FishMap 
The goal of FishMap is to map fish distribution using both preferentially sampled data (e.g., commercial catch data) and standardized data (e.g., survey data). It is based on a joint spatio-temporal hierarchical model that accounts for preferential sampling - see Alglave et al. (2022) and Alglave et al. (2023). The inference of the spatio-temporal field is performed using TMB and the SPDE approach to enhance the algorithm's speed.
N.b. The package is currently being updated as some functions rely on the packages rgdal, raster and sp that are now deprecated.
Installation
Dependencies
You must install INLA. Checkout INLA doc to upgrade INLA. Your INLA version should satisfy the minimal required version for running FishMap (22.12.16).
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(c("graph", "Rgraphviz"), dep=TRUE)
install.packages("INLA",repos=c(getOption("repos"),INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE)You must install TMB. The installation of TMB requires compiler tools. If you are installing from Windows machine, make sure that your Rtools installation is matching your R-version. Your TMB version should satisfy the minimal required version for running FishMap (1.9.2).
install.packages("TMB")FishMap
You can install the development version of FishMap from GitHub with:
# install.packages("remotes")
remotes::install_github("balglave/FishMap")Vignettes
You can install the development version of FishMap together with the vignettes with:
# install.packages("remotes")
remotes::install_github("balglave/FishMap", build_vignettes = TRUE)Installing with build_vignettes = TRUE will allow you to access the
vignettes locally. Once you have installed FishMap with vignettes, you
can access them using the command vignette(package = "FishMap").
Documentation
Full documentation website on: https://balglave.github.io/FishMap
In particular, you can follow this vignette:
vignette("user-running-fishmap", package = "FishMap")Unit tests in the package :
Writing unit tests is an essential practice in software development that can greatly improve the quality and maintainability of your code. Here are some of the key reasons why writing unit tests is important:
-
Ensuring correctness: Unit tests help ensure that your code works as intended and meets the requirements of the system. By writing tests that cover a wide range of scenarios and edge cases, you can catch bugs and errors before they become bigger problems down the line.
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Facilitating code changes: Unit tests can also help you make changes to your code with confidence. By having a comprehensive suite of tests that can quickly validate changes, you can refactor and optimize your codebase without fear of introducing new bugs or breaking existing functionality.
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Saving time and money: Catching bugs early in the development process can save time and money in the long run. By reducing the time spent on debugging and fixing issues, you can focus more on delivering new features and improvements to your application.
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Promoting code quality: Writing tests can also encourage good coding practices, such as writing modular and reusable code. By breaking down your code into smaller, testable units, you can make your code more flexible and maintainable over time.
Overall, unit testing is a critical practice that can improve the quality, maintainability, and reliability of your software. By investing time and effort into writing good tests, you can ensure that your code works as intended and can adapt to changing requirements and user needs.
You’ll find under this link, the testdown report that describe the tests implemented for this package and you will find in this vignette the technical choices made for the realization of these tests.
Workflow
This workflow is intended for developers, it summaries the structure of
the package. For each flat file in the dev/ folder, you have a set of
defined functions as well as the list of files generated from the flat
file when running the fusen::inflate() command.
Example
This is a basic example which shows you how to solve a common problem:
library(FishMap)
# Read internal data as Rds files
survey_data_file <- system.file("original_data",
"Solea_solea",
"survey_data.Rds",
package = "FishMap"
)
survey_data <- readr::read_rds(file = survey_data_file)
vmslogbook_data_file <- system.file("original_data",
"Solea_solea",
"vmslogbook_data.Rds",
package = "FishMap"
)
vmslogbook_data <- readr::read_rds(file = vmslogbook_data_file)
study_domain_file <- system.file("original_data",
"Solea_solea",
"study_domain.Rds",
package = "FishMap"
)
study_domain <- readr::read_rds(file = study_domain_file)
# prepare and load model inputs
fm_data_inputs <- fm_load_data(species = "Solea_solea",
fleet = c("OTB_DEF_>=70_0","OTB_CEP_>=70_0","OTT_DEF_>=70_0"),
fitted_data = "biomass",
survey_data = survey_data,
vmslogbook_data = vmslogbook_data,
study_domain = study_domain,
year_start = 2018,
year_end = 2018,
month_start = 11,
month_end = 11,
time_step = "Month",
k = 0.25,
grid_xmin = -6,
grid_xmax = 0,
grid_ymin = 42,
grid_ymax = 48)
# Fit the model
fm_model_results <- fm_fit_model(fm_data_inputs = fm_data_inputs,
SE = 1,
data_source = 1,
data_obs = 2,
samp_process = 0,
b_constraint = 2,
cov_samp_process = 0,
biomass_temporal = 1,
sampling_temporal = 0,
lf_link = 0,
ref_data = "com",
EM = "est_b",
month_ref = 1)
# Generate figure outputs
fm_generate_graphs(fm_model_results = fm_model_results)Code of Conduct
Please note that the FishMap project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.