2020-knitrdata.Rmd

---
title: "knitrdata: A Tool for Creating Standalone Rmarkdown Source Documents"
date: "2023-01-18"
author:
  - name: David M. Kaplan
    url: https://www.davidmkaplan.fr
    email: david.kaplan@ird.fr
    orcid: 0000-0001-6087-359X
    affiliation: MARBEC
    address:
    - Univ Montepllier, CNRS, Ifremer, IRD
    - Sète, France
    affiliation2: Institute de Recherche pour le Developpement (IRD)
    address2:
    - UMR MARBEC
    - av. Jean Monnet
    - CS 30171
    - 34203 Sète cedex, France
abstract: >
  Though Rmarkdown is a powerful tool for integrating text with code for analyses in a single source document exportable to a variety of output formats, until now there has been no simple way to integrate the data behind analyses into Rmarkdown source documents. The `knitrdata` package makes it possible for arbitrary text and binary data to be integrated directly into Rmarkdown source documents via implementation of a new `data` chunk type. The package includes command-line and graphical tools that facilitate creating and inserting `data` chunks into Rmarkdown documents, and the treatment of `data` chunks is highly configurable via chunk options. These tools allow one to easily create fully standalone Rmarkdown source documents integrating data, ancillary formatting files, analysis code and text in a single file. Used properly, the package can facilitate open distribution of source documents that demonstrate computational reproducibility of scientific results. 
preamble: |
  % Any extra LaTeX you need in the preamble
  \usepackage{longtable}
  \usepackage{calc} % for calculating minipage widths
  \usepackage{etoolbox}

  
# per R journal requirement, the bib filename should be the same as the output 
# tex file. Don't forget to rename the bib file and change this example value.
type: package
output: 
  rjtools::rjournal_web_article:
    self_contained: yes
    toc: no
bibliography: "2020-knitrdata.bib"
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
options(tinytex.verbose = TRUE)

library(tidyverse)

# Install knitrdata if not already installed
if (!requireNamespace("knitrdata")) {
  if (!requireNamespace("remotes"))
    install.packages("remotes")
  remotes::install_github("dmkaplan2000/knitrdata",ref="v0.6.1")  
}
library(knitrdata)
```

```{r r-packages-bib,eval=FALSE}
# Add bib references for R packages,
knitr::write_bib(c(.packages(),"rticles","starticles","rmarkdown","knitr",
                   "remotes","bookdown",
                   "kableExtra","knitrdata","gpg","xfun","citr"),
                 "2020-knitrdata.r-packages.bib")
```

```{r zotero-bib,eval=FALSE}
# Download refs from zotero
download.file("http://127.0.0.1:23119/better-bibtex/collection?/1/papers/2020-knitrdata.bibtex","2020-knitrdata.zotero.bib")
```

```{r combined-bib,eval=FALSE}
x = readLines("2020-knitrdata.zotero.bib")
y = readLines("2020-knitrdata.r-packages.bib")
writeLines(c(x,y),"2020-knitrdata.bib")
```


# Introduction

The basic principles of open science are that the data, research methodologies and analysis tools (e.g., the specific computational tools) used for scientific research should be made publicly available so that others can confirm and validate scientific analyses. Open science is particularly important for studies and disciplines for which true experimental replication is often difficult or impossible due to spatial, temporal or individual specificity [e.g., we cannot replicate Earth; @powersOpenScienceReproducibility2019]. In these cases, computational reproducibility, i.e., the ability to reproduce analytic results given the original data and analysis code, can still be achieved and can provide significant credibility to results [@powersOpenScienceReproducibility2019]. Though scientists, governments and journals often place great emphasis on access to raw data [@casseyReproducibilityRepeatabilityEcology2006; @lowndesOurpathbetter2017], it is important to remember that computational reproducibility can only be assured if data, methods, computational tools and the relationships between these are *all* openly accessible. Even when data are made publicly available, there are often significant gaps between the Methods section of a publication and the raw data that complicate reproducibility without access to the detailed code used to generate results. It is, therefore, essential for computational reproducibility that the code used to generate results be distributed along with the data and the publication itself. Though there are a number of potential ways to distribute all these elements together, probably the most common current approach is to place the data in a publicly accessible data store (e.g., [Dryad](https://datadryad.org)) and to associate the code with the publication via the supplementary material and/or by including it in the data store. Though this is a perfectly viable approach that can greatly enhance transparency of research, it physically separates data from analysis code and interpretation of results, potentially leading to confusion and/or loss of information regarding how these different element interrelate. At times, it would be more convenient, transparent and/or effective to join all the elements into a single document. The R package presented here, \CRANpkg{knitrdata} [@R-knitrdata], provides tools for doing just that - integrating data directly into Rmarkdown source documents so that data, code for analyses and text interpreting results are all available in a single file.

Rmarkdown [@R-rmarkdown] has become an increasingly popular tool for generating rich scientific documents while maintaining a source document that makes explicit the relationship between text and specific analyses used to produce results [@knitr2014; @lowndesOurpathbetter2017]. In a nutshell, Rmarkdown source documents are text documents comprised of two major elements: structured text that make up the headings and paragraphs of the document, and blocks of code (typically, but not exclusively, R code) for doing analyses and generating figures and tables. Rmarkdown source documents can be processed into a variety of final output formats, including PDF documents formatted for scientific publication. During this processing, the blocks of code in the source document are executed and used to augment the final output document with figures, tables and analytic results. In addition to providing a single source document that includes both written text and code for carrying out analyses, Rmarkdown has other benefits for open science, such as requiring the user to provide fully functioning code that runs from start to end without errors and facilitating reuse and updating of documents when new data arrives.

Until now, however, it has been difficult to integrate the raw data itself that are the bases for analyses directly into Rmarkdown source documents. Typically, data are placed in external files that are accessed via R code contained in the Rmarkdown source document that is executed during the knitting. As previously mentioned, this has the disadvantage of physically separating data from analysis code and text contained in the Rmarkdown source document, potentially leading to confusion and/or information loss. Furthermore, on a practical level, it often can be extremely convenient to merge all pertinent information into a single source document (e.g., to facilitate collaboration on an Rmarkdown source document). `knitrdata` provides a simple mechanism for integrating arbitrary text or binary data directly into Rmarkdown source documents, thereby allowing one to create standalone source documents that include all the elements necessary for conducting analyses. This integration is done with minimal additional formatting of the data (e.g., allowing one to insert comma-separated value (CSV) data without escaping quotation marks directly into Rmarkdown documents) and in a way that clearly visually separates data from R code, thereby facilitating comprehension of the different elements that contribute to analyses. `knitrdata` also facilitates encryption of data integrated into Rmarkdown source documents, thereby allowing one to merge data with analysis code and text even in cases where industrial or ethical privacy constraints restrict data access to a specific group of individuals.

Below, I briefly provide a conceptual overview of how `knitrdata` works, presenting some simple examples of its use and the tools available to facilitate integrating data into Rmarkdown source documents. I then discuss typical use cases and workflows for development of Rmarkdown source documents with `knitrdata`, as well as a number of potential caveats for its use. I conclude by reflecting on the value of `knitrdata` for achieving computational reproducibility and its place within the growing pantheon of tools that make Rmarkdown an increasingly essential tool for research.

# knitrdata installation and usage

The `knitrdata` package is available on [CRAN](https://cran.r-project.org/package=knitrdata), though the latest version can be installed from [github](https://github.com/dmkaplan2000/knitrdata) using the \CRANpkg{remotes} [@R-remotes] package:

```{r eval=FALSE,echo=TRUE}
remotes::install_github("dmkaplan2000/knitrdata",
                        build_vignettes=TRUE)
```

Once the package has been installed, all that is needed to use the functionality provided by the package in a Rmarkdown source document is to load the library at the start of the document, typically in the `setup` chunk:

```{r echo=TRUE}
library(knitrdata)
```

# Conceptual overview of `knitrdata`

To understand how `knitrdata` works and the functionality it provides, one must first understand some of the terminology and functioning of Rmarkdown itself. As previously mentioned, Rmarkdown documents are a combination of text written in *markdown*, a simple, structured text format that can be translated into a large number of final output formats, and code for doing analyses that can augment the final output document with analytic results, tables and figures. The code is contained in specially delimited blocks, referred to as *chunks*. For example, adding the following to an Rmarkdown document:

````markdown
`r ''````{r}
plot(-5:5,(-5:5)^2,type="l")
`r ''````
````

would add a plot of a parabola to the final output document. The process of translating a Rmarkdown document into a final output document is known as *knitting*, and this process is carried out using (often implicitly via RStudio) the \CRANpkg{knitr} package [@knitr2015].

Though code chunks typically contain R code, `knitr` supports a large number of other *language engines*, allowing one to integrate analyses in a number of other computer languages, including C, Python and SQL. For example, one could use the SQL language engine to import the contents of a database table into the R environment by including the following chunk in a Rmarkdown source document:

````markdown
`r ''````{sql connection="dbcon",output.var="d"}
SELECT * FROM "MyTable";
`r ''````
````

During knitting, this will create in the R environment a variable `d` containing the contents of the table `MyTable` accessible via the (previously created) active R database connection `dbcon`. Note that the name of the database connection and the name of the output variable are supplied in the chunk header via what are known as *chunk options*. Though this database table could be imported into the R environment without the SQL language engine using R code:

````markdown
`r ''````{r}
d = dbGetQuery(dccon,"SELECT * FROM \"MyTable\";")
`r ''````
````

the use of the SQL language engine has both practical and conceptual advantages. On the practical side, it avoids the need to escape quotation marks and allows text editors to recognize and highlight the code as SQL, both of which becoming increasingly valuable as the length and complexity of SQL queries increase. On the conceptual side, using the SQL engine visually separates database queries from R code and text, thereby better communicating the structure and functioning of analyses in Rmarkdown documents.

The `knitrdata` package works in many ways analogously to the SQL language engine, adding a new `data` language engine to the list of language engines known to `knitr` that is specifically designed to import raw "data" into the R environment and/or export it to external files. Here the term "data" is used in a very wide sense, including not only standard data tables (e.g., CSV text files) or binary data (e.g., RDS files, NetCDF files, images), but also text and formatted text (e.g., XML files, BibTeX files). For example, placing the following chunk in a Rmarkdown source document will, during the knitting process, create in the R environment a data frame `d` containing the contents of the comma-separated values (CSV) data in the chunk (provided that the `knitrdata` package has been previously loaded as described above):

````markdown
`r ''````{data output.var="d",loader.function=read.csv}
name,score
David M. Kaplan,1.2
The man formerly known as "Prince",3.4
Peter O'Toole,5.6
`r ''````
````

```{data output.var="d",loader.function=read.csv,echo=FALSE}
name,score
David M. Kaplan,1.2
The man formerly known as "Prince",3.4
Peter O'Toole,5.6
```

As with the SQL language engine, the name of the output variable for the chunk is supplied with a chunk option and in this example a `loader.function` option instructs `knitrdata` how to translate the contents of the chunk into a usable R object (in this example the R function `read.csv` is used to translate the CSV data into a data frame).

There are of course a number of other ways that such a simple data table could be imported into the R environment, including via an external data file or directly in R code, one approach to which might be:

````markdown
`r ''````{r}
d = read.csv(textConnection(
"name,score
David M. Kaplan,1.2
The man formerly known as \"Prince\",3.4
Peter O'Toole,5.6
"))
`r ''````
````

However, using the `data` language engine has much the same practical and conceptual advantages as the SQL data language engine, avoiding the need for escaping certain characters and visually separating data from code, both of which become increasingly valuable as dataset size increases.

Incorporating binary data into Rmarkdown source documents is a bit more complicated as the data must first be *encoded* as ASCII text (see the Section below on [Binary data chunks](#binary) for details), but the basic principles are the same - encoded binary data is incorporated into a `data` chunk and chunk options are used to tell `knitrdata` how to decode the data and load it into the R environment during knitting (see Table \@ref(tab:knitrdatachunkops) for a full list of `data` chunk options). There is also the possibility of saving `data` chunk contents out to external files using the `output.file` chunk option. This option is particularly useful for integrating into Rmarkdown source documents ancillary text files used in the final formatting of the output of the knitting process, such as BibTeX files with references, LaTeX style files for PDF output and CSS style files for HTML output. For example, the following chunk would export a BibTeX reference to a file named `refs.bib`, taking care not to overwrite an existing file with the same name [though note that similar functionality can also be achieved with the `cat` language engine; @XieMarkdownCookbook2020]:

````markdown
`r ''````{data output.file = "refs.bib", eval=!file.exists("refs.bib")}

@book{allaireRmarkdownDynamicDocuments2020,
  title = {Rmarkdown: {{Dynamic}} Documents for r},
  author = {Allaire, JJ and Xie, Yihui and McPherson, Jonathan and Luraschi, Javier and Ushey, Kevin and Atkins, Aron and Wickham, Hadley and Cheng, Joe and Chang, Winston and Iannone, Richard},
  year = {2020}
}
`r ''````
````

As code chunks are processed during knitting before generating the final output document, these files can be generated at any point during the knitting process using `data` chunks (in particular, it is often most practical to place this information at the end of a Rmarkdown document).

```{data output.var="t",loader.function=read.csv,loader.ops=list(sep="|",check.names=FALSE),echo=FALSE}
Chunk option|Description
`format`|One of `'"text"'` or `'"binary"'`. Defaults to `'"text"'`.
`encoding`|One of `'"asis"'`, `'"base64"'` or `'"gpg"'`. Defaults to `'"asis"'` for `format='"text"'` and `'"base64"'` for `format='"binary"'`.
`decoding.ops`|A list with additional arguments for `data_decode`. Currently only useful for passing the `verify` argument to \CRANpkg{gpg}::`gpg_decrypt` [@R-gpg] for `gpg` encrypted chunks.
`external.file`|A character string with the name of a file whose text contents will be used as if they were the contents of the data chunk.
`md5sum`|A character string giving the correct md5sum of the *decoded* chunk data. If supplied, the md5sum of the decoded data will be calculated and compared to the supplied value, returning an error if the two do not match.
`output.var`|A character string with the variable name to which the chunk output will be assigned. At least one of `output.var` or `output.file` must always be supplied.
`output.file`|A character string with the filename to which the chunk output will be written. At least one of `output.var` or `output.file` must always be supplied.
`loader.function`|A function that will be passed (as the first argument) the name of a file containing the (potentially decoded) contents of the data chunk.
`loader.ops`|A list of additional arguments to be passed to `loader.function`.
`line.sep`|Only used when `encoding='"asis"'`. In this cases, specifies the character string that will be used to join the lines of the data chunk before export to an external file, further processing or returning the data. Defaults to `knitrdata::platform.newline()`.
`eval`|A boolean indicating whether or not to process the chunk. Defaults to `TRUE`.
`echo`|A boolean indicating whether or not to include chunk contents in Rmarkdown output. Defaults to `FALSE`.
`max.echo`|An integer specifying the maximum number of lines of data to echo in the final output document. Defaults to 20. If the data exceeds this length, only the first 20 lines will be shown and a final line indicating the number of ommitted lines will be added.
```

```{r knitrdatachunkops,echo=FALSE,eval=TRUE,results='asis'}
t = t[order(t$`Chunk option`),]
label = knitr::opts_current$get('label')
pander::panderOptions("table.alignment.default","left")
pander::pander(
  t,caption=paste0("(\\#tab:",label,") Full list of knitrdata chunk options."),
  split.cell=50,row.names=FALSE)
```


```{r knitrdatachunkopskable,echo=FALSE,eval=FALSE}
t = t[order(t$`Chunk option`),]
knitr::kable(t,caption="Full list of knitrdata chunk options.",
             booktabs=TRUE,row.names=FALSE) |>
  kableExtra::column_spec(2,width="30em")
```

Using `data` chunks, just about any data or information that would typically be stored in external files can be integrated directly into Rmarkdown source documents. In particular, this permits creating standalone Rmarkdown source documents that can be knitted without need for external data files, thereby uniting text, code and data in a single source document. 

Note that this is different from the `self_contained` YAML header option permitted by some Rmarkdown output formats, notably HTML output formats. This option attempts to create a single *output* file that contains everything needed to visualize the final output document (e.g., in the case of HTML documents, the output HTML file will contain any CSS styles, javascript libraries and/or images used by the document), but it says nothing about whether or not external files are needed to knit the Rmarkdown source document (i.e., it is relevant to the output side of knitting, not the input side). In fact, a source document can be standalone in that all data and formatting files needed for knitting are incorporated within it using `data` chunks, but the final output (HTML) document may not be self contained because it relies on external files or libraries for visualization, and vice-versa (i.e., standalone source documents and standalone output documents are two separate and independent concerns).

Under the hood, the way `knitrdata` works is by adding (using the `knitr::knit_engines$set()` function) to the list of language engines that `knitr` maintains internally a `data` entry that points to a function inside the `knitrdata` package that processes `data` chunks (specifically the `eng_data` function, though users would typically not interact directly with this function). When knitting a Rmarkdown document, `knitr` will call this function each time a `data` chunk is encountered, passing it both the textual contents of the chunk and any chunk options. The function then uses this information to process the chunk, decoding it if necessary (via the `format` and `encoding` chunk options) and returning it as either a variable in the R environment (`output.var` chunk option) and/or an external file (`output.file` chunk option) after any additional processing has been carried out (via, e.g., the `loader.function` chunk option).  

## Text data chunks

Though a basic example of a `data` chunk containing CSV tabular data has been presented in the previous section, it is useful to develop that example a bit more to better understand the functioning of `knitrdata`. The simplest `data` chunks contain plain text that is read, but not processed by `knitrdata`. For example, omitting the `loader.function` chunk option from the previously presented `data` chunk with CSV data produces a different outcome:

````markdown
`r ''````{data output.var="txt"}
site,density
a,1.2
b,3.4
c,5.6
`r ''````
````

During the knitting process, this will place the text contents of the chunk into a R variable named `txt`, but no further processing of the text will be carried out (i.e., the variable `txt` will contain the literal text contents of the chunk, excluding the header with the chunk options and the tail). One could later convert the text into a R `data.frame` using the `read.csv` command in a R chunk placed after the `data` chunk:

````markdown
`r ''````{r}
d <- read.csv(text=txt)
`r ''````
````

The `loader.function` chunk option used in the initial `data` chunk example above causes `knitrdata` to combine into one process the two steps of (1) reading in the chunk contents and (2) converting them into a usable R data object. Whereas the first of these steps, reading the chunk contents, is carried out for all `data` chunks, the second only occurs if the `loader.function` chunk option is given. `loader.function` should be a function that takes in the name of a file containing the chunk contents and returns the processed contents. Though `read.csv` is likely to be a common choice, there are many other possibilities including `readRDS`, `read.csv2`, `scan`, `png::readPNG` and custom, user-defined functions. One can also supply a list of additional input arguments to the loader function using the `loader.ops` chunk option (e.g., one could change the expected separator in CSV data using `loader.ops=list(sep="|")`).

## Binary data chunks {#binary}

Though text data chunks can integrate into Rmarkdown source documents many small- to medium-sized tabular data sets, binary data formats, such as RDS files, are more convenient for more complicated and/or larger data sets. Incorporating binary data into Rmarkdown documents requires additional steps relative to text data: encoding the binary data as text and telling the `data` chunk how to decode the encoded data. `knitrdata` provides tools for simplifying these two steps that currently support encoding and decoding of two widely-used encodings: `base64` and `gpg`. [`base64`](https://en.wikipedia.org/wiki/Base64) is a standard format for encoding binary data as ASCII text based on translation of 6 bits of information into one of 64 alphanumeric and symbolic ASCII characters. Base64 encoded data looks like a somewhat intimidating jumble of characters, but the format is extremely widely used behind the scenes in many common web applications, such as email attachments and embedding images in HTML pages. In particular, `base64` is widely supported by a number of software packages and programming languages, including R, Python, Matlab and Julia, so `base64` encoded data is highly readable and likely to remain so for a very long time. 

`gpg`, standing for [GNU Privacy Guard](https://gnupg.org/), is a standard protocol for encrypting information so that only those with specific decryption keys can have access. This format can be used to ensure that only specific individuals can actually read and utilize the data contained in a Rmarkdown source document, as might be necessary when dealing with confidential (e.g., medical or trade-secret) data. Here, I focus primarily on `base64` encoding as this is the simplest and likely most common format for binary `data` chunks, and a full description of the configuration and use of GPG is beyond the scope of this document. The detailed use of `gpg` is, however, described in the package vignette.

Though `knitrdata` users rarely need to encode data by hand as the package provides [graphical tools](#addins) for this, it is instructive to have a basic understanding of the underlying functions for encoding and decoding data: `data_encode` and `data_decode`. `data_encode` takes the name of the file containing the data and the name of the encoding format, and it returns the encoded data that one would incorporate into a `data` chunk, either to the R command line or to a file. For example, if one saves the data frame `d` created in the previous section to a binary `RDS` file:

```{r echo=TRUE}
saveRDS(d,"data.RDS")
```

then one can encode this data as `base64` using:

```{r echo=TRUE}
b64 = knitrdata::data_encode("data.RDS","base64")
cat(b64)
```

This jumble of characters starting with `"H4sI"` is the base64 encoded contents of the binary file that one would place in a `data` chunk. For large files, it is often more practical to output the encoded data to a file by supplying the `output` argument:

```{r echo=TRUE,comment=NA}
knitrdata::data_encode("data.RDS","base64",
                       output="data.RDS.base64")
```

GPG encoding works similarly to base64 encoding, but one must change the format from `"base64"` to `"gpg"` and specify the encryption key (i.e., the receiver ID) to be used to encrypt the data.

Once one has the encoded data, one can use it in a `data` chunk by supplying the `format="binary"` chunk option and, optionally, an appropriate `loader.function` to convert the data into a R object:

````markdown
`r ''````{data format="binary",output.var="d",loader.function=readRDS}
H4sIAAAAAAAAA12OvQvCMBDFz48OKn6A4Hybk11c3HQQFxE7VHA9aopimkgiFjf/
Z2etl5oOesO93I/3crdvA0ADmnXuAT8h2MWryYynIQ9MYfA1QIu1v6Tb6YCbENd0
kaQ8xvgoMCOFqTaZMPKOZ6VzhWQxMieVCO/rRuIqDG7HsdZSMOi5v+fPaVmLjtdR
WhaUV0HNhwNFmbD+oLqHTQcrg020Ef+pRJKtUhVsH+hKYWpc9tfeMjoPq0Vdt+jB
rSiKF8v7A6bdy9EtAQAA
`r ''````
````

```{data format="binary",output.var="d",loader.function=readRDS}
H4sIAAAAAAAAA12OvQvCMBDFz48OKn6A4Hybk11c3HQQFxE7VHA9aopimkgiFjf/
Z2etl5oOesO93I/3crdvA0ADmnXuAT8h2MWryYynIQ9MYfA1QIu1v6Tb6YCbENd0
kaQ8xvgoMCOFqTaZMPKOZ6VzhWQxMieVCO/rRuIqDG7HsdZSMOi5v+fPaVmLjtdR
WhaUV0HNhwNFmbD+oLqHTQcrg020Ef+pRJKtUhVsH+hKYWpc9tfeMjoPq0Vdt+jB
rSiKF8v7A6bdy9EtAQAA
```

During knitting, this chunk will be processed, decoding the encoded binary RDS file and loading it into the variable `d` using the `readRDS` function. `knitrdata` will by default assume that the encoding is base64 when `format="binary"`, but one can also specify the chunk option `encoding="base64"` for increased clarity. For GPG encoded data, one would use `encoding="gpg"`. As with text `data` chunks, one can alternatively output the decoded contents of the chunk to a file (`output.file` option) or return it to the R session as a `raw` binary vector (by not supplying a `loader.function`).

## RStudio add-ins for creating data chunks {#addins}

```{data insertdialogdatachunk, format = "binary", encoding = "base64", output.var = "insertdialog", echo = FALSE, md5sum = "2cbca10413739a943476bcf5c7ebe3a7", eval=TRUE,loader.function=png::readPNG}
iVBORw0KGgoAAAANSUhEUgAAAdMAAAJACAYAAADW5IzdAAAYrXpUWHRSYXcgcHJv
ZmlsZSB0eXBlIGV4aWYAAHja7ZtXdmU5dkT/MQoNAd4MB3YtzUDD1w7cR5eW1d1/
UrIySd6HC3NMnAgAZfb//Pcx/8WfEm02MZWaW86WP7HF5js/VPv8afdfZ+P99/5Z
4fWT+/rc8LO/P3keBb6H54PSn++u8zx9vPA2hhtfn5v6+sTXV0evD946DBpZQ63P
k+S5f567+Oqo7eeH3Gr5PNXxzNPOV8M7ldffUJ5VvHWi383nB7FgpZVoFbzfwQV7
/63PDMLzt/PE8y9zpZ0NjZ9T8IZvPtjXTDDIl+W9fbf2s4G+GHme19J+tP77Tz8Y
3/fX8/CDLfNbR/nXH7j0a+NfE38aOLx+Mjz+8sEoOPbH5bz+nrPqOftZXY8Zi+ZX
RFnzZh29Q0M6ieG+lvkq/E38XO5X46vabifOWXbawdd0zXm8coyLbrnujtv3+3ST
KUa/feG799OH+6yG4puf8lWI+nLHFzy2QsVZ028TAo/9+1zcHbfd8aarjLwcTb2j
M7n9t1/mTx/+ky9zzpSJnK2PnYgL5uUVuUxDntO/tMIh7rz8lq6B375e7ref4keh
GmkmM1cW2O14uhjJfcRWuH4OtEt8f7LCmbJeHWAixk5MxgU8YLMLyWVni/fFOexY
cVBn5j5EP/CAS8kvJuljCNmb4qvX2LxT3G3rk89ej8EmHJFCDgXfkFM4K8ZE/JRY
iaGeQooppZxKqia11HPIMaecc8kCuV5CiSWVXEqppZVeQ4011VxLrbXV3nwLYGBq
uZVWW2u9e9MZqNNXp33nyfAjjDjSyKOMOtrok/CZcaaZZ5l1ttmXX2EBEyuvsupq
q29nNkix404777LrbrsfYu2EE086+ZRTTzv93WvulbY/fv0Dr7mX1/z1lNqVd6/x
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```{r addinfigure,fig.cap="The (a) 'Insert filled data chunk' and (b) 'Remove chunks' RStudio add-ins included with knitrdata. The dialogues will open when selected from the 'Addins' menu of RStudio. They allow one to (a) insert a data chunk containing the contents of an existing external data file into an open Rmarkdown document, and (b) delete one or more chunks from an open Rmarkdown document.",out.width=400}
op = par(mfrow=c(1,2),mar=c(0.1,3.1,0.1,0.1),oma=rep(0.1,4))

x = insertdialog
d = dim(x)
plot(c(0,d[2]),c(0,d[1]),type="n",axes=FALSE,xlab="",ylab="",asp=1)
yt = par()$usr[4]
yd = yt - d[1]
rasterImage(x,0,yd,d[2],d[1]+yd)
mtext("(a)",2,line=0,adj=1,padj=1,las=1,at=yt)

x = removedialog
d = dim(x)
plot(c(0,d[2]),c(0,d[1]),type="n",axes=FALSE,xlab="",ylab="",asp=1)
yt = par()$usr[4]
yd = yt - d[1]
rasterImage(x,0,yd,d[2],d[1]+yd)
mtext("(b)",2,line=0,adj=1,padj=1,las=1,at=yt)

par(op)
```

As manually encoding data and creating `data` chunks can be complicated, particularly for large data files, `knitrdata` includes graphical RStudio add-ins that do all the hard work of incorporating data in Rmarkdown documents. The principal add-in is called `Insert filled data chunk` (Fig. \@ref(fig:addinfigure)a). Though its use is meant to be largely self-explanatory, an instructional video is available on YouTube [@kaplanUsingKnitrdataCreate2020]. The basic idea is that one opens a Rmarkdown source document in the RStudio editor, places the cursor at the location one wants to insert a `data` chunk and then activates the add-in. The add-in prompts for the name of the data file to be incorporated, as well as values for various `data` chunk output and processing options. Based on the type of data file selected, the add-in will attempt to select or suggest various appropriate options. For example, if a RDS file is chosen, then format will be set to `binary`, encoding will be set to `base64` and the loader function will be set to `readRDS`. These defaults can be manually modified if not appropriate. The add-in also greatly facilitates and encourages the use of [MD5 sum data integrity checks](#integrity). After all options have been set, one clicks on `Create chunk` and an appropriately-formatted `data` chunk will be inserted in the active Rmarkdown source document at the cursor location.

`knitrdata` also provides a `Remove chunks` add-in that allows ones one to quickly delete unwanted (`data` and non-`data`) chunks (Fig. \@ref(fig:addinfigure)b), as well as a set of functions for command-line examination, creation and removal of chunks from Rmarkdown documents (e.g., `create_chunk`, `insert_chunk`, `list_rmd_chunks`, `remove_chunks`).

If one is not using RStudio to edit and knit Rmarkdown documents, then one can invoke the Skiny dialog to create `data` chunks directly from the command line using the `create_data_chunk_dialog` function contained in the `knitrdata` package. In this case, chunk contents will be (silently) returned on the command line for later insertion in a Rmarkdown document.

# Use cases

There are a number of use cases for the functionality provided by `knitrdata`, primary among them providing a single source for public diffusion of all information related to a publication or report, and/or making collaboration on Rmarkdown source documents simpler by eliminating or reducing the need for external files. A simple example of the prior is the Rmarkdown source document used to generate [this publication](https://github.com/dmkaplan2000/knitrdata_examples/blob/master/2020-knitrdata.Rmd), which includes text `data` chunks for the tabular data in Table \@ref(tab:knitrdatachunkops), as well as the ancillary formatting files associated with the document (BibTeX and LaTeX style files), and encoded binary `data` chunks for the PNG images in Figs. \@ref(fig:addinfigure) & \@ref(fig:openclosechunkfig). 

A more complicated example is the Rmarkdown source document for @wainQuantifyingIncreaseFishing2021, [publicly available on github](https://github.com/dmkaplan2000/knitrdata_examples/blob/master/wain.et.al.2020.standalone.Rmd). In this case, we wished to provide a permanent public record of the methods used in the paper and ensure that results could be verified, while at the same time respecting confidentiality agreements with respect to fine scale fishing activity data used in the paper. To achieve this we integrated the fine scale data in the Rmarkdown source document as an encrypted GPG `data` chunk. This approach may have value for a wide number of other studies using sensitive economic, social or medical data. To provide a complete record of the paper in a single document, we also integrated the Rmarkdown source document for the online supplementary materials into a `data` chunk within the Rmarkdown source document for the paper itself. As this supplementary materials document contains Rmarkdown chunks that would otherwise confuse the knitting process if integrated as raw text inside a `data` chunk, we base64 encoded this source document before including it in a `data` chunk. The document also contains `data` chunks for small data tables and for exporting to external files the ancillary formatting files required for knitting the document (BibTeX references, the LaTeX style file, the CSL citation style file, etc.). Finally, during the knitting process, the document also generates a lightweight version of itself that does not include the main data chunk, using the functionality of the `knitrdata` package to remove large data chunks. Overall, `knitrdata` provided a convenient way of generating a single document that contained all the necessary information for generating the final publication, thereby demonstrating computational reproducibility for the publication.

The uses of `data` chunks tend to fall into one of four general, not mutually-exclusive use cases:

1) Integration of ancillary formatting files into the Rmarkdown source document, thereby reducing the number of external files needed to knit a document
2) Inclusion of small- to medium-sized tabular data used in analyses and/or for tables
3) Inclusion of larger data sets using encoded binary data
4) Inclusion of confidential data using GPG-encrypted `data` chunks

Though the first of these use cases, integration of ancillary formatting files, can also be achieved with the `cat` language engine that is included with the `knitr` package [@XieMarkdownCookbook2020], `knitrdata` provides functionality that make this task easier and more secure. First, `knitrdata` allows for integrity checks on chunk contents that can control for unintentional modification of chunk contents (see the section on [data integrity](#integrity) below). Second, RStudio add-ins provided by the `knitrdata` package facilitate the integration of data into Rmarkdown source documents and the use of integrity checks. Finally, encoding of text documents permits integrating files that contain Rmarkdown chunks or other formatting that would otherwise be problematic within a `cat` chunk.

The second of these use cases, tabular data, can also in principle be achieved using other tools in R, such as a `textConnection` as shown above or via functionality in the \CRANpkg{tibble} package [@R-tibble]. Nevertheless, the use of `data` chunks is generally more ergonomic and flexible for anything but the smallest data tables as it allows the user to format data exactly as it would be in an external CSV file, without additional markup or the need to escape quotation marks. As an example, the information contained in Table \@ref(tab:knitrdatachunkops) was implemented in the source document for this paper as a `data` chunk as it contains lots of quotations and formatting that would have been tedious to include using other approaches.

The third and fourth use cases for `data` chunks, involving encoded binary data, are unique to `knitrdata` and allow for integration of complex data sets that would otherwise be very difficult to include in a Rmarkdown source document. 

# Workflow

When and in what ways to use the functionality provided by `knitrdata` during the development of a Rmarkdown source document requires some thought and depends to some degree on the project goals. If the goal is to create a final Rmarkdown source document that demonstrates computational reproducibility of a set of results, then it may not be necessary or practical to use `data` chunks during the development stages of the project as the use of `data` chunks necessarily weighs down a Rmarkdown source document with information (e.g., binary data) that may not be immediately useful to authors during development. In this case, it may be best to work initially as one has always done, relying on external files for data and formatting. External data and formatting files can be incorporated in `data` chunks at the end of development when it is time to generate a final archival/public version of a Rmarkdown source document.

On the other hand, if the objective of using `knitrdata` also includes reducing the complexity of collaborating on a Rmarkdown source document by reducing the number of external files necessary for knitting a document, then certain types of `data` chunks can be incorporated in a Rmarkdown source document during the initial phases of development with little impact on authors. Small- to medium-sized tabular data sets can be incorporated and this can have the benefit of making the tabular data visually available during the development process. Similarly, most ancillary formatting files can be placed at the very end of the Rmarkdown source document as these are only used after all chunks have been processed and, therefore, will not encumber the development process. Larger data sets, and in particular binary data sets, are a bit more problematic as they necessarily appear in the Rmarkdown source document before the data is used for analyses and will introduce significant amounts of text that are not human readable into the Rmarkdown source document. For this reason it may be best to leave incorporation of these data until the final stages of development, though see the sections below on [file size](#filesize) and [readability](#readable) for workarounds to these issues.

This latter workflow involving incorporation of `data` chunks in two distinct stages of development is what was used when creating the source document for @wainQuantifyingIncreaseFishing2021. Small data tables and formatting files were incorporated directly into the document from the start, but the larger data set that was the basis for statistical analyses and the Rmarkdown source document for the supplementary materials were only incorporated at the end of development to provide an archival source document for the paper capable of demonstrating computational reproducibility.

# Caveats and concerns

There are a number caveats and concerns with respect to the use of `knitrdata`, all of which have some validity, but for which a number of simple approaches exist to limit their impact. Below, I discuss four of them: [file size](#filesize), [document readability](#readable), [data integrity](#integrity) and [security](#security).

## Won't this create huge Rmarkdown files? {#filesize}

Incorporation of large data sets into `data` chunks will significantly increase the file size of Rmarkdown source documents, potentially making them more difficult to work with. Though it is unlikely to be practical to place extremely large data sets in Rmarkdown source documents, there are many contexts where data sets are sufficiently small so as to be included directly in a Rmarkdown source document. For example, the 8 years of fine scale fishing data used in @wainQuantifyingIncreaseFishing2021 added about 2 MB to the size of the Rmarkdown document when incorporated as a (compressed) RDS file, a size that is manageable and well within the limits of typical email attachments. RStudio currently will not open Rmarkdown documents larger than 5 MB in size, effectively limiting the amount of data that can be placed in a document unless one is willing to forgo graphical editing tools (larger documents can be rendered from the command line using the `rmarkdown::render` function, but not the more convenient and common "Knit" button of RStudio). Despite being in the era of big data, many scientific studies use primary data sets that are smaller than this size limit. As for @wainQuantifyingIncreaseFishing2021, many experimental or field studies may rely on data sets that are relatively small, and building a standalone Rmarkdown source document for these studies is an effective approach to documenting all quantitative information needed to reproduce results.

## Won't Rmarkdown source documents become unreadable? {#readable}

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```

```{r openclosechunkfig,fig.cap="Images demonstrating before (top) and after (bottom) a data chunk has been hidden from view in the RStudio editor. The top image shows two base64-encoded data chunks, one of which is hidden, whereas the other is visible. In the bottom image, both chunks have been hidden. The control for hiding chunk contents (top) and an indicator of a hidden chunk (bottom) are highlighted with red boxes.",out.width=400}
knitr::include_graphics("open-close-chunk-fig.pdf")
```

Large amounts of encoded binary data are undoubtedly not pretty to look at, but readability is not necessarily the primary benefit of using Rmarkdown. Rather, completeness and the articulation of text and analyses are the strengths of Rmarkdown, benefits that `data` chunks enhance as opposed to diminish. Many Rmarkdown documents are already a complex mix of text and code that is difficult to read and manage without the tools RStudio and other editors provide to navigate the document, such as the ability to jump between sections and chunks. `data` chunks are no different in this sense, and use of informative chunk labels can greatly facilitate document navigation. Furthermore, RStudio includes the possibility of hiding chunk contents with a single click (Fig. \@ref(fig:openclosechunkfig)), which can be quite practical when dealing with large `data` chunks. Once hidden, `data` chunk contents can be ignored, allowing one to edit the document unhindered.

## Won't a misplaced keystroke mess up my data? {#integrity}

It is possible to unintentionally corrupt a `data` chunk due to a misplaced keystroke, particularly if the data is encoded and not readily human readable. However, the use of navigation tools and hiding of `data` chunk contents as described [above](#readable) (Fig. \@ref(fig:openclosechunkfig)) can drastically reduce interaction with chunk contents, thereby limiting the possibility for error. Furthermore, there are a number of methods to validate chunk contents, the simplest of which is to do a MD5 sum check using functionality included in `knitrdata`. A MD5 sum is a very large number (typically encoded in hexadecimal) derived from a file's contents that has a vanishingly small probability of being equal to that of another file if the files are not identical. `data` chunks can include a `md5sum` chunk option that specifies a MD5 sum that will be checked against that of the decoded chunk contents, generating an error if the two do not match. In this way, data corruption can be swiftly identified and corrected. The `Insert filled data chunk` RStudio add-in will by default calculate and include a MD5 sum check when inserting binary `data` chunks (and such a check can be optionally requested for text `data` chunks) so that users can easily benefit from these checks without having to worry about the details.

## Are there security concerns when using `knitrdata`?

Any time one runs code from a third party, there are security risks. Typically, code can write files to disk, potentially modifying essential files or installing malicious software. Rmarkdown source documents using the functionality of `knitrdata` are no different in this sense, though the practical risks may be more important as `knitrdata` may encourage users to knit entire documents to gain access to raw data and arbitrary data may be encoded in formats that are not human readable. Reducing these risks involves responsibilities for both the authors and the users of Rmarkdown source documents. For authors, the primary responsibilities are to assure that source documents cannot be modified by third parties between the author and the user, and to use best practices when carrying out file input and output during the knitting process. Integrity of source documents can be protected by using reputable websites with established security protocols for publishing Rmarkdown source documents, including, but not limited to, [github](https://github.com/), [Zenodo](https://zenodo.org/) and the [Dryad](https://datadryad.org). Authors can also publish MD5 sums for Rmarkdown source documents so users can verify the integrity of those documents, though the security of those MD5 sums is only as strong as the websites on which MD5 sums and Rmarkdown source documents are published. Best practices for file input and output include using temporary files and/or relative paths entirely within the base directory containing the Rmarkdown source document when writing files to disk, using file names that are unique (e.g., avoiding generic names like `data.csv`) and performing checks for the existence of files with the same name before writing information to disk. The `Insert filled data chunk` RStudio add-in provided by `knitrdata` encourages the use of file existence checks in the `eval` chunk option controlling whether or not to process `data` chunks that write data to disk using the `output.file` chunk option, thereby avoiding overwriting existing files. 

For users of Rmarkdown source documents, there are a number of simple steps one can take to avoid the most severe security risks. Knitting Rmarkdown source documents from an unprivileged user account and placing Rmarkdown source documents in new, empty directories can reduce the risks of the most malicious attacks. Users should also familiarize themselves with the workings of Rmarkdown source documents before knitting them and check for potentially problematic actions, such as use of absolute file paths and/or communication with external internet resources. This includes examination of the chunk options associated with `data` chunks (in particular, the `output.file` and `loader.function`). If one is primarily interested in just the raw data contained in `data` chunks within a Rmarkdown source document, then RStudio permits manual execution of individual chunks. This includes execution of `data` chunks, which can be processed individually using the `Run current chunk` button of RStudio once the `knitrdata` library has been loaded.

# Conclusion

`knitrdata` provides a simple, but effective, tool for integrating arbitrary data into Rmarkdown source documents. If used appropriately, this can help assure computational reproducibility of many scientific documents by allowing one to integrate all relevant external files and data directly into a single Rmarkdown source document. Anyone who has attempted to validate the results in a publication by requesting the associated data has potentially encountered, if they managed to get the data, a set of one or more data tables with limited metadata and only the publication itself as documentation of the methods. Validating publication results under these conditions is often difficult and time consuming. By encouraging the integration of data, code for carrying out analyses, and text interpreting results in standalone Rmarkdown source documents, Rmarkdown with `knitrdata` can make it much easier to understand, reproduce and validate the details of scientific analyses. This combination can be particularly powerful when combined with other enhancements to Rmarkdown that make it possible to produce a wide variety of sophisticated scientific documents entirely within the confines of Rmarkdown, such as \CRANpkg{bookdown} [@R-bookdown], \CRANpkg{rticles} [@R-rticles] and \CRANpkg{starticles} [@R-starticles] for producing books and scientific publications with Rmarkdown, \CRANpkg{citr} [@R-citr] for bibliographic citations, and \CRANpkg{kableExtra} [@R-kableExtra] for producing sophisticated data tables.

# Online supporting information

The Rmarkdown source documents for this publication and @wainQuantifyingIncreaseFishing2021 are available online at [https://github.com/dmkaplan2000/knitrdata_examples](https://github.com/dmkaplan2000/knitrdata_examples). Additional examples and the package vignette are available in the `knitrdata` package itself.

# Acknowledgements

I would like to thank my colleagues at the MARBEC laboratory in Sète, France for numerous conversations that encouraged me to develop the `knitrdata` package. I would also like to thank Yihui Xie for advice and encouragement regarding the development of the package. The handling editor and an anonymous reviewer provided valuable feedback that significantly improved the manuscript.

<!-- Bibtex references -->
```{data knitrdatabib, format = "text", encoding = "asis", output.file = "2020-knitrdata.bib", echo = FALSE, md5sum="773bd18ca0c17589195a4f44e859d35d", eval=!file.exists("2020-knitrdata.bib"),line.sep="\n"}

@book{allaireRmarkdownDynamicDocuments2020,
  title = {Rmarkdown: {{Dynamic}} Documents for r},
  author = {Allaire, JJ and Xie, Yihui and McPherson, Jonathan and Luraschi, Javier and Ushey, Kevin and Atkins, Aron and Wickham, Hadley and Cheng, Joe and Chang, Winston and Iannone, Richard},
  year = {2020}
}

@book{allaireRticlesArticleFormats2020,
  title = {Rticles: {{Article}} Formats for r Markdown},
  author = {Allaire, JJ and Xie, Yihui and {R Foundation} and Wickham, Hadley and {Journal of Statistical Software} and Vaidyanathan, Ramnath and {Association for Computing Machinery} and Boettiger, Carl and {Elsevier} and Broman, Karl and Mueller, Kirill and Quast, Bastiaan and Pruim, Randall and Marwick, Ben and Wickham, Charlotte and Keyes, Oliver and Yu, Miao and Emaasit, Daniel and Onkelinx, Thierry and Gasparini, Alessandro and Desautels, Marc-Andre and Leutnant, Dominik and {MDPI} and {Taylor and Francis} and {\"O}{\u g}reden, O{\u g}uzhan and Hance, Dalton and N{\"u}st, Daniel and Uvesten, Petter and Campitelli, Elio and Muschelli, John and Kamvar, Zhian N. and Ross, Noam and Cannoodt, Robrecht and Luguern, Duncan and Kaplan, David M.},
  year = {2020}
}

@article{allenOpenScienceChallenges2019,
  title = {Open Science Challenges, Benefits and Tips in Early Career and Beyond},
  author = {Allen, Chris and Mehler, David M. A.},
  year = {2019},
  month = may,
  journal = {PLOS Biology},
  volume = {17},
  number = {5},
  pages = {e3000246},
  publisher = {{Public Library of Science}},
  issn = {1545-7885},
  doi = {10.1371/journal.pbio.3000246},
  abstract = {The movement towards open science is a consequence of seemingly pervasive failures to replicate previous research. This transition comes with great benefits but also significant challenges that are likely to affect those who carry out the research, usually early career researchers (ECRs). Here, we describe key benefits, including reputational gains, increased chances of publication, and a broader increase in the reliability of research. The increased chances of publication are supported by exploratory analyses indicating null findings are substantially more likely to be published via open registered reports in comparison to more conventional methods. These benefits are balanced by challenges that we have encountered and that involve increased costs in terms of flexibility, time, and issues with the current incentive structure, all of which seem to affect ECRs acutely. Although there are major obstacles to the early adoption of open science, overall open science practices should benefit both the ECR and improve the quality of research. We review 3 benefits and 3 challenges and provide suggestions from the perspective of ECRs for moving towards open science practices, which we believe scientists and institutions at all levels would do well to consider.},
  langid = {english},
  keywords = {Careers,Experimental design,Neuroimaging,Open data,Open science,Peer review,Reproducibility,Statistical data},
  file = {/allen.mehler.2019.open_science_challenges,_benefits_and_tips_in_early_career_and_beyond.pdf}
}

@article{casseyReproducibilityRepeatabilityEcology2006,
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  author = {Cassey, Phillip and Blackburn, Tim M.},
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  month = dec,
  journal = {BioScience},
  volume = {56},
  number = {12},
  pages = {958--959},
  publisher = {{Oxford Academic}},
  issn = {0006-3568},
  doi = {10.1641/0006-3568(2006)56[958:RARIE]2.0.CO;2},
  abstract = {The quantitative synthesis of research results is of fundamental importance in seeking to develop ecological generalities and construct refutable theories. It i},
  langid = {english},
  file = {/cassey.blackburn.2006.reproducibility_and_repeatability_in_ecology.pdf}
}

@article{cheruvelilDataIntensiveEcologicalResearch2018,
  title = {Data-{{Intensive Ecological Research Is Catalyzed}} by {{Open Science}} and {{Team Science}}},
  author = {Cheruvelil, Kendra Spence and Soranno, Patricia A.},
  year = {2018},
  month = oct,
  journal = {BioScience},
  volume = {68},
  number = {10},
  pages = {813--822},
  publisher = {{Oxford Academic}},
  issn = {0006-3568},
  doi = {10.1093/biosci/biy097},
  abstract = {Abstract.  Many problems facing society and the environment need ecologists to use increasingly larger volumes and heterogeneous types of data and approaches de},
  langid = {english},
  file = {/cheruvelil.soranno.2018.data-intensive_ecological_research_is_catalyzed_by_open_science_and_team_science.pdf}
}

@article{coutureFunderimposedDataPublication2018,
  title = {A Funder-Imposed Data Publication Requirement Seldom Inspired Data Sharing},
  author = {Couture, Jessica L. and Blake, Rachael E. and McDonald, Gavin and Ward, Colette L.},
  editor = {Wicherts, Jelte M.},
  year = {2018},
  month = jul,
  journal = {PLOS ONE},
  volume = {13},
  number = {7},
  pages = {e0199789},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0199789},
  langid = {english},
  file = {/home/dmk/snap/zotero-snap/common/Zotero/storage/EDV3FUAN/file.pdf}
}

@article{fidlerMetaresearchEvaluatingReproducibility2017,
  title = {Metaresearch for {{Evaluating Reproducibility}} in {{Ecology}} and {{Evolution}}},
  author = {Fidler, Fiona and Chee, Yung En and Wintle, Bonnie C. and Burgman, Mark A. and McCarthy, Michael A. and Gordon, Ascelin},
  year = {2017},
  month = mar,
  journal = {BioScience},
  volume = {67},
  number = {3},
  pages = {282--289},
  publisher = {{Oxford Academic}},
  issn = {0006-3568},
  doi = {10.1093/biosci/biw159},
  abstract = {Abstract.  Recent replication projects in other disciplines have uncovered disturbingly low levels of reproducibility, suggesting that those research literature},
  langid = {english},
  file = {/fidler.et.al.2017.metaresearch_for_evaluating_reproducibility_in_ecology_and_evolution.pdf}
}

@article{fraserQuestionableResearchPractices2018,
  title = {Questionable Research Practices in Ecology and Evolution},
  author = {Fraser, Hannah and Parker, Tim and Nakagawa, Shinichi and Barnett, Ashley and Fidler, Fiona},
  editor = {Wicherts, Jelte M.},
  year = {2018},
  month = jul,
  journal = {PLOS ONE},
  volume = {13},
  number = {7},
  pages = {e0200303},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0200303},
  abstract = {We surveyed 807 researchers (494 ecologists and 313 evolutionary biologists) about their use of Questionable Research Practices (QRPs), including cherry picking statistically significant results, p hacking, and hypothesising after the results are known (HARKing). We also asked them to estimate the proportion of their colleagues that use each of these QRPs. Several of the QRPs were prevalent within the ecology and evolution research community. Across the two groups, we found 64\% of surveyed researchers reported they had at least once failed to report results because they were not statistically significant (cherry picking); 42\% had collected more data after inspecting whether results were statistically significant (a form of p hacking) and 51\% had reported an unexpected finding as though it had been hypothesised from the start (HARKing). Such practices have been directly implicated in the low rates of reproducible results uncovered by recent large scale replication studies in psychology and other disciplines. The rates of QRPs found in this study are comparable with the rates seen in psychology, indicating that the reproducibility problems discovered in psychology are also likely to be present in ecology and evolution.},
  langid = {english},
  file = {/home/dmk/snap/zotero-snap/common/Zotero/storage/2C7F7TRW/file.pdf}
}

@misc{kaplanDataLanguageEngine2020,
  title = {Data {{Language Engine}} for Knitr / Rmarkdown [{{R}} Package Knitrdata Version 0.6.1]},
  author = {Kaplan, David M.},
  year = {2020},
  month = dec,
  publisher = {{Comprehensive R Archive Network (CRAN)}},
  abstract = {Implements a data language engine for incorporating data directly in 'rmarkdown' documents so that they can be made completely standalone.},
  howpublished = {https://CRAN.R-project.org/package=knitrdata}
}

@manual{kaplanKnitrdataDataLanguage2020,
  type = {Manual},
  title = {Knitrdata: {{Data Language Engine}} for 'knitr' / 'Rmarkdown'},
  author = {Kaplan, David M.},
  year = {2020}
}

@misc{kaplanUsingKnitrdataCreate2020,
  title = {Using Knitrdata to Create Standalone {{Rmarkdown}} Documents in {{Rstudio}}},
  author = {Kaplan, David M.},
  year = {2020},
  month = jul,
  abstract = {This video explains how to use the knitrdata R package to create standalone Rmarkdown documents, focusing on Rstudio add-ins provided by the package to facilitate its use. The video includes a detailed presentation of two real world examples.},
  howpublished = {https://www.youtube.com/watch?v=xX4YRAXYFxE}
}

@article{laiEvaluatingPopularityEcology2019,
  title = {Evaluating the Popularity of {{R}} in Ecology},
  author = {Lai, Jiangshan and Lortie, Christopher J. and Muenchen, Robert A. and Yang, Jian and Ma, Keping},
  year = {2019},
  journal = {Ecosphere},
  volume = {10},
  number = {1},
  pages = {e02567},
  issn = {2150-8925},
  doi = {10.1002/ecs2.2567},
  abstract = {The programming language R is widely used in many fields. We explored the extent of reported R use in the field of ecology using the Web of Science and text mining. We analyzed the frequencies of R packages reported in more than 60,000 peer-reviewed articles published in 30 ecology journals during a 10-yr period ending in 2017. The number of studies reported using R as their primary tool in data analysis increased linearly from 11.4\% in 2008 to 58.0\% in 2017. The top 10 packages reported were lme4, vegan, nlme, ape, MuMIn, MASS, mgcv, ade4, multcomp, and car. The increasing popularity of R has most likely furthered open science in ecological research because it can improve reproducibility of analyses and captures workflows when scripts and codes are included and shared. These findings may not be entirely unique to R because there are other programming languages used by ecologists, but they do strongly suggest that given the relatively high frequency of reported use of R, it is a significant component of contemporary analytics in the field of ecology.},
  copyright = {\textcopyright{} 2019 The Authors.},
  langid = {english},
  keywords = {code,ecology journal,open science,packages,R,reproducibility,statistical programming,Web of Science},
  annotation = {\_eprint: https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/ecs2.2567},
  file = {/lai.et.al.2019.evaluating_the_popularity_of_r_in_ecology.pdf}
}

@article{lowndesOurpathbetter2017,
  title = {Our Path to Better Science in Less Time Using Open Data Science Tools},
  author = {Lowndes, Julia S. Stewart and Best, Benjamin D. and Scarborough, Courtney and Afflerbach, Jamie C. and Frazier, Melanie R. and O'Hara, Casey C. and Jiang, Ning and Halpern, Benjamin S.},
  year = {2017},
  month = may,
  journal = {Nature Ecology \& Evolution},
  volume = {1},
  number = {6},
  pages = {1--7},
  publisher = {{Nature Publishing Group}},
  issn = {2397-334X},
  doi = {10.1038/s41559-017-0160},
  abstract = {Reproducibility starts with having a transparent and streamlined workflow. Here, the authors describe how they achieved this using open data tools for the collaborative Ocean Health Index project.},
  copyright = {2017 Macmillan Publishers Limited},
  langid = {english},
  file = {/home/dmk/papers2/lowndes.et.al.2017.our_path_to_better_science_in_less_time_using_open_data_science_tools.pdf}
}

@article{powersOpenScienceReproducibility2019,
  title = {Open Science, Reproducibility, and Transparency in Ecology},
  author = {Powers, Stephen M. and Hampton, Stephanie E.},
  year = {2019},
  journal = {Ecological Applications},
  volume = {29},
  number = {1},
  pages = {e01822},
  issn = {1939-5582},
  doi = {10.1002/eap.1822},
  abstract = {Reproducibility is a key tenet of the scientific process that dictates the reliability and generality of results and methods. The complexities of ecological observations and data present novel challenges in satisfying needs for reproducibility and also transparency. Ecological systems are dynamic and heterogeneous, interacting with numerous factors that sculpt natural history and that investigators cannot completely control. Observations may be highly dependent on spatial and temporal context, making them very difficult to reproduce, but computational reproducibility can still be achieved. Computational reproducibility often refers to the ability to produce equivalent analytical outcomes from the same data set using the same code and software as the original study. When coded workflows are shared, authors and editors provide transparency for readers and allow other researchers to build directly and efficiently on primary work. These qualities may be especially important in ecological applications that have important or controversial implications for science, management, and policy. Expectations for computational reproducibility and transparency are shifting rapidly in the sciences. In this work, we highlight many of the unique challenges for ecology along with practical guidelines for reproducibility and transparency, as ecologists continue to participate in the stewardship of critical environmental information and ensure that research methods demonstrate integrity.},
  copyright = {\textcopyright{} 2018 The Authors Ecological Applications published by Wiley Periodicals, Inc. on behalf of Ecological Society of America},
  langid = {english},
  keywords = {collaborative tools,data policy,data science,ecoinformatics,ecosystem,environmental science,open science,repeatability,replicability,reproducible,transparent,workflows},
  annotation = {\_eprint: https://esajournals.onlinelibrary.wiley.com/doi/pdf/10.1002/eap.1822},
  file = {/powers.hampton.2019.open_science,_reproducibility,_and_transparency_in_ecology.pdf}
}

@book{rcoreteamLanguageEnvironmentStatistical2021,
  ids = {R-base},
  title = {R: {{A Language}} and {{Environment}} for {{Statistical Computing}}},
  author = {{R Core Team}},
year = {2021},
  publisher = {{R Foundation for Statistical Computing}},
  address = {{Vienna, Austria}},
  organization = {{R Foundation for Statistical Computing}}
}

@book{riederer15WriteChunk,
  title = {15.6 {{Write}} the Chunk Content to a File via the Cat Engine | {{R Markdown Cookbook}}},
  author = {Riederer, Christophe Dervieux, Emily, Yihui Xie},
  abstract = {This book showcases short, practical examples of lesser-known tips and tricks to helps users get the most out of these tools. After reading this book, you will understand how R Markdown documents are transformed from plain text and how you may customize nearly every step of this processing. For example, you will learn how to dynamically create content from R code, reference code in other documents or chunks, control the formatting with customer templates, fine-tune how your code is processed, and incorporate multiple languages into your analysis.}
}

@article{tenopirResearchDataSharing2018,
  title = {Research {{Data Sharing}}: {{Practices}} and {{Attitudes}} of {{Geophysicists}}},
  shorttitle = {Research {{Data Sharing}}},
  author = {Tenopir, C. and Christian, L. and Allard, S. and Borycz, J.},
  year = {2018},
  journal = {Earth and Space Science},
  volume = {5},
  number = {12},
  pages = {891--902},
  issn = {2333-5084},
  doi = {10.1029/2018EA000461},
  abstract = {Open data policies have been introduced by governments, funders, and publishers over the past decade. Previous research showed a growing recognition by scientists of the benefits of data-sharing and reuse, but actual practices lag and are not always compliant with new regulations. The goal of this study is to investigate motives, attitudes, and data practices of the community of Earth and planetary geophysicists, a discipline believed to have accepting attitudes toward data sharing and reuse. A better understanding of the attitudes and current data-sharing practices of this scientific community could enable funders, publishers, data managers, and librarians to design systems and services that help scientists understand and adhere to mandates and to create practices, tools, and services that are scientist-focused. An online survey was distributed to the members of the American Geophysical Union, producing 1,372 responses from 116 countries. The attitudes of researchers to data sharing and reuse were generally positive, but in practice, scientists had concerns about sharing their own research data. These concerns include the possibility of potential data misuse and the need for assurance of proper citation and acknowledgement. Training and assistance in good data management practices are lacking in many scientific fields and might help to alleviate these doubts.},
  copyright = {\textcopyright 2018. The Authors.},
  langid = {english},
  keywords = {attitudes of scientists,barriers to data sharing,data management,data sharing,open science,research data curation},
  annotation = {\_eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018EA000461},
  file = {/tenopir.et.al.2018.research_data_sharing.pdf}
}

@article{wainQuantifyingIncreaseFishing2021,
  title = {Quantifying the Increase in Fishing Efficiency Due to the Use of Drifting {{FADs}} Equipped with Echosounders in Tropical Tuna Purse Seine Fisheries},
  author = {Wain, Gwena{\"e}lle and Gu{\'e}ry, Lorele{\"i} and Kaplan, David Michael and Gaertner, Daniel},
  year = {2021},
  month = feb,
  journal = {ICES Journal of Marine Science},
  volume = {78},
  number = {1},
  pages = {235--245},
  issn = {1054-3139},
  doi = {10.1093/icesjms/fsaa216},
  abstract = {Numerous pelagic species are known to associate with floating objects (FOBs), including tropical tunas. Purse seiners use this behaviour to facilitate the capture of tropical tunas by deploying artificial drifting fish aggregating devices (dFADs). One major recent change has been the integration of echosounders in satellite-tracked GPS buoys attached to FOBs, allowing fishers to remotely estimate fishable biomass. Understanding the effects of this new technology on catch of the three main tuna species (yellowfin tuna, Thunnus albacares; bigeye tuna, Thunnus obesus; and skipjack tuna, Katsuwonus pelamis) is important to accurately correct for this change in catch-per-unit-effort (CPUE) indices used for stock assessments. We analysed catch data from the French purse seine fleet for the period 2010\textendash 2017 in the Indian Ocean to assess the impact of this fleet's switch to echosounder buoys around 2012. Results indicate that echosounders do not increase the probability a set will be succesful, but they have a positive effect on catch per set, with catches on average increasing by {$\approx$}2-2.5 tonnes per set ({$\approx$}10\%) when made on the vessel's own dFADs equipped with an echosounder buoy. Increases were due to a decrease in sets below {$\approx$}25 tonnes and an increase in those greater than {$\approx$}25 tonnes, with a non-linear transition around this threshold. This increase explains the considerable investment of purse seiners in echosounder buoys, but also raises concerns about bias in stock size estimates based on CPUE if we do not correct for this fishing efficiency increase.},
  file = {/home/dmk/MEGA/papers/wain.et.al.2021.quantifying_the_increase_in_fishing_efficiency_due_to_the_use_of_drifting_fads.pdf;/home/dmk/papers2/wain.et.al.2020.quantifying_the_increase_in_fishing_efficiency_due_to_the_use_of_drifting_fads.online_supplementary.pdf;/home/dmk/papers2/wain.et.al.2020.quantifying_the_increase_in_fishing_efficiency_due_to_the_use_of_drifting_fads.pdf}
}

@article{wilsonOntologyConfoundsReproducibility2014,
  title = {Ontology {{Confounds Reproducibility}} in {{Ecology}} and {{Climate Science}}},
  author = {Wilson, Brent and Hayek, Lee-Ann C.},
  year = {2014},
  month = may,
  journal = {Life: The Excitement of Biology},
  volume = {2},
  number = {1},
  pages = {13--30},
  issn = {2325-081X, 2325-0828},
  doi = {10.9784/LEB2(1)Wilson.01},
  abstract = {The ability to reproduce ecological or climate change experiments or quantitative results is a hallmark of science, but not the only one. In addition, a scientist who subsequently examines original research must be able to reproduce the interpretation of the observations. Confidence in both ecology and climate science research is undermined when an a priori interpretation cannot be confirmed. This will happen when an a priori (before investigation) assumption was introduced by the original author(s), even inadvertently, regarding the nature of the universe or a subset of it. Such an assumption is here termed an ontological assumption when it then is treated as invariant; that is, it is not changed as unsupportive evidence, whether positive or negative, is accumulated. It thus conditions the interpretation of observed scientific phenomena. The results of the scientific investigation are therefore interpreted within the framework of this assumption, even though this framework is at variance with actual results obtained.},
  langid = {english},
  file = {/home/dmk/snap/zotero-snap/common/Zotero/storage/87ERSW6J/Ontology-Confounds-Reproducibility-in-Ecology-and-Climate-Science.pdf}
}

@book{xieDynamicDocumentsKnitr2015,
  title = {Dynamic Documents with {{R}} and Knitr},
  author = {Xie, Yihui},
  year = {2015},
  edition = {Second},
  publisher = {{Chapman and Hall/CRC}},
  address = {{Boca Raton, Florida}}
}

@incollection{xieKnitrComprehensiveTool2014,
  title = {Knitr: {{A}} Comprehensive Tool for Reproducible Research in {{R}}},
  booktitle = {Implementing Reproducible Computational Research},
  author = {Xie, Yihui},
  editor = {Stodden, Victoria and Leisch, Friedrich and Peng, Roger D.},
  year = {2014},
  publisher = {{Chapman and Hall/CRC}}
}

@book{xieKnitrGeneralpurposePackage2020,
  title = {Knitr: {{A}} General-Purpose Package for Dynamic Report Generation in r},
  author = {Xie, Yihui},
  year = {2020}
}

@book{XieMarkdownCookbook2020,
  title = {R {{Markdown Cookbook}}},
  author = {Xie, Yihui and Dervieux, Christophe and Riederer, Emily},
  year = {2020},
  month = oct,
  series = {The {{R Series}}},
  edition = {1st edition},
  publisher = {{CRC Press}},
  address = {{Boca Raton, Florida}},
  abstract = {This new book written by the developers of R Markdown is an essential reference that will help users learn and make full use of the software.  Those new to R Markdown will appreciate the short, practical examples that address the most common issues users encounter. Frequent users will also benefit from the wide ranging tips and tricks that expose `hidden' features, support customization and demonstrate the many new and varied applications of the software.  After reading this book users will learn how to:   Enhance your R Markdown content with diagrams, citations, and dynamically generated text Streamline your workflow with child documents, code chunk references, and caching Control the formatting and layout with Pandoc markdown syntax or by writing custom HTML and LaTeX templates Utilize chunk options and hooks to fine-tune how your code is processed Switch between different language engineers to seamlessly incorporate python, D3, and more into your analysis},
  isbn = {978-0-367-56383-7},
  langid = {english}
}

@book{xieMarkdownDefinitiveGuide2018,
  title = {R Markdown: {{The}} Definitive Guide},
  author = {Xie, Yihui and Allaire, J.J. and Grolemund, Garrett},
  year = {2018},
  publisher = {{Chapman and Hall/CRC}},
  address = {{Boca Raton, Florida}}
}


@Manual{R-base,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2022},
  url = {https://www.R-project.org/},
}

@Manual{R-bookdown,
  title = {bookdown: Authoring Books and Technical Documents with R Markdown},
  author = {Yihui Xie},
  year = {2022},
  note = {R package version 0.26},
  url = {https://CRAN.R-project.org/package=bookdown},
}

@Manual{R-citr,
  title = {citr: RStudio Add-in to Insert Markdown Citations},
  author = {Frederik Aust},
  year = {2019},
  note = {R package version 0.3.2},
  url = {https://github.com/crsh/citr},
}

@Manual{R-dplyr,
  title = {dplyr: A Grammar of Data Manipulation},
  author = {Hadley Wickham and Romain François and Lionel Henry and Kirill Müller},
  year = {2022},
  note = {R package version 1.0.10},
  url = {https://CRAN.R-project.org/package=dplyr},
}

@Manual{R-forcats,
  title = {forcats: Tools for Working with Categorical Variables (Factors)},
  author = {Hadley Wickham},
  year = {2021},
  note = {R package version 0.5.1},
  url = {https://CRAN.R-project.org/package=forcats},
}

@Manual{R-ggplot2,
  title = {ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics},
  author = {Hadley Wickham and Winston Chang and Lionel Henry and Thomas Lin Pedersen and Kohske Takahashi and Claus Wilke and Kara Woo and Hiroaki Yutani and Dewey Dunnington},
  year = {2022},
  note = {R package version 3.3.6},
  url = {https://CRAN.R-project.org/package=ggplot2},
}

@Manual{R-gpg,
  title = {gpg: GNU Privacy Guard for R},
  author = {Jeroen Ooms},
  year = {2022},
  note = {R package version 1.2.7},
  url = {https://github.com/jeroen/gpg},
}

@Manual{R-kableExtra,
  title = {kableExtra: Construct Complex Table with kable and Pipe Syntax},
  author = {Hao Zhu},
  year = {2021},
  note = {R package version 1.3.4},
  url = {https://CRAN.R-project.org/package=kableExtra},
}

@Manual{R-knitr,
  title = {knitr: A General-Purpose Package for Dynamic Report Generation in R},
  author = {Yihui Xie},
  year = {2022},
  note = {R package version 1.40},
  url = {https://yihui.org/knitr/},
}

@Manual{R-knitrdata,
  title = {knitrdata: Data Language Engine for knitr / rmarkdown},
  author = {David M. Kaplan},
  year = {2020},
  note = {R package version 0.6.1},
  url = {https://github.com/dmkaplan2000/knitrdata},
}

@Manual{R-purrr,
  title = {purrr: Functional Programming Tools},
  author = {Lionel Henry and Hadley Wickham},
  year = {2020},
  note = {R package version 0.3.4},
  url = {https://CRAN.R-project.org/package=purrr},
}

@Manual{R-readr,
  title = {readr: Read Rectangular Text Data},
  author = {Hadley Wickham and Jim Hester and Jennifer Bryan},
  year = {2022},
  note = {R package version 2.1.2},
  url = {https://CRAN.R-project.org/package=readr},
}

@Manual{R-remotes,
  title = {remotes: R Package Installation from Remote Repositories, Including
GitHub},
  author = {Gábor Csárdi and Jim Hester and Hadley Wickham and Winston Chang and Martin Morgan and Dan Tenenbaum},
  year = {2021},
  note = {R package version 2.4.2},
  url = {https://CRAN.R-project.org/package=remotes},
}

@Manual{R-rmarkdown,
  title = {rmarkdown: Dynamic Documents for R},
  author = {JJ Allaire and Yihui Xie and Jonathan McPherson and Javier Luraschi and Kevin Ushey and Aron Atkins and Hadley Wickham and Joe Cheng and Winston Chang and Richard Iannone},
  year = {2022},
  note = {R package version 2.16},
  url = {https://CRAN.R-project.org/package=rmarkdown},
}

@Manual{R-rticles,
  title = {rticles: Article Formats for R Markdown},
  author = {JJ Allaire and Yihui Xie and Christophe Dervieux and {R Foundation} and Hadley Wickham and {Journal of Statistical Software} and Ramnath  Vaidyanathan and {Association for Computing Machinery} and Carl Boettiger and {Elsevier} and Karl Broman and Kirill Mueller and Bastiaan Quast and Randall  Pruim and Ben Marwick and Charlotte Wickham and Oliver Keyes and Miao Yu and Daniel Emaasit and Thierry Onkelinx and Alessandro Gasparini and Marc-Andre Desautels and Dominik Leutnant and {MDPI} and {Taylor and Francis} and Oğuzhan Öğreden and Dalton Hance and Daniel Nüst and Petter Uvesten and Elio Campitelli and John Muschelli and Alex Hayes and Zhian N. Kamvar and Noam Ross and Robrecht Cannoodt and Duncan Luguern and David M. Kaplan and Sebastian Kreutzer and Shixiang Wang and Jay Hesselberth and Rob Hyndman},
  year = {2022},
  note = {R package version 0.24},
  url = {https://github.com/rstudio/rticles},
}

@Manual{R-starticles,
  title = {starticles: A Generic, Publisher-Independent Template for Writing Scientific
Documents in rmarkdown},
  author = {David M. Kaplan},
  year = {2022},
  note = {R package version 0.1.0},
  url = {https://github.com/dmkaplan2000/starticles},
}

@Manual{R-stringr,
  title = {stringr: Simple, Consistent Wrappers for Common String Operations},
  author = {Hadley Wickham},
  year = {2022},
  note = {R package version 1.4.1},
  url = {https://CRAN.R-project.org/package=stringr},
}

@Manual{R-tibble,
  title = {tibble: Simple Data Frames},
  author = {Kirill Müller and Hadley Wickham},
  year = {2022},
  note = {R package version 3.1.8},
  url = {https://CRAN.R-project.org/package=tibble},
}

@Manual{R-tidyr,
  title = {tidyr: Tidy Messy Data},
  author = {Hadley Wickham and Maximilian Girlich},
  year = {2022},
  note = {R package version 1.2.1},
  url = {https://CRAN.R-project.org/package=tidyr},
}

@Manual{R-tidyverse,
  title = {tidyverse: Easily Install and Load the Tidyverse},
  author = {Hadley Wickham},
  year = {2022},
  note = {R package version 1.3.2},
  url = {https://CRAN.R-project.org/package=tidyverse},
}

@Manual{R-xfun,
  title = {xfun: Supporting Functions for Packages Maintained by Yihui Xie},
  author = {Yihui Xie},
  year = {2022},
  note = {R package version 0.33},
  url = {https://github.com/yihui/xfun},
}

@Book{bookdown2016,
  title = {bookdown: Authoring Books and Technical Documents with {R} Markdown},
  author = {Yihui Xie},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2016},
  note = {ISBN 978-1138700109},
  url = {https://bookdown.org/yihui/bookdown},
}

@Book{ggplot22016,
  author = {Hadley Wickham},
  title = {ggplot2: Elegant Graphics for Data Analysis},
  publisher = {Springer-Verlag New York},
  year = {2016},
  isbn = {978-3-319-24277-4},
  url = {https://ggplot2.tidyverse.org},
}

@Book{knitr2015,
  title = {Dynamic Documents with {R} and knitr},
  author = {Yihui Xie},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2015},
  edition = {2nd},
  note = {ISBN 978-1498716963},
  url = {https://yihui.org/knitr/},
}

@InCollection{knitr2014,
  booktitle = {Implementing Reproducible Computational Research},
  editor = {Victoria Stodden and Friedrich Leisch and Roger D. Peng},
  title = {knitr: A Comprehensive Tool for Reproducible Research in {R}},
  author = {Yihui Xie},
  publisher = {Chapman and Hall/CRC},
  year = {2014},
  note = {ISBN 978-1466561595},
  url = {http://www.crcpress.com/product/isbn/9781466561595},
}

@Book{rmarkdown2018,
  title = {R Markdown: The Definitive Guide},
  author = {Yihui Xie and J.J. Allaire and Garrett Grolemund},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2018},
  note = {ISBN 9781138359338},
  url = {https://bookdown.org/yihui/rmarkdown},
}

@Book{rmarkdown2020,
  title = {R Markdown Cookbook},
  author = {Yihui Xie and Christophe Dervieux and Emily Riederer},
  publisher = {Chapman and Hall/CRC},
  address = {Boca Raton, Florida},
  year = {2020},
  note = {ISBN 9780367563837},
  url = {https://bookdown.org/yihui/rmarkdown-cookbook},
}

@Article{tidyverse2019,
  title = {Welcome to the {tidyverse}},
  author = {Hadley Wickham and Mara Averick and Jennifer Bryan and Winston Chang and Lucy D'Agostino McGowan and Romain François and Garrett Grolemund and Alex Hayes and Lionel Henry and Jim Hester and Max Kuhn and Thomas Lin Pedersen and Evan Miller and Stephan Milton Bache and Kirill Müller and Jeroen Ooms and David Robinson and Dana Paige Seidel and Vitalie Spinu and Kohske Takahashi and Davis Vaughan and Claus Wilke and Kara Woo and Hiroaki Yutani},
  year = {2019},
  journal = {Journal of Open Source Software},
  volume = {4},
  number = {43},
  pages = {1686},
  doi = {10.21105/joss.01686},
}

```



<!-- Latex style file -->

```{data rjournalsty, format = "text", encoding = "asis", output.file = "RJournal.sty", echo = FALSE, md5sum = "4c20669567ebf36534cdc6b7f15ed115", eval=TRUE,line.sep="\n"}
% Package `RJournal' to use with LaTeX2e
% Copyright (C) 2010 by the R Foundation
% Copyright (C) 2013 by the R Journal
%
% Originally written by Kurt Hornik and Friedrich Leisch with subsequent 
% edits by the editorial board
%
% CAUTION:
% Do not modify this style file. Any changes to this file will be reset when your
% article is submitted.
% If you must modify the style or add LaTeX packages to the article, these 
% should be specified in RJwrapper.tex

\NeedsTeXFormat{LaTeX2e}[1995/12/01]
\ProvidesPackage{RJournal}[2013/08/27 v0.13 RJournal package]

\RequirePackage{tikz}

% Overall page layout, fonts etc -----------------------------------------------

% Issues of of \emph{The R Journal} are created from the standard \LaTeX{}
% document class \pkg{report}. 

\RequirePackage{geometry}
\geometry{a4paper, 
   textwidth=14cm, top=1cm, bottom=1cm,
   includehead,includefoot,centering,
   footskip=1.5cm}
\raggedbottom

\RequirePackage{fancyhdr}
\fancyhead{}
\fancyheadoffset{2cm} 
\fancyhead[L]{\textsc{\RJ@sectionhead}}
\fancyhead[R]{\thepage}
\fancyfoot{}
\fancyfoot[L]{The R Journal Vol. \RJ@volume/\RJ@number, \RJ@month~\RJ@year}
\fancyfoot[R]{ISSN 2073-4859}
\pagestyle{fancy}

% We use the following fonts (all with T1 encoding):
% 
%   rm   & palatino
%   tt   & inconsolata
%   sf   & helvetica
%   math & palatino

\RequirePackage{microtype}

\RequirePackage[scaled=0.92]{helvet}
\RequirePackage{palatino,mathpazo}
\RequirePackage[scaled=1.02]{inconsolata}
\RequirePackage[T1]{fontenc}

\RequirePackage[hyphens]{url}
\RequirePackage[pagebackref]{hyperref}
\renewcommand{\backref}[1]{[p#1]}

% Dark blue colour for all links
\RequirePackage{color}
\definecolor{link}{rgb}{0.45,0.51,0.67}
\hypersetup{
  colorlinks,%
  citecolor=link,%
  filecolor=link,%
  linkcolor=link,%
  urlcolor=link
}

% Give the text a little room to breath
\setlength{\parskip}{3pt}
\RequirePackage{setspace}
\setstretch{1.05}

% Issue and article metadata ---------------------------------------------------

% Basic front matter information about the issue: volume, number, and
% date.

\newcommand{\volume}[1]{\def\RJ@volume{#1}}
\newcommand{\volnumber}[1]{\def\RJ@number{#1}}
\renewcommand{\month}[1]{\def\RJ@month{#1}}
\renewcommand{\year}[1]{\def\RJ@year{#1}}


% Individual articles correspond to
% chapters, and are contained in |article| environments.  This makes it
% easy to have figures counted within articles and hence hyperlinked
% correctly.

% An article has an author, a title, and optionally a subtitle.  We use
% the obvious commands for specifying these. Articles will be put in certain
% journal sections, named by \sectionhead.

\newcommand  {\sectionhead}  [1]{\def\RJ@sectionhead{#1}}
\renewcommand{\author}       [1]{\def\RJ@author{#1}}
\renewcommand{\title}        [1]{\def\RJ@title{#1}}
\newcommand  {\subtitle}     [1]{\def\RJ@subtitle{#1}}

% Control appearance of titles: make slightly smaller than usual, and 
% suppress section numbering. See http://tex.stackexchange.com/questions/69749
% for why we don't use \setcounter{secnumdepth}{-1} 

\usepackage[medium]{titlesec}
\usepackage{titletoc}
\titleformat{\section}   {\normalfont\large\bfseries}{}{0em}{}
\titleformat{\subsection}{\normalfont\normalsize\bfseries}{}{0em}{}
\titlecontents{chapter}   [0em]{}{}{}{\titlerule*[1em]{.}\contentspage}

% Article layout ---------------------------------------------------------------

% Environment |article| clears the article header information at its beginning. 
% We use |\FloatBarrier| from the placeins package to keep floats within
% the article.
\RequirePackage{placeins}
\newenvironment{article}{\author{}\title{}\subtitle{}\FloatBarrier}{\FloatBarrier}

% Refereed articles should have an abstract, so we redefine |\abstract| to
% give the desired style

\renewcommand{\abstract}[1]{%
\setstretch{1}%
\noindent%
\small%
\textbf{Abstract} #1 
}

% The real work is done by a redefined version of |\maketitle|.  Note
% that even though we do not want chapters (articles) numbered, we
% need to increment the chapter counter, so that figures get correct
% labelling.

\renewcommand{\maketitle}{%
\noindent
  \chapter{\RJ@title}\refstepcounter{chapter}
  \ifx\empty\RJ@subtitle
  \else
    \noindent\textbf{\RJ@subtitle}
    \par\nobreak\addvspace{\baselineskip}
  \fi
  \ifx\empty\RJ@author
  \else
    \noindent\textit{\RJ@author}
    \par\nobreak\addvspace{\baselineskip}
  \fi
  \@afterindentfalse\@nobreaktrue\@afterheading
}

% Now for some ugly redefinitions.  We do not want articles to start a
% new page. (Actually, we do, but this is handled via explicit
% \newpage
%
% The name@of@eq is a hack to get hyperlinks to equations to work
% within each article, even though there may be multiple eq.(1)
%    \begin{macrocode}
\renewcommand\chapter{\secdef\RJ@chapter\@schapter}
\providecommand{\nohyphens}{%
  \hyphenpenalty=10000\exhyphenpenalty=10000\relax}
\newcommand{\RJ@chapter}{%
  \edef\name@of@eq{equation.\@arabic{\c@chapter}}%
  \renewcommand{\@seccntformat}[1]{}%
  \@startsection{chapter}{0}{0mm}{%
    -2\baselineskip \@plus -\baselineskip \@minus -.2ex}{\p@}{%
    \phantomsection\normalfont\huge\bfseries\raggedright}}

% Book reviews should appear as sections in the text and in the pdf bookmarks,
% however we wish them to appear as chapters in the TOC. Thus we define an
% alternative to |\maketitle| for reviews.
\newcommand{\review}[1]{
  \pdfbookmark[1]{#1}{#1}
  \section*{#1}
  \addtocontents{toc}{\protect\contentsline{chapter}{#1}{\thepage}{#1.1}}
}

% We want bibliographies as starred sections within articles.
% 
\RequirePackage[sectionbib,round]{natbib}
\bibliographystyle{abbrvnat}

% Equations, figures and tables are counted within articles, but we do
% not show the article number. For equations it becomes a bit messy to avoid
% having hyperref getting it wrong. 

% \numberwithin{equation}{chapter}
\renewcommand{\theequation}{\@arabic\c@equation}
\renewcommand{\thefigure}{\@arabic\c@figure}
\renewcommand{\thetable}{\@arabic\c@table}

% Issue layout -----------------------------------------------------------------

% Need to provide our own version of |\tableofcontents|. We use the
% tikz package to get the rounded rectangle. Notice that |\section*|
% is really the same as |\chapter*|.
\renewcommand{\contentsname}{Contents}
\renewcommand\tableofcontents{%
   \vspace{1cm}
   \section*{\contentsname}
   { \@starttoc{toc} }
}

\renewcommand{\titlepage}{%
  \thispagestyle{empty}
  \hypersetup{
    pdftitle={The R Journal Volume \RJ@volume/\RJ@number, \RJ@month \RJ@year},%
    pdfauthor={R Foundation for Statistical Computing},%
  }
  \noindent
  \begin{center}
    \fontsize{50pt}{50pt}\selectfont
    The \raisebox{-8pt}{\includegraphics[height=77pt]{Rlogo-5}}\hspace{10pt}
    Journal
  
  \end{center}
  {\large \hfill Volume \RJ@volume/\RJ@number, \RJ@month{} \RJ@year \quad}

  \rule{\textwidth}{1pt}
  \begin{center}
    {\Large A peer-reviewed, open-access publication of the \\
    R Foundation for Statistical Computing}
  \end{center}

  % And finally, put in the TOC box. Note the way |tocdepth| is adjusted
  % before and after producing the TOC: thus, we can ensure that only
  % articles show up in the printed TOC, but that in the PDF version,
  % bookmarks are created for sections and subsections as well (provided
  % that the non-starred forms are used).
  \setcounter{tocdepth}{0}
  \tableofcontents
  \setcounter{tocdepth}{2}
  \clearpage
}

% Text formatting --------------------------------------------------------------

\newcommand{\R}{R}
\newcommand{\address}[1]{\addvspace{\baselineskip}\noindent\emph{#1}}
\newcommand{\email}[1]{\href{mailto:#1}{\normalfont\texttt{#1}}}

% Simple font selection is not good enough.  For example, |\texttt{--}|
% gives `\texttt{--}', i.e., an endash in typewriter font.  Hence, we
% need to turn off ligatures, which currently only happens for commands
% |\code| and |\samp| and the ones derived from them.  Hyphenation is
% another issue; it should really be turned off inside |\samp|.  And
% most importantly, \LaTeX{} special characters are a nightmare.  E.g.,
% one needs |\~{}| to produce a tilde in a file name marked by |\file|.
% Perhaps a few years ago, most users would have agreed that this may be
% unfortunate but should not be changed to ensure consistency.  But with
% the advent of the WWW and the need for getting `|~|' and `|#|' into
% URLs, commands which only treat the escape and grouping characters
% specially have gained acceptance

\DeclareRobustCommand\code{\bgroup\@noligs\@codex}
\def\@codex#1{\texorpdfstring%
{{\normalfont\ttfamily\hyphenchar\font=-1 #1}}%
{#1}\egroup}
\newcommand{\kbd}[1]{{\normalfont\texttt{#1}}}
\newcommand{\key}[1]{{\normalfont\texttt{\uppercase{#1}}}}
\DeclareRobustCommand\samp{`\bgroup\@noligs\@sampx}
\def\@sampx#1{{\normalfont\texttt{#1}}\egroup'}
\newcommand{\var}[1]{{\normalfont\textsl{#1}}}
\let\env=\code
\newcommand{\file}[1]{{`\normalfont\textsf{#1}'}}
\let\command=\code
\let\option=\samp
\newcommand{\dfn}[1]{{\normalfont\textsl{#1}}}
% \acronym is effectively disabled since not used consistently
\newcommand{\acronym}[1]{#1}
\newcommand{\strong}[1]{\texorpdfstring%
{{\normalfont\fontseries{b}\selectfont #1}}%
{#1}}
\let\pkg=\strong
\newcommand{\CRANpkg}[1]{\href{https://CRAN.R-project.org/package=#1}{\pkg{#1}}}%
\let\cpkg=\CRANpkg
\newcommand{\ctv}[1]{\href{https://CRAN.R-project.org/view=#1}{\emph{#1}}}
\newcommand{\BIOpkg}[1]{\href{https://www.bioconductor.org/packages/release/bioc/html/#1.html}{\pkg{#1}}}

% Example environments ---------------------------------------------------------
\RequirePackage{fancyvrb}
\RequirePackage{alltt}

\DefineVerbatimEnvironment{example}{Verbatim}{}
\renewenvironment{example*}{\begin{alltt}}{\end{alltt}}

% Support for output from Sweave, and generic session style code
% These used to have fontshape=sl for Sinput/Scode/Sin, but pslatex
% won't use a condensed font in that case.

% Update (2015-05-28 by DS): remove fontsize=\small to match example environment

\DefineVerbatimEnvironment{Sinput}{Verbatim}{}
\DefineVerbatimEnvironment{Soutput}{Verbatim}{}
\DefineVerbatimEnvironment{Scode}{Verbatim}{}
\DefineVerbatimEnvironment{Sin}{Verbatim}{}
\DefineVerbatimEnvironment{Sout}{Verbatim}{}
\newenvironment{Schunk}{}{}

% Mathematics ------------------------------------------------------------------

% The implementation of |\operatorname| is similar to the mechanism
% \LaTeXe{} uses for functions like sin and cos, and simpler than the
% one of \AmSLaTeX{}.  We use |\providecommand| for the definition in
% order to keep the one of the \pkg{amstex} if this package has
% already been loaded.
%    \begin{macrocode}
\providecommand{\operatorname}[1]{%
  \mathop{\operator@font#1}\nolimits}
\RequirePackage{amsfonts}

\renewcommand{\P}{%
  \mathop{\operator@font I\hspace{-1.5pt}P\hspace{.13pt}}}
\newcommand{\E}{%
  \mathop{\operator@font I\hspace{-1.5pt}E\hspace{.13pt}}}
\newcommand{\VAR}{\operatorname{var}}
\newcommand{\COV}{\operatorname{cov}}
\newcommand{\COR}{\operatorname{cor}}

% Figures ----------------------------------------------------------------------

\RequirePackage[font=small,labelfont=bf]{caption}

% Wide environments for figures and tables -------------------------------------
\RequirePackage{environ}

% An easy way to make a figure span the full width of the page
\NewEnviron{widefigure}[1][]{
\begin{figure}[#1]
\advance\leftskip-2cm
\begin{minipage}{\dimexpr\textwidth+4cm\relax}%
  \captionsetup{margin=2cm}
  \BODY
\end{minipage}%
\end{figure}
}

\NewEnviron{widetable}[1][]{
\begin{table}[#1]
\advance\leftskip-2cm
\begin{minipage}{\dimexpr\textwidth+4cm\relax}%
  \captionsetup{margin=2cm}
  \BODY
\end{minipage}%
\end{table}
}
```