https://github.com/pbs-assess/cpue-reports

:books: Standardized commercial catch-per-unit-effort reports for Pacific groundfish
https://github.com/pbs-assess/cpue-reports

Last synced: 3 days ago
JSON representation

:books: Standardized commercial catch-per-unit-effort reports for Pacific groundfish

• Host: GitHub
• URL: https://github.com/pbs-assess/cpue-reports
• Owner: pbs-assess
• Created: 2018-08-10T20:10:59.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2024-12-17T23:23:38.000Z (24 days ago)
• Last Synced: 2024-12-18T00:25:26.689Z (24 days ago)
• Language: R
• Homepage:
• Size: 85.9 KB
• Stars: 2
• Watchers: 9
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Groundfish CPUE Reports

Requires the following R packages:

```r

pkgs <- c(

  "here", "glmmTMB", "ggplot2", "dplyr", "readr", "lubridate",

  "reshape2", "plyr", "purrr", "remotes"

)

install.packages(pkgs)

remotes::install_github("pbs-assess/gfplot")

remotes::install_github("pbs-assess/gfdata")

```

See examples in `make.R` to run a report.

Contact Sean Anderson for more details.

## Parameter descriptions

* `author`: Character: author name. Whatever you want.

* `species_proper`: Character: common name of species.

* `area`: R code regular expression for groundfish areas to include. For example: `!r c("^5A|^5B|^5C|^5D|^5E|^3C|^3D")` would filter for 5A or 5B or 5C etc. Can be a vector where each element would be processed as a separate series.

* `area_name`: R code for names of areas specified in previous argument (same length). E.g. `!r c("Coastwide")`.

* `skip_single_variable_models`: Logical: if `FALSE`, models with each individual predictor are run you understand their contributions. If `TRUE`, this is skipped to save time.

* `era`: Character: `"modern"` runs 1996 onwards. `"historical"` runs on data from `gfdata::get_cpue_historical()`, which includes data from `min_year_historic`--1995. The data go back to roughly 1955.

* `use_alt_year`: Logical: use alternative year starting date? If `FALSE`, uses January 1. If `TRUE`, uses `alt_year_start_date`.

* `alt_year_start_date`: Character: month and day of alternative year starting date. E.g. `"02-21"`.

* `min_year_historic`: Numeric: minimum year to filter if using the historical data. E.g. `1956`.

* `discard_only`: Logical: use only the discards for a discard-CPUE?

* `parallel`: Logical: parallel processing. Because the data are rather large you usually want to leave this as `FALSE`.

* `min_positive_tows`: Numeric: The minimum number of positive tows over all years. E.g. `140`.

*  `min_positive_trips`: Numeric: The minimum number of annual positive trips. E.g., `10`.

* `min_yrs_with_trips`: Numeric: The number of years in which the `min_positive_trips` criteria needs to be met. E.g., `5`.

* `final_year`: Numeric: Final year to include. E.g. `2021`.

* `final_date`: Character representing the final day to include: `"2021-12-31"`

* `depth_bin_quantiles`: R code representing minimal and maximum depth quantiles beyond which to discard fishing events. E.g. `!r c(0.001, 0.999)`. Set to `!r c(0, 1)` to not discard any fishing events based on extreme depths.

* `lat_range`: R code representing minimum and maximum values for latitude beyond which to discard fishing events. E.g. `!r c(48, Inf)`.

* `depth_range`: R code representing minimum and maximum depth values beyond which to discard fishing events. E.g. `!r c(-Inf, Inf)` to do nothing. `!r c(50, 350)` would only include fishing events `>=` 50 and `<=` 350.