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

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 analyses.

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 using R Markdown. Additionally, development has started on a Shiny app and 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 and may contain bugs. If you find any, please let me know by creating an issue here on Github.

tidystats can be installed from CRAN, but 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:

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

Optionally you can also add an identifier, type, a subset of the statistics, whether the analysis was confirmatory or exploratory, and additional notes using the identifier, type, statistics, 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 statistics argument is used to select a subset of statistics that you want to add to the results list, in case this is desired.

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()
  • aov()
  • chisq.test()
  • wilcox.test()

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.

# Paired t-test
M1_condition <- t.test(call_parent ~ condition, data = cox, paired = TRUE)
results <- add_stats(M1_condition, results)
 
# Correlation
M2_parent_siblings <- cor.test(cox$call_parent, cox$call_siblings, alternative = "greater")
results <- add_stats(M2_parent_siblings, results)
 
# Regression
M3_condition_anxiety <- lm(call_parent ~ condition * anxiety , data = cox)
results <- add_stats(M3_condition_anxiety, results)
 
# ANOVA
M4_condition_sex <- aov(call_parent ~ condition * sex, data = cox)
results <- add_stats(M4_condition_sex, results)

Having added the statistical results to the list, you can convert the list to a table or to a data file, ready for sharing. The example below shows how to produce a table containing all of the statistical results.

library(dplyr)
library(knitr)
options(knitr.kable.NA = '-')
 
results %>%
  stats_list_to_df() %>%
  select(-notes) %>%
  kable()
identifier 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 - - cor -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 1 (Intercept) b 29.4466534 Linear regression
M3_condition_anxiety 1 (Intercept) SE 9.9311192 Linear regression
M3_condition_anxiety 1 (Intercept) t 2.9650891 Linear regression
M3_condition_anxiety 1 (Intercept) p 0.0034017 Linear regression
M3_condition_anxiety 1 (Intercept) df 196.0000000 Linear regression
M3_condition_anxiety 2 conditionmortality salience b 20.2945974 Linear regression
M3_condition_anxiety 2 conditionmortality salience SE 14.0193962 Linear regression
M3_condition_anxiety 2 conditionmortality salience t 1.4476085 Linear regression
M3_condition_anxiety 2 conditionmortality salience p 0.1493242 Linear regression
M3_condition_anxiety 2 conditionmortality salience df 196.0000000 Linear regression
M3_condition_anxiety 3 anxiety b -1.5511207 Linear regression
M3_condition_anxiety 3 anxiety SE 3.0119376 Linear regression
M3_condition_anxiety 3 anxiety t -0.5149910 Linear regression
M3_condition_anxiety 3 anxiety p 0.6071396 Linear regression
M3_condition_anxiety 3 anxiety df 196.0000000 Linear regression
M3_condition_anxiety 4 conditionmortality salience:anxiety b -5.5666889 Linear regression
M3_condition_anxiety 4 conditionmortality salience:anxiety SE 4.3104789 Linear regression
M3_condition_anxiety 4 conditionmortality salience:anxiety t -1.2914316 Linear regression
M3_condition_anxiety 4 conditionmortality salience:anxiety p 0.1980750 Linear regression
M3_condition_anxiety 4 conditionmortality salience:anxiety df 196.0000000 Linear regression
M3_condition_anxiety 5 (Model) R squared 0.0360246 Linear regression
M3_condition_anxiety 5 (Model) adjusted R squared 0.0212698 Linear regression
M3_condition_anxiety 5 (Model) F 2.4415618 Linear regression
M3_condition_anxiety 5 (Model) numerator df 3.0000000 Linear regression
M3_condition_anxiety 5 (Model) denominator df 196.0000000 Linear regression
M3_condition_anxiety 5 (Model) p 0.0655150 Linear regression
M4_condition_sex 1 condition df 1.0000000 ANOVA
M4_condition_sex 1 condition SS 383.6450000 ANOVA
M4_condition_sex 1 condition MS 383.6450000 ANOVA
M4_condition_sex 1 condition F 1.7299360 ANOVA
M4_condition_sex 1 condition p 0.1899557 ANOVA
M4_condition_sex 2 sex df 1.0000000 ANOVA
M4_condition_sex 2 sex SS 1140.4861329 ANOVA
M4_condition_sex 2 sex MS 1140.4861329 ANOVA
M4_condition_sex 2 sex F 5.1426918 ANOVA
M4_condition_sex 2 sex p 0.0244352 ANOVA
M4_condition_sex 3 condition:sex df 1.0000000 ANOVA
M4_condition_sex 3 condition:sex SS 66.1529617 ANOVA
M4_condition_sex 3 condition:sex MS 66.1529617 ANOVA
M4_condition_sex 3 condition:sex F 0.2982976 ANOVA
M4_condition_sex 3 condition:sex p 0.5855728 ANOVA
M4_condition_sex 4 Residuals df 196.0000000 ANOVA
M4_condition_sex 4 Residuals SS 43466.5909054 ANOVA
M4_condition_sex 4 Residuals MS 221.7683209 ANOVA

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

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

This produces a .csv file that can be shared and that can also be used to write your Results section. The report functions will be demonstrated below.

Report functions

To start reporting your results, first load in the previously saved data file containing the results. This will create a list, just like it was when it was originally saved.

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

Additionally, you can use options() to set the default tidystats list to use. This way the report() functions below require one fewer argument. You set the default tidystats list by running the following code.

options(tidystats_list = results)

The main function for reporting is report(). To figure out how to report the output in APA style, tidystats uses the method information stored in the results list. For example, the model with identifier 'M1' is a paired t-test. tidystats will parse this, see that it is part of the t-test family, and produce results accordingly. tidystats() also has test-specific reporting functions, such as report_t_test() that are used under the hood, but they are also available for you to use.

Below we show a list of common report examples:

code output
report("M1_condition") t(99) = -1.26, p = .21
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 when a model identifier is provided (and a term when the model consists of multiple terms). You can also only retrieve a single statistic by using the statistic argument. Additionally, you can refer to terms using either the term label or the term number. Although this latter method might be less descriptive, 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 will now, using tidystats look like this:

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.

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 anxiety       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
##   <chr>   <chr>           <int> <int> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
## 1 anxiety dental pain         0   100  3.26 0.497 0.0497  1.62  4.38  2.75
## 2 anxiety mortality sa…       0   100  3.17 0.485 0.0485  1.38  4.38  3   
## # ... with 4 more variables: median <dbl>, 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.

results <- cox %>%
  describe_data(anxiety) %>%
  tidy_describe_data() %>%
  add_stats(results, identifier = "anxiety", type = "d", notes = "Anxious attachment style")

In the add_stats() function you can also specify which of the statistics you would like to store in the results list, using the statistics argument. Of course, the results can also be tidied when the data is grouped.

News

tidystats 0.2

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.

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.

Bugfixes

  • Fixed bug that it was always 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, 2 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