CRAN release: 2022-01-04
CRAN release: 2020-09-21
- Changed the way certain model results are parsed. The estimate is now parsed as a list containing the name of the estimate and the value of the estimate. Models are now parsed to extract the following types of lists: statistics, terms, pairs, groups, and effects. This new parsing unites t-tests, ANOVA, and regression, including multilevel regression.
- Added support for generic tests. If
tidystatsdoes not support a particular analysis, you can create your own generic test by providing a list of statistics.
- Improved support for
- Added support for more
- Added a
pkgdownwebsite for the package.
- Added several vignettes, including an introduction to tidystats, how
to use the
tidy_stats_to_data_framefunction, and a description of the
CRAN release: 2020-06-15
- Fixed a bug in
describe_data()caused by the
CRAN release: 2019-09-12
tidystatshas been completely redesigned in terms of how statistics are combined together. While previously the output of statistical models was converted to a tidy data frame, the output is now converted to a list, with an entirely different structure. The reason for this change is that lists are more machine-readable, enabling more interesting features down the line. It is still possible to convert the list of statistics to a single data frame with a new function called
- The significant changes made to
tidystatshas resulted in the loss of some previously supported statistical functions. For a list of currently supported statistical functions, see the help document of
add_stats()or the README.
reportfunctions have been removed for now. These may return (if I get the impression these are liked) but for now I am focusing my development time on creating a Word add-in that will enable researchers to use a
tidystats-produced file for reporting statistics in Microsoft Word.
describe_data()no longer accepts multiple column names. The goal of the function is now to calculate the descriptives of a single column (which can still be grouped to calculate the descriptives for each group level).
count_data()has been removed.
CRAN release: 2019-01-03
- Changed the argument order in the family of
add_stats()functions. Previously, the model output or tidy data frame was the first argument. This allowed you to directly pipe the model output into
add_stats()(using magrittr’s %>%). However, an alternative approach is to have the tidystats list to be the first argument. This allows you create a long sequence of pipes. You start with the results list, add a model via
add_stats(), pipe the result into the next
add_stats(), and so on. Since you often store your model output in variable names anyway, this is probably more convenient. Additionally, this probably also keeps your script more tidy (you can do this at the end of your data analysis script).
- Certain statistical models are now tidied differently due to the
addition of a ‘group’ column. Several models like multilevel models,
meta-analytic models, and arguably also regression models have more than
just terms (e.g., model fit), so to distinguish between coefficients and
other parts of the output, a ‘group’ column has been added. This also
means usage of the
report()is affected, as now the group should be specified when necessary. Affected models are regression, within-subjects ANOVA, multilevel models, and meta-analysis models.
- Added the class argument to
add_stats_to_model(). Rather than having to manually tidy the data first, you can make use of some custom
tidy_stats()functions by specifying the class argument. Run
?add_statsto see a list of supported classes and see the help document of
tidy_stats.confint()for an example.
- Under the hood: Added a generic report function for single values
report_statistic(). Consequently, all report functions have been updated to use this new generic function.
- Removed the
identifiercolumn from each list element when using
- Reordered the columns of
tidy_stats.glm()to be consistent with the other
- Added a new function called
inspect(). This function accepts a tidystats results list or the output of a statistical model and will display all results in RStudio’s Viewer pane. This allows the user to visually inspect the results and, importantly, copy results in APA style to their clipboard. This function is aimed at users who prefer not to use R Markdown or when you want to quickly run a model and get the results in APA-style. This new function works well with Microsoft Word, but does not work with Apple Pages (some of the styling is lost when copying the results).
- Added support for
- Added support for lme4’s
- Added support for psych’s
- Added support for psych’s
- Added support for stats’
confint()via the new
- Added check for an existing identifier in
- Added a
add_stats_to_model(). Some statistical tests return a normal data.frame or matrix, which does not specify which test produced the results. This makes it difficult for tidystats to figure out how to tidy the result. Previously, we solved this by
add_stats()accepting pre-tidied data frames. Now we added a the
classargument to specify the name of the function that produced the results, so that we can then tidy it for you.
- Added warnings in case unsupported output is added (e.g., a pre-tided data frame).
read_stats()now removes empty columns from each list element.
- Improved documentation.
- Fixed a bug that would incorrectly classify ANOVAs as One-way ANOVAs when character variables were used rather than factors.
- Prepared for
CRAN release: 2018-05-06
describe_data()so that it no longer conflicts with psych’s
describe_data()to no longer accept non-numeric variables, but added the feature that descriptives can be calculated for more than 1 variable at a time. It is recommended to use the
count_data()function for non-numeric variables.
tidy_describe_data()and improved the function. A notable change is that var information is now returned to identify which descriptives belong to which variable. Also changed the group delimiter to ’ - ’.
write_stats()now prettifies the numbers using
prettyNum()when saving them to disk.
report()function. The method now supports the option to retrieve a single statistic from any tidy stats data frame. This will allow you to report all statistics, even when reporting functions for a specific method are not yet supported.
- Added quick report functions for means and standard deviations.
Instead of using
report()you can use
SD()to quickly report the mean or standard deviation, without having to specify that particular statistic. Less typing!
- Added an option called ‘tidystats_list’ in
options()to set a default list. By setting the tidystats list in
options(), you do not need to specify the list in the results argument of
report(). Less typing!
- Reporting regression results will now include a check for whether confidence intervals are included, and report them.
- Added skewness and kurtosis to
- Added new
count_data()function to calculate count descriptives of categorical data. Also added a
tidy_count_data()function to tidy the output of this new function.
- Added support for
- Added a better default
add_stats(). If you supply a variable to be added to the tidystats list, and no identifier is provided, it will take the variable name as the identifier. If you pipe the results into
add_stats()then the old default identifier will be used (e.g., “M1”).
- Added identifier check to
report(). The function will now throw an error when the identifier does not exist.
- Added statistic check to all report functions. The function will now throw an error when the statistic does not exist.
report_p_value()to support multiple p-values.
- Updated documentation to be more consistent and to take into account the changes made in the current update.
- Fixed bug that it was assumed that confidence intervals in
htestswere always 95% confidence intervals.
- Fixed bug in report functions that would occur when no statistic argument was provided.
- Removed spaces from terms in
- Removed a leading space from the method information of a Two Sample t-test.
add_stats_to_model(). The method previously required a term and did not automatically complete information (e.g., method information).