scrub

{scrub} - Spatial Cleaned Resistance and Unified Base

This is a working R compendium (think R package but for reproducible analysis). A good overview on research compendiums, see the R for Reproducible Research course.

Installation

git clone https://github.com/IDEELResearch/scrub.git
cd scrub
open scrub.Rproj

devtools::install_dev_deps() will install all required packages, as specified in the Imports in DESCRIPTION. (At a later date when analysis is finalised, renv can be used to create a reproducible R environment that anyone can use by calling renv::restore to set up package dependencies.)

Overview

The structure within analysis is as follows:

R/                            # Packaged R functions 

analysis/
    |
    ├── 01_xxxxx /            # analysis scripts used for generating figures
    |
    ├── plots/                # location of figures produced by the analysis scripts
    |
    ├── tables/               # location of any tables produced by the analysis scripts
    |
    ├── data_raw/             # data obtained from elsewhere and treated read-only    
    |
    ├── data_derived/         # intermediate data generated during the analysis
    |
    ├── data_out/             # final data objects to be used in other analyses

• Analysis scripts are to be run in the numbered order they are included. If there are shared numbers, then any order of these scripts works.

• Data that is read only, e.g. data shared from elsewhere and not generated using code in this repository, is stored in data_raw

• Data that is generated using code in this repository is stored in data_derived.

• Outputs from the analysis scripts, such as plots and tables are stored in plots and tables respectively.

Milestones (This can probably be deleted once all in Github issues)

• V1 of scrub csv table that is ready to go into stave
• Finish systematic lit review abstract screening for inclusion
• Finish full text extraction of lit review screening
• Checking and cross validating the extracted seekdeep server data
• Pf7k, WWARN, WHO pulling:
• Meta study formatting correctly
• Cleaning and deduplication
• Write a validation suite (lat long) - jeff/cecile sits in python
• Final merged two
• Deduplication
• Writing up what we did to get this data

Compendium DOI:

https://zenodo.org/record/XXX

The files at the URL above will generate the results as found in the publication.

The R package

This repository is organized as an R package. There are only a few R functions exported in this package - the majority of the R code is in the analysis directory. The R package structure is here to help manage dependencies, to take advantage of continuous integration, and so we can keep file and data management simple. For any R packages that are used frequently in this repository, they are documented in R/ and are used in the analysis folder using devtools::load_all().

To download the package source as you see it on GitHub, for offline browsing, use this line at the shell prompt (assuming you have Git installed on your computer):

git clone https://github.com/IDEELResearch/scrub.git

Once the download is complete, open the scrub.Rproj in RStudio to begin working with the package and compendium files. We will endeavour to keep all package dependencies required listed in the DESCRIPTION.

Licenses

Code: MIT year: 2024, copyright holder: OJ Watson

Data: CC-0 attribution requested in reuse