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README.Rmd
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README.Rmd
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---
output: github_document
bibliography: ["vignettes/packages.bib"]
biblio-style: apalike
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
out.width = "100%"
)
```
```{r pkgs, include=FALSE, message=FALSE, eval=FALSE}
# Required packages
pkgs <- c(
# tidy data manipulation
"purrr", "dplyr", "tibble", "tidyr", "stringr", "magrittr",
# plots
"ggplot2", "scales", "ggrepel", "RColorBrewer",
# package dev
"devtools", "roxygen2", "rlang", "fs", "testthat", "vdiffr", "cpp11",
# documentation and paper
"knitr", "rmarkdown", "bookdown", "rticles", "kableExtra", "bibtex", "pkgdown",
# reading and downloading data
"readmat", "R.matlab", "zen4R",
# statistics
"point", "MASS", "nortest"
)
knitr::write_bib(pkgs, fs::path("vignettes", "packages.bib"), prefix = "")
fs::file_copy(
fs::path("inst/paper/preprint/SIMS_method.bib"),
fs::path("vignettes", "SIMS_method.bib"),
overwrite = TRUE
)
hadley <- bibentry(
key = "Wickham2015",
bibtype = "Book",
title = "R packages: organize, test, document, and share your code",
author = person("Hadley","Wickham"),
year = "2015",
publisher = " O'Reilly Media, Inc.",
url = "https://r-pkgs.org/"
)
# Get the R reference
rref <- citation()
# Create ref key
rref$key <- "rversion"
# Write the R reference
pkgs <- bibtex::read.bib("vignettes/packages.bib")
bibtex::write.bib(
purrr::reduce(list(rref, hadley, pkgs), append),
file = fs::path("vignettes", "packages.bib")
)
```
# pointapply
<!-- badges: start -->
[](https://github.com/MartinSchobben/pointapply/actions)
[](https://app.codecov.io/gh/MartinSchobben/pointapply?branch=master)
[](https://zenodo.org/badge/latestdoi/340077309)
<!-- badges: end -->
The R package *pointapply* contains the code and data to reconstruct the publication: Martin Schobben, Michiel Kienhuis, and Lubos Polerecky. 2021. *New methods to detect isotopic heterogeneity with Secondary Ion Mass Spectrometry*, preprint on [Eartharxiv](https://eartharxiv.org/).
This paper assesses the performance of the application of the `diag_R()` function of the sister package *point* [@point] in detecting isotope heterogeneity in natural substrates.
# Data
The data is stored on the [Zenodo](https://doi.org/10.5281/zenodo.4580159) repository *pointdata*.
The data can be directly downloaded with `download_point()`, which is build around *zen4R* [@zen4R].
``` r
# Download
download_point()
```
# Render the paper
Use the function `render_paper()` and specify the directory to save the paper. Make sure to have latex installed on your system. Check e.g., https://yihui.org/tinytex/ for a light weight LaTeX version.
``` r
# install.packages('tinytex')
# Knit
render_paper(title = "Schobbenetal_SIMS_method", output_dir = "mydir")
```
## Credits
The construction of the R [@rversion] package *pointapply* and associated documentation was aided by the packages; *devtools* [@devtools], *roxygen2* [@roxygen2], *testthat* [@testthat], *vdiffr* [@vdiffr], *cpp11* [@cpp11], and *fs* [@fs].
The book: *R packages: organize, test, document, and share your code*, by @Wickham2015 is a great guide for learning how to build packages.
In addition, this package relies on a set of external packages from the tidyverse universe, including: *dplyr* [@dplyr], *tidyr* [@tidyr], *tibble* [@tibble], *stringr* [@stringr], *magrittr* [@magrittr], and *purrr* [@purrr] for data manipulation.
Data plots are constructed with *ggplot2* [@ggplot2; @ggplot22016], *ggrepel* [@ggrepel], *RColorBrewer* [@RColorBrewer], and *scales* [@scales]
The package *rlang* [@rlang] was used for tidy evaluation.
Some specialised packages where used, notably; *readmat* for loading the matlab LANS files [@readmat] and *MASS* [@MASS; @MASS2002] for 2D density estimates.
The data download from Zenodo with an api is facilitated by *zen4R* [@zen4R].
The documentation and paper was written with *knitr* [@knitr; @knitr2014; @knitr2015], *rmarkdown* [@rmarkdown; @rmarkdown2018; @rmarkdown2020], *bookdown* [@bookdown; @bookdown2016], *pkgdown* [@pkgdown], *rticles* [@rticles] and *bibtex* [@bibtex].
## Installation
You can install the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("MartinSchobben/pointapply", build_vignettes = TRUE)
```
## Reconstruct the paper from scratch
The data figures can be constructed with the functions contain in this package. The following vignettes detail all these operations in a coherent story line.
**Data:**
* Synthetic data for validation of model performance and Figure 2 and 3 and Supplementary Figure 1 (`vignette("simulation")`).
* Real data reading and processing and Supplementary Figure 2 (`vignette("data")`).
Because of the excessive file-sizes of ion count data, the data is stored externally on [Zenodo](https://doi.org/10.5281/zenodo.4564170), and can be accessed with the function `download_point()`.
**Figures:**
* Evaluation of model performance; Figures 4 and 5 (`vignette("performance")`).
* Raster images and scatter plots of real SIMS ^13^C/^12^C analyses; Figures 6--8 and Supplementary Figures 7 and 8 (`vignette("raster")`).
* Regression diagnostics; Supplementary Figures 3--5 (`vignette("regression")`).
* Accuracy of SIMS isotope analysis; Supplementary Figures 3 and 7 (`vignette("accuracy")`).
# References