我已经将代码部分翻译好了：
x <- data.frame(
  old = c("start1", "start2", "start3", "start4", "inter1", "inter2", "inter3"),
  new = c("final1", "final1", "inter1", "inter3", "inter2", "final2", "final3")
)
old    new
start1 final1
start2 final1
start3 inter1
start4 inter3
inter1 inter2
inter2 final2
inter3 final3
我希望直接得到每一行的“最终节点”。
在上面的示例中，它将是：
res <- data.frame(
  old= c("start1", "start2", "start3", "start4", "inter1", "inter2", "inter3"),
  new = c("final1", "final1", "final2", "final3", "final3", "final2", "final3")
)
old    new
start1 final1
start2 final1
start3 final2
start4 final3
inter1 final3
inter2 final2
inter3 final3

你可以在这里使用循环

在这里，我们迭代，只要有任何 "new" 值在 "old" 列中。我们找到它们，然后使用 `match` 查找每个中间值的 "new" 值。我们循环，直到没有更多要更新。
如果您使用类似图的数据，`igraph` 库可能会有帮助。在这种情况下，您可以这样使用它

它通过将输入视为有向图来工作。然后，我们将其分解为不重叠的部分，并对于每个部分，我们找到没有出度连接的节点。
<details>
<summary>英文:</summary>
You could use a loop here

Here we iterate while any of the "new" values are in the "old" column. We find them, then use `match` to look up what the "new" value is each each of those intermediate values. We loop until there are no more to update.
And if you are using graph-like data, the `igraph` library can be helpful. Here's how you might use it in this case

It works by treating the input as a directed graph. We then decompose it into non-overlapping parts and for each part, we find the node that has no out-going connections.
[![enter image description here][1]][1]
  [1]: https://i.stack.imgur.com/HYbzm.png
</details>
# 答案2
**得分**: 1
If you would like to use `igraph`, you can try `subcomponent` like below (but this might be not efficient due to rowwise operations, e.g., `sapply`):
如果你想要使用 `igraph`，你可以尝试以下的 `subcomponent`（但这可能不是高效的，因为涉及到逐行操作，例如 `sapply`）：
```R
g <- graph_from_data_frame(x)
x %>%
  mutate(new = sapply(
    old,
    function(v) tail(names(subcomponent(g, v, "out")), 1)
  ))

     old    new
1 start1 final1
2 start2 final1
3 start3 final2
4 start4 final3
5 inter1 final2
6 inter2 final2
7 inter3 final3

x %>%
  left_join(
    {.} %>%
      graph_from_data_frame() %>%
      components() %>%
      membership() %>%
      enframe(),
    by = join_by(old == name)
  ) %>%
  group_by(value) %>%
  mutate(new = grep("^final", new, value = TRUE)) %>%
  ungroup() %>%
  select(-value)

# A tibble: 7 × 2
  old    new
  <chr>  <chr>
1 start1 final1
2 start2 final1
3 start3 final2
4 start4 final3
5 inter1 final2
6 inter2 final2
7 inter3 final3