将dplyr mutate函数与搜索整个表结合起来

我对R很陌生，尤其是对整洁的诗句。我想写一个脚本，用它我们可以重写分类单元列表。我们已经有一个使用了很多for和if循环，我想尝试用tidyverse来简化它，但我有点被困在如何做到这一点上

我有一张看起来像那样的桌子（非常简单）

我尝试将mutate与ifelse语句结合起来，但这只是将NA添加到整个order列中

tibble被命名为分类单元列表

taxon_list %>%    
   mutate(order = ifelse(parent_ID == Id, Name, NA))

---

我知道这是行不通的，因为它不会在整个数据集中搜索正确的行（这是我之前使用alle for循环所做的）。也许有人能给我指出正确的方向？

一种方法是对每个列组类型进行

筛选
以获得2个单独的dfs，使用
选择子集
，然后
合并
2个

  df <- tibble(name = c( "cockroach","cockroach2", "grasshopper", "spider",    "lobster",  "insect",   "crustacea",    "arachnid"), 
                  Id = c(445,448,446,778,543,200,400,300),
                  parent_ID = c(200,200,200,300,400,200,400,300),
                  rank = c("genus","genus","genus","genus","genus","order","order","order"))     

library(tidyverse)

df_order <- df %>%
  filter(rank == "order") %>% 
  select(order = name, parent_ID)

df_genus <- df %>%
  filter(rank == "genus") %>% 
  select(name, Id, parent_ID) %>% 
  merge(df_order, by = "parent_ID")

一种方法是对每个列组类型进行
筛选
，以获得两个单独的dfs，使用
选择子集
，然后
合并这两个dfs

  df <- tibble(name = c( "cockroach","cockroach2", "grasshopper", "spider",    "lobster",  "insect",   "crustacea",    "arachnid"), 
                  Id = c(445,448,446,778,543,200,400,300),
                  parent_ID = c(200,200,200,300,400,200,400,300),
                  rank = c("genus","genus","genus","genus","genus","order","order","order"))     

library(tidyverse)

df_order <- df %>%
  filter(rank == "order") %>% 
  select(order = name, parent_ID)

df_genus <- df %>%
  filter(rank == "genus") %>% 
  select(name, Id, parent_ID) %>% 
  merge(df_order, by = "parent_ID")

你好有几件事；确保提供可复制的示例。您可以使用
dput（head（您的_-df））
来实现这一点。还有，你是什么意思？在您的示例中，Parent_ID不等于任何
ID
，但您仍然拥有
订单
栏谢谢您的回复。我编辑了一点原始帖子，希望它能解释得更多一些。我现在有一个可行的解决方案。你好。有几件事；确保提供可复制的示例。您可以使用
dput（head（您的_-df））
来实现这一点。还有，你是什么意思？在您的示例中，Parent_ID不等于任何
ID
，但您仍然拥有
订单
栏谢谢您的回复。我编辑了一点原始帖子，希望它能解释得更多一些。我现在有一个可行的解决办法。
  df <- tibble(name = c( "cockroach","cockroach2", "grasshopper", "spider",    "lobster",  "insect",   "crustacea",    "arachnid"), 
                  Id = c(445,448,446,778,543,200,400,300),
                  parent_ID = c(200,200,200,300,400,200,400,300),
                  rank = c("genus","genus","genus","genus","genus","order","order","order"))     

library(tidyverse)

df_order <- df %>%
  filter(rank == "order") %>% 
  select(order = name, parent_ID)

df_genus <- df %>%
  filter(rank == "genus") %>% 
  select(name, Id, parent_ID) %>% 
  merge(df_order, by = "parent_ID")

  parent_ID        name  Id     order
1       200   cockroach 445    insect
2       200  cockroach2 448    insect
3       200 grasshopper 446    insect
4       300      spider 778  arachnid
5       400     lobster 543 crustacea