Question

假设我想根据某些特定标准修剪由R中嵌套列表的层次结构组成的树。我可以做到这一点＆＃34;轻松＆＃34;足够使用lapply：

# Based an example from the NetworkD3 documentation
# https://christophergandrud.github.io/networkD3/

URL <- paste0(
  "https://cdn.rawgit.com/christophergandrud/networkD3/",
  "master/JSONdata//flare.json")

flare <- jsonlite::fromJSON(URL, simplifyDataFrame = FALSE)

# Leaf nodes have a "size" attribute. Let's say we want to 
# prune all the nodes with size < 5000.

prune <- function(tree) {
  if ("children" %in% names(tree)) {
    p <- lapply(tree$children, prune)
    pp <- p[!unlist(lapply(p, is.null))]
    copied_tree = list()
    copied_tree$name = tree$name
    copied_tree$children = pp
    return(copied_tree)
  } else if (tree$size < 5000) {
    return(NULL)
  }
  return(tree)
}

pruned <- prune(flare)

R for Data Science ，Hadley Wickham discusses许多场景，其中purrr可以替换apply系列函数来处理分层数据。但是，这些示例似乎处理单个嵌套列表，或处理深层嵌套列表的特定节点。

有没有办法使用purrr来完成上面讨论过的递归任务？

Answer 1

library(purrr)
prune_2 <- function(tree) {
  # print(tree$name)
  # print(map_lgl(tree$children, ~ "size" %in% names(.x)))
  tree$children %<>%  
    map_if(~ "children" %in% names(.x), prune_2) %>% 
    discard(~ if ("size" %in% names(.x)) .x$size < 5000 else FALSE)
  tree
}
pruned_2 <- prune_2(flare)
identical(pruned, pruned_2)
# [1] TRUE

使用purrr递归处理任意层次结构

1 个答案: