Class: RailsDataExplorer::DataType::Categorical

Inherits:
RailsDataExplorer::DataType show all
Defined in:
lib/rails_data_explorer/data_type/categorical.rb

Overview

Responsibilities:

* Provide available charts and statistics for categorical data type.
* Provide methods for categorical data type.

Collaborators:

* DataSet

Instance Method Summary collapse

Methods inherited from RailsDataExplorer::DataType

#available_chart_types

Instance Method Details

#all_available_chart_types ⇒ Object 



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# File 'lib/rails_data_explorer/data_type/categorical.rb', line 15

def all_available_chart_types
  [
    {
      chart_class: Chart::HistogramCategorical,
      chart_roles: [:x],
      dimensions_count_min: 1,
      dimensions_count_max: 1,
    },
    {
      chart_class: Chart::PieChart,
      chart_roles: [:any],
      dimensions_count_min: 1,
      dimensions_count_max: 1,
    },
    {
      chart_class: Chart::BoxPlotGroup,
      chart_roles: [:y],
      dimensions_count_min: 2,
      dimensions_count_max: 2,
    },
    {
      chart_class: Chart::Scatterplot,
      chart_roles: [:color],
      dimensions_count_min: 3,
    },
    {
      chart_class: Chart::ParallelCoordinates,
      chart_roles: [:dimension],
      dimensions_count_min: 3,
    },
    {
      chart_class: Chart::StackedBarChartCategorical,
      chart_roles: [:x, :y],
      dimensions_count_min: 2,
      dimensions_count_max: 2,
    },
    {
      chart_class: Chart::StackedBarChartCategoricalPercent,
      chart_roles: [:x, :y],
      dimensions_count_min: 2,
      dimensions_count_max: 2,
    },
    # {
    #   chart_class: Chart::StackedHistogramTemporal,
    #   chart_roles: [:y],
    #   dimensions_count_min: 2,
    #   dimensions_count_max: 2,
    # },
    {
      chart_class: Chart::ContingencyTable,
      chart_roles: [:any],
      dimensions_count_min: 2,
      dimensions_count_max: 2,
    },
    {
      chart_class: Chart::DescriptiveStatisticsTable,
      chart_roles: [:any],
      dimensions_count_min: 1,
      dimensions_count_max: 1,
    },
    {
      chart_class: Chart::ParallelSet,
      chart_roles: [:dimension],
      dimensions_count_min: 3,
    },
  ].freeze
end

#axis_tick_format(values) ⇒ Object 



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# File 'lib/rails_data_explorer/data_type/categorical.rb', line 181

def axis_tick_format(values)
  %(function(d) { return d })
end

#descriptive_statistics(values) ⇒ Object 



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# File 'lib/rails_data_explorer/data_type/categorical.rb', line 83

def descriptive_statistics(values)
  frequencies = compute_histogram(values)
  labels_ds = DataSeries.new('_', values.uniq)
  total_count = values.length
  ruby_formatters = {
    integer: Proc.new { |v| number_with_delimiter(v.round) },
    percent: Proc.new { |v| number_to_percentage(v, precision: 3, significant: true, strip_insignificant_zeros: true, delimiter: ',') },
  }
  r = frequencies.inject([]) { |m, (k,v)|
    m << { label: "#{ k.to_s }_count", value: v, ruby_formatter: ruby_formatters[:integer] }
    m << { label: "#{ k.to_s }_percent", value: (v / total_count.to_f) * 100, ruby_formatter: ruby_formatters[:percent] }
    m
  }.sort(
    &labels_ds.label_sorter(
      :label,
      lambda { |a,b| b[:value] <=> a[:value] }
    )
  )
  r.insert(0, { label: '[Total]_count', value: total_count, ruby_formatter: ruby_formatters[:integer] })
  r.insert(0, { label: '[Total]_percent', value: 100, ruby_formatter: ruby_formatters[:percent] })
  r
end

#descriptive_statistics_table(values) ⇒ Object

Returns an object that describes a statistics table.



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# File 'lib/rails_data_explorer/data_type/categorical.rb', line 107

def descriptive_statistics_table(values)
  desc_stats = descriptive_statistics(values)
  if desc_stats.length < DataSeries.many_uniq_vals_threshold
    descriptive_statistics_table_horizontal(desc_stats)
  else
    descriptive_statistics_table_vertical(desc_stats)
  end
end

#descriptive_statistics_table_horizontal(desc_stats) ⇒ Object 



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# File 'lib/rails_data_explorer/data_type/categorical.rb', line 116

def descriptive_statistics_table_horizontal(desc_stats)
  labels = desc_stats.map { |e| e[:label].gsub(/_count|_percent/, '') }.uniq
  table = Utils::RdeTable.new([])
  table.rows << Utils::RdeTableRow.new(
    :tr,
    labels.map { |label|
      Utils::RdeTableCell.new(:th, label, ruby_formatter: Proc.new { |e| e }, css_class: 'rde-cell-label')
    },
    css_class: 'rde-column_header'
  )
  table.rows << Utils::RdeTableRow.new(
    :tr,
    labels.map { |label|
      stat = desc_stats.detect { |e| "#{ label }_count" == e[:label] }
      Utils::RdeTableCell.new(:td, stat[:value], ruby_formatter: stat[:ruby_formatter], css_class: 'rde-cell-value')
    },
    css_class: 'rde-data_row'
  )
  table.rows << Utils::RdeTableRow.new(
    :tr,
    labels.map { |label|
      stat = desc_stats.detect { |e| "#{ label }_percent" == e[:label] }
      Utils::RdeTableCell.new(:td, stat[:value], ruby_formatter: stat[:ruby_formatter], css_class: 'rde-cell-value')
    },
    css_class: 'rde-data_row'
  )
  table
end

#descriptive_statistics_table_vertical(desc_stats) ⇒ Object 



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# File 'lib/rails_data_explorer/data_type/categorical.rb', line 145

def descriptive_statistics_table_vertical(desc_stats)
  labels = desc_stats.map { |e| e[:label].gsub(/_count|_percent/, '') }.uniq
  table = Utils::RdeTable.new([])
  table.rows << Utils::RdeTableRow.new(
    :tr,
    %w[Value Count Percent].map { |label|
      Utils::RdeTableCell.new(:th, label, css_class: 'rde-cell-label')
    },
    css_class: 'rde-column_header',
  )
  labels.each { |label|
    count_stat = desc_stats.detect { |e| "#{ label }_count" == e[:label] }
    percent_stat = desc_stats.detect { |e| "#{ label }_percent" == e[:label] }
    table.rows << Utils::RdeTableRow.new(
      :tr,
      [
        Utils::RdeTableCell.new(:td, label, css_class: 'rde-cell-value'),
        Utils::RdeTableCell.new(
          :td,
          count_stat[:value],
          ruby_formatter: count_stat[:ruby_formatter],
          css_class: 'rde-cell-value'
        ),
        Utils::RdeTableCell.new(
          :td,
          percent_stat[:value],
          ruby_formatter: percent_stat[:ruby_formatter],
          css_class: 'rde-cell-value'
        ),
      ],
      css_class: 'rde-data_row',
    )
  }
  table
end

#label_sorter(label_val_key, data_series, value_sorter) ⇒ Proc

Returns a Proc that will be used by #sort.

Parameters:

  • label_val_key (Symbol, nil) —

    the hash key to use to get the label value during sort (sent to a,b)

  • data_series (DataSeries) —

    the ds that contains the uniq vals

  • value_sorter (Proc) —

    the sorting proc to use if not sorted numerically

Returns:

  • (Proc) —

    a Proc that will be used by #sort



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# File 'lib/rails_data_explorer/data_type/categorical.rb', line 189

def label_sorter(label_val_key, data_series, value_sorter)
  if data_series.uniq_vals.any? { |e| e.to_s =~ /^[\+\-]?\d+/ }
    # Sort numerical categories by key ASC
    # This lambda can be used in conjunction with `#sort`.
    # It returns -1, 0, or 1
    lambda { |a,b|
      number_and_full_string_extractor = lambda { |val|
        str = label_val_key ? val[label_val_key] : val
        number = str.gsub(/^[^\d\+\-]*/, '') # remove non-digit leading chars
                    .gsub(',', '') # remove delimiter commas, they throw off to_f parsing
        if '' != number
          # label contains digits
          number = number.to_f
          number += 1  if str =~ /^>/ # increase highest threshold by one for proper sorting
          number -= 1  if str =~ /^</ # decrease lowest threshold by one for proper sorting
        else
          # label doesn't contain digits, set to nil to sort at end
          number = nil
        end
        [number, str]
      }
      a_num, a_str = number_and_full_string_extractor.call(a)
      b_num, b_str = number_and_full_string_extractor.call(b)
      if a_num && b_num
        # Both numbers are present, compare them
        [a_num, a_str] <=> [b_num, b_str]
      elsif a_num
        # a_num is present, b_num isn't. Sort a before b
        -1
      else
        # a_num is not present, b_num is, Sort a after b
        1
      end
    }
  else
    # Use provided value sorter
    value_sorter
  end
end

#limit_distinct_values(values, max_num_vals, val_for_others = nil) ⇒ Object

Returns the top N max frequent distinct observations in values. Groups less frequent observations under val_for_others.

Parameters:

  • values (Array)
  • max_num_vals (Integer) —

    the max number of distinct values to return (including val_for_others)

  • val_for_others (String, optional) (defaults to: nil) —

    defaults to ‘[Other]’



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# File 'lib/rails_data_explorer/data_type/categorical.rb', line 234

def limit_distinct_values(values, max_num_vals, val_for_others = nil)
  distinct_values = values.uniq
  # Return values if they already have lte max_num_vals distinct observations
  return values  if distinct_values.length <= max_num_vals

  val_for_others ||= '[Other]'
  frequencies = compute_histogram(values)
  top_vals = frequencies.to_a.sort { |a,b|
    # a = [value, frequency]
    # Sort by frequency DESC, value ASC
    [b.last, a.first] <=> [a.last, b.first]
  }.first(max_num_vals - 1).map { |e| e.first }
  values.map { |e| top_vals.include?(e) ? e : val_for_others }
end