statsExpressions: R Package for Tidy Dataframes and Expressions with Statistical Details

Indrajeet Patil

2022-05-20

This vignette can be cited as:


To cite package 'statsExpressions' in publications use:

  Patil, I., (2021). statsExpressions: R Package for Tidy Dataframes
  and Expressions with Statistical Details. Journal of Open Source
  Software, 6(61), 3236, https://doi.org/10.21105/joss.03236

A BibTeX entry for LaTeX users is

  @Article{,
    doi = {10.21105/joss.03236},
    url = {https://doi.org/10.21105/joss.03236},
    year = {2021},
    publisher = {{The Open Journal}},
    volume = {6},
    number = {61},
    pages = {3236},
    author = {Indrajeet Patil},
    title = {{statsExpressions: {R} Package for Tidy Dataframes and Expressions with Statistical Details}},
    journal = {{Journal of Open Source Software}},
  }

Summary

The {statsExpressions} package has two key aims: to provide a consistent syntax to do statistical analysis with tidy data, and to provide statistical expressions (i.e., pre-formatted in-text statistical results) for plotting functions. Currently, it supports common types of statistical approaches and tests: parametric, nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation analyses, contingency table analyses, and meta-analyses. The functions are pipe-friendly and compatible with tidy data.

Statement of Need

Statistical packages exhibit substantial diversity in terms of their syntax and expected input and output data type. For example, some functions expect vectors as inputs, while others expect dataframes. Depending on whether it is a repeated measures design or not, functions from the same package might expect data to be in wide or tidy format. Some functions can internally omit missing values, while others do not. Furthermore, the statistical test objects returned by the test functions might not have all required information (e.g., degrees of freedom, significance, Bayes factor, etc.) accessible in a consistent data type. Depending on the specific test object and statistic in question, details may be returned as a list, a matrix, an array, or a dataframe. This diversity can make it difficult to easily access all needed information for hypothesis testing and estimation, and to switch from one statistical approach to another.

This is where {statsExpressions} comes in: It can be thought of as a unified portal through which most of the functionality in these underlying packages can be accessed, with a simpler interface and with tidy data format.

Comparison to Other Packages

Unlike {broom} (Robinson, Hayes, & Couch, 2021) or {parameters} (Lüdecke, Ben-Shachar, Patil, & Makowski, 2020), the goal of {statsExpressions} is not to convert model objects into tidy dataframes, but to provide a consistent and easy syntax to carry out statistical tests. Additionally, none of these packages return statistical expressions.

Consistent Syntax for Statistical Analysis

The package offers functions that allow users choose a statistical approach without changing the syntax (i.e., by only specifying a single argument). The functions always require a dataframe in tidy format (Wickham et al., 2019), and work with missing data. Moreover, they always return a dataframe that can be further utilized downstream in the workflow (such as visualization).

A summary table listing the primary functions in the package and the statistical approaches they support. More detailed description of the tests and outputs from these functions can be found on the package website: https://indrajeetpatil.github.io/statsExpressions/articles/.
Function Parametric Non-parametric Robust Bayesian
one_sample_test
two_sample_test
oneway_anova
corr_test
contingency_table -
meta_analysis -

{statsExpressions} internally relies on stats package for parametric and non-parametric (R Core Team, 2021), WRS2 package for robust (Mair & Wilcox, 2020), and BayesFactor package for Bayesian statistics (Morey & Rouder, 2020). The random-effects meta-analysis is carried out using metafor (parametric) (Viechtbauer, 2010), metaplus (robust) (Beath, 2016), and metaBMA (Bayesian) (Heck et al., 2019) packages. Additionally, it relies on easystats packages (Ben-Shachar, Lüdecke, & Makowski, 2020; Lüdecke et al., 2020; Lüdecke, Ben-Shachar, Patil, Waggoner, & Makowski, 2021; Lüdecke, Waggoner, & Makowski, 2019; Makowski, Ben-Shachar, & Lüdecke, 2019; Makowski, Ben-Shachar, Patil, & Lüdecke, 2020) to compute appropriate effect size/posterior estimates and their confidence/credible intervals.

Tidy Dataframes from Statistical Analysis

To illustrate the simplicity of this syntax, let’s say we want to run a one-way ANOVA. If we first run a non-parametric ANOVA and then decide to run a robust ANOVA instead, the syntax remains the same and the statistical approach can be modified by changing a single argument:

mtcars %>% oneway_anova(cyl, wt, type = "nonparametric")
#> # A tibble: 1 × 15
#>   parameter1 parameter2 statistic df.error   p.value
#>   <chr>      <chr>          <dbl>    <int>     <dbl>
#> 1 wt         cyl             22.8        2 0.0000112
#>   method                       effectsize      estimate conf.level conf.low
#>   <chr>                        <chr>              <dbl>      <dbl>    <dbl>
#> 1 Kruskal-Wallis rank sum test Epsilon2 (rank)    0.736       0.95    0.624
#>   conf.high conf.method          conf.iterations n.obs expression
#>       <dbl> <chr>                          <int> <int> <list>    
#> 1         1 percentile bootstrap             100    32 <language>

mtcars %>% oneway_anova(cyl, wt, type = "robust")
#> # A tibble: 1 × 12
#>   statistic    df df.error p.value
#>       <dbl> <dbl>    <dbl>   <dbl>
#> 1      12.7     2     12.2 0.00102
#>   method                                           
#>   <chr>                                            
#> 1 A heteroscedastic one-way ANOVA for trimmed means
#>   effectsize                         estimate conf.level conf.low conf.high
#>   <chr>                                 <dbl>      <dbl>    <dbl>     <dbl>
#> 1 Explanatory measure of effect size     1.05       0.95    0.843      1.50
#>   n.obs expression
#>   <int> <list>    
#> 1    32 <language>

These functions are also compatible with other popular data manipulation packages. For example, we can use combination of dplyr and {statsExpressions} to repeat the same statistical analysis across grouping variables.

# running one-sample proportion test for all levels of `cyl`
mtcars %>%
  group_by(cyl) %>%
  group_modify(~ contingency_table(.x, am), .keep = TRUE) %>%
  ungroup()
#> # A tibble: 3 × 14
#>     cyl statistic    df p.value method                                  
#>   <dbl>     <dbl> <dbl>   <dbl> <chr>                                   
#> 1     4     2.27      1 0.132   Chi-squared test for given probabilities
#> 2     6     0.143     1 0.705   Chi-squared test for given probabilities
#> 3     8     7.14      1 0.00753 Chi-squared test for given probabilities
#>   effectsize  estimate conf.level conf.low conf.high conf.method
#>   <chr>          <dbl>      <dbl>    <dbl>     <dbl> <chr>      
#> 1 Pearson's C    0.414       0.95    0             1 ncp        
#> 2 Pearson's C    0.141       0.95    0             1 ncp        
#> 3 Pearson's C    0.581       0.95    0.265         1 ncp        
#>   conf.distribution n.obs expression
#>   <chr>             <int> <list>    
#> 1 chisq                11 <language>
#> 2 chisq                 7 <language>
#> 3 chisq                14 <language>

Expressions for Plots

In addition to other details contained in the dataframe, there is also a column titled expression, which contains a pre-formatted text with statistical details. These expressions (Figure 1) attempt to follow the gold standard in statistical reporting for both Bayesian (Doorn et al., 2020) and Frequentist (American Psychological Association and others, 2019) frameworks.

The templates used in `{statsExpressions}` to display statistical details in a plot.

The templates used in {statsExpressions} to display statistical details in a plot.

This expression be easily displayed in a plot (Figure 2). Displaying statistical results in the context of a visualization is indeed a philosophy adopted by the {ggstatsplot} package (Patil, 2021), and {statsExpressions} functions as its statistical processing backend.

# needed libraries
library(statsExpressions)
library(ggplot2)

# creating a dataframe
res <- oneway_anova(iris, Species, Sepal.Length, type = "nonparametric")

# create a ridgeplot using `ggridges` package
ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_boxplot() + # use 'expression' column to display results in the subtitle
  labs(
    x = "Penguin Species",
    y = "Body mass (in grams)",
    title = "Kruskal-Wallis Rank Sum Test",
    subtitle = res$expression[[1]]
  )
Example illustrating how `{statsExpressions}` functions can be used to display results from a statistical test in a plot.

Example illustrating how {statsExpressions} functions can be used to display results from a statistical test in a plot.

Licensing and Availability

{statsExpressions} is licensed under the GNU General Public License (v3.0), with all source code stored at GitHub. In the spirit of honest and open science, requests and suggestions for fixes, feature updates, as well as general questions and concerns are encouraged via direct interaction with contributors and developers by filing an issue while respecting Contribution Guidelines.

Acknowledgements

I would like to acknowledge the support of Mina Cikara, Fiery Cushman, and Iyad Rahwan during the development of this project. {statsExpressions} relies heavily on the easystats ecosystem, a collaborative project created to facilitate the usage of R for statistical analyses. Thus, I would like to thank the members of easystats as well as the users.

References

American Psychological Association and others. (2019). Publication Manual of the American Psychological Association (7th Edition.). American Psychological Association.
Beath, K. J. (2016). metaplus: An R package for the analysis of robust meta-analysis and meta-regression. R Journal, 8(1), 5–16. doi:10.32614/RJ-2016-001
Ben-Shachar, M. S., Lüdecke, D., & Makowski, D. (2020). effectsize: Estimation of effect size indices and standardized parameters. Journal of Open Source Software, 5(56), 2815. doi:10.21105/joss.02815
Doorn, J. van, Bergh, D. van den, Böhm, U., Dablander, F., Derks, K., Draws, T., Etz, A., et al. (2020). The JASP guidelines for conducting and reporting a bayesian analysis. Psychonomic Bulletin & Review, 1–14. doi:10.3758/s13423-020-01798-5
Heck, W., D., Gronau, F., Q., Wagenmakers, &, & E.-J. (2019). metaBMA: Bayesian model averaging for random and fixed effects meta-analysis. Retrieved from https://CRAN.R-project.org/package=metaBMA
Lüdecke, D., Ben-Shachar, M. S., Patil, I., & Makowski, D. (2020). parameters: Extracting, computing and exploring the parameters of statistical models using R. Journal of Open Source Software, 5(53), 2445. doi:10.21105/joss.02445
Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P., & Makowski, D. (2021). performance: An R package for assessment, comparison and testing of statistical models. Journal of Open Source Software, 6(60), 3139. doi:10.21105/joss.03139
Lüdecke, D., Waggoner, P., & Makowski, D. (2019). insight: A unified interface to access information from model objects in R. Journal of Open Source Software, 4(38), 1412. doi:10.21105/joss.01412
Mair, P., & Wilcox, R. (2020). Robust Statistical Methods in R Using the WRS2 Package. Behavior Research Methods, 52, 464–488. doi:10.3758/s13428-019-01246-w
Makowski, D., Ben-Shachar, M. S., & Lüdecke, D. (2019). bayestestR: Describing effects and their uncertainty, existence and significance within the bayesian framework. Journal of Open Source Software, 4(40), 1541. doi:10.21105/joss.01541
Makowski, D., Ben-Shachar, M. S., Patil, I., & Lüdecke, D. (2020). Methods and algorithms for correlation analysis in r. Journal of Open Source Software, 5(51), 2306. doi:10.21105/joss.02306
Morey, R. D., & Rouder, J. N. (2020). BayesFactor: Computation of bayes factors for common designs. Retrieved from https://richarddmorey.github.io/BayesFactor/
Patil, I. (2021). Visualizations with statistical details: The ’ggstatsplot’ approach. PsyArxiv. doi:10.31234/osf.io/p7mku
R Core Team. (2021). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
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