Interpretive/Deadpan Project

Overview

This paper contains the data, code, and figures from Anderson, Ling, & Schutz (under review). We performed analyses using R (version 4.2.3) on the Windows 10 operating system (version 22H2). To use this code, you must first install R and RStudio, along with git.

File organization

Analysis code is organized in .qmd and .r files within the repository.

• Data are contained in the /Data folder.
• R dependencies and custom functions are viewable in the /R folder. It contains the following files:
  • Libraries.R. Module containing all dependencies required for the analysis code to run.
  • Functions.R Module containing custom functions written for this project.
  • Preprocessing.R Script including additional code for figure and data formatting.
• Figures from the analyses are viewable in the /Figures folder.
• Analysis/Analysis.qmd contains the analyses from the manuscript.

Getting started

Analyses can be replicated in one of two ways: one involves using the renv package to restore dependencies and versions from the original analysis in an isolated project folder. The second involves manually installing packages. I recommend the first method, as it reduces the chance of package conflicts affecting analysis reproducibility. Before this, you must have git installed and configured, as well as R. I also recommend installing RStudio as it will allow you to open .Rproj files.

Cloning repository

In a terminal, navigate to the location you would like to write the project files to. Then, clone the repository by running the following line of code: git install https://github.com/cmndrsn/interpretive-deadpan-project

Reproducing analyses with renv (recommended)

• Ensure the package renv is installed for R.
  • Inside R, run the command install.packages("renv").
  • Once installed, open the project file Interpretive-vs-Deadpan.Rproj and run renv::restore() in the console. This will load the required dependencies to reproduce analyses.

Reproducing analyses after manually installing packages (not recommended)

• To get started, open /R/Libraries.R and ensure all libraries are installed.
  • The R command to install a package is install.packages(). For example, to install the dplyr package, run the following line of code: install.packages('dplyr')
  • The success of reproducing analyses may depend on specific package versions. If you encounter issues through this method, you can see the versions used to produce the analyses in the renv.lock file. This process will likely be quite tedious and time-consuming, so I recommend using renv instead.

Running analysis script

• Once all dependencies are installed, open Interpretive-vs-Deadpan.Rproj in RStudio. From the file navigator, open Analysis/Analysis.qmd from within the RStudio to reproduce analyses.