In idea-labs/comsldpsy: Data and analysis code for Visser et. al (2020)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)

# load development package
devtools::load_all(getwd())
pkgenv <- export_pkgenv()

comsldpsy

This repository contains a reproducible research compendium for our r pkgenv$product:

r pkgenv$authors (r pkgenv$year). r pkgenv$title. r pkgenv$journal. r pkgenv$doi_published

How to cite

Please cite this compendium as:

r pkgenv$authors, (r format(Sys.Date(), "%Y")). Compendium of R code and data for 'r pkgenv$title'. Accessed r format(Sys.Date(), "%d %b %Y"). Online at r pkgenv$doi_osf

Contents

The compendium contains all data, code, and text associated with the r pkgenv$product. It is organized as follows:

• The R/ directory contains all the R code.
• The inst/analysis_dir/analysis/ directory contains 4 subfolders with the following contents
• data/: the raw data
• figures/: the figures used in the manuscript
• manuscript/: the manuscript as accepted by the journal
• templates/: the Microsoft word template used to prepare the manuscript

How to download or install

There are several ways to use the compendium's contents and reproduce the analysis:

• Download the compendium as a zip archive from this GitHub repository or a registered/frozen version from the associated OSF project (click on "GitHub: ...", then select "Download as zip").

  • After unpacking the downloaded zip archive, you can explore the files on your computer.

• Install the compendium as an R package from GitHub using the devtools package:

  install.packages("devtools") devtools::install_github("`r pkgenv$url_github`")

  • This will install the R package locally including all its dependencies.
  • The package includes functions to
  • check the analysis for consistency
  • inspect the analysis pipeline and intermediate results
  • reproduce the analysis results on the user's computer
  • For more details on how to use the package and its functions, see here.

• Reproduce the analysis locally inside a Docker container. This allows to replicate the exact computational environment used by the authors. The compendium includes a Dockerfile that specifies how to build a Docker image that contains all the software dependencies needed to run the code and also includes the compendium's R package, ready to use. After installing Docker (needs administrator privileges), one can simply download the image and run the container using:

  docker run -p 8888:8888 `r pkgenv$url_docker`

  • This command will print a URL to the terminal.
  • Copy this URL and paste it into a web browser.
  • This will open a Jupyter Notebook.
  • Start an interactive RStudio session in the browser by selecting:

• Reproduce the analysis in the cloud without having to install any software. The same Docker container replicating the computational environment used by the authors can be run using BinderHub on mybinder.org:

  • Click to launch an interactive RStudio session in your web browser.

Licenses

Text and figures : CC-BY-4.0

Code : See the DESCRIPTION file

Data : CC-0 attribution requested in reuse

idea-labs/comsldpsy documentation built on April 14, 2020, 4:28 p.m.