tidystats

Author: Willem Sleegers License: MIT

tidystats is an R package aimed at sharing the output of statistical models. tidystats extracts the output of statistical models (e.g., t-tests, regression models) and combines them into a structured file. This file can then be used to report the statistics in a manuscript or shared with others so that they can extract the statistics (e.g., for meta-analyses).

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!).

Installation

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. You can name the list whatever you want (in the example below I create an empty list called results).

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 specify an identifier, the type of analysis, whether the analysis was preregistered, and/or additional notes.

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 specifies the type of analysis as primary, secondary, or exploratory.

The preregistered argument is used to indicate whether the analysis was preregistered or not.

Finally the notes argument is used to add additional information which you may find fruitful.

Supported statistical functions

Package: stats

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

Package: lme4

• lmer()

Package: lmerTest

• lmer()

Package: BayesFactor

• generalTestBF()
• lmBF()
• regressionBF()
• ttestBF()
• anovaBF()
• correlationBF()
• contingencyTableBF()
• proportionBF()
• meta.ttestBF()

Package: tidystats

• describe_data()
• count_data()

Example

In the following example we perform several tests, add them to a list, and save the list to a file.

# Conduct three different analyses
# t-test:
sleep_test <- t.test(extra ~ group, data = sleep, paired = TRUE)

# lm:
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2, 10, 20, labels = c("Ctl","Trt"))
weight <- c(ctl, trt)
lm_D9 <- lm(weight ~ group)

# ANOVA:
npk_aov <- aov(yield ~ block + N*P*K, npk)

# Create an empty list
results <- list()

# Add the analyses to the empty list
results <- results %>%
  add_stats(sleep_test, type = "primary") %>%
  add_stats(lm_D9, preregistered = FALSE) %>%
  add_stats(npk_aov, notes = "An ANOVA example")

# Save the results to a file
write_stats(results, "results.json")

This results is a .json file that contains all the statistics from the three models. If you want to see what this file looks like, you can inspect it here.

Reporting statistics

If you want to report the statistics in a manuscript, you can do so with a Word add-in that is currently in development here.

More resources

Do you have questions or comments about tidystats? Create an issue here on Github or contact me via Twitter.