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取决于的值applyFilter.

Solution

For applyFilter <- 1使用以下内容过滤行"Merc"字符串，不带过滤器all返回行。

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))

Problem

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迈出一步applyFilter <- 1.

Attempt

The 效率低下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。这只是理想工作流程的示例。

想要的答案

• 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是否filter呼叫是否正在评估

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grepl仅当 applyfilter 为 1 时才进行评估，否则filter只是回收一个TRUE.

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))

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))