lega: Substituting meat, poultry, and fish with legumes and risk of gallbladder diseases in a large prospective cohort

• Protocol:

Western diets high in animal foods and saturated fats has been shown to cause a multitude of non-communicable diseases while also having great and negative impacts on the environment. Based on a combined environmental and health related focus, legumes are increasingly being recommended as a meat substitute. Previous research has however indicated an increased risk of developing gallbladder related diseases when consuming large amounts of legumes and this study therefore investigates the association between substituting legumes for meats, poultry, and fish and the risk of developing gallbladder diseases.

Installing and setting up the project

If dependencies have been managed by using usethis::use_package("packagename") through the DESCRIPTION file, installing dependencies is as easy as opening the .Rproj file and running this command in the console:

pak::pak()

Steps to select and download the data

The data-raw/ folder contains the scripts to select, process, and prepare the data on the RAP to eventually be downloaded.

The steps to take to select the variables you want, create the CSV file on the RAP, convert it to Parquet format (for faster loading), and download to your project on RAP. The order is:

1. Select the variables you want in data-raw/project-variables.csv.

2. Follow the instructions in the data-raw/create-data.R script and run it to create the CSV file on the RAP server.

3. Run targets::tar_make() to download the CSV file to data/.

Processing and saving progress

It is very timely to rerun all code every time you have made changes in the data, so you can save and reload your work along the way by following the layout in data-raw/processing.R script. This is also helpful to do, when you have completed all data management tasks and want to save your changes to have a "ready to go" data frame to run your analysis on.

Once you've created your dataset using data-raw/create-data.R, you can uncomment the lines in the _targets.R file and afterwards run this code whenever you enter the RAP project.

targets::tar_make()

Brief description of folder and file contents

The following folders contain:

• data/: Will contain the UK Biobank data (not saved to Git) as well as the intermediate results output files.

• data-raw/: Contains the R script to download the data, as well as the CSV files that contain the project variables and the variable list as named in the RAP.

• doc/: This file contains the R Markdown, Word, or other types of documents with written content, like the manuscript and protocol.

• R/: Contains the R scripts and functions to create the figures, tables, and results for the project.