Raku Data::Reshapers
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:
- Cross tabulation,
cross-tabulate
- Long format conversion,
to-long-format
- Wide format conversion,
to-wide-format
- Join across (aka
SQL JOIN
),join-across
- Transpose,
transpose
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 $res; # {female => {1st => 144, 2nd => 106, 3rd => 216}, male => {1st => 179, 2nd => 171, 3rd => 493}} say to-pretty-table($res); # +--------+-----+-----+-----+ # | | 1st | 2nd | 3rd | # +--------+-----+-----+-----+ # | female | 144 | 106 | 216 | # | male | 179 | 171 | 493 | # +--------+-----+-----+-----+
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; .say for to-long-format( @tbl1 ); my @lfRes1 = to-long-format( @tbl1, 'id', [], variablesTo => "VAR", valuesTo => "VAL2" ); .say for @lfRes1;
Wide format
Here we transform the long format result @lfRes1
above into wide format --
the result has the same records as the @tbl1
:
say to-pretty-table( to-wide-format( @lfRes1, 'id', 'VAR', 'VAL2' ) ); # +-------------------+----------------+--------------+--------------+-----+ # | passengerSurvival | passengerClass | passengerAge | passengerSex | id | # +-------------------+----------------+--------------+--------------+-----+ # | died | 1st | 20 | male | 308 | # | died | 2nd | 40 | female | 412 | # | survived | 2nd | 50 | female | 441 | # | died | 3rd | 20 | male | 741 | # | died | 3rd | -1 | male | 932 | # +-------------------+----------------+--------------+--------------+-----+
Transpose
Using cross tabulation result above:
my $tres = transpose( $res ); say to-pretty-table($res, title => "Original"); # +--------------------------+ # | Original | # +--------+------+----------+ # | | died | survived | # +--------+------+----------+ # | female | 127 | 339 | # | male | 682 | 161 | # +--------+------+----------+ say to-pretty-table($tres, title => "Transposed"); # +--------------------------+ # | Transposed | # +----------+--------+------+ # | | female | male | # +----------+--------+------+ # | died | 127 | 682 | # | survived | 339 | 161 | # +----------+--------+------+
TODO
Simpler more convenient interface.
Currently, a user have to specify four different namespaces in order to be able to use all package functions.
More extensive long format tests.
More extensive wide format tests.
Implement verifications for
Positional-of-hashes
Positional-of-arrays
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
Implement "nice tabular visualization" using Pretty::Table and/or Text::Table::Simple.
Document examples using pretty tables.
Implement transposing operation for:
- hash of hashes
- hash of arrays
- array of hashes
- array of arrays
- array of key-to-array pairs
Implement to-pretty-table for:
- hash of hashes
- hash of arrays
- array of hashes
- array of arrays
- array of key-to-array pairs
Implemented join-across:
- inner, left, right, outer
- single key-to-key pair
- multiple key-to-key pairs
- optional fill-in of missing values
- handling collisions
Implement to long format conversion for:
- hash of hashes
- hash of arrays
Speed/performance profiling.
- Come up with profiling tests
- Comparison with R
- Comparison with Python
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..