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tidystats

Author: Willem Sleegers License: MIT

tidystats is an R package aimed at sharing the output of statistical models. To achieve this, tidystats combines the output of multiple statistical models and saves these in a file. This file can then be shared with others or used to report the statistics in a manuscript.

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 in. 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:

Optionally you can also specify an identifier and add 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

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)

# Add the analyses to an 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 in 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 soon do so with a Word add-in that is currently in development.

Reading in a tidystats file

An additional usage of the tidystats-produced file is that it can be read back into R and converted into a data frame. This enables researchers to then extract specific statistics to perform additional analyses with (e.g., meta-analyses). Below is an example.

# Read in a tidystats-produced .json file
results <- read_stats("results.json")

# Convert the list to a data frame
results_df <- tidy_stats_to_data_frame(results)

# Select the p-values
p_values <- filter(results_df, statistic == "p")

With the current example, this results in the following data frame:

identifier method group term statistic value type preregistered
sleep_test Paired t-test p 0.0028 primary
lm_D9 Linear regression coefficients (Intercept) p 0.0000 no
lm_D9 Linear regression coefficients groupTrt p 0.2490 no
lm_D9 Linear regression model p 0.2490 no
npk_aov ANOVA block p 0.0159
npk_aov ANOVA N p 0.0044
npk_aov ANOVA P p 0.4749
npk_aov ANOVA K p 0.0288
npk_aov ANOVA N:P p 0.2632
npk_aov ANOVA N:K p 0.1686
npk_aov ANOVA P:K p 0.8628

More resources

For more information on this package, see the tidystats project page on my website.