NAME

Data::Prepare - prepare CSV (etc) data for automatic processing

SYNOPSIS

  use Text::CSV qw(csv);
  use Data::Prepare qw(
    cols_non_empty non_unique_cols
    chop_lines chop_cols header_merge
  );
  my $data = csv(in => 'unclean.csv', encoding => "UTF-8");
  chop_cols([0, 2], $data);
  header_merge($spec, $data);
  chop_lines(\@lines, $data); # mutates the data

  # or:
  my @non_empty_counts = cols_non_empty($data);
  print Dumper(non_unique_cols($data));

DESCRIPTION

A module with utility functions for turning spreadsheets published for human consumption into ones suitable for automatic processing. Intended to be used by the supplied data-prepare script. See that script's documentation for a suggested workflow.

All the functions are exportable, none are exported by default. All the $data inputs are an array-ref-of-array-refs.

FUNCTIONS

chop_cols

  chop_cols([0, 2], $data);

Uses splice to delete each zero-based column index. The example above deletes the first and third columns.

chop_lines

  chop_lines([ 0, (-1) x $n ], $data);

Uses splice to delete each zero-based line index, in the order given. The example above deletes the first, and last $n, lines.

header_merge

  header_merge([
    { line => 1, from => 'up', fromspec => 'lastnonblank', to => 'self', matchto => 'HH', do => [ 'overwrite' ] },
    { line => 1, from => 'self', matchfrom => '.', to => 'down', do => [ 'prepend', ' ' ] },
    { line => 2, from => 'self', fromspec => 'left', to => 'self', matchto => 'Year', do => [ 'prepend', '/' ] },
    { line => 2, from => 'self', fromspec => 'literal:Country', to => 'self', tospec => 'index:0', do => [ 'overwrite' ] },
  ], $data);
  # Turns:
  # [
  #   [ '', 'Proportion of households with', '', '', '' ],
  #   [ '', '(HH1)', 'Year', '(HH2)', 'Year' ],
  #   [ '', 'Radio', 'of data', 'TV', 'of data' ],
  # ]
  # into (after a further chop_lines to remove the first two):
  # [
  #   [
  #     'Country',
  #     'Proportion of households with Radio', 'Proportion of households with Radio/Year of data',
  #     'Proportion of households with TV', 'Proportion of households with TV/Year of data'
  #   ]
  # ]

Applies the given transformations to the given data, so you can make the given data have the first row be your desired headers for the columns. As shown in the above example, this does not delete lines so further operations may be needed.

Broadly, each hash-ref specifies one operation, which acts on a single (specified) line-number. It scans along that line from left to right, unless tospec matches index:\d+ in which case only one operation is done.

The above merge operations in YAML format:

    spec:
      - do:
          - overwrite
        from: up
        fromspec: lastnonblank
        line: 2
        matchto: HH
        to: self
      - do:
          - prepend
          - ' '
        from: self
        line: 2
        matchfrom: .
        to: down
      - do:
          - prepend
          - /
        from: self
        fromspec: left
        line: 3
        matchto: Year
        to: self
      - do:
          - overwrite
        from: self
        fromspec: literal:Country
        line: 3
        to: self
        tospec: index:0

This turns the first three lines of data excerpted from the supplied example data (shown in CSV with spaces inserted for alignment reasons only):

        ,Proportion of households with,       ,     ,
        ,(HH1)                        ,Year   ,(HH2),Year
        ,Radio                        ,of data,TV   ,of data
  Belize,58.7                         ,2019   ,78.7 ,2019

into the following. Note that the first two lines will still be present (not shown), possibly modified, so you will need your chop_lines to remove them. The columns of the third line are shown, one per line, for readability:

  Country,
  Proportion of households with Radio,
  Proportion of households with Radio/Year of data,
  Proportion of households with TV,
  Proportion of households with TV/Year of data

This achieves a single row of column-headings, with each column-heading being unique, and sufficiently meaningful.

cols_non_empty

  my @col_non_empty = cols_non_empty($data);

In the given data, iterates through all rows and returns a list of quantities of non-blank entries in each column. This can be useful to spot columns with only a couple of entries, which are more usefully chopped.

non_unique_cols

  my $col2count = non_unique_cols($data);

Takes the first row of the given data, and returns a hash-ref mapping any non-unique column-names to the number of times they appear.

SEE ALSO

Text::CSV

LICENSE AND COPYRIGHT

Copyright (C) Ed J

This library is free software; you can redistribute it and/or modify it under the same terms as Perl itself.

cpanm Data::Prepare

perl -MCPAN -e shell
install Data::Prepare