miller/docs6/docs/statistics-examples.md

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# Statistics examples

Computing interquartile ranges

For one or more specified field names, simply compute p25 and p75, then write the IQR as the difference of p75 and p25:

mlr --oxtab stats1 -f x -a p25,p75 \
    then put '$x_iqr = $x_p75 - $x_p25' \
    data/medium 

x_p25 0.24667037823231752
x_p75 0.7481860062358446
x_iqr 0.5015156280035271

For wildcarded field names, first compute p25 and p75, then loop over field names with p25 in them:

mlr --oxtab stats1 --fr '[i-z]' -a p25,p75 \
    then put 'for (k,v in $*) {
      if (k =~ "(.*)_p25") {
        $["\1_iqr"] = $["\1_p75"] - $["\1_p25"]
      }
    }' \
    data/medium 

Computing weighted means

This might be more elegantly implemented as an option within the stats1 verb. Meanwhile, it's expressible within the DSL:

mlr --from data/medium put -q '
  # Using the y field for weighting in this example
  weight = $y;

  # Using the a field for weighted aggregation in this example
  @sumwx[$a] += weight * $i;
  @sumw[$a] += weight;

  @sumx[$a] += $i;
  @sumn[$a] += 1;

  end {
    map wmean = {};
    map mean  = {};
    for (a in @sumwx) {
      wmean[a] = @sumwx[a] / @sumw[a]
    }
    for (a in @sumx) {
      mean[a] = @sumx[a] / @sumn[a]
    }
    #emit wmean, "a";
    #emit mean, "a";
    emit (wmean, mean), "a";
  }'

a=pan,wmean=4979.563722208067,mean=5028.259010091302
a=eks,wmean=4890.3815931472145,mean=4956.2900763358775
a=wye,wmean=4946.987746229947,mean=4920.001017293998
a=zee,wmean=5164.719684856538,mean=5123.092330239375
a=hat,wmean=4925.533162478552,mean=4967.743946419371