How to find the n number of largest values in an R vector?

A vector may have thousands of values and each of them could be different or same also. It is also possible that values can be grouped or randomly selected but having few similar values. Irrespective of the values in a vector, to find some largest values we need to sort the vector in ascending order then the largest values will be selected.

Examples

> x1<-rnorm(50)
> x1
[1] -1.4447473195 3.2906645299 -0.4680055849 0.1611487482 -0.7715094280
[6] 0.4442103640 0.3702444686 0.0783124252 1.3476432299 1.0140576107
[11] -0.0968917066 0.4628821017 0.3102594626 -0.2946001275 0.1498108166
[16] -0.6002154305 0.5905382364 1.3892651534 0.1008921325 -0.6486318692
[21] -0.0562831933 -0.6887431711 0.4907512082 -0.3994662410 0.7827897030
[26] 0.5294704584 -1.3802965730 -0.6159076490 -0.0009408529 1.6182294859
[31] 0.2539617286 -1.9173056766 0.9534899983 -0.0849641284 0.0726055903
[36] 0.8863460104 0.2516331008 -0.0296849348 0.2086944365 0.1112500667
[41] -0.2346866849 -1.6693864582 -1.2794848686 0.4355160606 1.1611288668
[46] -0.4128822851 0.3113799053 0.4608703922 -0.4767432276 -0.4108699626
> tail(sort(x1),10)
[1] 0.5905382 0.7827897 0.8863460 0.9534900 1.0140576 1.1611289 1.3476432
[8] 1.3892652 1.6182295 3.2906645
> x2<-rpois(50,2)
> x2
[1] 3 0 2 3 1 3 2 3 5 1 0 5 3 2 1 2 1 2 4 2 3 2 2 5 3 3 0 1 3 3 1 1 2 1 0 2 1 1
[39] 3 4 0 1 2 2 1 5 2 5 1 6
> tail(sort(x2),10)
[1] 3 3 4 4 5 5 5 5 5 6
> x2<-rpois(50,2)
> x2
[1] 3 0 2 3 1 3 2 3 5 1 0 5 3 2 1 2 1 2 4 2 3 2 2 5 3 3 0 1 3 3 1 1 2 1 0 2 1 1
[39] 3 4 0 1 2 2 1 5 2 5 1 6
> tail(sort(x2),10)
[1] 3 3 4 4 5 5 5 5 5 6
> x3<-runif(50,2,5)
> x3
[1] 3.619601 4.015782 4.927389 3.871766 2.559403 3.654698 3.636688 2.435611
[9] 3.919934 3.385902 2.155832 4.343270 2.306058 4.331264 3.824110 3.421138
[17] 3.014716 4.809355 3.545865 4.109747 4.496385 4.476492 4.824152 3.999915
[25] 2.369429 4.419645 3.556565 3.421748 3.185124 3.239173 4.180487 2.653179
[33] 4.674133 3.477992 3.933203 3.618354 2.064820 2.032384 3.086342 4.491011
[41] 4.361386 2.789445 3.881738 2.521680 3.185342 2.708259 2.023868 4.963704
[49] 4.574970 3.443716
> tail(sort(x3),10)
[1] 4.419645 4.476492 4.491011 4.496385 4.574970 4.674133 4.809355 4.824152
[9] 4.927389 4.963704
> x4<-rexp(50,0.75)
> x4
[1] 1.66408581 0.23668114 2.60394558 0.37745569 1.51734607 0.68286297
[7] 2.37845758 2.34748084 1.08916016 0.87455649 0.22715427 0.08631177
[13] 1.38793359 0.63791999 0.08081514 0.46960890 0.76566002 0.07207330
[19] 0.73923112 2.79757298 2.41873012 0.21448042 0.60012030 1.98638409
[25] 0.08985795 1.61284962 2.04608139 0.28587935 0.23873098 3.84622620
[31] 1.04341525 1.27033301 0.75144631 0.27834051 0.35531788 1.85149528
[37] 0.57331483 0.28346725 0.01938860 1.44158534 0.42863950 0.19755680
[43] 0.90512264 0.32139020 2.93323666 4.36947212 1.22103199 0.33063906
[49] 1.15281344 0.19477133
> tail(sort(x4),10)
[1] 1.986384 2.046081 2.347481 2.378458 2.418730 2.603946 2.797573 2.933237
[9] 3.846226 4.369472
> x5<-sample(1:100,50)
> x5
[1] 68 95 78 46 7 19 35 34 11 38 86 45 61 63 77 91 79 92 44
[20] 24 43 23 8 22 70 97 84 88 37 62 51 2 98 72 16 39 80 67
[39] 20 28 96 56 57 65 40 18 5 76 87 100
> tail(sort(x5),10)
[1] 86 87 88 91 92 95 96 97 98 100
> x6<-sample(500:1000,50)
> x6
[1] 699 622 523 634 547 986 929 774 612 725 607 752 686 796 891 859 553 810 720
[20] 900 712 745 769 604 626 990 511 874 609 942 723 509 747 549 534 679 751 896
[39] 881 892 706 694 613 775 606 705 521 637 651 709
> tail(sort(x6),10)
[1] 874 881 891 892 896 900 929 942 986 990
> x7<-sample(1:5,50,replace=TRUE)
> x7
[1] 1 5 5 5 3 4 5 3 2 5 2 4 3 4 3 2 5 5 5 3 2 3 1 2 1 3 4 3 3 2 5 4 5 5 1 5 3 1
[39] 3 4 3 4 3 1 5 5 3 3 2 1
> tail(sort(x7),10)
[1] 5 5 5 5 5 5 5 5 5 5
> x8<-rlnorm(50,meanlog=0,sdlog=1)
> x8
[1] 3.28565379 0.33035002 4.83159451 0.37365934 2.06412742 0.39767931
[7] 0.84730808 0.48102938 0.51692884 0.18420942 0.15586761 4.01837070
[13] 1.00295917 0.40872245 0.09060854 0.55788680 3.02236904 0.48929409
[19] 0.39801790 0.22944458 1.36532857 4.56598722 2.85700417 0.58897804
[25] 1.54894520 0.75960321 3.48848189 2.62556191 1.94963069 1.69519791
[31] 1.24759682 0.63056343 1.20232731 0.44990949 1.55569080 0.26933108
[37] 1.04039486 0.97568753 3.16183492 2.31072475 5.33267314 0.28704556
[43] 0.82927315 1.68154657 3.29524392 4.05284285 0.44683256 0.33032771
[49] 0.23528317 1.56624060
> tail(sort(x8),10)
[1] 3.022369 3.161835 3.285654 3.295244 3.488482 4.018371 4.052843 4.565987
[9] 4.831595 5.332673

Nizamuddin Siddiqui

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Updated on: 04-Sep-2020

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