R 避免禁用流水线处理的延迟判断

假设我有一组用于数据处理的函数，例如:

procA <- function(input){
  cat('\n Now processing #A') # message just to log pipeline flow 
  
  # Actual data processing, may include some diagnostic messaging:
  cat('\n #A: ', dim(input))
  input$procA <- 'procA'
  
  return(input)
}

procB <- function(input){
  cat('\n Now processing #B') # message just to log pipeline flow 
  
  # Actual data processing, may include some diagnostic messaging:
  cat('\n #B: ', dim(input))
  input$procB <- 'procB' 
  
  return(input)
}

procC <- function(input){
  cat('\n Now processing #C') # message just to log pipeline flow 
  
  # Actual data processing, may include some diagnostic messaging:
  cat('\n #C: ', dim(input))
  input$procC <- 'procC' 
  
  return(input)
}

我将它们组合成一条管道，例如:

data(iris)

iris_processed <-
  iris %>% 
  procA %>% 
  procB %>% 
  procC

消息传送输出如下所示:

Now processing #C
Now processing #B
Now processing #A
#A: 150 5
#B: 150 6
#C: 150 7

由于延迟计算，这些日志(log)消息以相反的顺序发送，这使得我更难调试管道.到目前为止，我的解决方案是在每个函数的开头添加input <- eval(input).是否有更好的解决方案、良好的实践标准等？