miller/docs6/docs/data-cleaning-examples.md

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# Data-cleaning examples

Here are some ways to use the type-checking options as described in the Type-checking page. Suppose you have the following data file, with inconsistent typing for boolean. (Also imagine that, for the sake of discussion, we have a million-line file rather than a four-line file, so we can't see it all at once and some automation is called for.)

cat data/het-bool.csv

name,reachable
barney,false
betty,true
fred,true
wilma,1

One option is to coerce everything to boolean, or integer:

mlr --icsv --opprint put '$reachable = boolean($reachable)' data/het-bool.csv

name   reachable
barney false
betty  true
fred   true
wilma  true

mlr --icsv --opprint put '$reachable = int(boolean($reachable))' data/het-bool.csv

name   reachable
barney 0
betty  1
fred   1
wilma  1

A second option is to flag badly formatted data within the output stream:

mlr --icsv --opprint put '$format_ok = is_string($reachable)' data/het-bool.csv

name   reachable format_ok
barney false     false
betty  true      false
fred   true      false
wilma  1         false

Or perhaps to flag badly formatted data outside the output stream:

mlr --icsv --opprint put '
  if (!is_string($reachable)) {eprint "Malformed at NR=".NR}
' data/het-bool.csv

Malformed at NR=1
Malformed at NR=2
Malformed at NR=3
Malformed at NR=4
name   reachable
barney false
betty  true
fred   true
wilma  1

A third way is to abort the process on first instance of bad data:

mlr --csv put '$reachable = asserting_string($reachable)' data/het-bool.csv

Miller: is_string type-assertion failed at NR=1 FNR=1 FILENAME=data/het-bool.csv