Create a Tidy Statistics Output File

Produce a data file containing the output of statistical models and assist with a workflow aimed at writing scientific papers using 'R Markdown'. Supported statistical functions include: t.test(), cor.test(), lm(), glm(), aov(), anova(), and several others. The package is based on tidy data principles and the 'tidyverse' (Wickham, 2017).


tidystats logo

tidystats

Authors: Willem Sleegers, Arnoud Plantinga
License: MIT

tidystats is a package to easily create a text file containing the output of statistical models. The goal of this package is to help researchers accompany their manuscript with an organized data file of statistical results in order to greatly improve the reliability of meta-research and to reduce statistical reporting errors.

To make this possible, tidystats relies on tidy data principles to combine the output of statistical analyses such as t-tests, correlations, ANOVAs, and regression.

Besides enabling you to create an organized data file of statistical results, the tidystats package also contains functions to help you report statistics in APA style. Results can be reported using R Markdown or using a new built-in Shiny app. Additionally, development has started on a Google Docs plugin that uses a tidystats data file to report statistics.

Please see below for instructions on how to install and use this package. Do note that the package is currently in development. This means the package may contain bugs and is subject to significant changes. If you find any bugs or if you have any feedback, please let me know by creating an issue here on Github (it's really easy to do!).

tidystats can be installed from CRAN and the latest version can be installed from Github using devtools.

library(devtools)
install_github("willemsleegers/tidystats")

Setup

Load the package and start by creating an empty list to store the results of statistical models in.

library(tidystats)
 
results <- list()

Usage

The main function is add_stats(). The function has 2 necessary arguments:

  • results: The list you want to add the statistical output to.
  • output: The output of a statistical test you want to add to the list (e.g., the output of t.test() or lm())

Optionally you can also add an identifier, type, whether the analysis was confirmatory or exploratory, and additional notes using the identifier, type, confirmatory, and notes arguments, respectively.

The identifier is used to identify the model (e.g., 'weight_height_correlation'). If you do not provide one, one is automatically created for you.

The type argument is used to indicate whether the statistical test is a hypothesis test, manipulation check, contrast analysis, or other kind of analysis such as descriptives. This can be used to distinguish the vital statistical tests from those less relevant.

The confirmatory argument is used to indicate whether the test was confirmatory or exploratory. It can also be ommitted.

The notes argument is used to add additional information which you may find fruitful. Some statistical tests have default notes output (e.g., t-tests), which will be overwritten when a notes argument is supplied to the add_stats() function.

Supported statistical functions

Package: stats

  • t.test()
  • cor.test()
  • lm()
  • glm()
  • aov()
  • chisq.test()
  • wilcox.test()
  • fisher.test()

Package: psych

  • alpha()
  • corr.test()
  • ICC()

Package: lme4 and lmerTest

  • lmer()

Example

In the following example we perform several statistical tests on a data set, add the output of these results to a list, and save the results to a file.

The data set is called cox and contains the data of a replication attempt of C.R. Cox, J. Arndt, T. Pyszczynski, J. Greenberg, A. Abdollahi, and S. Solomon (2008, JPSP, 94(4), Exp. 6) by Wissink et al. The replication study was part of the Reproducibility Project (see https://osf.io/ezcuj/). The data set is part of the tidystats package.

# Perform analyses
M1_condition <- t.test(call_parent ~ condition, data = cox, paired = TRUE)
M2_parent_siblings <- cor.test(cox$call_parent, cox$call_siblings, 
  alternative = "greater")
M3_condition_anxiety <- lm(call_parent ~ condition * anxiety , data = cox)
M4_condition_sex <- aov(call_parent ~ condition * sex, data = cox)
 
# Add results
results <- results %>%
  add_stats(M1_condition) %>%
  add_stats(M2_parent_siblings) %>%
  add_stats(M3_condition_anxiety) %>%
  add_stats(M4_condition_sex)

To write the results to a file, use write_stats() with the results list as the first argument.

write_stats(results, "data/results.csv")

To see how the data was actually tidied, you can open the .csv file or you can convert the tidystats results list to a table, as shown below.

library(dplyr)
library(knitr)
options(knitr.kable.NA = '-')
 
results %>%
  stats_list_to_df() %>%
  select(-notes) %>%
  kable()
identifier group term_nr term statistic value method
M1_condition - - - mean of the differences -2.7700000 Paired t-test
M1_condition - - - t -1.2614135 Paired t-test
M1_condition - - - df 99.0000000 Paired t-test
M1_condition - - - p 0.2101241 Paired t-test
M1_condition - - - 95% CI lower -7.1272396 Paired t-test
M1_condition - - - 95% CI upper 1.5872396 Paired t-test
M1_condition - - - null value 0.0000000 Paired t-test
M2_parent_siblings - - - r -0.0268794 Pearson's product-moment correlation
M2_parent_siblings - - - t -0.3783637 Pearson's product-moment correlation
M2_parent_siblings - - - df 198.0000000 Pearson's product-moment correlation
M2_parent_siblings - - - p 0.6472171 Pearson's product-moment correlation
M2_parent_siblings - - - 95% CI lower -0.1430882 Pearson's product-moment correlation
M2_parent_siblings - - - 95% CI upper 1.0000000 Pearson's product-moment correlation
M2_parent_siblings - - - null value 0.0000000 Pearson's product-moment correlation
M3_condition_anxiety coefficients 1 (Intercept) b 29.4466534 Linear model
M3_condition_anxiety coefficients 1 (Intercept) SE 9.9311192 Linear model
M3_condition_anxiety coefficients 1 (Intercept) t 2.9650891 Linear model
M3_condition_anxiety coefficients 1 (Intercept) p 0.0034017 Linear model
M3_condition_anxiety coefficients 1 (Intercept) df 196.0000000 Linear model
M3_condition_anxiety coefficients 2 conditionmortality salience b 20.2945974 Linear model
M3_condition_anxiety coefficients 2 conditionmortality salience SE 14.0193962 Linear model
M3_condition_anxiety coefficients 2 conditionmortality salience t 1.4476085 Linear model
M3_condition_anxiety coefficients 2 conditionmortality salience p 0.1493242 Linear model
M3_condition_anxiety coefficients 2 conditionmortality salience df 196.0000000 Linear model
M3_condition_anxiety coefficients 3 anxiety b -1.5511207 Linear model
M3_condition_anxiety coefficients 3 anxiety SE 3.0119376 Linear model
M3_condition_anxiety coefficients 3 anxiety t -0.5149910 Linear model
M3_condition_anxiety coefficients 3 anxiety p 0.6071396 Linear model
M3_condition_anxiety coefficients 3 anxiety df 196.0000000 Linear model
M3_condition_anxiety coefficients 4 conditionmortality salience:anxiety b -5.5666889 Linear model
M3_condition_anxiety coefficients 4 conditionmortality salience:anxiety SE 4.3104789 Linear model
M3_condition_anxiety coefficients 4 conditionmortality salience:anxiety t -1.2914316 Linear model
M3_condition_anxiety coefficients 4 conditionmortality salience:anxiety p 0.1980750 Linear model
M3_condition_anxiety coefficients 4 conditionmortality salience:anxiety df 196.0000000 Linear model
M3_condition_anxiety model - - R squared 0.0360246 Linear model
M3_condition_anxiety model - - adjusted R squared 0.0212698 Linear model
M3_condition_anxiety model - - F 2.4415618 Linear model
M3_condition_anxiety model - - numerator df 3.0000000 Linear model
M3_condition_anxiety model - - denominator df 196.0000000 Linear model
M3_condition_anxiety model - - p 0.0655150 Linear model
M4_condition_sex - 1 condition df 1.0000000 Factorial ANOVA
M4_condition_sex - 1 condition SS 383.6450000 Factorial ANOVA
M4_condition_sex - 1 condition MS 383.6450000 Factorial ANOVA
M4_condition_sex - 1 condition F 1.7299360 Factorial ANOVA
M4_condition_sex - 1 condition p 0.1899557 Factorial ANOVA
M4_condition_sex - 2 sex df 1.0000000 Factorial ANOVA
M4_condition_sex - 2 sex SS 1140.4861329 Factorial ANOVA
M4_condition_sex - 2 sex MS 1140.4861329 Factorial ANOVA
M4_condition_sex - 2 sex F 5.1426918 Factorial ANOVA
M4_condition_sex - 2 sex p 0.0244352 Factorial ANOVA
M4_condition_sex - 3 condition:sex df 1.0000000 Factorial ANOVA
M4_condition_sex - 3 condition:sex SS 66.1529617 Factorial ANOVA
M4_condition_sex - 3 condition:sex MS 66.1529617 Factorial ANOVA
M4_condition_sex - 3 condition:sex F 0.2982976 Factorial ANOVA
M4_condition_sex - 3 condition:sex p 0.5855728 Factorial ANOVA
M4_condition_sex - 4 Residuals df 196.0000000 Factorial ANOVA
M4_condition_sex - 4 Residuals SS 43466.5909054 Factorial ANOVA
M4_condition_sex - 4 Residuals MS 221.7683209 Factorial ANOVA

Report functions

There are two ways to report your results using tidystats: Using R Markdown or using a built-in Shiny app. In both cases, you need the tidystats list that contains the tidied output of your statistical tests.

If you have previously created a tidystats file, you can read in this file to re-create the tidystats list, using the read_stats() function.

results <- read_stats("data/results.csv")

Shiny app

If you do not want to use R Markdown, you can use the built-in Shiny app to interactively produce APA-output and copy it to your manuscript. To start the app, run the inspect() function.

The inspect() function takes the tidystats list as its first argument, optionally followed by one or more identifiers. If no identifiers are provided, all models will be displayed. The results of each model will be displayed in a table and you can click on a row to produce APA output. This APA output will appear in a textbox at the bottom, next to a copy button that can be pressed to copy the results into your clipboard. See below for an example.

inspect

R Markdown

You can use the report() function to report your results via R Markdown. This function requires at minimum the tidystats list and an identifier identifying the exact test you want to report. It may also be necessary to provide additional information, such as a term in a regression, for the report() function to figure out what you want to report.

To reduce repetition, you can use options() to set the default tidystats list to use. This way the report() function requires one fewer argument. You set the default tidystats list by running the following code:

options(tidystats_list = results)

To figure out how to report the output in APA style, the report() function uses the method information stored in the tidied model. For example, the model with identifier 'M1' is a paired t-test. report() will parse this, see that it is part of the t-test family, and produce results accordingly.

Below is a list of common report examples:

code output
report("M1_condition") t(99) = -1.26, p = .21, 95% CI [-7.13, 1.59]
report("M1_condition", statistic = "t") -1.26
report("M2_parent_siblings") r(198) = -.027, p = .65
report("M3_condition_anxiety", term = "conditionmortality salience") b = 20.29, SE = 14.02, t(196) = 1.45, p = .15
report("M3_condition_anxiety", term_nr = 2) b = 20.29, SE = 14.02, t(196) = 1.45, p = .15
report("M3_condition_anxiety", term = "(Model)") adjusted R2 = .0035, F(1, 198) = 1.70, p = .19
report("M4_condition_sex", term = "condition:sex") F(1, 196) = 0.30, p = .59

As you can see in the examples above, you can use report() to produce a full line of output. You can also retrieve a single statistic by using the statistic argument. Additionally, you can refer to terms using either the term label or the term number (and in some cases, using a group). Although it may be less descriptive to use a term number, it reduces the amount of code clutter in your Markdown document. Our philosophy is, in line with Markdown's general writing philosophy, that the code should not distract from writing. To illustrate, writing part of a results section with tidystats will look like this: and the dental pain condition on the number of minutes allocated to calling one's parents, r report("M1_condition").

To execute the code, the code segment should be surrounded by backward ticks (see http://rmarkdown.rstudio.com/lesson-4.html), which results in:

We found no significant difference between the mortality salience condition and the dental pain condition on the number of minutes allocated to calling one's parents, t(99) = -1.26, p = .21, 95% CI [-7.13, 1.59].

Helper functions

Descriptives

Since it's common to also report descriptives in addition to the statistical results, we have added a hopefully useful describe_data() and count_data() function to calculate common descriptive statistics that can be tidied and added to a results data frame. Several examples follow using the cox data.

# Descriptives of the 'anxiety' variable
describe_data(cox, anxiety)
## # A tibble: 1 x 13
##   var   missing     n     M    SD     SE   min   max range median  mode
##   <chr>   <int> <int> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl>
## 1 anxi…       0   200  3.22 0.492 0.0348  1.38  4.38     3   3.25   3.5
## # ... with 2 more variables: skew <dbl>, kurtosis <dbl>
# By condition
cox %>%
  group_by(condition) %>%
  describe_data(anxiety)
## # A tibble: 2 x 14
## # Groups:   condition [2]
##   var   condition missing     n     M    SD     SE   min   max range median
##   <chr> <chr>       <int> <int> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl>
## 1 anxi… dental p…       0   100  3.26 0.497 0.0497  1.62  4.38  2.75   3.38
## 2 anxi… mortalit…       0   100  3.17 0.485 0.0485  1.38  4.38  3      3.25
## # ... with 3 more variables: mode <dbl>, skew <dbl>, kurtosis <dbl>
# Descriptives of a non-numeric variable
count_data(cox, condition)
## # A tibble: 2 x 4
##   var       group                  n   pct
##   <chr>     <chr>              <int> <dbl>
## 1 condition dental pain          100    50
## 2 condition mortality salience   100    50

If you use the describe_data() and count_data() function from the tidystats package to get the descriptives, you can use the tidy_describe_data() and tidy_count_data() function to tidy the output, and consequently add it to a results list.

(Note: This will soon be improved)

anxiety_tidy <- cox %>%
  describe_data(anxiety) %>%
  tidy_describe_data()
 
results <- results %>%
  add_stats(anxiety_tidy, type = "d", notes = "Anxious attachment style")
## Warning in add_stats.data.frame(., anxiety_tidy, type = "d", notes =
## "Anxious attachment style"): You added a data.frame to your results list.
## Please make sure it is properly tidied.

News

tidystats 0.3

Changes

  • 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() and 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_stats to 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 called report_statistic(). Consequently, all report functions have been updated to use this new generic function.
  • Removed the identifier column from each list element when using read_stats().
  • Reordered the columns of tidy_stats.lm() and tidy_stats.glm() to be consistent with the other tidy_stats() functions.

Features

  • 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 glm().
  • Added support for lme4's lmer() and lmerTest's lmer().
  • Added support for psych's alpha().
  • Added support for psych's ICC().
  • Added support for stats' confint() via the new class argument in add_stats() and add_stats_to_model().

Improvements

  • Added check for an existing identifier in add_stats_to_model().
  • Added a 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.
  • 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.

Bugfixes

  • Fixed a bug that would incorrectly classify ANOVAs as One-way ANOVAs when character variables were used rather than factors.
  • Prepared for dplyr 0.8

Misc

  • Added tests to the R package to minimize bugs.
  • Made the code and documentation more consistent
  • Added an under-the-hood helper function to rename statistics columns

tidystats 0.2

Changes

  • Renamed describe() to describe_data() so that it no longer conflicts with psych's describe().
  • Changed 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.
  • Renamed 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.

New features

  • Improved 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 M() and 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 describe_data()
  • 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 chisq.test and wilcox.test.
  • Added a better default 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").

Improvements

  • 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.
  • Improved 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.

Bugfixes

  • Fixed bug that it was assumed that confidence intervals in htests were always 95% confidence intervals.
  • Fixed bug in report functions that would occur when no statistic argument was provided.
  • Removed spaces from terms in aov() output.
  • Removed a leading space from the method information of a Two Sample t-test.
  • Improved add_stats_to_model(). The method previously required a term and did not automatically complete information (e.g., method information).

tidystats 0.1

  • Initial release

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("tidystats")

0.3 by Willem Sleegers, 6 months ago


Browse source code at https://github.com/cran/tidystats


Authors: Willem Sleegers [aut, cre]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports tibble, readr, tidyr, dplyr, magrittr, purrr, stringr, knitr, kableExtra, miniUI, shiny, rlang

Suggests testthat, lme4, lmerTest, psych


See at CRAN