使用rlang在表达式的准引用中使用预先存在的字符向量 [英] Using pre-existing character vectors in quasiquotation of an expression with rlang

问题描述

有时，当使用dplyr时，其中一个具有用于处理数据的列名的字符向量，例如:

Sometimes when working with dplyr one has a character vector of column names that's to be used to operate on data, for instance:

 cols_of_interest <- c("Petal.Width", "Petal.Length")

在dplyr 0.5.0及更早版本中，建议的解决此问题的方法是使用verb_下划线构造，如下所示:

In dplyr 0.5.0 and earlier, the recommended approach to this problem was to use the verb_ underscore construct as follows:

library("tidyverse")
my_cols <- c("Petal.Width", "Petal.Length")
iris %>%
  select_(.dots = my_cols)

现在不推荐使用verb_函数，而推荐使用rlang库引入的新的整洁评估框架(dplyr.tidyverse.org/articles/programming.html).

The verb_ functions are now deprecated in favour of the new tidy evaluation framework (dplyr.tidyverse.org/articles/programming.html) introduced by the rlang library.

从dplyr 0.7.0开始，以下工作无需任何特殊调整:

As of dplyr 0.7.0 the following works without any special accommodations:

library("tidyverse")
# library("rlang")
my_cols <- c("Petal.Width", "Petal.Length")
iris %>%
  select(my_cols)

请注意，在dplyr的开发版本中，情况并非如此

Note that in development builds of dplyr, this was not the case

在Shiny应用程序中选择列是一个很好的示例用例，这就是使用verb_表示法的方式

Selecting columns in Shiny apps is a good example use case, this is how one could do it using verb_ notation

library("shiny")
library("tidyverse")
library("DT")

shinyApp(
  ui = fluidPage(
    selectInput("cols_to_show",
                "Columns to show",
                choices = colnames(iris),
                multiple = TRUE),
    dataTableOutput("verb_table")
  ),
  server = function(input, output){
    output$verb_table <- renderDataTable({
      iris %>%
        select_(.dots = input$cols_to_show)

    })
  }
)

推荐答案

在0.5.0版以前的dplyr中，用于非标准评估的基础框架为lazyeval，并且需要对字符串进行特殊考虑. Hadley Wickham发布了dplyr的根本新版本，并带有一个名为rlang的新肋骨，它为非标准评估提供了更一致的框架.这是版本0.70-这是为什么跳过0.6.0的解释-

In pre 0.5.0 dplyr the underlying framework for non-standard evaluation was lazyeval and required special consideration for strings. Hadley Wickham released a fundamentally new version of dplyr with a new underbelly called rlang which provides a more consistent framework for non-standard evaluation. This was version 0.70 - here's an explanation of why 0.6.0 was skipped - https://blog.rstudio.org/2017/06/13/dplyr-0-7-0/

以下内容无需任何特殊考虑即可工作:

The following now works without any special considerations:

library("tidyverse")
my_cols <- c("Petal.Width", "Petal.Length")
iris %>%
  select(my_cols)

请注意，新的rlang框架增加了使用quores拥有裸符号向量的功能

Note that the new rlang framework adds the ability to have a vector of naked symbols using quosures

my_quos <- quos(Petal.Width, Petal.Length)
iris %>%
  select(!!!my_quos)

您可以在此处了解有关使用dplyr进行编程的更多信息- http://dplyr.tidyverse .org/articles/programming.html

You can read more about programming with dplyr here - http://dplyr.tidyverse.org/articles/programming.html

library("shiny")
library("tidyverse")
library("DT")
library("rlang")
shinyApp(
  ui = fluidPage(
    selectInput(
      "cols_to_show",
      "Columns to show",
      choices = colnames(iris),
      multiple = TRUE
    ),
    dataTableOutput("verb_table"),
    dataTableOutput("tidyeval_table")
  ),
  server = function(input, output) {
    output$verb_table <- renderDataTable({
      iris %>%
        select_(.dots = input$cols_to_show)

    })

    output$tidyeval_table <- renderDataTable({
      iris %>%
        select(!!!syms(input$cols_to_show))

    })
  }
)

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