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README.Rmd
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README.Rmd
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---
output: github_document
bibliography: "./inst/REFERENCES.bib"
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
)`-brightgreen.svg) [](https://cran.r-project.org/) [](http://orcid.org/0000-0002-3993-1182)
# redoxpeat
### Compendium URL
<https://github.com/henningte/redoxpeat> or <https://doi.org/10.5281/zenodo.5792970>
### Authors
Henning Teickner (henning.teickner@uni-muenster.de), Chuanyu Gao, Klaus-Holger Knorr
### Contents
This repository contains the data and code for our paper [@Teickner.2022]:
> Henning Teickner, Chuanyu Gao, Klaus-Holger Knorr (2022). _Electrochemical Properties of Peat Particulate Organic Matter on a Global Scale: Relation to Peat Chemistry and Degree of Decomposition_. Global Biochemical Cycles. DOI: [10.1029/2021GB007160](https://www.doi.org/10.1029/2021GB007160).
### How to download or install
You can download the compendium as a zip from from this URL: <https://doi.org/10.5281/zenodo.5792970>
Or you can install this compendium as an R package, redoxpeat, from GitHub with:
```{r installation, eval=FALSE}
remotes::install_github("henningte/redoxpeat")
```
### How to use
#### Reproduce the analyses
To reproduce the analyses for the paper, open the Rstudio project included in this research compendium and run the Rmarkdown script in `analysis/paper/001-paper-main.rmd`. To also get the final reference list, run `analysis/paper/007-latex.Rmd` afterwards.
Running the whole script takes about two hours and occupies additional disk space of ~11 Gb.
Alternatively, the [Dockerfile](Dockerfile) can be used to build a Docker image from which all analyses can be reproduced. The Dockerfile ensures that all required dependencies are installed (e.g. specific R packages; this is managed using the R package [renv](https://rstudio.github.io/renv/articles/renv.html)).
The [Dockerfile](Dockerfile) provides instructions how to build a Docker image from the Dockerfile and how to run the image in a Docker container. It occupies disk space of ~7 Gb.
When the Docker image runs in a container, go to `localhost:8787` in your Browser. You will find an RStudio interface where you can log in with username `rstudio` and password `redoxpeat`. Here you can find the Rmarkdown scripts (`redoxpeat/analysis/paper/001-paper-main.rmd` and `analysis/paper/007-latex.Rmd`) as described above.
#### Access to the R scripts
All R scripts used to produce the results for the analyses are in folder [analysis/paper](analysis/paper).
#### Access to the data
The compendium contains all data used during the analyses of the paper:
1. General information on peat samples: `d`
2. General information on sites: `d_sites`
3. Peat electron accepting capacity (EAC) and electron donating capacity (EDC) of suspensions of freeze dried peat: `el_t0`
4. Peat iron content and speciation (from acid extraction): `fe_t0`
5. Peat elemental contents (C, N) and isotope signatures ($\delta^{13}$C, $\delta^{15}$N): `d_irms`
6. Peat elemental contents from wavelength-dispersive X-ray fluorescence: `d_xrf`
7. Peat mid infrared spectra (not preprocessed): `d_mir`
8. EAC/EDC data extracted from @Aeschbacher.2012: `aeschbacher2012`
9. EAC/EDC data extracted from @Tan.2017: `tan2017`
10. EAC/EDC data extracted from @Walpen.2018: `walpen2018`
There is a full documentation available for each dataset. For example, to read the documentation for `el_t0`, type:
```{r data-doc, eval=FALSE}
library(redoxpeat)
?el_t0
```
#### Access to models for predicting the EAC/EDC from mid infrared spectra
Functions to get predictions from the fitted models to predict the EAC/EDC from id infrared spectra are available via the R package [irpeat](https://github.com/henningte/irpeat/) [@Teickner.2021].
#### Detailed overview on the files contained in the compendium
- `README.md`/`README.Rmd`: Readme for the compendium.
- `DESCRIPTION`: The R package DESCRIPTION file for the compendium.
- `NAMESPACE`: The R package NAMESPACE file for the compendium.
- `LICENSE.md`: Details on the license for the code in the compendium.
- `CONTRIBUTING.md` and `CONDUCT.md`: Files with information on how to contribute to the compendium.
- `Dockerfile`: Dockerfile to build a Docker image for the compendium.
- `.Rbuildignore`, `.gitignore`, `.dockerignore`: Files to ignore during R package building, to ignore by Git, and to ignore while bulding a Docker image, respectively.
- `configure`/`configure.win`: Files created with rstantools in order to integrate Stan models into the R package.
- `renv.lock`: renv lock file (Lists all R package dependencies and versions and can be used to restore the R package library using renv). `renv.lock` was created by running `renv::snapshot()` in the R package directory and it uses the information included in the `DESCRIPTION` file.
- `.Rprofile`: Code to run upon project start.
- `R`, `man`, `inst`, `data-raw`, `data`, `src`: Default folders for making the R package and integration with Stan via rstatools run.
- `analysis`:
- `data`: Folder with the input data included in the R package (see scripts in `data-raw`).
- `scripts`:
- `figures`: Folder with figures for the manuscript not derived from data (conceptual figures, etc.).
- `templates`: Folder with LaTeX and Rmarkdown templates for the manuscript.
- `paper`: Folder with template files and scripts to create the manuscript.
- `000-preamble.tex`: LaTeX setup for the manuscript.
- `001-paper-main.Rmd`: Main Rmarkdown file that represents the body of code and text of the manuscript. Run this to reproduce the manuscript.
- `002-paper-sites.Rmd`: Child Rmarkdown file of `001-paper-main.Rmd` to process general information on the sampling sites.
- `003-regression-elco.Rmd`: Child Rmarkdown file of `001-paper-main.Rmd` to compute the element ratio-based regression models to predict the EAC~POM~ and EDC~POM~.
- `004-regression-mirs.Rmd`: Child Rmarkdown file of `001-paper-main.Rmd` to compute the mid infrared spectra-based regression models to predict the EAC~POM~ and EDC~POM~.
- `005-paper-abstract-graphical.Rmd`: Rmarkdown document used to create parts of the figures for the graphical abstract.
- `006-paper-supplementary.Rmd`: Rmarkdown file to be run from within `001-paper-main.Rmd`. This script contains code and text for the supporting information.
- `007-latex.Rmd`: Rmarkdown file to be run after `001-paper-main.Rmd` to generate the full reference lists in the rendered pdf file and clean up `006-paper-supplementary.tex`.
- `references.bib`: Literature references for the manuscript.
- `references_or.bib`: Original Literature references for the manuscript. `references.bib` is created from `references_or.bib` by `007-latex.Rmd`.
- `agujournal2018.cls`: The Agu Journal class derived from the rticles package.
- `trackchanges.sty`: The trackchanges TeX package needed to run the LaTeX template from rticles.
- `si.aux`: Auxiliary file containing the definitions for the supplementary references in `001-paper-main.tex`. This file is generated from `007-latex.Rmd`.
- `001-paper-main.tex`/`001-paper-main.pdf`: The LaTeX/PDF document resulting from rendering `001-paper-main.Rmd`.
- `006-paper-supplementary.tex`/`006-paper-supplementary.pdf`: The LaTeX/PDF document resulting from rendering `006-paper-supplementary.Rmd`.
### Acknowledgements
For their support during sample collection/provision, we would like to thank Svenja Agethen (DE), Werner Borken (SKY I-1, SKY I-6), Tanja Broder (PBR, SKY II, LT, MK), Mariusz Gałka (LT, MK), Liam Heffernan (LP, LB), Norbert Hölzel (KR, ISH), Annkathrain Hömberg (TX, DT), Tim Moore (MB), Sindy Wagner (BB), Tim-Martin Wertebach (KR, ISH), and Zhi-Guo Yu (TX, DT).
Analyses of this study were carried out in the laboratory of the Institute of Landscape Ecology. Svenja Agethen and Michael Sander provided analytical support. The assistance of Ulrike Berning-Mader, Madeleine Supper, Victoria Ratachin, and numerous student assistants is greatly acknowledged. We thank Dr. Hendrik Wetzel, Fraunhofer Institute for Applied Polymer Research, Dept. Starch Modification/Molecular Properties, Potsdam, Germany, for analysis of O and H. The workflow was reproduced by the Reproducible Research Support Service of the University of Münster. We thank two anonymous reviewers whose comments helped to improve the manuscript.
This Study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) grant no. KN 929/12-1 to Klaus-Holger Knorr; Chuanyu Gao was supported by the Youth Innovation Promotion Association CAS (No. 2020235).
### How to cite
Please cite this compendium as:
> Henning Teickner, Chuanyu Gao, Klaus-Holger Knorr, (`r format(Sys.Date(), "%Y")`). _Reproducible research compendium with R code and data for: 'Electrochemical Properties of Peat Particulate Organic Matter on a Global Scale: Relation to Peat Chemistry and Degree of Decomposition'_. Accessed `r format(Sys.Date(), "%d %b %Y")`. Online at <https://doi.org/10.5281/zenodo.5792970>
### Licenses
**Text and figures:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
**Code:** See the [DESCRIPTION](DESCRIPTION) file
**Data:** [CC-0](http://creativecommons.org/publicdomain/zero/1.0/) attribution requested in reuse
**LaTeX templates:** The LaTeX templates are derived from the rticles R package.
### Contributions
We welcome contributions from everyone. Before you get started, please see our [contributor guidelines](CONTRIBUTING.md). Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms.
### References/Sources
The format of this research compendium is inspired by @Marwick.2017. The Rmarkdown template for the main article is from the rticles package [@Allaire.2020].