -0.13153881   0.81685914  1.0504160307  virginica
#> 149   0.43072244  0.78617383   0.93015445  1.4439940605  virginica
#> 150   0.06843254 -0.13153881   0.76021149  0.7880306775  virginica

创建于2023-04-03带有reprex v2.0.2

3条答案

按热度按时间

ibrsph3r1#

赞(0）回复(0）举报 2023-04-09

hl0ma9xz2#

你已经接受了答案，但是这个答案对于评论来说有点太长了。dufei是对的，你想在上游修复这个问题。另外，scale()返回一个矩阵，但是它也可以取一个矩阵-所以你不需要mutate(across())。你可以只做：

numeric_cols  <- sapply(iris, is.numeric)
iris[numeric_cols]  <- scale(iris[numeric_cols])

我想这样会快一些。

r7knjye23#

在data.frame或tibble中，有一个矩阵列。当显示data.frame时，它将添加.1，.2，...添加到矩阵的每一列的列名。在tibble中，它将添加[,1]。为了能够重命名这些列，您可以通过将矩阵转换为list再转换回data.frame来将矩阵转换为数据框的单列。

DF <- data.frame(a = 1:3, b = letters[1:3])
DF$c <- matrix(1:6, 3)
DF
#  a b c.1 c.2
#1 1 a   1   4
#2 2 b   2   5
#3 3 c   3   6

as_tibble(DF)
## A tibble: 3 × 3
#      a b     c[,1]  [,2]
#  <int> <chr> <int> <int>
#1     1 a         1     4
#2     2 b         2     5
#3     3 c         3     6

names(DF)
#[1] "a" "b" "c"

x <- as.data.frame(as.list(DF))
x
#  a b c.1 c.2
#1 1 a   1   4
#2 2 b   2   5
#3 3 c   3   6

names(x)
#[1] "a"   "b"   "c.1" "c.2"

as.tibble(x)
## A tibble: 3 × 4
#      a b       c.1   c.2
#  <int> <chr> <int> <int>
#1     1 a         1     4
#2     2 b         2     5
#3     3 c         3     6

names(as.tibble(x))
#[1] "a"   "b"   "c.1" "c.2"

library(dplyr)
library(tibble)

DF <- tibble(a = 1:3, b = letters[1:3])
DF$c <- matrix(1:6, 3)

DF %>% mutate(across(where(is.numeric), scale)) #Given is question of @thole
## A tibble: 3 × 3
#  a[,1] b     c[,1]  [,2]
#  <dbl> <chr> <dbl> <dbl>
#1    -1 a        -1    -1
#2     0 b         0     0
#3     1 c         1     1

DF |> mutate(across(where(is.numeric), \(x) scale(x)[, 1])) #@dufei
## A tibble: 3 × 3
#      a b         c
#  <dbl> <chr> <dbl>
#1    -1 a        -1
#2     0 b         0
#3     1 c         1

numeric_cols  <- sapply(DF, is.numeric)  #@SamR
DF[numeric_cols]  <- scale(DF[numeric_cols])
#Error in `[<-`:
#! Can't recycle `scale(DF[numeric_cols])` (size 3) to size 2.
#Run `rlang::last_trace()` to see where the error occurred.

DF
## A tibble: 3 × 3
#      a b     c[,1]  [,2]
#  <int> <chr> <int> <int>
#1     1 a         1     4
#2     2 b         2     5
#3     3 c         3     6

如何删除R中列名上的特殊字符

3条答案

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