更新R中的数据表(使用data.table)

我有两张DT

X = data.table(names = c("a", "a", "b", "b", "c", "c"), years = c("2001", "2002", "2001", "2002", "2001", "2002"), val.1 = 1:6, key = c("names", "years"))

X

| names | years | val.1 |
| -------- | -------- |
| a   | 2001   |  1 |
| a   | 2002   |  2 |
| b   | 2001   |  3 |
| b   | 2002   |  4 |
| c   | 2001   |  5 |
| c   | 2002   |  6 |

和

X.update = data.table(names = c("a", "b", "b", "c", "d", "d", "d"), years = c("2003", "2002", "2003", "2003", "2001", "2002", "2003"), val.1 = 11:17, key = c("names", "years"))

X.update

| names | years | val.1 |
| -------- | -------- |
| a   | 2003   |  11 |
| b   | 2002   |  12 |
| b   | 2003   |  13 |
| c   | 2003   |  14 |
| d   | 2001   |  15 |
| d   | 2002   |  16 |
| d   | 2003   |  17 |

The task looks natural to me. X.update supersedes all old values (val.1) for the same c("names", "year") 和 adds new entries everywhere else.

这里的意思是:

1. 2003年所有名称的新行
2. 每年都有一个新的d行(基本上是添加了d)
3. B在2002年的修正

X.Final X.Final

| names | year | val.1 |
| -------- | -------- |
| a   | 2001   |  1 |
| a   | 2002   |  2 |
| a   | 2003   |  11 |       <-added for a for 2003
| b   | 2001   |  3 |        # edited, R2Evans 和 Sam are right
| b   | 2002   |  12 |       <-corrected for b for 2002
| b   | 2003   |  13 |       <-added for b for 2003
| c   | 2001   |  5 |
| c   | 2002   |  6 |
| c   | 2003   |  14 |       <-added for c for 2003
| d   | 2001   |  15 |       <-added
| d   | 2002   |  16 |       <-added
| d   | 2003   |  17 |       <-added

因为我需要对有100,000行的表使用它，所以我想在DT中寻求一个惯用的(=FAST)解决方案.