Munger Ruby Reporting Library

Munger is basically a simple data munging and reporting library for Ruby as an alternative to Ruport, which did not fill my needs in ways that convinced me to start over rather than try to fork or patch it. Apologies to the Ruport chaps, whom I am sure are smashing blokes - it just didn’t wiggle my worm.

See the Wiki for details :

3-Part Reporting

Munger creates reports in three stages, much like an Apollo rocket. My main problem with Ruport was the coupling of different parts of these stages in ways that didn’t make the data easily re-usable, cacheable or didn’t give me enough control. I like to have my data separate from my report, which should be renderable however I want.

  • Stage 1 - Data Munging *

The first stage is getting a dataset that has all the information you need. I like to call this stage ‘munging’ (pronounced: ‘MON’-day + chan-‘GING’), which is taking a simple set of data (from a SQL query, perhaps) and transforming fields, adding derived data, pivoting, etc - and making it into a table of all the actual data-points you need.

  • Stage 2 - Report Formatting *

Then there is the Reporting. To me, this means taking your massaged dataset and doing all the fun reporting to it. This includes grouping, subgrouping, sorting, column ordering, multi-level aggregation (sums, avg, etc) and highlighting important information (values that are too small, too high, etc).

It can be argued that pivoting should be at this level, rather than the first, but I decided to put it there instead, mostly because I really think of the pivoted data as a different data set and also for performance reasons - the pivot data can be a bear to produce, and I plan on caching the first stage and then running different reporting options on it.

  • Stage 3 - Output Rendering *

Now that I have my super spiffy report, I want to be able to render it however I want, possibly in multiple formats - HTML and XLS are the most important to me, but PDF, text, csv, etc will also likely be produced eventually.


The starting data can be ActiveRecord collections or an array of Hashes.

webpage_hit table has ip_address, hit_date, action, referrer

  • Simple Example *

hits = WebpageHits.find(:all, :conditions => [‘hit_date > ?‘, 1.days.ago]) @table_data = => data) @table_data.sort(‘hit_date’).aggregate(:count => :action) html_table =

  • More Complex Example *

hits = WebpageHits.find(:all, :conditions => [‘hit_date > ?‘, 7.days.ago])

data = data.transform_column(‘hit_date’) { |row| } data.add_column(‘controller’) { |row| row.action.split(‘/’).first }

day_columns = data.pivot(‘hit_date’, ‘action’, ‘ip_address’, :count)

@table_data = => data, :columns => [:action] + day_columns, :aggregate => {:sum => day_columns})

@table_data.sort(‘action’).subgroup(‘controller’) @table_data.process.style_cells(‘low_traffic’, :only => new_columns) do |cell, row| # highlight any index pages that have < 500 hits cell.to_i < 500 if row.action =~ /index/

html_table =