Web application for the simulation of experimental data

Bernabeu, P., & Lynott, D. (2020) web application, research and teaching applications, R
Code GitHub repository Web application Google Scholar
  Purposes

This open-source, R-based web application is suitable for educational and research purposes in experimental and quantitative sciences. It allows the creation of varied data sets with specified structures, such as between-group and within-participant variables, that can be categorical or continuous. These parameters can be set throughout the various tabs (sections) from the top menu. In the last tab, the data set can be downloaded. The benefits of this application include time-saving and flexibility in the control of parameters.

How it works

graph TD A["Set parameters across tabs"] --> B["Define between-group
variables"] A --> C["Define within-participant
variables"] B --> D["Choose categorical
or continuous"] C --> D D --> E["Simulated data set"] E --> F["Build custom summary
(penultimate tab)"] F --> G["Download parameters and
data set (last tab)"]

Guidelines

General guidelines include the following:

  • In the names of variables, it is recommended to use only alphanumeric characters and underscore signs. The latter can be used to separate characters or words (e.g., variable_name). Different names should be used for each variable.

  • In the levels of categorical variables, alphanumeric, special characters and spaces are allowed.

  • In numeric fields (e.g., ‘Mean’, ‘Standard deviation’, ‘Relative probability [0, 1]’), only numbers and decimal points are allowed.

  • As the data set increases, so does the processing time.

More specific guidelines are available in each section.

 The web application can be launched here.

Screenshot of the Dependent tab (view larger)

Screenshot of the Dependent tab in the experimental data simulation web app, showing parameter settings for the dependent variable

Reference

Bernabeu, P., & Lynott, D. (2020). Web application for the simulation of experimental data (Version 1.4). https://github.com/pablobernabeu/Experiment-simulation-app/

Code

This web application was developed in R (R Core Team, 2020). The code is available on Github, where contributions may be made. The initial code for this application was influenced by Section 5.7 (Simulating data for multi-factor designs) in Crump (2017). The R packages used include ‘dplyr’ (Wickham, François, Henry, & Müller, 2018), ‘DT’ (Xie, 2020), ‘flexdashboard’ (Iannone, Allaire, & Borges, 2020), ‘shiny’ (Chang, Cheng, Allaire, Xie, & McPherson, 2020) and ‘stringr’ (Wickham, 2019).

Options for development and local use of the app

Option A) Using local R/RStudio or RStudio Cloud project or Binder RStudio environment

  1. [Step only necessary in R/RStudio] Install the packages in the versions used in the latest release of this application, by running:

    install.packages('devtools')
library(devtools)
install_version('dplyr', '1.0.2', 'http://cran.us.r-project.org')
install_version('DT', '0.15', 'http://cran.us.r-project.org')
install_version('flexdashboard', '0.5.2', 'http://cran.us.r-project.org')
install_version('htmltools', '0.5.0', 'http://cran.us.r-project.org')
install_version('knitr', '1.30', 'http://cran.us.r-project.org')
install_version('ngram', '3.0.4', 'http://cran.us.r-project.org')
install_version('purrr', '0.3.4', 'http://cran.us.r-project.org')
install_version('shiny', '1.5.0', 'http://cran.us.r-project.org')
install_version('stringr', '1.4.0', 'http://cran.us.r-project.org')
install_version('tidyr', '1.1.2', 'http://cran.us.r-project.org')

  2. Open the index.Rmd script.

  3. Run the application by clicking on ▶️ Run document at the top left, or by running rmarkdown::run('index.Rmd') in the console.

  4. Click on Open in Browser at the top left.

Option B) Using Dockerfile (see instructions)

Acknowledgements

Thank you to RStudio for the free hosting server used by this application, shinyapps.io.

References

Chang, W., Cheng, J., Allaire, J., Xie, Y., & McPherson, J. (2020). shiny: Web Application Framework for R. R package version 1.4.0. Available at http://CRAN.R-project.org/package=shiny.

Crump, M. J. C. (2017). Programming for Psychologists: Data Creation and Analysis (Version 1.1). https://crumplab.github.io/programmingforpsych/.

Iannone, R., Allaire, J. J., & Borges, B. (2020). Flexdashboard: R Markdown Format for Flexible Dashboards. http://rmarkdown.rstudio.com/flexdashboard.

R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Wickham, H. (2019). stringr: Simple, Consistent Wrappers for Common String Operations. R package version 1.4.0. https://CRAN.R-project.org/package=stringr.

Wickham, H., François, R., Henry, L., & Müller, K. (2018). dplyr: A Grammar of Data Manipulation. R package version 0.7.6. https://CRAN.R-project.org/package=dplyr.

Xie, Y. (2020). DT: A Wrapper of the JavaScript Library “DataTables”. R package version 0.14. Available at https://CRAN.R-project.org/package=DT.

Contact

To submit any questions or feedback, please post an issue, or email Pablo Bernabeu at pcbernabeu@gmail.com.

web application data simulation randomisation research methods experiment statistics data science R Tidyverse R Shiny Flexdashboard HTML CSS Software Sustainability Institute Fellowship
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