The {normalize} R package offers convenient tools to normalize (centering to zero mean and scaling to unit variance) numeric data:
works for vector, matrix, data.frame, and list objects
can normalize by row or by column
can ignore rows or columns when normalizing
allows for joint normalizing of rows or columns
provides the normalizing constants as attributes
preserves object attributes
You can install the released version from CRAN with:
install.packages("normalize")
Can normalize a vector:
x <- 1:10 normalize(x) #> [1] -1.4863011 -1.1560120 -0.8257228 -0.4954337 -0.1651446 0.1651446 #> [7] 0.4954337 0.8257228 1.1560120 1.4863011 #> attr(,"center") #> [1] 5.5 #> attr(,"scale") #> [1] 3.02765 normalize(x, center = FALSE) #> [1] 0.3302891 0.6605783 0.9908674 1.3211565 1.6514456 1.9817348 2.3120239 #> [8] 2.6423130 2.9726022 3.3028913 #> attr(,"scale") #> [1] 3.02765 normalize(x, scale = FALSE) #> [1] -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 #> attr(,"center") #> [1] 5.5
Can normalize a matrix:
normalize( matrix(1:12, nrow = 3, ncol = 4), jointly = list(1:2, 3:4) # joint normalization of columns 1, 2 and 3, 4 ) #> [,1] [,2] [,3] [,4] #> [1,] -2.5 0.5 -2.5 0.5 #> [2,] -1.5 1.5 -1.5 1.5 #> [3,] -0.5 2.5 -0.5 2.5 #> attr(,"center") #> [1] 3.5 3.5 9.5 9.5 #> attr(,"scale") #> [1] 1 1 1 1
Can normalize a data.frame:
normalize(
data.frame(a = 1:3, b = c("A", "B", "C"), c = 7:9, d = 10:12),
ignore = 2 # ignore character column 2 for normalization
)
#> a b c d
#> 1 -1 A -1 -1
#> 2 0 B 0 0
#> 3 1 C 1 1
Can work on a list:
normalize(list(1:5, diag(3), data.frame(1:3, 2:4))) #> [[1]] #> [1] -1.2649111 -0.6324555 0.0000000 0.6324555 1.2649111 #> attr(,"center") #> [1] 3 #> attr(,"scale") #> [1] 1.581139 #> #> [[2]] #> [,1] [,2] [,3] #> [1,] 1.1547005 -0.5773503 -0.5773503 #> [2,] -0.5773503 1.1547005 -0.5773503 #> [3,] -0.5773503 -0.5773503 1.1547005 #> attr(,"center") #> [1] 0.3333333 0.3333333 0.3333333 #> attr(,"scale") #> [1] 0.5773503 0.5773503 0.5773503 #> #> [[3]] #> X1.3 X2.4 #> 1 -1 -1 #> 2 0 0 #> 3 1 1
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