In BenWilliams-NOAA/swo: Survey workload optimization

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

# load ----
library(swo)

This is a basic example which shows you how to query the data for the GOA or AI:

library(swo)
yrs = 2017 # min year to consider
species = c(10110, 21740) # AFSC species codes
region = 'GOA'
afsc_user = 'your_afsc_username'
afsc_pwd = 'your_afsc_pwd'

# query the database, results are stored in 4 files in the "data" folder
query_data(region, species, yrs, afsc_user, afsc_pwd)

# bring data into the global environment
cpue <- vroom::vroom(here::here('data', 'cpue_goa.csv'))
lfreq <- vroom::vroom(here::here('data', 'lfreq_goa.csv'))
strata <- vroom::vroom(here::here('data', 'strata_goa.csv'))
specimen <- vroom::vroom(here::here('data', 'specimen_goa.csv'))

Since the query will not work unless one is connect to the AFSC (and has associated permissions) some example data have been provided.

library(swo)

# get data 
data(example_cpue) # numerical CPUE by species/year/haul/strata
data(example_lfreq) # lengthed fish sample data
data(example_specimen) # aged fish sample data 
data(example_strata) # survey strata area sizes km^2

In the following example a simple example of two replicates is run that incorporates: bootstrap haul, length, and age variability, and reduces the length sample size to 100 per haul. The results are saved in nested folders output/goa with file names example_comp_age.csv etc.

swo_sim(iters = 2, example_lfreq, example_specimen, example_cpue, example_strata, boot_hauls = TRUE,
    boot_lengths = TRUE, boot_ages = TRUE, length_samples = 100, save = 'example', region = 'goa')