Standardize / Normalize / Z-score / Scale

The standardize() function allows you to easily scale and center all numeric variables of a dataframe. It is similar to the base function scale(), but presents some advantages: it is tidyverse-friendly, data-type friendly (i.e., does not transform it into a matrix) and can handle dataframes with categorical data.

library(psycho)
library(tidyverse)

z_iris <- iris %>% 
  psycho::standardize() 

summary(z_iris)

       Species    Sepal.Length       Sepal.Width       Petal.Length    
 setosa    :50   Min.   :-1.86378   Min.   :-2.4258   Min.   :-1.5623  
 versicolor:50   1st Qu.:-0.89767   1st Qu.:-0.5904   1st Qu.:-1.2225  
 virginica :50   Median :-0.05233   Median :-0.1315   Median : 0.3354  
                 Mean   : 0.00000   Mean   : 0.0000   Mean   : 0.0000  
                 3rd Qu.: 0.67225   3rd Qu.: 0.5567   3rd Qu.: 0.7602  
                 Max.   : 2.48370   Max.   : 3.0805   Max.   : 1.7799  
  Petal.Width     
 Min.   :-1.4422  
 1st Qu.:-1.1799  
 Median : 0.1321  
 Mean   : 0.0000  
 3rd Qu.: 0.7880  
 Max.   : 1.7064  

But beware, standardization does not change (and “normalize”) the distribution!

z_iris %>% 
  dplyr::select(-Species) %>% 
  gather(Variable, Value) %>% 
  ggplot(aes(x=Value, fill=Variable)) +
      geom_density(alpha=0.5) +
      geom_vline(aes(xintercept=0)) +
      theme_bw() +
      scale_fill_brewer(palette="Spectral")