R/zscore.R

In liver: "Eating the Liver of Data Science"

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#     Copyright (C) 2020  Reza Mohammadi & Kevin Burke                         |
#                                                                              |
#     This file is part of 'liver' package.                                    |
#                                                                              |
#     liver is free software: you can redistribute it and/or modify it under   |
#     the terms of the GNU General Public License as published by the Free     |
#     Software Foundation; see <https://cran.r-project.org/web/licenses/GPL-3>.|
#                                                                              |
#     Maintainer: Reza Mohammadi <a.mohammadi@uva.nl>                          |
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#     Z-score normalization
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zscore = function( x, columns = NULL, na.rm = FALSE ) 
{
    #    if( !methods::is( x )[ 1 ] %in% c( "integer", "numeric" ) ) stop( "Categorical variable not support" )
    
    if( !is.vector( x ) & !is.matrix( x ) & !is.data.frame( x ) ) stop( " x must be a vector, matrix, or dataframe" )
    
    if( any( is.na( x ) ) & ( na.rm == FALSE ) ) na.rm = TRUE
    
    if( is.vector( x ) ){
        z = ( x - mean( x, na.rm = na.rm ) ) / stats::sd( x, na.rm = na.rm )
    }
    
    if( is.null( columns ) )
    {
        data_frame = FALSE
        if( is.data.frame( x ) ){
            data_frame = TRUE
            x = data.matrix( x )
        }
        
        if( is.matrix( x ) ){
            if( nrow( x ) == 1 ) stop( " x, for the case of matrix, must have more than 1 row." )
            
            z = t( ( t( x ) - apply( x, 2, mean, na.rm = na.rm ) ) / apply( x, 2, stats::sd, na.rm = na.rm ) )
            
            z[ is.na( z ) ] = 0
        }
        
        if( data_frame == TRUE ) z = as.data.frame( z )
    }else{
        z = x
        if( is.numeric( columns ) ){
            for( i in columns ){
                x_i = as.integer( x[ , i ] )
                
                z[ , i ] = ( x_i - mean( x_i, na.rm = na.rm ) ) / stats::sd( x_i, na.rm = na.rm )
            }
        }
        
        if( is.character( columns ) ){
            for( i in columns ){
                x_i = as.integer( x[ i ] )
                
                z[ i ] = ( x_i - mean( x_i, na.rm = na.rm ) ) / stats::sd( x_i, na.rm = na.rm )
            }
        }
    }
    
    return( z )
}

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