如何使用 merge() 避免重复条目？
How do I avoid duplicate entries using merge()?
如果这听起来像是一个非常愚蠢的问题，请原谅我，我刚刚开始重新编码。
我有 6 个数据库，每个数据库有 3 列和 16 行，我正在尝试使用 merge() 函数进行分析。使用合并函数时，by.x 和 by.y 参数重复 7 或 8 次，我不知道为什么以及如何修复它。
这是它的样子：
mycob1 <- read.csv("MYCOB_1.csv")
mycob2 <- read.csv("MYCOB_2.csv")
mycob1
Lot_210927 RFU Ct
1 0 6.3588 9.164329
2 0 5.0394 11.350701
3 0 4.9946 37.334669
4 0 4.8604 8.168337
5 0 4.9032 37.306613
6 0 4.9502 22.176353
7 0 4.7858 23.713427
8 0 5.2778 10.496994
9 1 1021.8458 32.119668
10 1 1020.1998 31.500716
11 1 1065.8000 31.979674
12 1 988.0452 31.019754
13 1 1085.2206 31.557973
14 1 1072.8540 31.745491
15 1 1020.6496 31.218151
16 1 983.4106 31.981162
mycob2
Lot_211020 RFU Ct
1 0 0.6876 47.72087
2 0 40.1056 38.37418
3 0 97.0882 37.72917
4 0 10.3170 36.18236
5 0 67.3742 37.39050
6 0 10.2540 40.16776
7 0 6.9624 28.07575
8 0 9.5718 28.84626
9 0 13.0306 38.87375
10 1 860.3956 29.15746
11 1 884.9338 30.03665
12 1 1552.2462 27.90839
13 1 738.2328 29.22760
14 1 1419.6448 29.13627
15 1 1441.6212 29.35351
16 1 424.9774 31.56446
>
M1 <- merge(mycob1, mycob2,
by.x = "Lot_210927",
by.y = "Lot_211020",
all.x = TRUE,
all.y = FALSE)
Lot_210927 RFU.x Ct.x RFU.y Ct.y
1 0 6.3588 9.164329 0.6876 47.72087
2 0 6.3588 9.164329 40.1056 38.37418
3 0 6.3588 9.164329 97.0882 37.72917
4 0 6.3588 9.164329 10.3170 36.18236
5 0 6.3588 9.164329 67.3742 37.39050
6 0 6.3588 9.164329 10.2540 40.16776
7 0 6.3588 9.164329 6.9624 28.07575
8 0 6.3588 9.164329 9.5718 28.84626
9 0 6.3588 9.164329 13.0306 38.87375
10 0 5.0394 11.350701 0.6876 47.72087
11 0 5.0394 11.350701 40.1056 38.37418
12 0 5.0394 11.350701 97.0882 37.72917
13 0 5.0394 11.350701 10.3170 36.18236
14 0 5.0394 11.350701 67.3742 37.39050
15 0 5.0394 11.350701 10.2540 40.16776
16 0 5.0394 11.350701 6.9624 28.07575
17 0 5.0394 11.350701 9.5718 28.84626
18 0 5.0394 11.350701 13.0306 38.87375
19 0 4.9946 37.334669 0.6876 47.72087
20 0 4.9946 37.334669 40.1056 38.37418
21 0 4.9946 37.334669 97.0882 37.72917
22 0 4.9946 37.334669 10.3170 36.18236
23 0 4.9946 37.334669 67.3742 37.39050
24 0 4.9946 37.334669 10.2540 40.16776
25 0 4.9946 37.334669 6.9624 28.07575
26 0 4.9946 37.334669 9.5718 28.84626
27 0 4.9946 37.334669 13.0306 38.87375
28 0 4.8604 8.168337 0.6876 47.72087
29 0 4.8604 8.168337 40.1056 38.37418
30 0 4.8604 8.168337 97.0882 37.72917
31 0 4.8604 8.168337 10.3170 36.18236
32 0 4.8604 8.168337 67.3742 37.39050
33 0 4.8604 8.168337 10.2540 40.16776
34 0 4.8604 8.168337 6.9624 28.07575
35 0 4.8604 8.168337 9.5718 28.84626
36 0 4.8604 8.168337 13.0306 38.87375
37 0 4.9032 37.306613 0.6876 47.72087
38 0 4.9032 37.306613 40.1056 38.37418
39 0 4.9032 37.306613 97.0882 37.72917
40 0 4.9032 37.306613 10.3170 36.18236
41 0 4.9032 37.306613 67.3742 37.39050
42 0 4.9032 37.306613 10.2540 40.16776
43 0 4.9032 37.306613 6.9624 28.07575
44 0 4.9032 37.306613 9.5718 28.84626
45 0 4.9032 37.306613 13.0306 38.87375
46 0 4.9502 22.176353 0.6876 47.72087
47 0 4.9502 22.176353 40.1056 38.37418
48 0 4.9502 22.176353 97.0882 37.72917
49 0 4.9502 22.176353 10.3170 36.18236
50 0 4.9502 22.176353 67.3742 37.39050
51 0 4.9502 22.176353 10.2540 40.16776
52 0 4.9502 22.176353 6.9624 28.07575
53 0 4.9502 22.176353 9.5718 28.84626
54 0 4.9502 22.176353 13.0306 38.87375
55 0 4.7858 23.713427 0.6876 47.72087
56 0 4.7858 23.713427 40.1056 38.37418
57 0 4.7858 23.713427 97.0882 37.72917
58 0 4.7858 23.713427 10.3170 36.18236
59 0 4.7858 23.713427 67.3742 37.39050
60 0 4.7858 23.713427 10.2540 40.16776
61 0 4.7858 23.713427 6.9624 28.07575
62 0 4.7858 23.713427 9.5718 28.84626
63 0 4.7858 23.713427 13.0306 38.87375
64 0 5.2778 10.496994 0.6876 47.72087
65 0 5.2778 10.496994 40.1056 38.37418
66 0 5.2778 10.496994 97.0882 37.72917
67 0 5.2778 10.496994 10.3170 36.18236
68 0 5.2778 10.496994 67.3742 37.39050
69 0 5.2778 10.496994 10.2540 40.16776
70 0 5.2778 10.496994 6.9624 28.07575
71 0 5.2778 10.496994 9.5718 28.84626
72 0 5.2778 10.496994 13.0306 38.87375
73 1 1021.8458 32.119668 860.3956 29.15746
74 1 1021.8458 32.119668 884.9338 30.03665
75 1 1021.8458 32.119668 1552.2462 27.90839
76 1 1021.8458 32.119668 738.2328 29.22760
77 1 1021.8458 32.119668 1419.6448 29.13627
78 1 1021.8458 32.119668 1441.6212 29.35351
79 1 1021.8458 32.119668 424.9774 31.56446
80 1 1020.1998 31.500716 860.3956 29.15746
81 1 1020.1998 31.500716 884.9338 30.03665
82 1 1020.1998 31.500716 1552.2462 27.90839
83 1 1020.1998 31.500716 738.2328 29.22760
84 1 1020.1998 31.500716 1419.6448 29.13627
85 1 1020.1998 31.500716 1441.6212 29.35351
86 1 1020.1998 31.500716 424.9774 31.56446
87 1 1065.8000 31.979674 860.3956 29.15746
88 1 1065.8000 31.979674 884.9338 30.03665
89 1 1065.8000 31.979674 1552.2462 27.90839
90 1 1065.8000 31.979674 738.2328 29.22760
91 1 1065.8000 31.979674 1419.6448 29.13627
92 1 1065.8000 31.979674 1441.6212 29.35351
93 1 1065.8000 31.979674 424.9774 31.56446
94 1 988.0452 31.019754 860.3956 29.15746
95 1 988.0452 31.019754 884.9338 30.03665
96 1 988.0452 31.019754 1552.2462 27.90839
97 1 988.0452 31.019754 738.2328 29.22760
98 1 988.0452 31.019754 1419.6448 29.13627
99 1 988.0452 31.019754 1441.6212 29.35351
100 1 988.0452 31.019754 424.9774 31.56446
101 1 1085.2206 31.557973 860.3956 29.15746
102 1 1085.2206 31.557973 884.9338 30.03665
103 1 1085.2206 31.557973 1552.2462 27.90839
104 1 1085.2206 31.557973 738.2328 29.22760
105 1 1085.2206 31.557973 1419.6448 29.13627
106 1 1085.2206 31.557973 1441.6212 29.35351
107 1 1085.2206 31.557973 424.9774 31.56446
108 1 1072.8540 31.745491 860.3956 29.15746
109 1 1072.8540 31.745491 884.9338 30.03665
110 1 1072.8540 31.745491 1552.2462 27.90839
111 1 1072.8540 31.745491 738.2328 29.22760
112 1 1072.8540 31.745491 1419.6448 29.13627
113 1 1072.8540 31.745491 1441.6212 29.35351
114 1 1072.8540 31.745491 424.9774 31.56446
115 1 1020.6496 31.218151 860.3956 29.15746
116 1 1020.6496 31.218151 884.9338 30.03665
117 1 1020.6496 31.218151 1552.2462 27.90839
118 1 1020.6496 31.218151 738.2328 29.22760
119 1 1020.6496 31.218151 1419.6448 29.13627
120 1 1020.6496 31.218151 1441.6212 29.35351
121 1 1020.6496 31.218151 424.9774 31.56446
122 1 983.4106 31.981162 860.3956 29.15746
123 1 983.4106 31.981162 884.9338 30.03665
124 1 983.4106 31.981162 1552.2462 27.90839
125 1 983.4106 31.981162 738.2328 29.22760
126 1 983.4106 31.981162 1419.6448 29.13627
127 1 983.4106 31.981162 1441.6212 29.35351
128 1 983.4106 31.981162 424.9774 31.56446
如有任何建议，我们将不胜感激！
也许是这样的：
left_join(mycob1, mycob2, by.x = "Lot_210927", by.y = "Lot_211020")
R 中的左连接是两个数据帧之间的合并操作，其中合并 returns 来自一个 table（左侧）的所有行和来自第二个 table.
如果您想要其他联接，请参阅此处：https://www.datasciencemadesimple.com/join-in-r-merge-in-r/ 以获得对联接选项的详细解释。
如果这听起来像是一个非常愚蠢的问题，请原谅我，我刚刚开始重新编码。
我有 6 个数据库，每个数据库有 3 列和 16 行，我正在尝试使用 merge() 函数进行分析。使用合并函数时，by.x 和 by.y 参数重复 7 或 8 次，我不知道为什么以及如何修复它。 这是它的样子：
mycob1 <- read.csv("MYCOB_1.csv")
mycob2 <- read.csv("MYCOB_2.csv")
mycob1
Lot_210927 RFU Ct
1 0 6.3588 9.164329
2 0 5.0394 11.350701
3 0 4.9946 37.334669
4 0 4.8604 8.168337
5 0 4.9032 37.306613
6 0 4.9502 22.176353
7 0 4.7858 23.713427
8 0 5.2778 10.496994
9 1 1021.8458 32.119668
10 1 1020.1998 31.500716
11 1 1065.8000 31.979674
12 1 988.0452 31.019754
13 1 1085.2206 31.557973
14 1 1072.8540 31.745491
15 1 1020.6496 31.218151
16 1 983.4106 31.981162
mycob2
Lot_211020 RFU Ct
1 0 0.6876 47.72087
2 0 40.1056 38.37418
3 0 97.0882 37.72917
4 0 10.3170 36.18236
5 0 67.3742 37.39050
6 0 10.2540 40.16776
7 0 6.9624 28.07575
8 0 9.5718 28.84626
9 0 13.0306 38.87375
10 1 860.3956 29.15746
11 1 884.9338 30.03665
12 1 1552.2462 27.90839
13 1 738.2328 29.22760
14 1 1419.6448 29.13627
15 1 1441.6212 29.35351
16 1 424.9774 31.56446
>
M1 <- merge(mycob1, mycob2,
by.x = "Lot_210927",
by.y = "Lot_211020",
all.x = TRUE,
all.y = FALSE)
Lot_210927 RFU.x Ct.x RFU.y Ct.y
1 0 6.3588 9.164329 0.6876 47.72087
2 0 6.3588 9.164329 40.1056 38.37418
3 0 6.3588 9.164329 97.0882 37.72917
4 0 6.3588 9.164329 10.3170 36.18236
5 0 6.3588 9.164329 67.3742 37.39050
6 0 6.3588 9.164329 10.2540 40.16776
7 0 6.3588 9.164329 6.9624 28.07575
8 0 6.3588 9.164329 9.5718 28.84626
9 0 6.3588 9.164329 13.0306 38.87375
10 0 5.0394 11.350701 0.6876 47.72087
11 0 5.0394 11.350701 40.1056 38.37418
12 0 5.0394 11.350701 97.0882 37.72917
13 0 5.0394 11.350701 10.3170 36.18236
14 0 5.0394 11.350701 67.3742 37.39050
15 0 5.0394 11.350701 10.2540 40.16776
16 0 5.0394 11.350701 6.9624 28.07575
17 0 5.0394 11.350701 9.5718 28.84626
18 0 5.0394 11.350701 13.0306 38.87375
19 0 4.9946 37.334669 0.6876 47.72087
20 0 4.9946 37.334669 40.1056 38.37418
21 0 4.9946 37.334669 97.0882 37.72917
22 0 4.9946 37.334669 10.3170 36.18236
23 0 4.9946 37.334669 67.3742 37.39050
24 0 4.9946 37.334669 10.2540 40.16776
25 0 4.9946 37.334669 6.9624 28.07575
26 0 4.9946 37.334669 9.5718 28.84626
27 0 4.9946 37.334669 13.0306 38.87375
28 0 4.8604 8.168337 0.6876 47.72087
29 0 4.8604 8.168337 40.1056 38.37418
30 0 4.8604 8.168337 97.0882 37.72917
31 0 4.8604 8.168337 10.3170 36.18236
32 0 4.8604 8.168337 67.3742 37.39050
33 0 4.8604 8.168337 10.2540 40.16776
34 0 4.8604 8.168337 6.9624 28.07575
35 0 4.8604 8.168337 9.5718 28.84626
36 0 4.8604 8.168337 13.0306 38.87375
37 0 4.9032 37.306613 0.6876 47.72087
38 0 4.9032 37.306613 40.1056 38.37418
39 0 4.9032 37.306613 97.0882 37.72917
40 0 4.9032 37.306613 10.3170 36.18236
41 0 4.9032 37.306613 67.3742 37.39050
42 0 4.9032 37.306613 10.2540 40.16776
43 0 4.9032 37.306613 6.9624 28.07575
44 0 4.9032 37.306613 9.5718 28.84626
45 0 4.9032 37.306613 13.0306 38.87375
46 0 4.9502 22.176353 0.6876 47.72087
47 0 4.9502 22.176353 40.1056 38.37418
48 0 4.9502 22.176353 97.0882 37.72917
49 0 4.9502 22.176353 10.3170 36.18236
50 0 4.9502 22.176353 67.3742 37.39050
51 0 4.9502 22.176353 10.2540 40.16776
52 0 4.9502 22.176353 6.9624 28.07575
53 0 4.9502 22.176353 9.5718 28.84626
54 0 4.9502 22.176353 13.0306 38.87375
55 0 4.7858 23.713427 0.6876 47.72087
56 0 4.7858 23.713427 40.1056 38.37418
57 0 4.7858 23.713427 97.0882 37.72917
58 0 4.7858 23.713427 10.3170 36.18236
59 0 4.7858 23.713427 67.3742 37.39050
60 0 4.7858 23.713427 10.2540 40.16776
61 0 4.7858 23.713427 6.9624 28.07575
62 0 4.7858 23.713427 9.5718 28.84626
63 0 4.7858 23.713427 13.0306 38.87375
64 0 5.2778 10.496994 0.6876 47.72087
65 0 5.2778 10.496994 40.1056 38.37418
66 0 5.2778 10.496994 97.0882 37.72917
67 0 5.2778 10.496994 10.3170 36.18236
68 0 5.2778 10.496994 67.3742 37.39050
69 0 5.2778 10.496994 10.2540 40.16776
70 0 5.2778 10.496994 6.9624 28.07575
71 0 5.2778 10.496994 9.5718 28.84626
72 0 5.2778 10.496994 13.0306 38.87375
73 1 1021.8458 32.119668 860.3956 29.15746
74 1 1021.8458 32.119668 884.9338 30.03665
75 1 1021.8458 32.119668 1552.2462 27.90839
76 1 1021.8458 32.119668 738.2328 29.22760
77 1 1021.8458 32.119668 1419.6448 29.13627
78 1 1021.8458 32.119668 1441.6212 29.35351
79 1 1021.8458 32.119668 424.9774 31.56446
80 1 1020.1998 31.500716 860.3956 29.15746
81 1 1020.1998 31.500716 884.9338 30.03665
82 1 1020.1998 31.500716 1552.2462 27.90839
83 1 1020.1998 31.500716 738.2328 29.22760
84 1 1020.1998 31.500716 1419.6448 29.13627
85 1 1020.1998 31.500716 1441.6212 29.35351
86 1 1020.1998 31.500716 424.9774 31.56446
87 1 1065.8000 31.979674 860.3956 29.15746
88 1 1065.8000 31.979674 884.9338 30.03665
89 1 1065.8000 31.979674 1552.2462 27.90839
90 1 1065.8000 31.979674 738.2328 29.22760
91 1 1065.8000 31.979674 1419.6448 29.13627
92 1 1065.8000 31.979674 1441.6212 29.35351
93 1 1065.8000 31.979674 424.9774 31.56446
94 1 988.0452 31.019754 860.3956 29.15746
95 1 988.0452 31.019754 884.9338 30.03665
96 1 988.0452 31.019754 1552.2462 27.90839
97 1 988.0452 31.019754 738.2328 29.22760
98 1 988.0452 31.019754 1419.6448 29.13627
99 1 988.0452 31.019754 1441.6212 29.35351
100 1 988.0452 31.019754 424.9774 31.56446
101 1 1085.2206 31.557973 860.3956 29.15746
102 1 1085.2206 31.557973 884.9338 30.03665
103 1 1085.2206 31.557973 1552.2462 27.90839
104 1 1085.2206 31.557973 738.2328 29.22760
105 1 1085.2206 31.557973 1419.6448 29.13627
106 1 1085.2206 31.557973 1441.6212 29.35351
107 1 1085.2206 31.557973 424.9774 31.56446
108 1 1072.8540 31.745491 860.3956 29.15746
109 1 1072.8540 31.745491 884.9338 30.03665
110 1 1072.8540 31.745491 1552.2462 27.90839
111 1 1072.8540 31.745491 738.2328 29.22760
112 1 1072.8540 31.745491 1419.6448 29.13627
113 1 1072.8540 31.745491 1441.6212 29.35351
114 1 1072.8540 31.745491 424.9774 31.56446
115 1 1020.6496 31.218151 860.3956 29.15746
116 1 1020.6496 31.218151 884.9338 30.03665
117 1 1020.6496 31.218151 1552.2462 27.90839
118 1 1020.6496 31.218151 738.2328 29.22760
119 1 1020.6496 31.218151 1419.6448 29.13627
120 1 1020.6496 31.218151 1441.6212 29.35351
121 1 1020.6496 31.218151 424.9774 31.56446
122 1 983.4106 31.981162 860.3956 29.15746
123 1 983.4106 31.981162 884.9338 30.03665
124 1 983.4106 31.981162 1552.2462 27.90839
125 1 983.4106 31.981162 738.2328 29.22760
126 1 983.4106 31.981162 1419.6448 29.13627
127 1 983.4106 31.981162 1441.6212 29.35351
128 1 983.4106 31.981162 424.9774 31.56446
如有任何建议，我们将不胜感激！
也许是这样的：
left_join(mycob1, mycob2, by.x = "Lot_210927", by.y = "Lot_211020")
R 中的左连接是两个数据帧之间的合并操作，其中合并 returns 来自一个 table（左侧）的所有行和来自第二个 table.
如果您想要其他联接，请参阅此处：https://www.datasciencemadesimple.com/join-in-r-merge-in-r/ 以获得对联接选项的详细解释。