Tplyr 0.1.3

• Bug Fixes
  • Fixes a bug where ‘N’ counts in column headers would display as 0 when a distinct_by and custom groupings were used.
• Other Changes
  • Ordering layer columns are now unnamed vectors. For varn and factor ordering columns they could previously be named which could be unexpected.
  • The names of the data.frames used in target and pop_data are now attributes of the tplyr table object and not the data.frames themselves.
  • The UAT application now gives a warning if an error happened during validation, or confirms that all tests pass.

Tplyr 0.1.2

• Bug Fixes
  • Fixes a bug where percentages in count layers would appear as ‘Inf’ when a distinct_by variable and custom groupings were used. GitHub Issue #8

Tplyr 0.1.1

Initial release onto CRAN.

• Bug Fixes/Enhancements
  • Count layers were re-factored to improve the execution efficiency
  • Auto-precision now works without a by variable
  • Several new assertions have been added to give clearer error messages
  • Treatment groups within the population data will produce columns in the resulting build, even if no records exist for that treatment group in the target dataset
  • Risk difference variable names will now populate properly when a cols argument is used
  • Data frame attributes are cleaned prior to processing to prevent any merge/bind warnings during processing
  • Total values within count layers are properly filled when the resulting count is 0 (largely impacts risk-difference calculations)
• Feature additions
  • Shift layers are here!
  • Flexibility when filling missing values has been enhanced for descriptive statistic layers
  • Layers can now be named, and those names can be used in get_numeric_data and the new function get_statistics_data to get risk difference raw numbers. Data may also be filtered directly from both functions.
  • Default formats can now be set via options or at the table level, which allows you to eliminate a great deal of redundant code

Tplyr 0.1.0

Beta release for Tplyr with introduction of numerous new features:

General updates: - Calculate your header N counts based on the population dataset or the target dataset. The alpha release had an option to set the population data but this wasn’t actually used anywhere in the internals. - Use these header N counts as token replacements when using the add_column_headers function. - Order variables are now added to the built dataset to allow you to sort the output dataset as you wish with numeric variables. - Count layer updates: - Optionally use the population data N counts as denominators for percent calculation. - For multi-level count summaries, nest the row label columns together to present both row labels in a single column - You can now present both distinct and non-distinct counts instead of one or the other - Sorting options allow you to order results from the target variable values or from derived counts within a specified column - Risk difference calculations can now be added as additional columns, with flexible options for presentation - Descriptive statistics layer updates: - The custom summary functionality has been updated to apply to multi-variable summaries, which results in an interface change - Automatic decimal precision has been added to allow you to base the presentation on the precision of the data as collected

Tplyr 0.1.0.9999

Initial alpha release of Tplyr