dplyr没有硬编码变量名 [英] dplyr without hard-coding the variable names

问题描述

是否可以使用dplyr的mutate函数，而无需对变量名进行硬编码？例如，以下代码工作，因为我硬编码名称Var1：

Is it possible to use dplyr's mutate function without hard-coding the variable names? For example, the following code works, because I hard-code the name Var1:

            > d=expand.grid(1:3, 20:22)
            > d
            Var1 Var2
            1    1   20
            2    2   20
            3    3   20
            4    1   21
            5    2   21
            6    3   21
            7    1   22
            8    2   22
            9    3   22
            > d=mutate(d, x=percent_rank(Var1))
            > d
            Var1 Var2     x
            1    1   20 0.000
            2    2   20 0.375
            3    3   20 0.750
            4    1   21 0.000
            5    2   21 0.375
            6    3   21 0.750
            7    1   22 0.000
            8    2   22 0.375
            9    3   22 0.750

但是，当我将变量的名称变为一个变量时，它不再有效：

However, when I make the variable's name a variable, it no longer works:

            > my.variable='Var1'
            > d=mutate(d, x=percent_rank(my.variable))
            > d
                Var1 Var2   x
            1    1   20 NaN
            2    2   20 NaN
            3    3   20 NaN
            4    1   21 NaN
            5    2   21 NaN
            6    3   21 NaN
            7    1   22 NaN
            8    2   22 NaN
            9    3   22 NaN

eval（）和as.symbol（）函数似乎也没有帮助。

The eval() and as.symbol() functions don't seem to help, either.

推荐答案

伟大的哈德利·威克姆（Hadley Wickham）自己（以他的名字而闻名），建议您此 mutatr Google Groups：

The great Hadley Wickham himself (hallowed be his name!) suggested this on the mutatr Google Groups:

d <- expand.grid(1:3, 20:22)
my.variable <- 'Var1'
percent_rank <- function(x) rank(x)/max(rank(x))
call <- substitute(mutate(d, percent_rank(var)), 
                   list(var = as.name(my.variable)))
eval(call)
#   Var1 Var2 percent_rank(Var1)
# 1    1   20              0.250
# 2    2   20              0.625
# 3    3   20              1.000
# 4    1   21              0.250
# 5    2   21              0.625
# 6    3   21              1.000
# 7    1   22              0.250
# 8    2   22              0.625
# 9    3   22              1.000

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