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