title: normalizing multivariate data
body: <p>Please is there any R package or function that I can use to normalize multivariate data? I'm not really concerned about the normalization method (min-max, z-score, anything works for me!!). I have a time-series of 32 objects measured along 12 hours. It looks somewhat like this:</p>
  
  <pre><code>df &lt;-  structure(list(hr1 = c(4343L, 153L, 159L, 3100L, 183L), hr2 = c(1098L, 
  123L, 65L, 3050L, 225L), hr3 = c(7L, 110L, 65L, 256L, 20L), hr4 = c(6400L, 
  141L, 69L, 251L, 313L), hr5 = c(112L, 107L, 70L, 5L, 245L), 
  hr6 = c(46L, 139L, 73L, 208L, 30L), hr7 = c(115L, 105L, 65L, 
  219L, 267L), hr8 = c(375L, 137L, 66L, 180L, 345L), hr9 = c(187L, 
  126L, 65L, 63L, 279L), hr10 = c(1613L, 277L, 66L, 134L, 30L
  ), hr11 = c(55L, 316L, 139L, 454L, 36L), hr12 = c(577L, 48L, 
  90L, 3581L, 88L)), .Names = c("hr1", "hr2", "hr3", "hr4", 
  "hr5", "hr6", "hr7", "hr8", "hr9", "hr10", "hr11", "hr12"), 
  class = "data.frame", row.names = c("1", "2", "3", "4", "5"))
  
  &gt; df
     hr1  hr2 hr3  hr4 hr5 hr6 hr7 hr8 hr9 hr10 hr11 hr12
  1 4343 1098   7 6400 112  46 115 375 187 1613   55  577
  2  153  123 110  141 107 139 105 137 126  277  316   48
  3  159   65  65   69  70  73  65  66  65   66  139   90
  4 3100 3050 256  251   5 208 219 180  63  134  454 3581
  5  183  225  20  313 245  30 267 345 279   30   36   88
  </code></pre>
  
  <p>I want to normalize the values for each object and run k-means clustering on them to group objects that exhibit similar shapes. At the moment their values are too widely dispersed and would yield inaccurate clusters. I'm still not very sure how to go about both the normalization and the clustering and would appreciate any pointers at all. Thanks.</p>