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hake-assessment

An R package which uses Bookdown and Rmarkdown to build the US/Canadian Pacific hake assessment document

Page links:

• Introduction
• How to create the hake assessment PDF document
• Debugging chunks of code and Rmarkdown text
• Adding new data to data tables
• Reference point text markup
• Plot settings
• 2024 Assessment cycle (Jan - Mar 2024)
  • Server setup for 2024
• How the models are run

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Introduction

In 2023, the project code used for building the assessment document and all the presentations was switched over from the original Sweave-based approach to the Bookdown system. Read NEWS.md for important details on this.

The assessment document is built using the following software packages:

• R Markdown: A simple yet powerful markup language designed to remove the need for tedious LaTeX macros embedded throughout the text. See the Rmarkdown reference guide for help on syntax.
• knitr: This R package is responsible for creating figures and tables during builds, and for dealing with inline R code chunks to create reproducible paragraphs of text. knitr chunks are written in a special R Markdown format.
• Pandoc: A very powerful document converter which in this case converts the R Markdown to LaTeX code.
• LuaTeX: The LaTeX compiler which converts the LaTeX created by Pandoc into the final PDF document.
• YAML: This is a simple configuration file format used to keep the project in order. It is used by the bookdown package.
• bookdown: An R package that facilitates writing complex documents by integrating all of the above packages into a single package, using a YAML configuration file.

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How to create the hake assessment PDF document

The same method works for creating the beamer presentation PDFs
The RDS files must have been created before the document can be built, To make them, see the section:
How the models are run

1. Install the hake package:
   • If you are on the Linux server, the hake package is already installed, go to step 2.
   • If you are on a local machine, open an R session, and install the hake package from GitHub: remotes::install_github("pacific-hake/hake-assessment")
2. Clone this GitHub repository: git clone https://github.com/pacific-hake/hake-assessment
3. If using Rstudio, open hake.Rproj. If not, open an R console however you like to and navigate to the hake project root directory. Either way, change your working directory to the doc directory (for beamer presentations change your working directory to the one containing all the RMD files):
   setwd(here::here("doc"))
4. Create the PDF document (same command for beamer presentations):
   hake::render()
5. The hake.pdf document will be rendered in the doc directory. The first time rendering the document in a new R session will take about 7-8 minutes because the model RDS files have to be loaded. After that, the build will be much quicker.
6. Clean the directory up from time to time by running hake::clean(). This will remove all files and subdirectories created by the render() function. This command also works for the beamer presentation directories. If you get an error stating that the variable assess_yr could not be found, run clean().

Alternatively, in RStudio you can click the knit button while the file 000-launcher.rmd is open in the editor window. This will be much slower though because it starts its own new R session, which means all the models have to be loaded every time you render the document this way.

For details on the hake::render() function, see NEWS.md.

Debugging chunks of code and Rmarkdown text

Run gotest() to enter a customized debug directory, paste the code into the 005-test.rmd file, save it and run render(). When done, run goback() to go back to the directory you were in originally. This works for all beamer presentations as well.

Details:

• If you haven't already done so in your current R session, run devtools::load_all() while your working directory is somewhere within the hake repo directory structure.
• Run gotest(), which will create a temporary directory containing copies of all files needed to do a minimal document build, and switch you to that directory.
  • If in RStudio, click the gear-arrow-down icon in the Files window and select Go to working directory. This will take the Rstudio file manager to the temporary directory, and show you the files that have been copied there by the gotest() function.
  • If not in RStudio, type dirclip(), which will copy the temporary directory name to the clipboard. You can now go to a file manager of your choice and paste the directory name into it, and it will take you to the temporary working directory.
• Open the 005-test.rmd file, and paste your chunk(s) of code into it. Save the file.
• Build the document by running render(). The PDF (hake.pdf) will be built in the current temporary directory, and contain only the output from your test code. If you haven't built the document yet in the current R session, this will take 7-8 minutes because all the mode files have to be loaded.
• Iteratively make changes to your code in the temporary 005-test.rmd file and build the document, until satisfied with your code. Copy the code to the clipboard for pasting into the real document. Be careful, once you leave the temporary directory your code in 005-test.rmd will be gone and unrecoverable.
• To go back to the directory you were in before testing, run goback().
  • If in Rstudio, click the gear-down-arrow icon in the Files window (bottom right panel in Rstudio) and select Go to working directory.

Adding new data to data tables

Data tables are package data and can be accessed directly from within the package like in this example, which gives you the U.S. at-sea bottom depth table:
hake::us_atsea_bottom_depth_df

To see a list of all package data available in the hake package:
data(package = "hake")

To update any package data, for example if we want to add new rows to the CSV files found in the data-tables directory:

1. Open the CSV file from the data-tables directory that you want to add data to.
2. Add the new data row(s), and save the file.
3. Do the first two steps with as many data tables as you want to update, then do then next steps.
4. Source the data-raw/pd-data-tables.R file to update the package data to reflect your changes: source(here::here(data-raw/pd-data-tables.R)). If you're using RStudio you can just press Ctrl-Shift-Enter with the file in focus to do this. This will update the *.rda files which are the binary package data files.
5. Make sure to commit the changes to the *.rda files in the GitHub repo.
6. The new data will not appear until you run devtools::load_all() or render() the document or a beamer presentation.

Reference point text markup

The reference points and other values which are referred to in text in numerous places and require complex markup and/or latex are located in the file data-raw/reference-points.R. They are stored as package data so can be referred to like this (example for F_SPR=40%):

• hake::fspr_40_10 for Rmarkdown (inside text in the document) or
• hake::fspr_40_10_for_latex_table for tables (which require LaTeX code)

To add to this list or change anything, follow the same method as laid out in the Adding new data to data tables section above. Test the new expression by using the gotest()/goback() debugging method. The only difference is that you will be editing and sourcing the file data-raw/pd-reference-points.R instead.

Plot settings

There are many standardized project-wide plot settings which are also package data. These can be found in the file data-raw/plot-settings.R. If any are modified or new ones are added, follow the same method as laid out in the Adding new data to data tables (done annually) section above. For example, if you wanted to change the cohort diagonal line color in all age bubble plots from dark green to red you would find this line of code:

File: data-raw/plot-settings.R
Line: create_data_hake("age_diag_linecolor", "darkgreen")

and change darkgreen to red. Then run that line of code and reload the package using:
devtools::load_all()

2025 Assessment cycle (Jan - Mar 2025)

• Model runs were done on an Ubuntu 22.04 LTS server with 80 Xeon Gold CPUs and 404 GB of RAM.

• All model runs, including the base, bridging, sensitivities, and retrospectives, were done using the main branch of the ADNUTS MCMC algorithm.

• extra-mcmc must be and was enabled for ALL models

Server setup for 2025

• Operating system: Ubuntu 22.04 LTS (Jammy Jellyfish)

• R version: 4.4.2 (2024-10-31 "Pile of Leaves")

• The R packages listed here are all installed site-wide; users do not need to (and should not) install them on their own accounts

• TexLive version: 2024 (tlmgr revision 73493 (2025-01-17 23:28:29 +0100))

• The TexLive packages listed here are all installed site-wide; users do not need to (and should not) install them on their own accounts

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How the models are run

There is a detailed vignette on this topic. To build the vignette, navigate in the R terminal to vignettes and run the following command: rmarkdown::render("vignettes/run-models.Rmd") Once this is done, there will be an HTML file called run-models.html. Open that in a browser.