Normalizes values based on possible range and new bounds
Usage
normalize(x, ...)
# S3 method for default
normalize(x, range = base::range(x, na.rm = TRUE), bounds = 0:1, ...)
# S3 method for data.frame
normalize(x, ...)Arguments
- x
An object that is (coercible to)
double;data.framesare transformed- ...
Additional arguments passed to methods
- range
The range of possible values of
x. See details for more info. Defaults to the range of non-NAvalues- bounds
The new boundaries for the normalized values of
x. Defaults to0and1.
Details
Parameters range and bounds are modified with base::range().
The largest and smallest values are then used to determine the
minimum/maximum values and lower/upper bounds. This allows for a vector of
more than two values to be passed.
The current implementation of normalize.data.frame() allows for list of
parameters passed for each column. However, it is probably best suited for
default values.
Examples
x <- c(0.23, 0.32, 0.12, 0.61, 0.26, 0.24, 0.23, 0.32, 0.29, 0.27)
data.frame(
x = normalize(x),
v = normalize(x, range = 0:2),
b = normalize(x, bounds = 0:10),
vb = normalize(x, range = 0:2, bounds = 0:10)
)
#> x v b vb
#> 1 0.2244898 0.115 2.244898 1.15
#> 2 0.4081633 0.160 4.081633 1.60
#> 3 0.0000000 0.060 0.000000 0.60
#> 4 1.0000000 0.305 10.000000 3.05
#> 5 0.2857143 0.130 2.857143 1.30
#> 6 0.2448980 0.120 2.448980 1.20
#> 7 0.2244898 0.115 2.244898 1.15
#> 8 0.4081633 0.160 4.081633 1.60
#> 9 0.3469388 0.145 3.469388 1.45
#> 10 0.3061224 0.135 3.061224 1.35
# maintains matrix
mat <- structure(c(0.24, 0.92, 0.05, 0.37, 0.19, 0.69, 0.43, 0.22, 0.85,
0.73, 0.89, 0.68, 0.57, 0.89, 0.61, 0.98, 0.75, 0.37, 0.24, 0.24,
0.34, 0.8, 0.25, 0.46, 0.03, 0.71, 0.79, 0.56, 0.83, 0.97), dim = c(10L, 3L))
mat
#> [,1] [,2] [,3]
#> [1,] 0.24 0.89 0.34
#> [2,] 0.92 0.68 0.80
#> [3,] 0.05 0.57 0.25
#> [4,] 0.37 0.89 0.46
#> [5,] 0.19 0.61 0.03
#> [6,] 0.69 0.98 0.71
#> [7,] 0.43 0.75 0.79
#> [8,] 0.22 0.37 0.56
#> [9,] 0.85 0.24 0.83
#> [10,] 0.73 0.24 0.97
normalize(mat, bounds = -1:1)
#> [,1] [,2] [,3]
#> [1,] -0.5578947 0.8105263 -0.34736842
#> [2,] 0.8736842 0.3684211 0.62105263
#> [3,] -0.9578947 0.1368421 -0.53684211
#> [4,] -0.2842105 0.8105263 -0.09473684
#> [5,] -0.6631579 0.2210526 -1.00000000
#> [6,] 0.3894737 1.0000000 0.43157895
#> [7,] -0.1578947 0.5157895 0.60000000
#> [8,] -0.6000000 -0.2842105 0.11578947
#> [9,] 0.7263158 -0.5578947 0.68421053
#> [10,] 0.4736842 -0.5578947 0.97894737
normalize(as.data.frame(mat), bounds = -1:1)
#> V1 V2 V3
#> 1 -0.5632184 0.7567568 -0.34042553
#> 2 1.0000000 0.1891892 0.63829787
#> 3 -1.0000000 -0.1081081 -0.53191489
#> 4 -0.2643678 0.7567568 -0.08510638
#> 5 -0.6781609 0.0000000 -1.00000000
#> 6 0.4712644 1.0000000 0.44680851
#> 7 -0.1264368 0.3783784 0.61702128
#> 8 -0.6091954 -0.6486486 0.12765957
#> 9 0.8390805 -1.0000000 0.70212766
#> 10 0.5632184 -1.0000000 1.00000000