我正在尝试在名为 multi_scenario 和 production_targets 的两个数据帧之间执行 left_join。我正在尝试基于以下代码执行连接，使用基于匹配列 "mean_needed" 的 left_join ：
library(dplyr)
comb <- left_join(multi_scenario, production_targets, by = "mean_needed")
这是我的两个数据框
1.multi_scenario：
structure(list(scenario_ID = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L,
5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
3L, 3L, 3L, 3L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L,
4L, 4L, 5L, 5L, 5L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 1L, 1L,
1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L,
3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L), yield.spect = c(3,
13, 19, 22, 23, 14, 16, 20, 23, 1, 7, 16, 26, 35, 4, 6, 10, 33,
47, 5, 6, 10, 28, 64, 7, 8, 10, 29, 59, 7, 12, 28, 35, 36, 12,
13, 15, 27, 58, 13, 16, 25, 57, 4, 12, 22, 33, 54, 2, 17, 28,
29, 50, 19, 21, 23, 28, 35, 14, 17, 25, 52, 21, 23, 24, 26, 45,
20, 21, 23, 30, 45, 9, 21, 31, 57, 12, 14, 50, 0, 10, 37, 44,
55, 2, 9, 34, 48, 54, 3, 28, 31, 40, 44, 13, 17, 28, 38, 50,
14, 26, 29, 34, 49, 13, 30, 31, 37, 41, 14, 15, 17, 44, 64, 11,
13, 15, 56, 57, 7, 25, 30, 44, 47, 2, 30, 32, 38, 58, 18, 25,
26, 36, 55, 13, 29, 32, 34, 57, 13, 26, 38, 40, 49, 1, 4, 44,
55, 63, 10, 21, 36, 40, 58, 23, 25, 38, 57, 6, 18, 44, 50, 54,
17, 27, 32, 39, 63, 14, 20, 34, 51, 60, 5, 6, 55, 56, 57, 11,
27, 50, 64, 22, 25, 40, 44, 54, 18, 24, 41, 43, 59, 24, 26, 35,
45, 63, 7, 21, 37, 62, 64, 13, 37, 46, 48, 49, 3, 19, 46, 62,
7, 50, 63, 65, 28, 30, 37, 49, 56, 2, 38, 42, 59, 64, 21, 30,
47, 53, 56, 13, 31, 41, 59, 62, 8, 27, 49, 62, 65, 20, 21, 51,
60, 31, 36, 39, 45, 62, 36, 37, 42, 45, 60, 18, 33, 52, 56, 59,
43, 44, 51, 27, 43, 49, 53, 61, 30, 41, 47, 53, 62, 18, 49, 53,
60, 64), number_parcel = c(1000, 1000, 1000, 1000, 1000, 2000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 2000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 2000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 2000, 1000, 1000, 1000, 2000, 2000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 2000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 2000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 2000, 2000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 2000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 2000, 2000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000,
1000, 1000, 1000, 1000, 1000, 1000, 1000), mean_needed = c(15.9204,
15.9204, 15.9204, 15.9204, 15.9204, 17.2471, 17.2471, 17.2471,
17.2471, 17.2471, 17.2471, 17.2471, 17.2471, 17.2471, 19.9005,
19.9005, 19.9005, 19.9005, 19.9005, 22.5539, 22.5539, 22.5539,
22.5539, 22.5539, 22.5539, 22.5539, 22.5539, 22.5539, 22.5539,
23.8806, 23.8806, 23.8806, 23.8806, 23.8806, 25.2073, 25.2073,
25.2073, 25.2073, 25.2073, 25.2073, 25.2073, 25.2073, 25.2073,
25.2073, 25.2073, 25.2073, 25.2073, 25.2073, 25.2073, 25.2073,
25.2073, 25.2073, 25.2073, 25.2073, 25.2073, 25.2073, 25.2073,
25.2073, 26.534, 26.534, 26.534, 26.534, 27.8607, 27.8607, 27.8607,
27.8607, 27.8607, 27.8607, 27.8607, 27.8607, 27.8607, 27.8607,
27.8607, 27.8607, 27.8607, 27.8607, 27.8607, 27.8607, 27.8607,
29.1874, 29.1874, 29.1874, 29.1874, 29.1874, 29.1874, 29.1874,
29.1874, 29.1874, 29.1874, 29.1874, 29.1874, 29.1874, 29.1874,
29.1874, 29.1874, 29.1874, 29.1874, 29.1874, 29.1874, 30.5141,
30.5141, 30.5141, 30.5141, 30.5141, 30.5141, 30.5141, 30.5141,
30.5141, 30.5141, 30.5141, 30.5141, 30.5141, 30.5141, 30.5141,
30.5141, 30.5141, 30.5141, 30.5141, 30.5141, 30.5141, 30.5141,
30.5141, 30.5141, 30.5141, 31.8408, 31.8408, 31.8408, 31.8408,
31.8408, 31.8408, 31.8408, 31.8408, 31.8408, 31.8408, 33.1675,
33.1675, 33.1675, 33.1675, 33.1675, 33.1675, 33.1675, 33.1675,
33.1675, 33.1675, 33.1675, 33.1675, 33.1675, 33.1675, 33.1675,
33.1675, 33.1675, 33.1675, 33.1675, 33.1675, 33.1675, 33.1675,
33.1675, 33.1675, 34.4942, 34.4942, 34.4942, 34.4942, 34.4942,
35.8209, 35.8209, 35.8209, 35.8209, 35.8209, 35.8209, 35.8209,
35.8209, 35.8209, 35.8209, 35.8209, 35.8209, 35.8209, 35.8209,
35.8209, 35.8209, 35.8209, 35.8209, 35.8209, 37.1476, 37.1476,
37.1476, 37.1476, 37.1476, 37.1476, 37.1476, 37.1476, 37.1476,
37.1476, 38.4743, 38.4743, 38.4743, 38.4743, 38.4743, 38.4743,
38.4743, 38.4743, 38.4743, 38.4743, 38.4743, 38.4743, 38.4743,
38.4743, 38.4743, 38.4743, 38.4743, 38.4743, 38.4743, 38.4743,
38.4743, 38.4743, 38.4743, 39.801, 39.801, 39.801, 39.801, 39.801,
41.1277, 41.1277, 41.1277, 41.1277, 41.1277, 41.1277, 41.1277,
41.1277, 41.1277, 41.1277, 41.1277, 41.1277, 41.1277, 41.1277,
41.1277, 42.4544, 42.4544, 42.4544, 42.4544, 42.4544, 42.4544,
42.4544, 42.4544, 42.4544, 42.4544, 42.4544, 42.4544, 42.4544,
42.4544, 43.7811, 43.7811, 43.7811, 43.7811, 43.7811, 43.7811,
43.7811, 43.7811, 43.7811, 43.7811, 46.4345, 46.4345, 46.4345,
46.4345, 46.4345, 46.4345, 46.4345, 46.4345, 46.4345, 46.4345,
46.4345, 46.4345, 46.4345, 49.0879, 49.0879, 49.0879, 49.0879,
49.0879)), row.names = c(NA, -277L), class = c("data.table",
"data.frame"), .internal.selfref = <pointer: 0x0000000000321ef0>)
- 生产目标：
structure(list(rel_prod = c(0.5, 0.55, 0.6, 0.65, 0.7, 0.75,
0.8, 0.85, 0.9, 0.95, 1, 1.05, 1.1, 1.15, 1.2, 1.25, 1.3, 1.35,
1.4, 1.45, 1.5, 1.55, 1.6, 1.65, 1.7, 1.75, 1.8, 1.85, 1.9, 1.95,
2), production_target = c(66335, 72968.5, 79602, 86235.5, 92869,
99502.5, 106136, 112769.5, 119403, 126036.5, 132670, 139303.5,
145937, 152570.5, 159204, 165837.5, 172471, 179104.5, 185738,
192371.5, 199005, 205638.5, 212272, 218905.5, 225539, 232172.5,
238806, 245439.5, 252073, 258706.5, 265340), mean_needed = c(13.267,
14.5937, 15.9204, 17.2471, 18.5738, 19.9005, 21.2272, 22.5539,
23.8806, 25.2073, 26.534, 27.8607, 29.1874, 30.5141, 31.8408,
33.1675, 34.4942, 35.8209, 37.1476, 38.4743, 39.801, 41.1277,
42.4544, 43.7811, 45.1078, 46.4345, 47.7612, 49.0879, 50.4146,
51.7413, 53.068)), row.names = c(NA, -31L), class = "data.frame")
我期待上面的代码将两个新列（rel_production 和 production_target）添加到我的新数据框comb 中，并带有关联的值。但是，由于某种原因，我的新数据框在某些区域显示 NA 而不是预期值。这似乎只发生在对 mean_needed 的某些观察中。但是，当我查看要加入的两个数据框中的 mean_needed 值时，它们看起来是相同的。没有任何额外的隐藏空格，并且两列都是数字。我包含一个打印件以显示两列中的 mean_needed 值似乎相同。我得到 NA 而不是期望值有什么原因吗？谢谢
梳数据框，显示某些平均值的 NA。加入后在其他 mean_values 处重复分配 NA
我试图通过以下方式离开的价值观：
production_targets$mean_needed
13.2670 14.5937 15.9204 17.2471 18.5738 19.9005 21.2272 22.5539 23.8806 25.2073 26.5340 27.8607 29.1874 30.5141 31.8408 33.1675 34.4942 35.8209 37.1476 38.4743 39.8010 41.1277 42.4544 43.7811 45.1078 46.4345 47.7612 49.0879 50.4146 51.7413 53.0680
comb$mean.needed %>% unique()
14.5937 15.9204 17.2471 18.5738 19.9005 21.2272 22.5539 25.2073 26.5340 27.8607 29.1874 30.5141 31.8408 33.1675 34.4942 35.8209 37.1476 38.4743 39.8010 41.1277 42.4544 43.7811 45.1078 46.4345 47.7612 49.0879 27] 50.4146