Convert large/infinite numeric values in a data.frame or task.
Source:R/capLargeValues.R
capLargeValues.RdConvert numeric entries which large/infinite (absolute) values in a data.frame or task. Only numeric/integer columns are affected.
Usage
capLargeValues(
obj,
target = character(0L),
cols = NULL,
threshold = Inf,
impute = threshold,
what = "abs"
)Arguments
- obj
(data.frame | Task)
Input data.- target
(character)
Name of the column(s) specifying the response. Target columns will not be capped. Default ischaracter(0).- cols
(character)
Which columns to convert. Default is all numeric columns.- threshold
(
numeric(1))
Threshold for capping. Every entry whose absolute value is equal or larger is converted. Default isInf.- impute
(
numeric(1))
Replacement value for large entries. Large negative entries are converted to-impute. Default isthreshold.- what
(
character(1))
What kind of entries are affected? “abs” meansabs(x) > threshold, “pos” meansabs(x) > threshold && x > 0, “neg” meansabs(x) > threshold && x < 0. Default is “abs”.
See also
Other eda_and_preprocess:
createDummyFeatures(),
dropFeatures(),
mergeSmallFactorLevels(),
normalizeFeatures(),
removeConstantFeatures(),
summarizeColumns(),
summarizeLevels()
Examples
capLargeValues(iris, threshold = 5, impute = 5)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1 5.0 3.5 1.4 0.2 setosa
#> 2 4.9 3.0 1.4 0.2 setosa
#> 3 4.7 3.2 1.3 0.2 setosa
#> 4 4.6 3.1 1.5 0.2 setosa
#> 5 5.0 3.6 1.4 0.2 setosa
#> 6 5.0 3.9 1.7 0.4 setosa
#> 7 4.6 3.4 1.4 0.3 setosa
#> 8 5.0 3.4 1.5 0.2 setosa
#> 9 4.4 2.9 1.4 0.2 setosa
#> 10 4.9 3.1 1.5 0.1 setosa
#> 11 5.0 3.7 1.5 0.2 setosa
#> 12 4.8 3.4 1.6 0.2 setosa
#> 13 4.8 3.0 1.4 0.1 setosa
#> 14 4.3 3.0 1.1 0.1 setosa
#> 15 5.0 4.0 1.2 0.2 setosa
#> 16 5.0 4.4 1.5 0.4 setosa
#> 17 5.0 3.9 1.3 0.4 setosa
#> 18 5.0 3.5 1.4 0.3 setosa
#> 19 5.0 3.8 1.7 0.3 setosa
#> 20 5.0 3.8 1.5 0.3 setosa
#> 21 5.0 3.4 1.7 0.2 setosa
#> 22 5.0 3.7 1.5 0.4 setosa
#> 23 4.6 3.6 1.0 0.2 setosa
#> 24 5.0 3.3 1.7 0.5 setosa
#> 25 4.8 3.4 1.9 0.2 setosa
#> 26 5.0 3.0 1.6 0.2 setosa
#> 27 5.0 3.4 1.6 0.4 setosa
#> 28 5.0 3.5 1.5 0.2 setosa
#> 29 5.0 3.4 1.4 0.2 setosa
#> 30 4.7 3.2 1.6 0.2 setosa
#> 31 4.8 3.1 1.6 0.2 setosa
#> 32 5.0 3.4 1.5 0.4 setosa
#> 33 5.0 4.1 1.5 0.1 setosa
#> 34 5.0 4.2 1.4 0.2 setosa
#> 35 4.9 3.1 1.5 0.2 setosa
#> 36 5.0 3.2 1.2 0.2 setosa
#> 37 5.0 3.5 1.3 0.2 setosa
#> 38 4.9 3.6 1.4 0.1 setosa
#> 39 4.4 3.0 1.3 0.2 setosa
#> 40 5.0 3.4 1.5 0.2 setosa
#> 41 5.0 3.5 1.3 0.3 setosa
#> 42 4.5 2.3 1.3 0.3 setosa
#> 43 4.4 3.2 1.3 0.2 setosa
#> 44 5.0 3.5 1.6 0.6 setosa
#> 45 5.0 3.8 1.9 0.4 setosa
#> 46 4.8 3.0 1.4 0.3 setosa
#> 47 5.0 3.8 1.6 0.2 setosa
#> 48 4.6 3.2 1.4 0.2 setosa
#> 49 5.0 3.7 1.5 0.2 setosa
#> 50 5.0 3.3 1.4 0.2 setosa
#> 51 5.0 3.2 4.7 1.4 versicolor
#> 52 5.0 3.2 4.5 1.5 versicolor
#> 53 5.0 3.1 4.9 1.5 versicolor
#> 54 5.0 2.3 4.0 1.3 versicolor
#> 55 5.0 2.8 4.6 1.5 versicolor
#> 56 5.0 2.8 4.5 1.3 versicolor
#> 57 5.0 3.3 4.7 1.6 versicolor
#> 58 4.9 2.4 3.3 1.0 versicolor
#> 59 5.0 2.9 4.6 1.3 versicolor
#> 60 5.0 2.7 3.9 1.4 versicolor
#> 61 5.0 2.0 3.5 1.0 versicolor
#> 62 5.0 3.0 4.2 1.5 versicolor
#> 63 5.0 2.2 4.0 1.0 versicolor
#> 64 5.0 2.9 4.7 1.4 versicolor
#> 65 5.0 2.9 3.6 1.3 versicolor
#> 66 5.0 3.1 4.4 1.4 versicolor
#> 67 5.0 3.0 4.5 1.5 versicolor
#> 68 5.0 2.7 4.1 1.0 versicolor
#> 69 5.0 2.2 4.5 1.5 versicolor
#> 70 5.0 2.5 3.9 1.1 versicolor
#> 71 5.0 3.2 4.8 1.8 versicolor
#> 72 5.0 2.8 4.0 1.3 versicolor
#> 73 5.0 2.5 4.9 1.5 versicolor
#> 74 5.0 2.8 4.7 1.2 versicolor
#> 75 5.0 2.9 4.3 1.3 versicolor
#> 76 5.0 3.0 4.4 1.4 versicolor
#> 77 5.0 2.8 4.8 1.4 versicolor
#> 78 5.0 3.0 5.0 1.7 versicolor
#> 79 5.0 2.9 4.5 1.5 versicolor
#> 80 5.0 2.6 3.5 1.0 versicolor
#> 81 5.0 2.4 3.8 1.1 versicolor
#> 82 5.0 2.4 3.7 1.0 versicolor
#> 83 5.0 2.7 3.9 1.2 versicolor
#> 84 5.0 2.7 5.0 1.6 versicolor
#> 85 5.0 3.0 4.5 1.5 versicolor
#> 86 5.0 3.4 4.5 1.6 versicolor
#> 87 5.0 3.1 4.7 1.5 versicolor
#> 88 5.0 2.3 4.4 1.3 versicolor
#> 89 5.0 3.0 4.1 1.3 versicolor
#> 90 5.0 2.5 4.0 1.3 versicolor
#> 91 5.0 2.6 4.4 1.2 versicolor
#> 92 5.0 3.0 4.6 1.4 versicolor
#> 93 5.0 2.6 4.0 1.2 versicolor
#> 94 5.0 2.3 3.3 1.0 versicolor
#> 95 5.0 2.7 4.2 1.3 versicolor
#> 96 5.0 3.0 4.2 1.2 versicolor
#> 97 5.0 2.9 4.2 1.3 versicolor
#> 98 5.0 2.9 4.3 1.3 versicolor
#> 99 5.0 2.5 3.0 1.1 versicolor
#> 100 5.0 2.8 4.1 1.3 versicolor
#> 101 5.0 3.3 5.0 2.5 virginica
#> 102 5.0 2.7 5.0 1.9 virginica
#> 103 5.0 3.0 5.0 2.1 virginica
#> 104 5.0 2.9 5.0 1.8 virginica
#> 105 5.0 3.0 5.0 2.2 virginica
#> 106 5.0 3.0 5.0 2.1 virginica
#> 107 4.9 2.5 4.5 1.7 virginica
#> 108 5.0 2.9 5.0 1.8 virginica
#> 109 5.0 2.5 5.0 1.8 virginica
#> 110 5.0 3.6 5.0 2.5 virginica
#> 111 5.0 3.2 5.0 2.0 virginica
#> 112 5.0 2.7 5.0 1.9 virginica
#> 113 5.0 3.0 5.0 2.1 virginica
#> 114 5.0 2.5 5.0 2.0 virginica
#> 115 5.0 2.8 5.0 2.4 virginica
#> 116 5.0 3.2 5.0 2.3 virginica
#> 117 5.0 3.0 5.0 1.8 virginica
#> 118 5.0 3.8 5.0 2.2 virginica
#> 119 5.0 2.6 5.0 2.3 virginica
#> 120 5.0 2.2 5.0 1.5 virginica
#> 121 5.0 3.2 5.0 2.3 virginica
#> 122 5.0 2.8 4.9 2.0 virginica
#> 123 5.0 2.8 5.0 2.0 virginica
#> 124 5.0 2.7 4.9 1.8 virginica
#> 125 5.0 3.3 5.0 2.1 virginica
#> 126 5.0 3.2 5.0 1.8 virginica
#> 127 5.0 2.8 4.8 1.8 virginica
#> 128 5.0 3.0 4.9 1.8 virginica
#> 129 5.0 2.8 5.0 2.1 virginica
#> 130 5.0 3.0 5.0 1.6 virginica
#> 131 5.0 2.8 5.0 1.9 virginica
#> 132 5.0 3.8 5.0 2.0 virginica
#> 133 5.0 2.8 5.0 2.2 virginica
#> 134 5.0 2.8 5.0 1.5 virginica
#> 135 5.0 2.6 5.0 1.4 virginica
#> 136 5.0 3.0 5.0 2.3 virginica
#> 137 5.0 3.4 5.0 2.4 virginica
#> 138 5.0 3.1 5.0 1.8 virginica
#> 139 5.0 3.0 4.8 1.8 virginica
#> 140 5.0 3.1 5.0 2.1 virginica
#> 141 5.0 3.1 5.0 2.4 virginica
#> 142 5.0 3.1 5.0 2.3 virginica
#> 143 5.0 2.7 5.0 1.9 virginica
#> 144 5.0 3.2 5.0 2.3 virginica
#> 145 5.0 3.3 5.0 2.5 virginica
#> 146 5.0 3.0 5.0 2.3 virginica
#> 147 5.0 2.5 5.0 1.9 virginica
#> 148 5.0 3.0 5.0 2.0 virginica
#> 149 5.0 3.4 5.0 2.3 virginica
#> 150 5.0 3.0 5.0 1.8 virginica