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The potential smoke-free dividend from quitting for smokers across local areas in England: A cross-sectional analysis - code repository

Project Status: Active – The project has reached a stable, usable state and is being actively developed. Lifecycle: maturing

Introduction

The purpose of this repository is to provide reproducible code and data inputs to produce the results of the paper “The potential smoke-free dividend from quitting for smokers across local areas in England: A cross-sectional analysis”.

Usage

Fork the project on GitHub to create your own repository. This project uses renv to aid reproducibility of results by ensuring users make use of the same environment.

When a new user first launches in this project, renv should automatically bootstrap itself, thereby downloading and installing the appropriate version of renv into the project library. After this has completed, they can then use renv::restore() to restore the project library locally on their machine.

Once the project library is restored, the repository is ready to be used to replicate the analysis once the requirements below are met.

The R scripts which reproduce the analysis are stored in the R/ directory. The script run_analyses.R in the top level of the repository is a metafile which runs all of the scripts in order. The key outputs are saved to outputs/main results/.

Requirements

All data inputs required are available in the repository or part of the R package smkfreediv which is open source and available on GitHub. The exception to this is the Smoking Toolkit Study (STS) data which cannot be provided.

R Package

The analysis requires the use of the smkfreediv R package, which contains functions and data inputs needed to calculate the amount of upshifting required to account for underreporting of spending data in the STS and to combine data sources to calculate the smoke free dividend. The version of smkfreediv which produced the analysis in the paper is 1.6.3. It is not guaranteed that the code in this repository will still work with later versions of the package.

To install the package from GitHub with the version used to produce the paper, run the follwowing code (also contained in the 001 - package installation.R script file). Note that smkfreediv is part of the renv environment and is restored with the other CRAN packages when using renv::restore(), so this step is not needed.

devtools::install_git(
  "https://github.com/STAPM/smkfreediv.git",
  ref = "1.6.3",
  build_vignettes = FALSE
)

Smoking Toolkit Study

In order to run the analysis the user must provide their own version of the STS data. The raw SPSS file for the STS data must be stored in the input_data/ directory within the project repository and the name of the file (including .sav extension) should be updated in the data_file object created in the run_analyses.R file located in the root directory of the repository. e.g. for the April 2021 data the paper was produced using:

data_file <- "omni174_39.1_65.2cot_31.3a_25.4s_recodes_60.5sa.sav"

Reproducibility

Note that this repository uses the renv R package to produce a reproducible environment for this analysis. see the package website for more information.

When first launching the project, the function renv::restore() will populate the renv/library folder, installing the R packages saved in the lockfifle.

The lockfile renv.lock is in the top level of the repository, and contains the metadata for all R packages used in the project. The libraries themselves are ignored by git and not uploaded to GitHub, and so a new user needs to install the packages themselves using the metadata.

The .Rprofile file in the top level of the repository is automatically run when the project is opened and this runs the script file renv/activate.R to set the renv folder as the source R package library.

The version of R used to produce the analysis is 4.3.1.

Citation

Please cite this code repository as:

Morris D (2023) The potential smoke-free dividend across local areas in England: A cross-sectional analysis - code and data repository 2023. University of Sheffield. doi:10.17605/OSF.IO/VZMP7.

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