R 尝试为两列的每个组合提取最高值。包括用于澄清的数据集_R_Dataframe

R 尝试为两列的每个组合提取最高值。包括用于澄清的数据集

r dataframe

R 尝试为两列的每个组合提取最高值。包括用于澄清的数据集,r,dataframe,R,Dataframe,首先，这里是数据集，您可以在本地使用它： dput(test) structure(list(Games = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 1L, 2L,

首先，这里是数据集，您可以在本地使用它：

dput(test)

structure(list(Games = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 
23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 
36L, 37L, 38L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 
25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 
38L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 
14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 
27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 1L, 
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 1L, 2L, 3L, 
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 
31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 1L, 2L, 3L, 4L, 5L, 6L, 
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 
20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 
33L, 34L, 35L, 36L, 37L, 38L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 
22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 
35L, 36L, 37L, 38L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 
37L, 38L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 
26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 
28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 
30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 1L, 2L, 3L, 4L, 
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 
32L, 33L, 34L, 35L, 36L, 37L, 38L), League = c("BPL", "BPL", 
"BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", 
"BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", 
"BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", 
"BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", 
"BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", 
"BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", 
"BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", 
"BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", 
"BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", 
"BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", 
"BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", 
"BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", "BPL", 
"BPL", "BPL", "BPL", "BPL", "LGA", "LGA", "LGA", "LGA", "LGA", 
"LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", 
"LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", 
"LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", 
"LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", 
"LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", 
"LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", 
"LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", 
"LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", 
"LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", 
"LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", 
"LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", 
"LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", "LGA", 
"LGA", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", 
"BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", 
"BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", 
"BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", 
"BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", 
"BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", 
"BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", 
"BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", 
"BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", 
"BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", 
"BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", 
"BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", 
"BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "BLI", "LG1", "LG1", 
"LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", 
"LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", 
"LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", 
"LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", 
"LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", 
"LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", 
"LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", 
"LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", 
"LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", 
"LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", 
"LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", 
"LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", "LG1", 
"LG1", "LG1", "LG1", "LG1"), line = c("Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Upper", "Upper", "Upper", "Upper", "Upper", 
"Upper", "Upper", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Prediction", 
"Prediction", "Prediction", "Prediction", "Prediction", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower", "Lower", "Lower", "Lower", "Lower", "Lower", 
"Lower", "Lower"), value = c(1.29298996115076, 2.40499166735689, 
3.69798162850766, 4.78660126564843, 5.83266953230409, 7.25470630289318, 
8.49759553439007, 9.6095972405962, 10.902587201747, 12.2552936000241, 
13.3439132371649, 14.5109638123662, 15.8636702106434, 17.0651422119924, 
18.2666142133414, 19.4680862146904, 20.7610761758412, 22.0039654073381, 
23.246854638835, 24.3354742759757, 25.6881806742529, 27.110217444842, 
28.2222191510481, 29.5152091121989, 30.8679155104761, 32.1609054716268, 
33.4037947031237, 34.6052667044727, 35.8982566656235, 36.9443249322792, 
38.1457969336282, 39.3472689349772, 40.6402588961279, 41.7288785332687, 
42.9717677647656, 44.0837694709717, 45.3266587024686, 46.4937092776699, 
1.14244921317105, 2.07994244701033, 3.22239166018138, 4.12353606349401, 
4.95615615314181, 6.19262849787408, 7.2907822523304, 8.22827548616968, 
9.37072469934073, 10.5592560861148, 11.4604004894275, 12.4750490564289, 
13.6635804432029, 14.7191561786698, 15.7747319141366, 16.8303076496034, 
17.9727568627745, 19.0709106172308, 20.1690643716871, 21.0702087749998, 
22.2587401617738, 23.4952125065061, 24.4327057403454, 25.5751549535164, 
26.7636863402905, 27.9061355534616, 29.0042893079179, 30.0598650433847, 
31.2023142565558, 32.0349343462036, 33.0905100816704, 34.1460858171372, 
35.2885350303083, 36.1896794336209, 37.2878331880772, 38.2253264219165, 
39.3234801763728, 40.3381287433743, 0.991908465191337, 1.75489322666377, 
2.7468016918551, 3.46047086133959, 4.07964277397953, 5.13055069285498, 
6.08396897027074, 6.84695373174317, 7.8388621969345, 8.8632185722055, 
9.57688774168998, 10.4391343004915, 11.4634906757625, 12.3731701453471, 
13.2828496149318, 14.1925290845164, 15.1844375497078, 16.1378558271235, 
17.0912741045393, 17.8049432740238, 18.8292996492948, 19.8802075681702, 
20.6431923296426, 21.635100794834, 22.659457170105, 23.6513656352963, 
24.6047839127121, 25.5144633822967, 26.5063718474881, 27.125543760128, 
28.0352232297126, 28.9449026992973, 29.9368111644886, 30.6504803339731, 
31.6038986113889, 32.3668833728613, 33.3203016502771, 34.1825482090786, 
1.29298996115076, 2.88008216870556, 3.96870180584633, 5.2616917669971, 
6.39944846523036, 7.64233769672725, 8.66837198271614, 10.0210783809933, 
11.0878947100969, 12.1998964163031, 13.6219331868922, 14.9746395851693, 
16.2175288166662, 17.4190008180152, 18.711990779166, 20.0049807403168, 
21.1169824465229, 22.4099724076737, 23.6114444090227, 24.7000640461634, 
25.9930540073142, 27.0190882933031, 28.2619775248, 29.3997342230332, 
30.5117359292394, 31.8047258903901, 33.0061978917391, 34.0730142208428, 
35.2744862221918, 36.3864879283979, 37.6794778895487, 38.9724678506994, 
40.3945046212885, 41.7472110195657, 43.0402009807164, 44.2830902122133, 
45.8701824197681, 47.2228888180453, 1.14244921317105, 2.4806827570886, 
3.38182716040123, 4.52427637357228, 5.49958461525208, 6.5977383697084, 
7.39807580873082, 8.58660719550491, 9.45281209844173, 10.390305332281, 
11.6267776770133, 12.8153090637874, 13.9134628182437, 14.9690385537105, 
16.1114877668816, 17.2539369800526, 18.1914302138919, 19.3338794270629, 
20.3894551625298, 21.2905995658424, 22.4330487790135, 23.2333862180359, 
24.3315399724922, 25.306848214172, 26.2443414480113, 27.3867906611823, 
28.4423663966492, 29.308571299586, 30.3641470350528, 31.3016402688921, 
32.4440894820631, 33.5865386952342, 34.8230110399664, 36.0115424267405, 
37.1539916399116, 38.2521453943679, 39.5903789382855, 40.7789103250595, 
0.991908465191337, 2.08128334547164, 2.79495251495612, 3.78686098014746, 
4.5997207652738, 5.55313904268955, 6.12777963474551, 7.1521360100165, 
7.81772948678652, 8.58071424825894, 9.63162216713439, 10.6559785424054, 
11.6093968198211, 12.5190762894058, 13.5109847545971, 14.5028932197885, 
15.2658779812609, 16.2577864464522, 17.1674659160369, 17.8811350855214, 
18.8730435507127, 19.4476841427686, 20.4011024201844, 21.2139622053107, 
21.9769469667832, 22.9688554319745, 23.8785349015592, 24.5441283783292, 
25.4538078479138, 26.2167926093862, 27.2087010745776, 28.2006095397689, 
29.2515174586444, 30.2758738339154, 31.2677822991067, 32.2212005765225, 
33.3105754568028, 34.3349318320738, 1.2428892314969, 2.59559562977408, 
3.79706763112308, 5.14977402940026, 6.35124603074926, 7.64423599190003, 
8.71105232100364, 10.0040422821544, 11.504440002718, 12.6164417089241, 
13.9094316700749, 15.1523209015718, 16.3952101330687, 17.8172469036578, 
19.1102368648085, 20.2479935630418, 21.5409835241926, 22.7838727556895, 
23.8506890847931, 25.1436790459438, 26.4366690070946, 27.7296589682454, 
29.1516957388344, 30.3945849703313, 31.5960569716803, 32.8890469328311, 
34.131936164328, 35.2439378705341, 36.7443355910977, 37.882092289331, 
39.08356429068, 40.3765542518308, 41.7292606501079, 43.0819670483851, 
NA, NA, NA, NA, 1.09815375445633, 2.28668514123042, 3.34226087669724, 
4.53079226347133, 5.58636799893815, 6.7288172121092, 7.59502211504602, 
8.73747132821707, 10.0238183917451, 10.9613116255843, 12.1037608387554, 
13.2019145932117, 14.300068347668, 15.5365406924003, 16.6789899055714, 
17.6542981472512, 18.7967473604222, 19.8949011148785, 20.7611060178154, 
21.9035552309864, 23.0460044441575, 24.1884536573285, 25.4249260020608, 
26.5230797565171, 27.5786554919839, 28.721104705155, 29.8192584596113, 
30.7567516934506, 32.0430987569786, 33.0184069986584, 34.0739827341252, 
35.2164319472963, 36.4049633340703, 37.5934947208444, NA, NA, 
NA, NA, 0.953418277415756, 1.97777465268675, 2.8874541222714, 
3.9118104975424, 4.82148996712705, 5.81339843231838, 6.4789919090884, 
7.47090037427973, 8.54319678077214, 9.30618154224456, 10.2980900074359, 
11.2515082848517, 12.2049265622674, 13.2558344811429, 14.2477429463342, 
15.0606027314605, 16.0525111966519, 17.0059294740676, 17.6715229508376, 
18.663431416029, 19.6553398812203, 20.6472483464116, 21.6981562652871, 
22.6515745427028, 23.5612540122875, 24.5531624774788, 25.5065807548946, 
26.269565516367, 27.3418619228594, 28.1547217079858, 29.0644011775704, 
30.0563096427617, 31.0806660180327, 32.1050223933037, NA, NA, 
NA, NA, 1.29298996115076, 2.58597992230153, 3.75303049750283, 
5.04602045865359, 6.93875492269199, 8.36079169328107, 10.1442930752809, 
11.6446907958445, 12.8461627971935, 14.1391527583443, 15.7262449658991, 
17.1482817364882, 18.735373944043, 20.1574107146321, 21.6578084351956, 
23.0798452057847, 24.2813172071337, 25.448367782335, 26.6912570138319, 
27.9341462453288, 29.5212384528836, 30.9432752234727, 32.7267766054726, 
34.2271743260362, 35.6492110966253, 37.0712478672143, 38.2727198685633, 
39.4397704437646, 40.6412424451136, 41.8082930203149, 43.3953852278697, 
44.6883751890205, 45.9813651501713, 47.8740996142097, 49.2961363847987, 
50.5891263459495, 51.8821163071003, 53.3825140276639, 1.14244921317105, 
2.2848984263421, 3.29954699334351, 4.44199620651456, 5.9487877292247, 
7.18526007395696, 8.63362972467803, 9.91997678820602, 10.9755525236728, 
12.1180017368439, 13.4562352807614, 14.6927076254937, 16.0309411694113, 
17.2674135141435, 18.5537605776715, 19.7902329224038, 20.8458086578706, 
21.860457224872, 22.9586109793284, 24.0567647337847, 25.3949982777022, 
26.6314706224345, 28.0798402731556, 29.3661873366836, 30.6026596814158, 
31.8391320261481, 32.8947077616149, 33.9093563286163, 34.9649320640831, 
35.9795806310846, 37.3178141750021, 38.4602633881732, 39.6027126013442, 
41.1095041240543, 42.3459764687866, 43.4884256819577, 44.6308748951287, 
45.9172219586567, 0.991908465191337, 1.98381693038267, 2.84606348918419, 
3.83797195437552, 4.95882053575741, 6.00972845463285, 7.12296637407513, 
8.19526278056753, 9.10494225015218, 10.0968507153435, 11.1862255956238, 
12.2371335144993, 13.3265083947796, 14.377416313655, 15.4497127201474, 
16.5006206390229, 17.4103001086075, 18.272546667409, 19.2259649448248, 
20.1793832222405, 21.2687581025208, 22.3196660213963, 23.4329039408386, 
24.505200347331, 25.5561082662064, 26.6070161850819, 27.5166956546665, 
28.378942213468, 29.2886216830527, 30.1508682418542, 31.2402431221345, 
32.2321515873258, 33.2240600525172, 34.344908633899, 35.3958165527745, 
36.3877250179658, 37.3796334831572, 38.4519298896496)), .Names = c("Games", 
"League", "line", "value"), row.names = 115:570, class = "data.frame")

我有4个栏目：游戏、联赛、线和价值。我想过滤上面的数据帧，使其只包含每个League+Line组合的最高值

例如，我只想要联盟“MLS”的“上限”范围的最高值。我想对联赛和一线队的每一个组合都这样做

如果有任何不清楚的地方，请详细说明，我会更新这篇文章。

这是你需要的吗

library(dplyr)
test %>% group_by(League, line) %>% filter(value == max(value, na.rm = T))

# Source: local data frame [12 x 4]
# Groups: League, line [12]
# 
# Games League       line    value
# <int>  <chr>      <chr>    <dbl>
# 1     38    BPL      Upper 46.49371
# 2     38    BPL Prediction 40.33813
# 3     38    BPL      Lower 34.18255
# 4     38    LGA      Upper 47.22289
# 5     38    LGA Prediction 40.77891
# 6     38    LGA      Lower 34.33493
# 7     34    BLI      Upper 43.08197
# 8     34    BLI Prediction 37.59349
# 9     34    BLI      Lower 32.10502
# 10    38    LG1      Upper 53.38251
# 11    38    LG1 Prediction 45.91722
# 12    38    LG1      Lower 38.45193

或更可读的版本：

setDT(test)[, .SD[value == max(value, na.rm = T)], .(League, line)]

我们也可以使用

ave

从

base R

test[with(test, ave(-value, League, line, FUN = rank)<2),]
#    Games League       line    value
#152    38    BPL      Upper 46.49371
#190    38    BPL Prediction 40.33813
#228    38    BPL      Lower 34.18255
#266    38    LGA      Upper 47.22289
#304    38    LGA Prediction 40.77891
#342    38    LGA      Lower 34.33493
#376    34    BLI      Upper 43.08197
#414    34    BLI Prediction 37.59349
#452    34    BLI      Lower 32.10502
#494    38    LG1      Upper 53.38251
#532    38    LG1 Prediction 45.91722
#570    38    LG1      Lower 38.45193

我们可以在

dplyr

中实现的相应方法是

library(dplyr)
test %>%
    group_by(League, line) %>%
    arrange(desc(value)) %>%
    slice(1L)

如果你不关心

Games

列，那么一个更简洁、更容易理解的liitle，可能是

test[，（maxval=max（value）），by=（League，line）]

@dww，这可能会更有效。顺便说一句，您需要以任何方式指定

na.rm=TRUE

，因为数据包含缺少的值。@Psidom所以，我刚刚注意到我得到了一个奇怪的错误。过滤器运行良好，但它遗漏了我的一个联盟变量。注意：我的实际数据集包含6个不同的联盟。。我只是把它删减了，这样它就可以放在这篇博文的正文中。不太确定，可能是联盟根本没有数据，即该联盟的所有记录的值都是NA。@Psidom令人惊讶的是，BLI联盟没有出现。

library(data.table)
setDT(test)[order(-value), head(.SD, 1) , by = .(League, line)]
#   League       line Games    value
#1:    LG1      Upper    38 53.38251
#2:    LGA      Upper    38 47.22289
#3:    BPL      Upper    38 46.49371
#4:    LG1 Prediction    38 45.91722
#5:    BLI      Upper    34 43.08197
#6:    LGA Prediction    38 40.77891
#7:    BPL Prediction    38 40.33813
#8:    LG1      Lower    38 38.45193
#9:    BLI Prediction    34 37.59349
#10:   LGA      Lower    38 34.33493
#11:   BPL      Lower    38 34.18255
#12:   BLI      Lower    34 32.10502

library(dplyr)
test %>%
    group_by(League, line) %>%
    arrange(desc(value)) %>%
    slice(1L)