This function allows you to scale vectors or an entire data frame using the max-min scaling method A numeric vector is always returned.
maxmin(x)Pass a vector or the required columns of a data frame through this argument.
library(magrittr)
library(dplyr)
rand <- rnorm(100, mean = 0, sd = 1)
data.frame(original = rand, transformed = maxmin(rand))
#> original transformed
#> 1 1.14981796 0.77594710
#> 2 -0.78765332 0.42310901
#> 3 0.26826200 0.61540458
#> 4 -0.20518300 0.52918424
#> 5 0.74279756 0.70182352
#> 6 0.60254205 0.67628122
#> 7 2.38011613 1.00000000
#> 8 -1.31955826 0.32624238
#> 9 0.44171575 0.64699271
#> 10 0.48254579 0.65442838
#> 11 -0.06953347 0.55388774
#> 12 -3.11098966 0.00000000
#> 13 -0.45764019 0.48320859
#> 14 0.94524439 0.73869166
#> 15 0.49736982 0.65712802
#> 16 1.00899808 0.75030201
#> 17 1.45581230 0.83167255
#> 18 0.16440494 0.59649089
#> 19 1.15712644 0.77727807
#> 20 -1.66889663 0.26262343
#> 21 1.06073733 0.75972439
#> 22 -0.95837288 0.39201882
#> 23 0.16214491 0.59607931
#> 24 -0.56929115 0.46287553
#> 25 -0.58084816 0.46077085
#> 26 0.42246842 0.64348753
#> 27 0.61784179 0.67906749
#> 28 1.21528694 0.78786983
#> 29 -0.24716022 0.52153966
#> 30 0.32504800 0.62574603
#> 31 0.12181530 0.58873478
#> 32 -0.84467626 0.41272441
#> 33 -0.27483548 0.51649964
#> 34 -0.51018477 0.47363955
#> 35 -0.21746201 0.52694808
#> 36 -0.78985095 0.42270879
#> 37 1.04141961 0.75620639
#> 38 -0.56182863 0.46423455
#> 39 1.71301275 0.87851202
#> 40 -1.99601312 0.20305137
#> 41 1.99990100 0.93075800
#> 42 1.82143948 0.89825790
#> 43 -0.04069326 0.55913991
#> 44 0.10361558 0.58542038
#> 45 -0.05611024 0.55633228
#> 46 0.65931815 0.68662087
#> 47 -2.70473999 0.07398322
#> 48 2.02559271 0.93543679
#> 49 0.32928153 0.62651701
#> 50 0.22049993 0.60670650
#> 51 0.71712234 0.69714774
#> 52 -0.44852785 0.48486806
#> 53 -0.93385633 0.39648359
#> 54 -0.51832345 0.47215740
#> 55 -0.20462133 0.52928653
#> 56 -0.31780813 0.50867378
#> 57 1.69439265 0.87512106
#> 58 0.70124243 0.69425581
#> 59 1.91679281 0.91562295
#> 60 0.42656997 0.64423447
#> 61 1.27621744 0.79896605
#> 62 -1.56728288 0.28112858
#> 63 -0.17839215 0.53406320
#> 64 1.49148765 0.83816948
#> 65 -0.32366781 0.50760666
#> 66 -1.06796656 0.37206042
#> 67 1.95495285 0.92257237
#> 68 1.11640855 0.76986282
#> 69 -0.93389982 0.39647567
#> 70 0.95855161 0.74111507
#> 71 -1.16993174 0.35349126
#> 72 0.15300981 0.59441570
#> 73 1.62575299 0.86262091
#> 74 0.28591075 0.61861864
#> 75 0.43740442 0.64620756
#> 76 0.33465417 0.62749544
#> 77 1.18127035 0.78167498
#> 78 1.12056451 0.77061968
#> 79 0.11604166 0.58768333
#> 80 -1.19385897 0.34913381
#> 81 -1.21538317 0.34521398
#> 82 -0.82542523 0.41623027
#> 83 -0.75722635 0.42865015
#> 84 1.48913248 0.83774058
#> 85 0.46455201 0.65115148
#> 86 -1.07346210 0.37105961
#> 87 -0.57718823 0.46143737
#> 88 0.97502478 0.74411505
#> 89 -1.01613502 0.38149960
#> 90 -0.49954551 0.47557710
#> 91 1.96017430 0.92352327
#> 92 0.22447541 0.60743049
#> 93 0.74576980 0.70236481
#> 94 -0.61232238 0.45503900
#> 95 1.40661441 0.82271299
#> 96 1.23247927 0.79100077
#> 97 -1.13110282 0.36056250
#> 98 0.08498987 0.58202840
#> 99 -1.79562504 0.23954458
#> 100 0.58996016 0.67398990
iris %>% mutate(Petal.Length2 = maxmin(Petal.Length))
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species Petal.Length2
#> 1 5.1 3.5 1.4 0.2 setosa 0.06779661
#> 2 4.9 3.0 1.4 0.2 setosa 0.06779661
#> 3 4.7 3.2 1.3 0.2 setosa 0.05084746
#> 4 4.6 3.1 1.5 0.2 setosa 0.08474576
#> 5 5.0 3.6 1.4 0.2 setosa 0.06779661
#> 6 5.4 3.9 1.7 0.4 setosa 0.11864407
#> 7 4.6 3.4 1.4 0.3 setosa 0.06779661
#> 8 5.0 3.4 1.5 0.2 setosa 0.08474576
#> 9 4.4 2.9 1.4 0.2 setosa 0.06779661
#> 10 4.9 3.1 1.5 0.1 setosa 0.08474576
#> 11 5.4 3.7 1.5 0.2 setosa 0.08474576
#> 12 4.8 3.4 1.6 0.2 setosa 0.10169492
#> 13 4.8 3.0 1.4 0.1 setosa 0.06779661
#> 14 4.3 3.0 1.1 0.1 setosa 0.01694915
#> 15 5.8 4.0 1.2 0.2 setosa 0.03389831
#> 16 5.7 4.4 1.5 0.4 setosa 0.08474576
#> 17 5.4 3.9 1.3 0.4 setosa 0.05084746
#> 18 5.1 3.5 1.4 0.3 setosa 0.06779661
#> 19 5.7 3.8 1.7 0.3 setosa 0.11864407
#> 20 5.1 3.8 1.5 0.3 setosa 0.08474576
#> 21 5.4 3.4 1.7 0.2 setosa 0.11864407
#> 22 5.1 3.7 1.5 0.4 setosa 0.08474576
#> 23 4.6 3.6 1.0 0.2 setosa 0.00000000
#> 24 5.1 3.3 1.7 0.5 setosa 0.11864407
#> 25 4.8 3.4 1.9 0.2 setosa 0.15254237
#> 26 5.0 3.0 1.6 0.2 setosa 0.10169492
#> 27 5.0 3.4 1.6 0.4 setosa 0.10169492
#> 28 5.2 3.5 1.5 0.2 setosa 0.08474576
#> 29 5.2 3.4 1.4 0.2 setosa 0.06779661
#> 30 4.7 3.2 1.6 0.2 setosa 0.10169492
#> 31 4.8 3.1 1.6 0.2 setosa 0.10169492
#> 32 5.4 3.4 1.5 0.4 setosa 0.08474576
#> 33 5.2 4.1 1.5 0.1 setosa 0.08474576
#> 34 5.5 4.2 1.4 0.2 setosa 0.06779661
#> 35 4.9 3.1 1.5 0.2 setosa 0.08474576
#> 36 5.0 3.2 1.2 0.2 setosa 0.03389831
#> 37 5.5 3.5 1.3 0.2 setosa 0.05084746
#> 38 4.9 3.6 1.4 0.1 setosa 0.06779661
#> 39 4.4 3.0 1.3 0.2 setosa 0.05084746
#> 40 5.1 3.4 1.5 0.2 setosa 0.08474576
#> 41 5.0 3.5 1.3 0.3 setosa 0.05084746
#> 42 4.5 2.3 1.3 0.3 setosa 0.05084746
#> 43 4.4 3.2 1.3 0.2 setosa 0.05084746
#> 44 5.0 3.5 1.6 0.6 setosa 0.10169492
#> 45 5.1 3.8 1.9 0.4 setosa 0.15254237
#> 46 4.8 3.0 1.4 0.3 setosa 0.06779661
#> 47 5.1 3.8 1.6 0.2 setosa 0.10169492
#> 48 4.6 3.2 1.4 0.2 setosa 0.06779661
#> 49 5.3 3.7 1.5 0.2 setosa 0.08474576
#> 50 5.0 3.3 1.4 0.2 setosa 0.06779661
#> 51 7.0 3.2 4.7 1.4 versicolor 0.62711864
#> 52 6.4 3.2 4.5 1.5 versicolor 0.59322034
#> 53 6.9 3.1 4.9 1.5 versicolor 0.66101695
#> 54 5.5 2.3 4.0 1.3 versicolor 0.50847458
#> 55 6.5 2.8 4.6 1.5 versicolor 0.61016949
#> 56 5.7 2.8 4.5 1.3 versicolor 0.59322034
#> 57 6.3 3.3 4.7 1.6 versicolor 0.62711864
#> 58 4.9 2.4 3.3 1.0 versicolor 0.38983051
#> 59 6.6 2.9 4.6 1.3 versicolor 0.61016949
#> 60 5.2 2.7 3.9 1.4 versicolor 0.49152542
#> 61 5.0 2.0 3.5 1.0 versicolor 0.42372881
#> 62 5.9 3.0 4.2 1.5 versicolor 0.54237288
#> 63 6.0 2.2 4.0 1.0 versicolor 0.50847458
#> 64 6.1 2.9 4.7 1.4 versicolor 0.62711864
#> 65 5.6 2.9 3.6 1.3 versicolor 0.44067797
#> 66 6.7 3.1 4.4 1.4 versicolor 0.57627119
#> 67 5.6 3.0 4.5 1.5 versicolor 0.59322034
#> 68 5.8 2.7 4.1 1.0 versicolor 0.52542373
#> 69 6.2 2.2 4.5 1.5 versicolor 0.59322034
#> 70 5.6 2.5 3.9 1.1 versicolor 0.49152542
#> 71 5.9 3.2 4.8 1.8 versicolor 0.64406780
#> 72 6.1 2.8 4.0 1.3 versicolor 0.50847458
#> 73 6.3 2.5 4.9 1.5 versicolor 0.66101695
#> 74 6.1 2.8 4.7 1.2 versicolor 0.62711864
#> 75 6.4 2.9 4.3 1.3 versicolor 0.55932203
#> 76 6.6 3.0 4.4 1.4 versicolor 0.57627119
#> 77 6.8 2.8 4.8 1.4 versicolor 0.64406780
#> 78 6.7 3.0 5.0 1.7 versicolor 0.67796610
#> 79 6.0 2.9 4.5 1.5 versicolor 0.59322034
#> 80 5.7 2.6 3.5 1.0 versicolor 0.42372881
#> 81 5.5 2.4 3.8 1.1 versicolor 0.47457627
#> 82 5.5 2.4 3.7 1.0 versicolor 0.45762712
#> 83 5.8 2.7 3.9 1.2 versicolor 0.49152542
#> 84 6.0 2.7 5.1 1.6 versicolor 0.69491525
#> 85 5.4 3.0 4.5 1.5 versicolor 0.59322034
#> 86 6.0 3.4 4.5 1.6 versicolor 0.59322034
#> 87 6.7 3.1 4.7 1.5 versicolor 0.62711864
#> 88 6.3 2.3 4.4 1.3 versicolor 0.57627119
#> 89 5.6 3.0 4.1 1.3 versicolor 0.52542373
#> 90 5.5 2.5 4.0 1.3 versicolor 0.50847458
#> 91 5.5 2.6 4.4 1.2 versicolor 0.57627119
#> 92 6.1 3.0 4.6 1.4 versicolor 0.61016949
#> 93 5.8 2.6 4.0 1.2 versicolor 0.50847458
#> 94 5.0 2.3 3.3 1.0 versicolor 0.38983051
#> 95 5.6 2.7 4.2 1.3 versicolor 0.54237288
#> 96 5.7 3.0 4.2 1.2 versicolor 0.54237288
#> 97 5.7 2.9 4.2 1.3 versicolor 0.54237288
#> 98 6.2 2.9 4.3 1.3 versicolor 0.55932203
#> 99 5.1 2.5 3.0 1.1 versicolor 0.33898305
#> 100 5.7 2.8 4.1 1.3 versicolor 0.52542373
#> 101 6.3 3.3 6.0 2.5 virginica 0.84745763
#> 102 5.8 2.7 5.1 1.9 virginica 0.69491525
#> 103 7.1 3.0 5.9 2.1 virginica 0.83050847
#> 104 6.3 2.9 5.6 1.8 virginica 0.77966102
#> 105 6.5 3.0 5.8 2.2 virginica 0.81355932
#> 106 7.6 3.0 6.6 2.1 virginica 0.94915254
#> 107 4.9 2.5 4.5 1.7 virginica 0.59322034
#> 108 7.3 2.9 6.3 1.8 virginica 0.89830508
#> 109 6.7 2.5 5.8 1.8 virginica 0.81355932
#> 110 7.2 3.6 6.1 2.5 virginica 0.86440678
#> 111 6.5 3.2 5.1 2.0 virginica 0.69491525
#> 112 6.4 2.7 5.3 1.9 virginica 0.72881356
#> 113 6.8 3.0 5.5 2.1 virginica 0.76271186
#> 114 5.7 2.5 5.0 2.0 virginica 0.67796610
#> 115 5.8 2.8 5.1 2.4 virginica 0.69491525
#> 116 6.4 3.2 5.3 2.3 virginica 0.72881356
#> 117 6.5 3.0 5.5 1.8 virginica 0.76271186
#> 118 7.7 3.8 6.7 2.2 virginica 0.96610169
#> 119 7.7 2.6 6.9 2.3 virginica 1.00000000
#> 120 6.0 2.2 5.0 1.5 virginica 0.67796610
#> 121 6.9 3.2 5.7 2.3 virginica 0.79661017
#> 122 5.6 2.8 4.9 2.0 virginica 0.66101695
#> 123 7.7 2.8 6.7 2.0 virginica 0.96610169
#> 124 6.3 2.7 4.9 1.8 virginica 0.66101695
#> 125 6.7 3.3 5.7 2.1 virginica 0.79661017
#> 126 7.2 3.2 6.0 1.8 virginica 0.84745763
#> 127 6.2 2.8 4.8 1.8 virginica 0.64406780
#> 128 6.1 3.0 4.9 1.8 virginica 0.66101695
#> 129 6.4 2.8 5.6 2.1 virginica 0.77966102
#> 130 7.2 3.0 5.8 1.6 virginica 0.81355932
#> 131 7.4 2.8 6.1 1.9 virginica 0.86440678
#> 132 7.9 3.8 6.4 2.0 virginica 0.91525424
#> 133 6.4 2.8 5.6 2.2 virginica 0.77966102
#> 134 6.3 2.8 5.1 1.5 virginica 0.69491525
#> 135 6.1 2.6 5.6 1.4 virginica 0.77966102
#> 136 7.7 3.0 6.1 2.3 virginica 0.86440678
#> 137 6.3 3.4 5.6 2.4 virginica 0.77966102
#> 138 6.4 3.1 5.5 1.8 virginica 0.76271186
#> 139 6.0 3.0 4.8 1.8 virginica 0.64406780
#> 140 6.9 3.1 5.4 2.1 virginica 0.74576271
#> 141 6.7 3.1 5.6 2.4 virginica 0.77966102
#> 142 6.9 3.1 5.1 2.3 virginica 0.69491525
#> 143 5.8 2.7 5.1 1.9 virginica 0.69491525
#> 144 6.8 3.2 5.9 2.3 virginica 0.83050847
#> 145 6.7 3.3 5.7 2.5 virginica 0.79661017
#> 146 6.7 3.0 5.2 2.3 virginica 0.71186441
#> 147 6.3 2.5 5.0 1.9 virginica 0.67796610
#> 148 6.5 3.0 5.2 2.0 virginica 0.71186441
#> 149 6.2 3.4 5.4 2.3 virginica 0.74576271
#> 150 5.9 3.0 5.1 1.8 virginica 0.69491525
maxmin(iris$Petal.Length)
#> [1] 0.06779661 0.06779661 0.05084746 0.08474576 0.06779661 0.11864407
#> [7] 0.06779661 0.08474576 0.06779661 0.08474576 0.08474576 0.10169492
#> [13] 0.06779661 0.01694915 0.03389831 0.08474576 0.05084746 0.06779661
#> [19] 0.11864407 0.08474576 0.11864407 0.08474576 0.00000000 0.11864407
#> [25] 0.15254237 0.10169492 0.10169492 0.08474576 0.06779661 0.10169492
#> [31] 0.10169492 0.08474576 0.08474576 0.06779661 0.08474576 0.03389831
#> [37] 0.05084746 0.06779661 0.05084746 0.08474576 0.05084746 0.05084746
#> [43] 0.05084746 0.10169492 0.15254237 0.06779661 0.10169492 0.06779661
#> [49] 0.08474576 0.06779661 0.62711864 0.59322034 0.66101695 0.50847458
#> [55] 0.61016949 0.59322034 0.62711864 0.38983051 0.61016949 0.49152542
#> [61] 0.42372881 0.54237288 0.50847458 0.62711864 0.44067797 0.57627119
#> [67] 0.59322034 0.52542373 0.59322034 0.49152542 0.64406780 0.50847458
#> [73] 0.66101695 0.62711864 0.55932203 0.57627119 0.64406780 0.67796610
#> [79] 0.59322034 0.42372881 0.47457627 0.45762712 0.49152542 0.69491525
#> [85] 0.59322034 0.59322034 0.62711864 0.57627119 0.52542373 0.50847458
#> [91] 0.57627119 0.61016949 0.50847458 0.38983051 0.54237288 0.54237288
#> [97] 0.54237288 0.55932203 0.33898305 0.52542373 0.84745763 0.69491525
#> [103] 0.83050847 0.77966102 0.81355932 0.94915254 0.59322034 0.89830508
#> [109] 0.81355932 0.86440678 0.69491525 0.72881356 0.76271186 0.67796610
#> [115] 0.69491525 0.72881356 0.76271186 0.96610169 1.00000000 0.67796610
#> [121] 0.79661017 0.66101695 0.96610169 0.66101695 0.79661017 0.84745763
#> [127] 0.64406780 0.66101695 0.77966102 0.81355932 0.86440678 0.91525424
#> [133] 0.77966102 0.69491525 0.77966102 0.86440678 0.77966102 0.76271186
#> [139] 0.64406780 0.74576271 0.77966102 0.69491525 0.69491525 0.83050847
#> [145] 0.79661017 0.71186441 0.67796610 0.71186441 0.74576271 0.69491525