当我了解在merge（）中默认为TRUE的附加参数'sort'时，我真的认为我有解决这个问题的方法。但是，将此设置为false并没有帮助。下面是我的代码演示，包括我得到的结果和我想要的结果：
df2 = structure(list(player = c("Marvin Williams", "Spencer Hawes",
"Jeremy Lin", "Kemba Walker", "P.J. Hairston", "Rudy Gay", "Rajon Rondo",
"DeMarcus Cousins", "Ben McLemore", "Willie Cauley-Stein"), global.player.id = c(263884L,
329824L, 340730L, 462980L, 609567L, 266358L, 262882L, 509450L,
604898L, 699950L), team.name = c("Hornets", "Hornets", "Hornets",
"Hornets", "Grizzlies", "Kings", "Kings", "Kings", "Kings", "Kings"
)), .Names = c("player", "global.player.id", "team.name"), class = "data.frame", row.names = c(47L,
48L, 52L, 53L, 225L, 389L, 390L, 395L, 398L, 401L))
df1 = structure(list(global.player.id = c(-1L, 262882L, 266358L, 509450L,
604898L, 699950L, 263884L, 329824L, 340730L, 462980L, 609567L,
-1L, 262882L, 266358L, 509450L, 604898L, 699950L, 263884L, 329824L,
340730L, 462980L, 609567L, -1L, 262882L, 266358L), x_loc = c(47.17753,
13.57165, 46.45843, 26.68803, 52.16717, 47.20201, 60.097, 47.20201,
52.16717, 65.1302, 46.45843, 47.19141, 13.61702, 46.5355, 26.71856,
52.25433, 47.27324, 60.08215, 47.27324, 52.25433, 65.11267, 46.5355,
46.82163, 13.66478, 46.57545), y_loc = c(26.44326, 25.18298,
18.46573, 25.48557, 33.09177, 31.09372, 22.79717, 31.09372, 33.09177,
26.39671, 18.46573, 26.5187, 25.17431, 18.42014, 25.53807, 33.11185,
31.01197, 22.76307, 31.01197, 33.11185, 26.40227, 18.42014, 26.72834,
25.17784, 18.35961), order = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25)), .Names = c("global.player.id",
"x_loc", "y_loc", "order"), row.names = c("1", "2", "3", "4",
"5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18", "19", "20", "21", "22", "23", "24", "25"), class = "data.frame")
以上是我正在使用的数据框架。当我将df2合并到它时，我想保持df1的顺序。我在这里处理时间序列数据，因此数据帧的顺序很重要。 df1中的order列只是为了测试df1是否正在被洗牌（我不想在合并后使用额外的代码对订单进行排序）。
这是我尝试过的：
merge(df1, df2, by = 'global.player.id', all.x = TRUE)
global.player.id x_loc y_loc order player team.name
1 -1 47.17753 26.44326 1 <NA> <NA>
2 -1 46.82163 26.72834 23 <NA> <NA>
3 -1 47.19141 26.51870 12 <NA> <NA>
4 262882 13.57165 25.18298 2 Rajon Rondo Kings
5 262882 13.61702 25.17431 13 Rajon Rondo Kings
6 262882 13.66478 25.17784 24 Rajon Rondo Kings
7 263884 60.08215 22.76307 18 Marvin Williams Hornets
8 263884 60.09700 22.79717 7 Marvin Williams Hornets
9 266358 46.53550 18.42014 14 Rudy Gay Kings
10 266358 46.45843 18.46573 3 Rudy Gay Kings
11 266358 46.57545 18.35961 25 Rudy Gay Kings
12 329824 47.27324 31.01197 19 Spencer Hawes Hornets
13 329824 47.20201 31.09372 8 Spencer Hawes Hornets
14 340730 52.16717 33.09177 9 Jeremy Lin Hornets
15 340730 52.25433 33.11185 20 Jeremy Lin Hornets
16 462980 65.13020 26.39671 10 Kemba Walker Hornets
17 462980 65.11267 26.40227 21 Kemba Walker Hornets
18 509450 26.71856 25.53807 15 DeMarcus Cousins Kings
19 509450 26.68803 25.48557 4 DeMarcus Cousins Kings
20 604898 52.16717 33.09177 5 Ben McLemore Kings
21 604898 52.25433 33.11185 16 Ben McLemore Kings
22 609567 46.53550 18.42014 22 P.J. Hairston Grizzlies
23 609567 46.45843 18.46573 11 P.J. Hairston Grizzlies
24 699950 47.20201 31.09372 6 Willie Cauley-Stein Kings
25 699950 47.27324 31.01197 17 Willie Cauley-Stein Kings
最初在df1中，订单按1-25排序，现在全部乱序。很明显，df1以我不想要的方式被洗牌。这是我将sort = FALSE传递给合并函数时的输出：
merge(df1, df2, by = 'global.player.id', all.x = TRUE, sort = FALSE)
global.player.id x_loc y_loc order player team.name
1 262882 13.57165 25.18298 2 Rajon Rondo Kings
2 262882 13.61702 25.17431 13 Rajon Rondo Kings
3 262882 13.66478 25.17784 24 Rajon Rondo Kings
4 266358 46.53550 18.42014 14 Rudy Gay Kings
5 266358 46.45843 18.46573 3 Rudy Gay Kings
6 266358 46.57545 18.35961 25 Rudy Gay Kings
7 509450 26.71856 25.53807 15 DeMarcus Cousins Kings
8 509450 26.68803 25.48557 4 DeMarcus Cousins Kings
9 604898 52.16717 33.09177 5 Ben McLemore Kings
10 604898 52.25433 33.11185 16 Ben McLemore Kings
11 699950 47.20201 31.09372 6 Willie Cauley-Stein Kings
12 699950 47.27324 31.01197 17 Willie Cauley-Stein Kings
13 263884 60.08215 22.76307 18 Marvin Williams Hornets
14 263884 60.09700 22.79717 7 Marvin Williams Hornets
15 329824 47.27324 31.01197 19 Spencer Hawes Hornets
16 329824 47.20201 31.09372 8 Spencer Hawes Hornets
17 340730 52.16717 33.09177 9 Jeremy Lin Hornets
18 340730 52.25433 33.11185 20 Jeremy Lin Hornets
19 462980 65.13020 26.39671 10 Kemba Walker Hornets
20 462980 65.11267 26.40227 21 Kemba Walker Hornets
21 609567 46.53550 18.42014 22 P.J. Hairston Grizzlies
22 609567 46.45843 18.46573 11 P.J. Hairston Grizzlies
23 -1 47.17753 26.44326 1 <NA> <NA>
24 -1 46.82163 26.72834 23 <NA> <NA>
25 -1 47.19141 26.51870 12 <NA> <NA>
也不是我想要的，因为订单再次出现故障。
无论如何调用合并函数而不完全改组传递的第一个数据帧参数，或者我完全没有运气。如果是这样，这似乎是merge（）函数的主要缺点。谢谢！
答案 0 :(得分：2)
您可以使用join
plyr
library(plyr)
plyr::join(df1,df2, by='global.player.id')
结果未排序。