High-Performance Open-Source Archive
rlang, checkmate,
stringr, readr, purrr,
tibble, dplyr, and tidyr
dependencies.by argument to describe_data() and
count_data() for grouping data by one or more
variables.remove_string,
detect_string, extract_string) to replace
stringr functionality.dots_to_names, group_names,
stack_rows) to replace dplyr,
tidyr, and purrr functionality.citation("tidystats") to see
how to cite tidystats.results argument in
add_stats() to listcount_data()’s
pct column to make it easier to present the numbers as
percentages (e.g., using scales::label_percent()). You can
still obtain percentages using the new pct argument.pct column from the output of
describe_data().anova() tests.add_stats():
args and class. The args argument
can be used to supply additional arguments to customize which statistics
are extracted from a particular analysis. For a list of supported
functions, see the “Details” section in the help document of
add_stats(). The class argument can be used to
explicitly indicate the class of the analysis you want to add. Sometimes
the output of a particular analysis returns an object with insufficient
information for tidystats to know how to extract the statistics. By
using the class argument, you can tell tidystats what kind
of object it is so that it can nevertheless extract the statistics. For
a list of supported classes, see the Details section of the help
document of add_stats().confint() function from the stats
package using the new class argument in `add_stats().alpha(),
corr.test(), mardia(), and ICC()
functions from the psych package.icc() function from the irr
package.cohen.d() function from the
effsize package.emmeans() function from the
emmeans package.test() function on the result of
contrast() on the result of emmeans() from the
emmeans package.lme(), nlme(),
gls(), and anova() from the nlme package.pct argument to count_data() to
return proportions instead of percentages.variable column in the output of
describe_data() to var.describe_data().variable column to var in
describe_data().describe_data() now (again) has support for multiple
variables. You can provide more than 1 column name (separated by commas)
to calculate descriptives for each variable.tidy_stats() on ungrouped count data produced
with count_data() is now properly tidied.describe_data() when the data
is grouped.anova().tidy_stats_to_data_frame() function, and a
description of the tidystats taxonomy.read_stats() now converts Inf character strings to
numeric.write_stats() now has a digits argument that determines
the number of decimals for saved numbers (default: 6).anova().count_data() again.describe_data() caused by the dplyr
1.0.0 update.tidy_stats_to_data_frame().add_stats() or the README.report() functions 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.add_stats() now has a type argument to
specify whether an analysis was a primary analysis, secondary analysis,
or exploratory analysis.add_stats() now has a preregistered
argument to specify whether an analysis was preregistered or not.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).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.identifier column from each list element
when using read_stats().tidy_stats.lm() and
tidy_stats.glm() to be consistent with the other
tidy_stats() functions.add_stats_to_model().read_stats() now removes empty columns from each list
element.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).class argument to add_stats() and
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 class
argument to specify the name of the function that produced the results,
so that we can then tidy it for you. Run ?add_stats to see
a list of supported classes and see the help document of
tidy_stats.confint() for an example.report_statistic(). Consequently, all report
functions have been updated to use this new generic function.glm().lmer() and lmerTest’s
lmer().alpha().ICC().confint() via the new
class argument in add_stats() and
add_stats_to_model().describe() to describe_data() so
that it no longer conflicts with psych’s
describe().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_descriptives() to
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.report() you can use M() and
SD() to quickly report the mean or standard deviation,
without having to specify that particular statistic. Less typing!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!describe_data()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.chisq.test() and
wilcox.test().identifier to
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”).report(). The function will
now throw an error when the identifier does not exist.report_p_value() to support multiple
p-values.htest objects were always 95% confidence intervals.aov() output.add_stats_to_model(). The method previously
required a term and did not automatically complete information (e.g.,
method information).
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