Last updated: 2019-04-10

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# Background

• Research is increasingly computational

• Code and data are important research outputs
• yet, we still focus mainly on curating papers.
• Calls for openness
• stick: reproducibility crisis
• carrot: huge rewards from working open

## Enter the Research Compendium

The goal of a research compendium is to provide a standard and easily recognizable way for organizing the digital materials of a project to enable others to inspect, reproduce, and extend the research.

### Three Generic Principles

1. Organize its files according to prevailing conventions:
• help other people recognize the structure of the project,
• supports tool building which takes advantage of the shared structure.
2. Separate of data, method, and output, while making the relationship between them clear.

3. Specify the computational environment that was used for the original analysis.

## R community response

R packages can be used as a research compendium for organising and sharing files!

### R package file system structure for reproducible research

• Take advantage of the power of convention.

• Make use of great package development tools.

1. Ben Marwick, Carl Boettiger & Lincoln Mullen (2018) Packaging Data Analytical Work Reproducibly Using R (and Friends), The American Statistician, 72:1, 80-88, DOI: <10.1080/00031305.2017.1375986>

Example use of the R package structure for a research compendium (source Marwick et al, 2018)

# Project Organisation

Good project layout helps ensure the

• Integrity of data
• Portability of the project
• Easier to pick the project back up after a break

## From raw to analytical data

### the reproducible pipeline

• Do not manually edit raw data

• Keep a clean pipeline of data processing from raw to analytical.

• Ideally, incorporate checks to ensure correct processing of data through to analytical.

## Separate function definition and application

• When your project is new and shiny, the script file usually contains many lines of directly executated code.

• As it matures, reusable chunks get pulled into their own functions.

• The actual analysis scripts then become relatively short, and use the functions defined in separate R scripts.

## Use Rstudio projects

### pkg here

Use function here::here("path", "to", "file") to create robust paths relative to the project root.

eg

here::here("path", "to", "file")

It’s like agreeing that we will all drive on the left or the right. A hallmark of civilization is following conventions that constrain your behavior a little, in the name of public safety.

Jenny Bryan on Project-oriented workflows

### A place for everything, everything in its place.

Benjamin Franklin

### The benefits of following convention:

• You will be able to find your way around any of your projects

• You will be able to find your way around any project by others following same convention

• You will be able to find your way around any r package on GitHub!

## Tools

### Leverage tools and functionality for R package development

• manage dependencies
• make functionality available
• document functionality
• validate functionality

#### A minimal analysis project in R would consist of:

1. An scripts/ directory that contains R scripts (.R), notebooks (.Rmd), and intermediate data.

2. A DESCRIPTION file that provides metadata about the compendium. Most importantly, it would list the packages needed to run the analysis. Would contain field to indicate that this is an analysis, not a package.

#### A reproducible analysis project would also contain:

1. An R/ directory which contains R files that provide high-stakes functions.

2. A data/ directory which contains high-stakes data.

3. A tests/ directory that contains unit tests for the code and data.

4. A vignettes/ directory that contains high-stakes reports.

#### Some other components would be automatically added/generated:

1. A man/ directory which contains roxygen2-generated documentation for the reusable functions and data.

2. Online documentation in a docs/ folder.

#### A shareable reproducible analysis project would also:

• Use Git + GitHub (or other public Git host)

• Use Travis or other continuous integration service

• Capture the computational environment so it can easily be recreated on a different computer. This involves at a minimum capturing package versions, but might also include capturing R version, and other external dependencies.

# rrtools: Research Compendia in R

The goal of rrtools is to provide instructions, templates, and functions for making a basic compendium suitable for writing reproducible research with R.

rrtools build on tools & conventions for R package development to

• organise files
• manage dependencies
• share code
• document code
• check and test code

rrtools extends and works with a number of R packages:

# Workshop materials

## Data

#### On github: https://github.com/annakrystalli/rrtools-wkshp-materials/

• Unzip the file

## Workshop aims and objectives

In this workshop we’ll use materials associated with a published paper (text, data and code) to create a research compendium around it.

By the end of the workshop, you should be able to:

• Be able to Create a Research Compendium to manage and share resources associated with an academic publication.

• Be able to produce a reproducible manuscript from a single rmarkdown document.

• Appreciate the power of convention!

# Setup

### Install packages

Next, let’s install the packages we’ll need, starting with rrtools (if you haven’t got devtools installed, you’ll need to before you can install rrtools from GitHub).

# install.packages("devtools")
devtools::install_github("benmarwick/rrtools")

Installing rrtools imports many of the packages we’ll need today (eg, have a look at the imports section of the DESCRIPTION file).

Imports: devtools, git2r, whisker, rstudioapi, rmarkdown, knitr,
bookdown, curl, RCurl, jsonlite, methods, httr, usethis, clisymbols,
crayon, glue, readr (>= 1.1.1)

Now, install some additional packages we’ll need for the workshop.

install.packages(c(
# source paper analysis
"ggthemes"
# bibliographic / publishing
"citr", "rticles",
# documentation
"roxygen2",
# graphics
"Cairo"))

### Get workshop materials

Today we’ll be working with a subset of materials from the published compendium of code, data, and author’s manuscript:

##### Carl Boettiger. (2018, April 17). cboettig/noise-phenomena: Supplement to: “From noise to knowledge: how randomness generates novel phenomena and reveals information” (Version revision-2). Zenodo. http://doi.org/10.5281/zenodo.1219780

accompanying the publication:

##### Carl Boettiger . From noise to knowledge: how randomness generates novel phenomena and reveals information. Published in Ecology Letters, 22 May 2018 https://doi.org/10.1111/ele.13085

You can download the materials using usethis::use_course() and supplying a path to a destination folder to argument destdir:

usethis::use_course(url = "bit.ly/rrtools_wks", destdir = "~/Desktop")

This will download everything we need from a GitHub repository as a .zip file, unzip it and launch it in a new Rstudio session for us to explore.

#### Inspect materials

├── README.md <- .......................repo README
├── analysis.R <- ......................analysis underlying paper
├── gillespie.csv <- ...................data
├── paper.pdf <- .......................LaTex pdf of the paper
├── paper.txt <- .......................text body of the paper
├── refs.bib <- ........................bibtex bibliographic file
└── rrtools-wkshp-materials.Rproj <- ...rstudio project file

In this workshop we’ll attempt a partial reproduction of the original paper using the materials we’ve just downloaded.

We’ll use this as an opportunity to create a new research compendium using rrtools and friends! 🎊

# Create compendium

Now that we’ve got all the materials we need, let’s start by *creating a blank research compendium for us to work in.

First we need to load rrtools

library(rrtools) 

This performs a quick check to confirm you have Git installed and configured

If you do, you should see the following output in the console.

✔ Git is installed on this computer, your username is annakrystalli

### create compendium

Now we’re ready to create our compendium. We use function rrtools::create_compendium and supply it with a path at which our compendium will be created. The final part of our path becomes the compendium name. Because the function effectively creates a package, only a single string of lowercase alpha characters is accepted as a name. so let’s go for rrcompendium as the final part of our path.

To create rrcompendium in a directory called Documents/workflows/ I use:

rrtools::create_compendium("~/Documents/workflows/rrcompendium")

Go ahead and create a compendium at a location of your choice. Stick with compendium name rrcompendium for ease of following the materials. If the call was successfull you should see the following console output:

✔ Setting active project to '/Users/Anna/Documents/workflows/rrcompendium'
✔ Creating 'R/'
✔ Creating 'man/'
✔ Writing 'DESCRIPTION'
✔ Writing 'NAMESPACE'
✔ Writing 'rrcompendium.Rproj'
✔ Adding '^rrcompendium\\.Rproj$', '^\\.Rproj\\.user$' to '.Rbuildignore'
✔ Opening new project 'rrcompendium' in RStudio
✔ The package rrcompendium has been created
✔ Opening the new compendium in a new RStudio session...

Next, you need to:  ↓ ↓ ↓
● Edit the DESCRIPTION file
● Use other 'rrtools' functions to add components to the compendium

and a new Rstudio session launched for the compendium:

### Initiate git

We can initialise our compendium with .git using:

usethis::use_git()

N.B. Beware, you may have ended up with two Rstudio sessions of rrcompendium. Make sure to only have one session of a single project at one time to avoid problems.

### Inspect templates

.
|                                               dependency management
├── NAMESPACE <- ...............................AUTO-GENERATED on build
├── R <- .......................................folder for functions
├── man <- .....................................AUTO-GENERATED on build
└── rrcompendium.Rproj <- ......................rstudio project file

rrtools::create_compendium() creates the bare backbone of infrastructure required for a research compendium. At this point it provides facilities to store general metadata about our compendium (eg bibliographic details to create a citation) and manage dependencies in the DESCRIPTION file and store and document functions in the R/ folder. Together these allow us to manage, install and share functionality associated with our project.

### update description file

Let’s update some basic details in the DESCRIPTION file:

Package: rrcompendium
Title: What the Package Does (One Line, Title Case)
Version: 0.0.0.9000
Authors@R:
person(given = "First",
family = "Last",
role = c("aut", "cre"),
email = "first.last@example.com")
Description: What the package does (one paragraph)
ByteCompile: true
Encoding: UTF-8
LazyData: true


### Title

Title: Partial Reproduction of Boettiger Ecology Letters 2018;21:1255–1267
with rrtools

### Version

We don’t need to change the version now but using semantic versioning for our compendium can be a really useful way to track versions. In general, versions below 0.0.1 are in development, hence the DESCRIPTION file defaults to 0.0.0.9000.

### Authors

Next let’s specify the author of the compendium. Edit with your own details.

Authors@R:
person(given = "Anna",
family = "Krystalli",
role = c("aut", "cre"),
email = "annakrystalli@googlemail.com")

For more details on specifying authors, check documentation for ?person

### Description

Let’s add a bit more detail about the contents of the compendium in the Description.

Description: This repository contains the research compendium of the
partial reproduction of Boettiger Ecology Letters 2018;21:1255–1267.
The compendium contains all data, code, and text associated with this       sub-section of the analysis

Finally, let’s add a license for the material we create. We’ll use an MIT license. Note however that his only covers the code. We can do this with:

 usethis::use_mit_license()
✔ Setting License field in DESCRIPTION to 'MIT + file LICENSE'
✔ Adding '^LICENSE\\.md$' to '.Rbuildignore' This creates files LICENSE and LICENSE.md and updates the DESCRIPTION file with details of the license. License: MIT + file LICENSE ### Recap We’ve finished updating our DESCRIPTION file! 🎉 It should look a bit like this: Package: rrcompendium Title: Partial Reproduction of Boettiger Ecology Letters 2018;21:1255–1267 with rrtools Version: 0.0.0.9000 Authors@R: person(given = "Anna", family = "Krystalli", role = c("aut", "cre"), email = "annakrystalli@googlemail.com") Description: This repository contains the research compendium of the partial reproduction of Boettiger Ecology Letters 2018;21:1255–1267. The compendium contains all data, code, and text associated with this sub-section of the analysis. License: MIT + file LICENSE ByteCompile: true Encoding: UTF-8 LazyData: true  and your project folder should contain: . ├── DESCRIPTION ├── LICENSE ├── LICENSE.md ├── NAMESPACE ├── R ├── man └── rrcompendium.Rproj Let’s commit our work and move on to preparing our compendium for sharing on GitHub. ## Sharing a compendium on GitHub ### Create GitHub repository Next, we’ll create a GitHub repository to share our compendium. We’ll make use of our GITHUB_PAT and go for https authentication. We can do this with function: usethis::use_github(protocol = "https") ✔ Setting active project to '/Users/Anna/Documents/workflows/rrcompendium' ● Check title and description Name: rrcompendium Description: Partial Reproduction of Boettiger Ecology Letters 2018;21:1255–1267 with rrtools  The function will prompt you to confirm the name and description for your GitHub repo which it parses from our DESCRIPTION file. If everything looks good select the affirmative option. Are title and description ok? 1: Not now 2: Definitely 3: Nope  If creation of the repo was successfull you should see the following console output: ✔ Creating GitHub repository ✔ Adding GitHub remote ✔ Adding GitHub links to DESCRIPTION ✔ Setting URL field in DESCRIPTION to 'https://github.com/annakrystalli/rrcompendium' ✔ Setting BugReports field in DESCRIPTION to 'https://github.com/annakrystalli/rrcompendium/issues' ✔ Pushing to GitHub and setting remote tracking branch ✔ Opening URL https://github.com/annakrystalli/rrcompendium ### Create README Every GitHub repository needs a README landing page. We can create an rrtools README template using: rrtools::use_readme_rmd()  ✔ Creating 'README.Rmd' from template. ✔ Adding 'README.Rmd' to .Rbuildignore. ● Modify 'README.Rmd' ✔ Rendering README.Rmd to README.md for GitHub. ✔ Adding code of conduct. ✔ Creating 'CONDUCT.md' from template. ✔ Adding 'CONDUCT.md' to .Rbuildignore. ✔ Adding instructions to contributors. ✔ Creating 'CONTRIBUTING.md' from template. ✔ Adding 'CONTRIBUTING.md' to .Rbuildignore.  This generates README.Rmd and renders it to README.md, ready to display on GitHub. It contains: • a template citation to show others how to cite your project. • license information for the text, figures, code and data in your compendium --- output: github_document --- <!-- README.md is generated from README.Rmd. Please edit that file --> {r, echo = FALSE} knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
)


# rrcompendium

This repository contains the data and code for our paper:

> Authors, (YYYY). _Title of paper_. Name of journal/book <https://doi.org/xxx/xxx>

Our pre-print is online here:

> Authors, (YYYY). _Title of paper_. Name of journal/book, Accessed 10 Apr 2019. Online at <https://doi.org/xxx/xxx>

### How to cite

> Authors, (2019). _Compendium of R code and data for 'Title of paper'_. Accessed 10 Apr 2019. Online at <https://doi.org/xxx/xxx>

You can download the compendium as a zip from from this URL: </archive/master.zip>

Or you can install this compendium as an R package, rrcompendium, from GitHub with:

**Code :** See the [DESCRIPTION](DESCRIPTION) file

**Data :** [CC-0](http://creativecommons.org/publicdomain/zero/1.0/) attribution requested in reuse

### 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.


The call also adds two other markdown files:

• CONDUCT.md: a code of conduct for users
• CONTRIBUTING.md:: basic instructions for people who want to contribute to our compendium

### update README

There’s five main edits we need to make to the template:

#### edit compendium DOI details

Although we don’t have a link to the DOI, we can complete the rest of the details and leave it as a place holder.

This repository contains the data and code for our reproduction paper:

> Krystalli, A, (2018). _Partial Reproduction of Boettiger Ecology Letters 2018;21:1255–1267 with rrtools_.  <https://doi.org/{DOI-to-paper}>

#### edit paper.pdf DOI

Our reproduction pre-print is online here:

> Krystalli, A, (2018). _Partial Reproduction of Boettiger Ecology Letters 2018;21:1255–1267 with rrtools_, Accessed 10 Apr 2019. Online at <https://doi.org/{DOI-to-compendium}>


#### edit compendium citation

Please cite this compendium as:

> Krystalli, A, (2019). _Compendium of R code and data for 'Partial Reproduction of Boettiger Ecology Letters 2018;21:1255–1267 with rrtools'_. Accessed 10 Apr 2019. Online at <https://doi.org/{DOI-to-compendium}>

#### update zip url

This is a link to download a zipped file of the repository. To update the template, just paste the url of your compendium repository like so:

### How to download or install

You can download the compendium as a zip from from this URL: <https://github.com/annakrystalli/rrcompendium/archive/master.zip>

**Text and figures :**  [CC-BY-4.0](http://creativecommons.org/licenses/by/4.0/), Copyright (c) 2018 Carl Boettiger.

**Code :** See the [DESCRIPTION](DESCRIPTION) file

**Data :** [CC-BY-4.0](http://creativecommons.org/licenses/by/4.0/), Copyright (c) 2018 Carl Boettiger.

### render README.md, commit and push to GitHub

#### We’ve now completed our rrtoolsREADME.Rmd! 🎉

• Render it to update the README.md file which is displayed on GitHub

• Commit and push to GitHub.

You’re Github repository README should look like this on the site:

and your project folder should contain:

.
├── CONDUCT.md
├── CONTRIBUTING.md
├── DESCRIPTION
├── NAMESPACE
├── R
├── man
└── rrcompendium.Rproj

## Setting up the analysis folder

### Create analysis

We now need an analysis folder to contain our analysis and paper. We can do this using function rrtools::use_analysis()

The function has three location = options:

• top_level to create a top-level analysis/ directory

• inst to create an inst/ directory (so that all the sub-directories are available after the package is installed)

• vignettes to create a vignettes/ directory (and automatically update the DESCRIPTION).

The default is a top-level analysis/.

rrtools::use_analysis()
✔ Adding bookdown to Imports
✔ Creating 'analysis' directory and contents
✔ Creating 'analysis'
✔ Creating 'analysis/paper'
✔ Creating 'analysis/figures'
✔ Creating 'analysis/templates'
✔ Creating 'analysis/data'
✔ Creating 'analysis/data/raw_data'
✔ Creating 'analysis/data/derived_data'
✔ Creating 'references.bib' from template.
✔ Creating 'paper.Rmd' from template.

Next, you need to:  ↓ ↓ ↓ ↓
● Write your article/report/thesis, start at the paper.Rmd file
● Add the citation style library file (csl) to replace the default provided here, see https://github.com/citation-style-language/
● Add bibliographic details of cited items to the 'references.bib' file
● For adding captions & cross-referencing in an Rmd, see https://bookdown.org/yihui/bookdown/
● For adding citations & reference lists in an Rmd, see http://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html

Note that:
⚠ Your data files are tracked by Git and will be pushed to GitHub

Regardless for location option, the contents of the created sub-directories are the same:

    analysis/
|
├── paper/
│   ├── paper.Rmd       # this is the main document to edit
│   └── references.bib  # this contains the reference list information

├── figures/            # location of the figures produced by the Rmd
|
├── data/
│   ├── DO-NOT-EDIT-ANY-FILES-IN-HERE-BY-HAND
│   ├── raw_data/       # data obtained from elsewhere
│   └── derived_data/   # data generated during the analysis
|
└── templates
├── journal-of-archaeological-science.csl
|                   # this sets the style of citations & reference list
├── template.docx   # used to style the output of the paper.Rmd
└── template.Rmd

Let’s inspect

#### paper.Rmd

paper.Rmd is ready to write in and render with bookdown. It includes:

• a YAML header that identifies the references.bib file and the supplied csl file (Citation Style Language) to style the reference list)

• a colophon that adds some git commit details to the end of the document. This means that the output file (HTML/PDF/Word) is always traceable to a specific state of the code.

#### references.bib

The references.bib file has just one item to demonstrate the format. It is ready to insert more reference details.

We can replace the supplied csl file with a different citation style from https://github.com/citation-style-language/

# Reproduce a paper in Rmd

In this section we’re going to create a literate programming document to reproduce the paper in a format suitable for journal submission or as a pre-print. We’ll do this using the course materials we downloaded.

In particular, we’re going to combine the code in analysis.R, the text in paper.txt and the references in the refs.bib file in an .Rmd document to reproduce paper.pdf.

## Setup data

### Copy data to data/

To begin, let’s copy gillespie.csv from the course materials you downloaded in rrtools-wkshp-materials-master/ to the subfolder analysis/data/raw_data/ in rrcompendium

Your data folder should now look like this:

analysis/data
├── DO-NOT-EDIT-ANY-FILES-IN-HERE-BY-HAND
├── derived_data
└── raw_data
└── gillespie.csv

## Inspect analysis.R file

Let’s also open analysis.R in the course materials and run the code. The script has some initial setup, then loads the data, recodes one of the columns for plotting and then plots the results of the simulation, which generates figure 1 in paper.pdf.

#### analysis.R

library(dplyr)
library(ggplot2)
library(ggthemes)
theme_set(theme_grey())
# load-data
data <- read_csv(here::here("gillespie.csv"), col_types = "cdiddd")
# create colour palette
colours <- ptol_pal()(2)
# recode-data
data <- data %>%
mutate(system_size = recode(system_size,
large = "A. 1000 total sites",
small = "B. 100 total sites"))

# plot-gillespie
data %>%
ggplot(aes(x = time)) +
geom_hline(aes(yintercept = mean), lty=2, col=colours[2]) +
geom_hline(aes(yintercept = minus_sd), lty=2, col=colours[2]) +
geom_hline(aes(yintercept = plus_sd), lty=2, col=colours[2]) +
geom_line(aes(y = n), col=colours[1]) +
facet_wrap(~system_size, scales = "free_y") 

## Create journal article template using rticles

The rticles package is designed to simplify the creation of documents that conform to submission standards. A suite of custom R Markdown templates for popular journals is provided by the package.

### delete paper/ subdirectory

First, let’s delete the current analysis/paper folder as we’re going to create a new paper.Rmd template.

### create new paper template

This particular paper was published in Ecology Letters, an Elsevier Journal. We can create a new paper.Rmd template from the templates provided by rticles package.

We can use the New R Markdown dialog

Select:

• Template: Elesevier Journal Article
• Name: paper
• Location: ~/Documents/workflows/rrcompendium/analysis

Or we can use rmarkdown::draft() to create articles:

rmarkdown::draft(here::here("analysis","paper.Rmd"), template = "elsevier_article", package = "rticles")

Both these functions create the following files in a new directory analysis/paper.

analysis/paper
├── elsarticle.cls
├── mybibfile.bib
├── numcompress.sty
└── paper.Rmd
• The elsarticle.cls contains contains the citation language style for the references.

• The mybibfile.bib contains an example reference list.

• The new paper.Rmd is the file we will be working in.

Let’s open it up and start editing it.

## Update YAML

The YAML header in Paper.Rmd contains document wide metadata and is pre-populated with some fields relevant to an academic publication.


---
title: Short Paper
author:
- name: Alice Anonymous
email: alice@example.com
affiliation: Some Institute of Technology
footnote: Corresponding Author
- name: Bob Security
email: bob@example.com
affiliation: Another University
- code: Some Institute of Technology
address: Department, Street, City, State, Zip
- code: Another University
address: Department, Street, City, State, Zip
abstract: |
This is the abstract.

It consists of two paragraphs.

journal: "An awesome journal"
date: "2019-04-10"
bibliography: mybibfile.bib
output: rticles::elsevier_article
---

Here we’re going to reproduce paper.pdf as is, so we’ll actually be editing the file with details from the original publication.

First, let’s clear all text BELOW the YAML header (which is delimited by ---. DO NOT delete the YAML header).

Next, let’s open paper.txt from the course material which contains all text from the in paper.pdf. We can use it to complete some of the fields in the YAML header.

### title

Add the paper title to this field

title: "From noise to knowledge: how randomness generates novel phenomena and reveals information"


### author

Here we specify author details.

author:
- name: "Carl Boettiger"
affiliation: a
email: "cboettig@berkeley.edu"

Here we specify the addresses associated with the affiliations specified in authors

address:
- code: a
address: "Dept of Environmental Science, Policy, and Management, University of California Berkeley, Berkeley CA 94720-3114, USA"

Note that the field code in address cross-references with the affiliations specified in author.

### bibliography

Before specifying the bibliography, we need to copy the refs.bib file associated with paper.pdf from the course materials and save it in our analysis/paper subdirectory.

Next we can set the refs.bib as the source for our paper’s bibliograpraphy:

bibliography: refs.bib

### layout

We can add an additional field called layout which specifies the layout of the output and takes the following values.

• review: doublespace margins
• 3p: singlespace margins
• 5p: two-column

Let’s use singlespace margins

layout:  3p

### preamble

We can add also an additional field called preamble. This allows us to include LaTeX packages and functions. We’ll use the following to add linenumbers and doublespacing.

preamble: |
\usepackage[nomarkers]{endfloat}
\linenumbers
\usepackage{setspace}
\doublespacing

### abstract

This field should contain the abstract

abstract: |

# Abstract

Noise, as the term itself suggests, is most often seen a nuisance to ecological insight, a inconvenient reality that must be acknowledged, a haystack that must be stripped away to reveal the processes of interest underneath. Yet despite this well-earned reputation, noise is often interesting in its own right: noise can induce novel phenomena that could not be understood from some underlying determinstic model alone.  Nor is all noise the same, and close examination of differences in frequency, color or magnitude can reveal insights that would otherwise be inaccessible.  Yet with each aspect of stochasticity leading to some new or unexpected behavior, the time is right to move beyond the familiar refrain of "everything is important" (Bjørnstad & Grenfell 2001).  Stochastic phenomena can suggest new ways of inferring process from pattern, and thus spark more dialog between theory and empirical perspectives that best advances the field as a whole. I highlight a few compelling examples, while observing that the study of stochastic phenomena are only beginning to make this translation into empirical inference.  There are rich opportunities at this interface in the years ahead.


### output

The output format. In this case, the template is correctly pre-populated with rticles::elsevier_article so no need to edit.

output: rticles::elsevier_article

Now let’s add the main body of the paper from paper.txt.

### add new page after abstract

First, let’s a add a new page after the abstract using:

\newpage


### copy and paste text from paper.txt

We do not need the details we’ve just completed the YAML with, so ignore the title, abstract etc and just copy everything in paper.txt from the Introduction header down to and including the reference section header.

# Introduction: Noise the nuisance

To many, stochasticity, or more simply, noise,
is just that -- something which obscures patterns we are

...
...
...
...
...

# Acknowledgements

The author acknowledges feedback and advice from the editor,
Tim Coulson and two anonymous reviewers. This work was supported in
part by  USDA National Institute of Food and Agriculture, Hatch
project CA-B-INS-0162-H.

# References


### Check pdf output

Let’s knit our document and have our first look at the resulting pdf by clicking on the Knit tab.

### Update references

Next we’ll replace the flat citations in the text with real linked citation which can be used to auto-generate formatted inline citations and the references section.

#### Insert formatted citations

We’ll use the citr package, which provides functions and an RStudio addin to search a BibTeX-file to create and insert formatted Markdown citations into the current document.

Once citr is installed and you have restarted your R session, the addin appears in the addin menu. The addin will automatically look up the Bib(La)TeX-file(s) specified in the YAML front matter.

##### To insert a citation
1. Select text to replace with a citation

2. Launch citr addin:

3. Search for citation to insert

4. Select citation to insert

5. Insert citation

Carry on updating the rest of the citations. Don’t forget to check the abstract for citations!

### Update math

For the sake of time today, and not to open this topic too deeply here, I’ve included the following LaTex equation syntax in the text:

\begin{align}
\frac{\mathrm{d} n}{\mathrm{d} t} = \underbrace{c n \left(1 - \frac{n}{N}\right)}_{\textrm{birth}} - \underbrace{e n}_{\textrm{death}}, \label{levins}
\end{align}

that generates equation 1 in the paper.pdf.

\begin{align} \frac{\mathrm{d} n}{\mathrm{d} t} = \underbrace{c n \left(1 - \frac{n}{N}\right)}_{\textrm{birth}} - \underbrace{e n}_{\textrm{death}}, \label{levins} \end{align}

So you don’t need to edit anything here.

#### update inline math

Inline LaTeX equations and parameters can be written between a pair of dollar signs using the LaTeX syntax, e.g., $f(x) = 1/x$ generates $$f(x) = 1/x$$.

Using paper.pdf to identify mathematical expressions in the text (generally they appear in italic), edit your paper.Rmd, enclosing them between dollar signs.

### Check pdf output

Let’s knit our document to check our references and maths annotations have been updated correctly by clicking on the Knit tab.

Now that we’ve set up the text for our paper, let’s insert the code to generate figure 1.

### Add libraries chunk

First let’s insert a libraries code chunk right at the top of the document to set up our analysis. Because it’s a setup chunk we set include = F which suppresses all output resulting from chunk evaluation.

    {r libraries, include=FALSE}



#### set document chunk options

Now, let’s set some knitr options for the whole document by adding the following code to our libraries chunk:

knitr::opts_chunk$set(echo = FALSE, message=FALSE, warning=FALSE, dev="cairo_pdf", fig.width=7, fig.height=3.5) We’re setting default chunk options to: • echo = FALSE • message = FALSE • warning = FALSE to suppress code, warnings and messages in the output, and • dev="cairo_pdf" • fig.width=7 • fig.height=3.5 to specify how figures will appear. #### add libraries Copy and paste the code for loading all the libraries from analysis.R. Add library rrcompendium so we can access function recode_system_size. The libraries chunk should now look like so: knitr::opts_chunk$set(echo = FALSE, message=FALSE, warning=FALSE,
dev="cairo_pdf", fig.width=7, fig.height=3.5)

library(dplyr)
library(ggplot2)
library(ggthemes)
library(rrcompendium)

### Add set-theme chunk

Right below the libraries chunk, insert a new chunk and call it set-theme

Copy the code to set the plot theme and pasted into the set-theme code:

theme_set(theme_grey())

### Add figure1 chunk

Now scroll down towards the bottom of the document and create a new chunk just above the Conclusions section. Call it figure1

Copy and paste the remaining code into a new chunk which will create figure 1.

# create colour palette
colours <- ptol_pal()(2)

data <- read_csv(here::here("gillespie.csv"), col_types = "cdiddd")

# recode-data
data <- data %>%
mutate(system_size = recode(system_size, large = "A. 1000 total sites", small= "B. 100 total sites"))

# plot-gillespie
data %>%
ggplot(aes(x = time)) +
geom_hline(aes(yintercept = mean), lty=2, col=colours[2]) +
geom_hline(aes(yintercept = minus_sd), lty=2, col=colours[2]) +
geom_hline(aes(yintercept = plus_sd), lty=2, col=colours[2]) +
geom_line(aes(y = n), col=colours[1]) +
facet_wrap(~system_size, scales = "free_y") 

Finally, let’s update chunk figure1 to include a figure caption. The text for the caption is at the bottom of paper.txt. We can include it in the chunk header through chunk option fig.cap like so:

    {r figure1, figure1, fig.cap="Population dynamics from a Gillespie simulation of the Levins model with large (N=1000, panel A) and small (N=100, panel B) number of sites (blue) show relatively weaker effects of demographic noise in the bigger system. Models are otherwise identical, with e = 0.2 and c = 1 (code in appendix A). Theoretical predictions for mean and plus/minus one standard deviation shown in horizontal re dashed lines."} 

## Render final document to pdf

Let’s check our final work by re-knitting to pdf. You should be looking at something that looks a lot like paper.pdf

Finally, before we’re finished, let’s ensure the additional dependencies introduced in the paper are included. We can use rrtools::add_dependencies_to_description()

rrtools::add_dependencies_to_description()

This function scans script files (.R, .Rmd, .Rnw, .Rpres, etc.) for external package dependencies indicated by library(), require() or :: and adds those packages to the Imports field in the package DESCRIPTION:

Imports:
bookdown,
dplyr,
ggplot2 (>= 3.0.0),
ggthemes (>= 3.5.0),
here (>= 0.1),
knitr (>= 1.20),
rticles (>= 0.6)


## Final compendium

You can see the resulting rrcompendium here

The complete compendium should contain the following files:

.
├── CONDUCT.md
├── CONTRIBUTING.md
├── DESCRIPTION
├── NAMESPACE
├── R
│   └── process-data.R
├── analysis
│   ├── data
│   │   ├── DO-NOT-EDIT-ANY-FILES-IN-HERE-BY-HAND
│   │   ├── derived_data
│   │   └── raw_data
│   │       └── gillespie.csv
│   ├── figures
│   ├── paper
│   │   ├── elsarticle.cls
│   │   ├── mybibfile.bib
│   │   ├── numcompress.sty
│   │   ├── paper.Rmd
│   │   ├── paper.fff
│   │   ├── paper.pdf
│   │   ├── paper.spl
│   │   ├── paper.tex
│   │   ├── paper_files
│   │   │   └── figure-latex
│   │   │       └── figure1-1.pdf
│   │   └── refs.bib
│   └── templates
│       ├── journal-of-archaeological-science.csl
│       ├── template.Rmd
│       └── template.docx
├── inst
│   └── testdata
│       └── gillespie.csv
├── man
│   └── recode_system_size.Rd
├── rrcompendium.Rproj
└── tests
├── testthat
│   └── test-process-data.R
└── testthat.R



sessionInfo()