- Bug Fixes
- Nested count layers with character values in the first position could error if multiple risk differences were added.
- Improved handling of factors in the treatment variable.
- In certain cases when creating a count layer, you may only want to keep certain factors from your target dataset. Tplyr now has this functionality built in! With the
keep_levels() you can define what factors you want to keep in your count layers without having to recode/drop factors at the table level.
- Tplyr would normally use the R native rounding method and that is the method we recommend. However, in certain cases you may be trying to match your Tplyr output with SAS. You can set the ‘tplyr.IBMRounding’ option to TRUE, and Tplyr will simulate IBM rounding.
set_denoms_by() has been enhanced for nested count layers. You can now your nested count target variables as denominators.
- Bug fixes
add_risk_difference() would error out when you used it in a nested count layer that had a character value as the first variable.
- Nested count layers could not be sorted
bycount if the layer level where logic caused a value to be droped. This was fixed and tested for future development.
- The process for determining
by variable indicies was changed from N -> factor -> alphabetical to factor -> N -> alphabetical to allow users to override variables that have N counterparts that might have additional values not present in the target.
- You can now use text strings as the first variable in nested count.
- Bug Fixes
- A bug fix where factors in by variables weren’t indexed properly was resolved.
- Several documentation updates for clarity and changed functionality.
- Improved error messages and error handling in some places.
- Other changes
- Event counts are now noted as ‘distinct_n’ instead of ‘distinct’ in count format strings. ‘distinct’ may still be used but results in a warning that it should no longer be used. Using both ‘distinct’ and ‘distinct_n’ results in an error.
- Updated for changes in how tibble uses attributes.
- Bug Fixes
- Fixed a bug caused by an update to
tibble 3.0.4 that caused factors to be displayed incorrectly in row labels and sorting columns to populate incorrectly.
- A bug where the factors used in the shift layers wouldn’t be reflected in the ordering columns.
add_total_row() interface has been updated. It now takes an f_str object can be formatted differently than the rest of the table. A parameter was also added note if total rows should include missing counts.
set_missing_count() interface was updated. The ‘string’ parameter was removed and replaced with the ellipsis. Instead of passing a vector, a user would pass any number of character vectors that are named.
- Build will error if
denom_ignore is used but no missing count strings are specified.
- A new function,
set_denom_where() was added to allow a user to change how the denominators are filtered when calculating percentages.
- Other changes
- The version of dplyr that gets imported was updated to 1.0.0. The version of tidyselect imported was updated to 1.1.0. This was updated to remove warnings in the count layer build process.
Fixes a bug where “Totals” in numeric data may not take into account the where logic at the layer level and thus give inaccurate percentages
add_total_row() function is more intuitive. It now uses the
denoms_by variables to determine how to calculate the totals.
- 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.
- 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
Initial release onto CRAN.
- Bug Fixes/Enhancements
- Count layers were re-factored to improve the execution efficiency
- Auto-precision now works without a
- 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
Beta release for Tplyr with introduction of numerous new features:
- 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
- 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
Initial alpha release of Tplyr