如何解决当值似乎在 by="x" 参数中匹配时，为什么 left_join 创建 NAs？
我正在尝试在两个名为 multi_scenario 和 production_targets 的数据帧之间执行 left_join。我正在尝试根据以下代码执行连接，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,2L,3L,4L,5L,1L),yield.spect = c(3,13,19,22,23,14,16,20,1,7,26,35,4,6,10,33,47,5,28,64,8,29,59,12,36,15,27,58,25,57,54,2,17,50,21,52,24,45,30,9,31,37,44,55,34,48,3,40,38,49,41,11,56,32,18,63,39,51,60,43,62,46,65,42,53,61,64),number_parcel = c(1000,1000,2000,1000),mean_needed = c(15.9204,15.9204,17.2471,19.9005,22.5539,23.8806,25.2073,26.534,27.8607,29.1874,30.5141,31.8408,33.1675,34.4942,35.8209,37.1476,38.4743,39.801,41.1277,42.4544,43.7811,46.4345,49.0879,49.0879)),row.names = c(NA,-277L),class = c("data.table","data.frame"),.internal.selfref = <pointer: 0x0000000000321ef0>)
- production_targets：
structure(list(rel_prod = c(0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.05,1.1,1.15,1.2,1.25,1.3,1.35,1.4,1.45,1.5,1.55,1.6,1.65,1.7,1.75,1.8,1.85,1.9,1.95,2),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(13.267,14.5937,18.5738,21.2272,45.1078,47.7612,50.4146,51.7413,53.068)),-31L),class = "data.frame")
我希望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，出于某种原因，我的新数据框有一些区域显示 NA 而不是预期值。这似乎只发生在 mean_needed 的某些观察中。但是当我查看我试图加入的两个数据帧中 mean_needed 的值时，它们看起来相同。没有任何额外的隐藏空间，两列都是数字。我包含了一个打印来显示 mean_needed 值在两列中似乎相同。我得到 NA 而不是预期值有什么原因吗？谢谢
我试图离开的值：
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
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