Reproduce paper in Rmd

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.

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"

address

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

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.

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.

Check Math expressions and Markdown extensions by bookdown for more information.

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.

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}
    
 ```

knitr::opts_chunk$set(echo = FALSE, message=FALSE, warning=FALSE, 
                      dev="cairo_pdf", fig.width=7, fig.height=3.5)

knitr::opts_chunk$set(echo = FALSE, message=FALSE, warning=FALSE, 
                      dev="cairo_pdf", fig.width=7, fig.height=3.5)

library(dplyr)
library(readr)
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

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

# load-data
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") 

read_csv(here::here("analysis", "data","raw_data", "gillespie.csv"), 
         col_types = "cdiddd")  %>% 
    # recode-data
    recode_system_size() %>% 
    # plot-gillespie
    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") 

  ```{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."} ```

🔨 Install and Restart

Commit and push to GitHub!