将参数传递给 dplyr 函数 [英] Pass arguments to dplyr functions

问题描述

我想使用 dplyr 参数化以下计算，以找出 Sepal.Length 的哪些值与多个 Sepal.Width:

I want to parameterise the following computation using dplyr that finds which values of Sepal.Length are associated with more than one value of Sepal.Width:

library(dplyr)

iris %>%
    group_by(Sepal.Length) %>%
    summarise(n.uniq=n_distinct(Sepal.Width)) %>%
    filter(n.uniq > 1)

通常我会这样写:

not.uniq.per.group <- function(data, group.var, uniq.var) {
    iris %>%
        group_by(group.var) %>%
        summarise(n.uniq=n_distinct(uniq.var)) %>%
        filter(n.uniq > 1)
}

然而，这种方法会引发错误，因为 dplyr 使用了 non-标准评价.这个函数应该怎么写?

However, this approach throws errors because dplyr uses non-standard evaluation. How should this function be written?

推荐答案

您需要使用 dplyr 函数的标准评估版本(只需在函数名称后附加_"，即.group_by_ & summarise_) 并将字符串传递给您的函数，然后您需要将其转换为符号.要参数化 summarise_ 的参数，您需要使用 interp()，它在 lazyeval 包中定义.具体:

You need to use the standard evaluation versions of the dplyr functions (just append '_' to the function names, ie. group_by_ & summarise_) and pass strings to your function, which you then need to turn into symbols. To parameterise the argument of summarise_, you will need to use interp(), which is defined in the lazyeval package. Concretely:

library(dplyr)
library(lazyeval)

not.uniq.per.group <- function(df, grp.var, uniq.var) {
    df %>%
        group_by_(grp.var) %>%
        summarise_( n_uniq=interp(~n_distinct(v), v=as.name(uniq.var)) ) %>%
        filter(n_uniq > 1)
}

not.uniq.per.group(iris, "Sepal.Length", "Sepal.Width")

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请注意，在最新版本的 dplyr 中，dplyr 函数的标准评估版本是 "软弃用" 支持非标准评估.

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Note that in recent versions of dplyr the standard evaluation versions of the dplyr functions have been "soft deprecated" in favor of non-standard evaluation.

请参阅使用dplyr 小插图 进行编程以了解更多信息处理非标准评估.

See the Programming with dplyr vignette for more information on working with non-standard evaluation.

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