r - 根据外部值有条件地应用管道步骤

Question

鉴于 dplyr 工作流程:

require(dplyr)                                      
mtcars %>% 
    tibble::rownames_to_column(var = "model") %>% 
    filter(grepl(x = model, pattern = "Merc")) %>% 
    group_by(am) %>% 
    summarise(meanMPG = mean(mpg))

我有兴趣有条件申请 filter取决于 applyFilter 的值.

解决方案

对于applyFilter <- 1使用 "Merc" 过滤行字符串，没有过滤器所有行都会被返回。

applyFilter <- 1


mtcars %>%
  tibble::rownames_to_column(var = "model") %>%
  filter(model %in%
           if (applyFilter) {
             rownames(mtcars)[grepl(x = rownames(mtcars), pattern = "Merc")]
           } else
           {
             rownames(mtcars)
           }) %>%
  group_by(am) %>%
  summarise(meanMPG = mean(mpg))

问题

建议的解决方案效率低下，因为 ifelse调用总是被评估；更理想的方法只会评估 filter步骤为applyFilter <- 1 .

尝试

效率低下的工作解决方案如下所示:

mtcars %>% 
    tibble::rownames_to_column(var = "model") %>% 
    # Only apply filter step if condition is met
    if (applyFilter) { 
        filter(grepl(x = model, pattern = "Merc"))
        }
    %>% 
    # Continue 
    group_by(am) %>% 
    summarise(meanMPG = mean(mpg))

当然，上面的语法是不正确的。这只是一个说明，理想的工作流程应该是什么样子。

<小时/>

所需答案

• 我对创建临时对象不感兴趣；工作流程应类似于:

  startingObject
      %>%
      ...
      conditional filter
      ...
      final object
  

• 理想情况下，我希望找到一个可以控制 filter 是否可用的解决方案。调用是否正在评估

Answer 1

这种方法怎么样:

mtcars %>% 
    tibble::rownames_to_column(var = "model") %>% 
    filter(if(applyfilter== 1) grepl(x = model, pattern = "Merc") else TRUE) %>% 
    group_by(am) %>% 
    summarise(meanMPG = mean(mpg))

这意味着只有当 applyfilter 为 1 时才会评估 grepl，否则 filter 只是回收 TRUE。

<小时/>

或者另一种选择是使用 {}:

mtcars %>% 
  tibble::rownames_to_column(var = "model") %>% 
  {if(applyfilter == 1) filter(., grepl(x = model, pattern = "Merc")) else .} %>% 
  group_by(am) %>% 
  summarise(meanMPG = mean(mpg))

<小时/>

显然还有另一种可能的方法，您可以简单地破坏管道，有条件地执行过滤器，然后继续管道(我知道OP没有要求这样做，只是想为其他读者提供另一个例子)

mtcars %<>% 
  tibble::rownames_to_column(var = "model")

if(applyfilter == 1) mtcars %<>% filter(grepl(x = model, pattern = "Merc"))

mtcars %>% 
  group_by(am) %>% 
  summarise(meanMPG = mean(mpg))