如何防止元素双向的数据帧查找中的无限循环 [英] How to prevent infinite loop in dataframe lookup where elements are bi-directional

问题描述

@thothal 向我解释了一个递归函数 here 这允许我基于在父数据帧中查找字符串来递归获取数据帧.使用我提供的示例，效果很好.

I have a recursive function kindly explained to me by @thothal here This allows me to recursively get dataframes based on looking up a character string in the parent dataframe. With the example I provided this works great.

然而，我现在正在处理更多表格，其中子元素存在于父元素中，反之亦然.这导致递归函数中的无限循环.

However I am now working on further tables where the elements in the child are present in the parent and vice versa. This leads to an infinte loop in the recursive function.

要重复原始问题并进行更改:

To repeat the original question with changes:

Numsdf1<-c("C123","C456","C789")
Textdf1<-c("Harry","Bobby","Terry")
df1<-data.frame(Numsdf1,Textdf1,stringsAsFactors=FALSE)

第二个数据帧是查找字符串C123"的结果

The second dataframe is the result of looking up the string "C123"

NumsC123<-c("C123","Noo","Too")
TextC123<-c("Tim","Slim","Shim")
C123<-data.frame(NumsC123,TextC123,stringsAsFactors=FALSE)

第三个数据帧是查找Coo"的结果

The third dataframe is a result of looking up "Coo"

NumsCoo<-c("S144","S199","S743")
TextCoo<-c("Ellie","Bellie","Tellie")
Coo<-data.frame(NumsCoo,TextCoo,stringsAsFactors=FALSE)

第四个是查找Noo"的结果

The fourth is the result of looking up "Noo"

NumsNoo<-c("GHS","THE","PAA")
TextNoo<-c("Front","Bunt","Shunt")
Noo<-data.frame(NumsNoo,TextNoo,stringsAsFactors=FALSE)

最初的解决方案是:

library(tidyverse)
get_all_dfs <- function(df) {
   lapply(df[, 1], function(elem) {
      print(paste("Looking for element", elem))
      # use mget because we can use ifnotfound despite we are requesting only one element
      next_df <- mget(elem, env = .GlobalEnv, ifnotfound = NA)
      if (!is.na(next_df)) {
         unlist(get_all_dfs(next_df[[1]]), F)
      } else {
         list(setNames(df, c("col1", "col2")))
      }
    })
}

flatten_dfr(get_all_dfs(df1)) %>% unique()

这意味着当我运行该函数时，我得到了一个无法跳出的循环.因此，而不是预期的结果:

This means that when I run the function I get a loop which I can't break out of. So instead of the intended result of:

C123 -> Coo -> S144 -> S199 -> S743 -> Noo -> GHS -> THE -> PAA -> Too -> C456 -> C789

我明白

C123 -> Coo -> C123 -> Coo -> C123 etc.

我可以做些什么来防止这种情况发生?

What can I do to prevent this?

我实施了@thothal 的解决方案.我遇到的问题是我使用的查找函数返回一个数据帧而不是一个列表，所以我也创建了一个列表来存储全局环境.但是循环仍然发生.这是更新后的代码:

I implemented the solution from @thothal. The problem I had was that the lookup function I use returns a dataframe rather than a list so I created a list to store the Global environment too. However the loop still occurs. Here is the updated code:

   get_all_dfs_rec <- function(df, my_env) {
        lapply(df$relatedIdEx, function(elem) {
            print(paste("Looking for element", elem))
            next_df <- myGIConcepts(elem) ###This returns a dataframe
            next_df<-list(next_df,my_env) ###Environment variable kept in a list
        if (!is.na(next_df)) {
          rm(list = elem, envir = my_env)
          unlist(get_all_dfs_rec(next_df[[1]], my_env), FALSE)
          } else {
          list(setNames(df, c("col1", "col2")))
        }
    })
  }
        
    get_all_dfs <- function(df_start) {
  ## create a new environment
  my_env <- new.env()
  ## and add all 'data.frames' from the global environment to it
  walk(ls(.GlobalEnv), ~ {
    elem <- get(.x, env = .GlobalEnv);
    if (class(elem) == "data.frame") my_env[[.x]] <- elem})
  flatten_dfr(get_all_dfs_rec(df_start, my_env)) %>% unique()
}

推荐答案

您可以将所有数据框放在自己的环境中，一旦找到就将其从那里删除:

You can put all of your data frames in an own environment and once they are found remove it from there:

get_all_dfs_rec <- function(df, my_env) {
   lapply(df[, 1], function(elem) {
      print(paste("Looking for element", elem))
      # use mget because we can use ifnotfound despite we are requesting only one element
      next_df <- mget(elem, env = my_env, ifnotfound = NA)
      if (!is.na(next_df)) {
         # use list, otherwise rm tries to remove elem (which does not exist in the env)
         rm(list = elem, envir = my_env)
         unlist(get_all_dfs_rec(next_df[[1]], my_env), FALSE)
      } else {
         list(setNames(df, c("col1", "col2")))
      }
    })
}

get_all_dfs <- function(df_start) {
   ## create a new environment
   my_env <- new.env()
   ## and add all 'data.frames' from the global environment to it
   walk(ls(.GlobalEnv), ~ {
       elem <- get(.x, env = .GlobalEnv); 
       if (class(elem) == "data.frame") my_env[[.x]] <- elem})
   flatten_dfr(get_all_dfs_rec(df_start, my_env)) %>% unique()
}

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