Add Euclidean distance
add_euclidean.RdAdd a Euclidean distance measure to the end of a data frame
Arguments
- df
A data.frame
- x, y
The two values to compute the distance from
- .name
The name of the new column for the distance value
Examples
add_euclidean(iris, Sepal.Width, Sepal.Length)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species eucd
#> 1 5.1 3.5 1.4 0.2 setosa 0.86515792
#> 2 4.9 3.0 1.4 0.2 setosa 0.94507401
#> 3 4.7 3.2 1.3 0.2 setosa 1.15220002
#> 4 4.6 3.1 1.5 0.2 setosa 1.24406520
#> 5 5.0 3.6 1.4 0.2 setosa 1.00284506
#> 6 5.4 3.9 1.7 0.4 setosa 0.95217202
#> 7 4.6 3.4 1.4 0.3 setosa 1.28968920
#> 8 5.0 3.4 1.5 0.2 setosa 0.91029202
#> 9 4.4 2.9 1.4 0.2 setosa 1.45188322
#> 10 4.9 3.1 1.5 0.1 setosa 0.94429774
#> 11 5.4 3.7 1.5 0.2 setosa 0.78074637
#> 12 4.8 3.4 1.6 0.2 setosa 1.09816433
#> 13 4.8 3.0 1.4 0.1 setosa 1.04490744
#> 14 4.3 3.0 1.1 0.1 setosa 1.54439790
#> 15 5.8 4.0 1.2 0.2 setosa 0.94366213
#> 16 5.7 4.4 1.5 0.4 setosa 1.35029561
#> 17 5.4 3.9 1.3 0.4 setosa 0.95217202
#> 18 5.1 3.5 1.4 0.3 setosa 0.86515792
#> 19 5.7 3.8 1.7 0.3 setosa 0.75637175
#> 20 5.1 3.8 1.5 0.3 setosa 1.05076078
#> 21 5.4 3.4 1.7 0.2 setosa 0.56032570
#> 22 5.1 3.7 1.5 0.4 setosa 0.98263161
#> 23 4.6 3.6 1.0 0.2 setosa 1.35660049
#> 24 5.1 3.3 1.7 0.5 setosa 0.78194089
#> 25 4.8 3.4 1.9 0.2 setosa 1.09816433
#> 26 5.0 3.0 1.6 0.2 setosa 0.84527997
#> 27 5.0 3.4 1.6 0.4 setosa 0.91029202
#> 28 5.2 3.5 1.5 0.2 setosa 0.78091712
#> 29 5.2 3.4 1.4 0.2 setosa 0.72890207
#> 30 4.7 3.2 1.6 0.2 setosa 1.15220002
#> 31 4.8 3.1 1.6 0.2 setosa 1.04420539
#> 32 5.4 3.4 1.5 0.4 setosa 0.56032570
#> 33 5.2 4.1 1.5 0.1 setosa 1.22516593
#> 34 5.5 4.2 1.4 0.2 setosa 1.19313239
#> 35 4.9 3.1 1.5 0.2 setosa 0.94429774
#> 36 5.0 3.2 1.2 0.2 setosa 0.85531567
#> 37 5.5 3.5 1.3 0.2 setosa 0.56020671
#> 38 4.9 3.6 1.4 0.1 setosa 1.08828530
#> 39 4.4 3.0 1.3 0.2 setosa 1.44447161
#> 40 5.1 3.4 1.5 0.2 setosa 0.81851383
#> 41 5.0 3.5 1.3 0.3 setosa 0.95245204
#> 42 4.5 2.3 1.3 0.3 setosa 1.54210837
#> 43 4.4 3.2 1.3 0.2 setosa 1.45036716
#> 44 5.0 3.5 1.6 0.6 setosa 0.95245204
#> 45 5.1 3.8 1.9 0.4 setosa 1.05076078
#> 46 4.8 3.0 1.4 0.3 setosa 1.04490744
#> 47 5.1 3.8 1.6 0.2 setosa 1.05076078
#> 48 4.6 3.2 1.4 0.2 setosa 1.25149173
#> 49 5.3 3.7 1.5 0.2 setosa 0.84156494
#> 50 5.0 3.3 1.4 0.2 setosa 0.87755240
#> 51 7.0 3.2 4.7 1.4 versicolor 1.16543192
#> 52 6.4 3.2 4.5 1.5 versicolor 0.57465777
#> 53 6.9 3.1 4.9 1.5 versicolor 1.05752772
#> 54 5.5 2.3 4.0 1.3 versicolor 0.83152363
#> 55 6.5 2.8 4.6 1.5 versicolor 0.70528828
#> 56 5.7 2.8 4.5 1.3 versicolor 0.29455880
#> 57 6.3 3.3 4.7 1.6 versicolor 0.51713785
#> 58 4.9 2.4 3.3 1.0 versicolor 1.14976732
#> 59 6.6 2.9 4.6 1.3 versicolor 0.77285071
#> 60 5.2 2.7 3.9 1.4 versicolor 0.73591092
#> 61 5.0 2.0 3.5 1.0 versicolor 1.35246622
#> 62 5.9 3.0 4.2 1.5 versicolor 0.08061155
#> 63 6.0 2.2 4.0 1.0 versicolor 0.87153020
#> 64 6.1 2.9 4.7 1.4 versicolor 0.30105075
#> 65 5.6 2.9 3.6 1.3 versicolor 0.28976696
#> 66 6.7 3.1 4.4 1.4 versicolor 0.85772852
#> 67 5.6 3.0 4.5 1.5 versicolor 0.24999644
#> 68 5.8 2.7 4.1 1.0 versicolor 0.35995123
#> 69 6.2 2.2 4.5 1.5 versicolor 0.92856424
#> 70 5.6 2.5 3.9 1.1 versicolor 0.60813778
#> 71 5.9 3.2 4.8 1.8 versicolor 0.15350860
#> 72 6.1 2.8 4.0 1.3 versicolor 0.36345319
#> 73 6.3 2.5 4.9 1.5 versicolor 0.72053098
#> 74 6.1 2.8 4.7 1.2 versicolor 0.36345319
#> 75 6.4 2.9 4.3 1.3 versicolor 0.57847347
#> 76 6.6 3.0 4.4 1.4 versicolor 0.75883566
#> 77 6.8 2.8 4.8 1.4 versicolor 0.99067227
#> 78 6.7 3.0 5.0 1.7 versicolor 0.85858307
#> 79 6.0 2.9 4.5 1.5 versicolor 0.22203203
#> 80 5.7 2.6 3.5 1.0 versicolor 0.47926842
#> 81 5.5 2.4 3.8 1.1 versicolor 0.74159618
#> 82 5.5 2.4 3.7 1.0 versicolor 0.74159618
#> 83 5.8 2.7 3.9 1.2 versicolor 0.35995123
#> 84 6.0 2.7 5.1 1.6 versicolor 0.39016862
#> 85 5.4 3.0 4.5 1.5 versicolor 0.44702523
#> 86 6.0 3.4 4.5 1.6 versicolor 0.37678228
#> 87 6.7 3.1 4.7 1.5 versicolor 0.85772852
#> 88 6.3 2.3 4.4 1.3 versicolor 0.88436317
#> 89 5.6 3.0 4.1 1.3 versicolor 0.24999644
#> 90 5.5 2.5 4.0 1.3 versicolor 0.65459776
#> 91 5.5 2.6 4.4 1.2 versicolor 0.57186673
#> 92 6.1 3.0 4.6 1.4 versicolor 0.26299218
#> 93 5.8 2.6 4.0 1.2 versicolor 0.45938171
#> 94 5.0 2.3 3.3 1.0 versicolor 1.13347470
#> 95 5.6 2.7 4.2 1.3 versicolor 0.43231727
#> 96 5.7 3.0 4.2 1.2 versicolor 0.15437472
#> 97 5.7 2.9 4.2 1.3 versicolor 0.21283379
#> 98 6.2 2.9 4.3 1.3 versicolor 0.38982674
#> 99 5.1 2.5 3.0 1.1 versicolor 0.92906668
#> 100 5.7 2.8 4.1 1.3 versicolor 0.29455880
#> 101 6.3 3.3 6.0 2.5 virginica 0.51713785
#> 102 5.8 2.7 5.1 1.9 virginica 0.35995123
#> 103 7.1 3.0 5.9 2.1 virginica 1.25797386
#> 104 6.3 2.9 5.6 1.8 virginica 0.48300955
#> 105 6.5 3.0 5.8 2.2 virginica 0.65916479
#> 106 7.6 3.0 6.6 2.1 virginica 1.75760203
#> 107 4.9 2.5 4.5 1.7 virginica 1.09567250
#> 108 7.3 2.9 6.3 1.8 virginica 1.46513875
#> 109 6.7 2.5 5.8 1.8 virginica 1.02200696
#> 110 7.2 3.6 6.1 2.5 virginica 1.46117472
#> 111 6.5 3.2 5.1 2.0 virginica 0.67198578
#> 112 6.4 2.7 5.3 1.9 virginica 0.66148688
#> 113 6.8 3.0 5.5 2.1 virginica 0.95838313
#> 114 5.7 2.5 5.0 2.0 virginica 0.57546928
#> 115 5.8 2.8 5.1 2.4 virginica 0.26095636
#> 116 6.4 3.2 5.3 2.3 virginica 0.57465777
#> 117 6.5 3.0 5.5 1.8 virginica 0.65916479
#> 118 7.7 3.8 6.7 2.2 virginica 1.99969120
#> 119 7.7 2.6 6.9 2.3 virginica 1.91216236
#> 120 6.0 2.2 5.0 1.5 virginica 0.87153020
#> 121 6.9 3.2 5.7 2.3 virginica 1.06625430
#> 122 5.6 2.8 4.9 2.0 virginica 0.35416318
#> 123 7.7 2.8 6.7 2.0 virginica 1.87441499
#> 124 6.3 2.7 4.9 1.8 virginica 0.57985477
#> 125 6.7 3.3 5.7 2.1 virginica 0.89037345
#> 126 7.2 3.2 6.0 1.8 virginica 1.36414743
#> 127 6.2 2.8 4.8 1.8 virginica 0.43980854
#> 128 6.1 3.0 4.9 1.8 virginica 0.26299218
#> 129 6.4 2.8 5.6 2.1 virginica 0.61326847
#> 130 7.2 3.0 5.8 1.6 virginica 1.35787759
#> 131 7.4 2.8 6.1 1.9 virginica 1.57779326
#> 132 7.9 3.8 6.4 2.0 virginica 2.18664848
#> 133 6.4 2.8 5.6 2.2 virginica 0.61326847
#> 134 6.3 2.8 5.1 1.5 virginica 0.52418021
#> 135 6.1 2.6 5.6 1.4 virginica 0.52443451
#> 136 7.7 3.0 6.1 2.3 virginica 1.85755167
#> 137 6.3 3.4 5.6 2.4 virginica 0.57093335
#> 138 6.4 3.1 5.5 1.8 virginica 0.55829940
#> 139 6.0 3.0 4.8 1.8 virginica 0.16682792
#> 140 6.9 3.1 5.4 2.1 virginica 1.05752772
#> 141 6.7 3.1 5.6 2.4 virginica 0.85772852
#> 142 6.9 3.1 5.1 2.3 virginica 1.05752772
#> 143 5.8 2.7 5.1 1.9 virginica 0.35995123
#> 144 6.8 3.2 5.9 2.3 virginica 0.96724603
#> 145 6.7 3.3 5.7 2.5 virginica 0.89037345
#> 146 6.7 3.0 5.2 2.3 virginica 0.85858307
#> 147 6.3 2.5 5.0 1.9 virginica 0.72053098
#> 148 6.5 3.0 5.2 2.0 virginica 0.65916479
#> 149 6.2 3.4 5.4 2.3 virginica 0.49460242
#> 150 5.9 3.0 5.1 1.8 virginica 0.08061155
df <- head(iris)
df[1, ] <- NA_real_
add_euclidean(df, Sepal.Length, Sepal.Width)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species eucd
#> 1 NA NA NA NA <NA> NA
#> 2 4.9 3.0 1.4 0.2 setosa 0.3605551
#> 3 4.7 3.2 1.3 0.2 setosa 0.2720294
#> 4 4.6 3.1 1.5 0.2 setosa 0.4123106
#> 5 5.0 3.6 1.4 0.2 setosa 0.2529822
#> 6 5.4 3.9 1.7 0.4 setosa 0.7224957