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• Get Unit Test coverage above 80%

  Self explanatory; we need to test all functionality.

  No due date

  •2/2 issues closed
  100% complete0 open2 closed

• Phase 1

  > Phase 1 focuses on providing consistent, stable, and reliable access to GDELT data. [Issues](https://github.com/linwoodc3/gdeltPyR/issues) in this phase will build out the main `Search` class to return [GDELT data, version 1.0 or version 2.0](http://gdeltproject.org/data.html#intro), or equally important, give a relevant error message when no data is returned. This also means the phase will focus on building documentation, a unit testing framework (shooting for 90% coverage), and creating a helper class that provides helpful information on column names/table descriptions.

  No due date

  •5/16 issues closed
  31% complete11 open5 closed

• 0.1.11

  Add methods to `gdeltPyR` that expand the analytic value of the Python library. Needs to do more than just mirror GDELT data given the memory limitation. Key features: * direct query to Google Big Query * shapefile return * automatic choropleth output * extract unique news providers * running average (for actor or event code) * analytic for fastest news provider on issue (epoch average) * extract content from every news article

  No due date
  0% complete0 open0 closed

• Expand output formats

  Implement ability to output returned data in scientific or web format. This means: * json * hdf Use the pandas output format to easily convert the data. The challenge is deciding where to put the `output` parameter.

  Overdue by 8 year(s)

  •

  Due by November 17, 2016
  0% complete0 open0 closed