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1.9 KiB
1.9 KiB
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