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Data::Reshapers zef:antononcube last updated on 2022-08-06

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Raku Data::Reshapers

SparkyCI Build Status License: Artistic-2.0

This Raku package has data reshaping functions for different data structures that are coercible to full arrays.

The supported data structures are: - Positional-of-hashes - Positional-of-arrays

The five data reshaping provided by the package over those data structures are:

The first four operations are fundamental in data wrangling and data analysis; see [AA1, Wk1, Wk2, AAv1-AAv2].

(Transposing of tabular data is, of course, also fundamental, but it also can be seen as a basic functional programming operation.)


Usage examples

Cross tabulation

Making contingency tables -- or cross tabulation -- is a fundamental statistics and data analysis operation, [Wk1, AA1].

Here is an example using the Titanic dataset (that is provided by this package through the function get-titanic-dataset):

use Data::Reshapers;

my @tbl = get-titanic-dataset();
my $res = cross-tabulate( @tbl, 'passengerSex', 'passengerClass');
say $res;
# {female => {1st => 144, 2nd => 106, 3rd => 216}, male => {1st => 179, 2nd => 171, 3rd => 493}}
to-pretty-table($res);
# +--------+-----+-----+-----+
# |        | 3rd | 1st | 2nd |
# +--------+-----+-----+-----+
# | female | 216 | 144 | 106 |
# | male   | 493 | 179 | 171 |
# +--------+-----+-----+-----+

Long format

Conversion to long format allows column names to be treated as data.

(More precisely, when converting to long format specified column names of a tabular dataset become values in a dedicated column, e.g. "Variable" in the long format.)

my @tbl1 = @tbl.roll(3);
.say for @tbl1;
# {id => 822, passengerAge => 30, passengerClass => 3rd, passengerSex => male, passengerSurvival => died}
# {id => 684, passengerAge => 40, passengerClass => 3rd, passengerSex => male, passengerSurvival => died}
# {id => 1243, passengerAge => -1, passengerClass => 3rd, passengerSex => male, passengerSurvival => died}
.say for to-long-format( @tbl1 );
# {AutomaticKey => 0, Value => died, Variable => passengerSurvival}
# {AutomaticKey => 0, Value => 3rd, Variable => passengerClass}
# {AutomaticKey => 0, Value => male, Variable => passengerSex}
# {AutomaticKey => 0, Value => 30, Variable => passengerAge}
# {AutomaticKey => 0, Value => 822, Variable => id}
# {AutomaticKey => 1, Value => died, Variable => passengerSurvival}
# {AutomaticKey => 1, Value => 3rd, Variable => passengerClass}
# {AutomaticKey => 1, Value => male, Variable => passengerSex}
# {AutomaticKey => 1, Value => 40, Variable => passengerAge}
# {AutomaticKey => 1, Value => 684, Variable => id}
# {AutomaticKey => 2, Value => died, Variable => passengerSurvival}
# {AutomaticKey => 2, Value => 3rd, Variable => passengerClass}
# {AutomaticKey => 2, Value => male, Variable => passengerSex}
# {AutomaticKey => 2, Value => -1, Variable => passengerAge}
# {AutomaticKey => 2, Value => 1243, Variable => id}
my @lfRes1 = to-long-format( @tbl1, 'id', [], variablesTo => "VAR", valuesTo => "VAL2" );
.say for @lfRes1;
# {VAL2 => male, VAR => passengerSex, id => 1243}
# {VAL2 => -1, VAR => passengerAge, id => 1243}
# {VAL2 => died, VAR => passengerSurvival, id => 1243}
# {VAL2 => 3rd, VAR => passengerClass, id => 1243}
# {VAL2 => male, VAR => passengerSex, id => 684}
# {VAL2 => 40, VAR => passengerAge, id => 684}
# {VAL2 => died, VAR => passengerSurvival, id => 684}
# {VAL2 => 3rd, VAR => passengerClass, id => 684}
# {VAL2 => male, VAR => passengerSex, id => 822}
# {VAL2 => 30, VAR => passengerAge, id => 822}
# {VAL2 => died, VAR => passengerSurvival, id => 822}
# {VAL2 => 3rd, VAR => passengerClass, id => 822}

Wide format

Here we transform the long format result @lfRes1 above into wide format -- the result has the same records as the @tbl1:

to-pretty-table( to-wide-format( @lfRes1, 'id', 'VAR', 'VAL2' ) );
# +----------------+-------------------+--------------+------+--------------+
# | passengerClass | passengerSurvival | passengerSex |  id  | passengerAge |
# +----------------+-------------------+--------------+------+--------------+
# |      3rd       |        died       |     male     | 1243 |      -1      |
# |      3rd       |        died       |     male     | 684  |      40      |
# |      3rd       |        died       |     male     | 822  |      30      |
# +----------------+-------------------+--------------+------+--------------+

Transpose

Using cross tabulation result above:

my $tres = transpose( $res );

to-pretty-table($res, title => "Original");
# +--------------------------+
# |         Original         |
# +--------+-----+-----+-----+
# |        | 2nd | 1st | 3rd |
# +--------+-----+-----+-----+
# | female | 106 | 144 | 216 |
# | male   | 171 | 179 | 493 |
# +--------+-----+-----+-----+
to-pretty-table($tres, title => "Transposed");
# +---------------------+
# |      Transposed     |
# +-----+--------+------+
# |     | female | male |
# +-----+--------+------+
# | 1st |  144   | 179  |
# | 2nd |  106   | 171  |
# | 3rd |  216   | 493  |
# +-----+--------+------+

Type system

There is a type "deduction" system in place. The type system conventions follow those of Mathematica's Dataset -- see the presentation "Dataset improvements".

Here we get the Titanic dataset, change the "passengerAge" column values to be numeric, and show dataset's dimensions:

my @dsTitanic = get-titanic-dataset(headers => 'auto');
@dsTitanic = @dsTitanic.map({$_<passengerAge> = $_<passengerAge>.Numeric; $_}).Array;
dimensions(@dsTitanic)
# (1309 5)

Here is a sample of dataset's records:

to-pretty-table(@dsTitanic.pick(5), field-names => <id passengerAge passengerClass passengerSex passengerSurvival>)
# +------+--------------+----------------+--------------+-------------------+
# |  id  | passengerAge | passengerClass | passengerSex | passengerSurvival |
# +------+--------------+----------------+--------------+-------------------+
# | 1305 |      10      |      3rd       |    female    |        died       |
# | 684  |      40      |      3rd       |     male     |        died       |
# | 721  |      20      |      3rd       |     male     |        died       |
# |  40  |      50      |      1st       |     male     |        died       |
# | 399  |      10      |      2nd       |     male     |      survived     |
# +------+--------------+----------------+--------------+-------------------+

Here is the type of a single record:

deduce-type(@dsTitanic[12])
# Struct([id, passengerAge, passengerClass, passengerSex, passengerSurvival], [Str, Int, Str, Str, Str])

Here is the type of single record's values:

deduce-type(@dsTitanic[12].values.List)
# Tuple([Atom((Str)), Atom((Str)), Atom((Str)), Atom((Str)), Atom((Int))])

Here is the type of the whole dataset:

deduce-type(@dsTitanic)
# Vector(Struct([id, passengerAge, passengerClass, passengerSex, passengerSurvival], [Str, Int, Str, Str, Str]), 1309)

TODO

  1. [X] Simpler more convenient interface.

    • ~~Currently, a user have to specify four different namespaces in order to be able to use all package functions.~~
  2. [ ] More extensive long format tests.

  3. [ ] More extensive wide format tests.

  4. [ ] Implement verifications for

    • [X] Positional-of-hashes

    • [X] Positional-of-arrays

    • [X] Positional-of-key-to-array-pairs

    • [ ] Positional-of-hashes, each record of which has:

      • [ ] Same keys
      • [ ] Same type of values of corresponding keys
    • [ ] Positional-of-arrays, each record of which has:

      • [ ] Same length
      • [ ] Same type of values of corresponding elements
  5. [X] Implement "nice tabular visualization" using Pretty::Table and/or Text::Table::Simple.

  6. [X] Document examples using pretty tables.

  7. [X] Implement transposing operation for:

    • [X] hash of hashes
    • [X] hash of arrays
    • [X] array of hashes
    • [X] array of arrays
    • [X] array of key-to-array pairs
  8. [X] Implement to-pretty-table for:

    • [X] hash of hashes
    • [X] hash of arrays
    • [X] array of hashes
    • [X] array of arrays
    • [X] array of key-to-array pairs
  9. [ ] Implemented join-across:

    • [X] inner, left, right, outer
    • [X] single key-to-key pair
    • [ ] multiple key-to-key pairs
    • [ ] optional fill-in of missing values
    • [ ] handling collisions
  10. [ ] Implement to long format conversion for:

    • [ ] hash of hashes
    • [ ] hash of arrays
  11. [ ] Speed/performance profiling.

    • [ ] Come up with profiling tests
    • [ ] Comparison with R
    • [ ] Comparison with Python
  12. [ ] Type system.

    • [X] Base type (Int, Str, Numeric)
    • [X] Homogenous list detection
    • [X] Association detection
    • [X] Struct discovery
    • [ ] Enumeration detection
    • [X] Dataset detection
      • [X] List of hashes
      • [X] Hash of hashes
      • [X] List of lists
  13. [ ] "Simple" or fundamental functions

    • [X] flatten
    • [X] take-drop
    • [ ] tally
      • Currently in "Data::Summarizers".

References

Articles

[AA1] Anton Antonov, "Contingency tables creation examples", (2016), MathematicaForPrediction at WordPress.

[Wk1] Wikipedia entry, Contingency table.

[Wk2] Wikipedia entry, Wide and narrow data.

Functions, repositories

[AAf1] Anton Antonov, CrossTabulate, (2019), Wolfram Function Repository.

[AAf2] Anton Antonov, LongFormDataset, (2020), Wolfram Function Repository.

[AAf3] Anton Antonov, WideFormDataset, (2021), Wolfram Function Repository.

[AAf4] Anton Antonov, RecordsSummary, (2019), Wolfram Function Repository.

Videos

[AAv1] Anton Antonov, "Multi-language Data-Wrangling Conversational Agent", (2020), YouTube channel of Wolfram Research, Inc.. (Wolfram Technology Conference 2020 presentation.)

[AAv2] Anton Antonov, "Data Transformation Workflows with Anton Antonov, Session #1", (2020), YouTube channel of Wolfram Research, Inc..

[AAv3] Anton Antonov, "Data Transformation Workflows with Anton Antonov, Session #2", (2020), YouTube channel of Wolfram Research, Inc..