如何在dplyr 0.7中参数化函数调用？ [英] How to parametrize function calls in dplyr 0.7?

问题描述

dplyr 0.7的发行版包括对dplyr进行编程的大修。我仔细阅读了本文档，并试图理解它会如何影响dplyr的使用。

The release of dplyr 0.7 includes a major overhaul of programming with dplyr. I read this document carefully, and I am trying to understand how it will impact my use of dplyr.

以下是在使用以下功能构建报表和汇总功能时常用的习惯用法： dplyr：

Here is a common idiom I use when building reporting and aggregation functions with dplyr:

my_report <- function(data, grouping_vars) {
  data %>%
    group_by_(.dots=grouping_vars) %>%
    summarize(x_mean=mean(x), x_median=median(x), ...)
}

在这里， grouping_vars 是字符串的向量。

Here, grouping_vars is a vector of strings.

我喜欢这个习惯用法，因为我可以从其他地方传入字符串向量，例如文件或Shiny应用程序的反应式UI，但是对于交互式工作也不错。

I like this idiom because I can pass in string vectors from other places, say a file or a Shiny app's reactive UI, but it's also not too bad for interactive work either.

但是，在新的使用dplyr小插图进行编程中，新的dplyr无法完成此类操作。我只看到传递字符串不再是正确方法的示例，而我不得不使用quosures。

However, in the new programming with dplyr vignette, I see no examples of how something like this can be done with the new dplyr. I only see examples of how passing strings is no longer the correct approach, and I have to use quosures instead.

我很乐意采用quosures，但是该怎么做呢？我从字符串到dplyr所期望的quoures？

I'm happy to adopt quosures, but how exactly do I get from strings to the quosures expected by dplyr here? It doesn't seem feasible to expect the entire R ecosystem to provide quosures to dplyr - lots of times we're going to get strings and they'll have to be converted.

这是一个示例，显示您现在应该做的事情以及我以前的成语如何不起作用：

Here is an example showing what you're now supposed to do, and how my old idiom doesn't work:

library(dplyr)
grouping_vars <- quo(am)
mtcars %>%
  group_by(!!grouping_vars) %>%
  summarise(mean_cyl=mean(cyl))
#> # A tibble: 2 × 2
#>      am mean_cyl
#>   <dbl>    <dbl>
#> 1     0 6.947368
#> 2     1 5.076923

grouping_vars <- "am"
mtcars %>%
  group_by(!!grouping_vars) %>%
  summarise(mean_cyl=mean(cyl))
#> # A tibble: 1 × 2
#>   `"am"` mean_cyl
#>    <chr>    <dbl>
#> 1     am   6.1875

推荐答案

dplyr 将具有专门的group_by函数 group_by_at 来处理多个分组变量。使用 _at 家族的新成员会容易得多：

dplyr will have a specialized group_by function group_by_at to deal with multiple grouping variables. It would be much easier to use the new member of the _at family:

# using the pre-release 0.6.0

cols <- c("am","gear")

mtcars %>%
    group_by_at(.vars = cols) %>%
    summarise(mean_cyl=mean(cyl))

# Source: local data frame [4 x 3]
# Groups: am [?]
# 
# am  gear mean_cyl
# <dbl> <dbl>    <dbl>
# 1     0     3 7.466667
# 2     0     4 5.000000
# 3     1     4 4.500000
# 4     1     5 6.000000

.vars 参数接受由 vars ：

.vars

.vars

由vars（）生成的列列表，或
列名或列位置的数字矢量。

A list of columns generated by vars(), or a character vector of column names, or a numeric vector of column positions.

这篇关于如何在dplyr 0.7中参数化函数调用？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！