📈 An R package for data extraction and plotting of BC groundfish data
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README.md

gfplot: An R package for data extraction and plotting of British Columbia groundfish data

Facilitates the creation of an annual groundfish data synopsis report with gfsynopsis.

Note that the documentation is incomplete in some places. Please post in the issue tracker if you have questions or suggestions on how the package or its documentation could be improved. We welcome pull requests!

In addition to the help available through the R console, a web version of the documentation is available.

Installation

The gfplot package can be installed and loaded with:

install.packages("INLA", repos = c(getOption("repos"), 
  INLA = "https://inla.r-inla-download.org/R/stable"), dep = TRUE)
# install.packages("devtools")
devtools::install_github("pbs-assess/gfplot")
library(gfplot)

Functions specific to PBS:

fns <- ls("package:gfplot")
sort(fns[grepl("get", fns)])
#>  [1] "get_age_methods"        "get_age_precision"     
#>  [3] "get_catch"              "get_commercial_samples"
#>  [5] "get_cpue_historic"      "get_cpue_historical"   
#>  [7] "get_cpue_index"         "get_cpue_spatial"      
#>  [9] "get_cpue_spatial_ll"    "get_fishery_ids"       
#> [11] "get_gear_types"         "get_iphc_1995"         
#> [13] "get_iphc_1996to2002"    "get_iphc_2013"         
#> [15] "get_iphc_hooks"         "get_iphc_sets"         
#> [17] "get_iphc_sets_info"     "get_iphc_skates_info"  
#> [19] "get_iphc_spp_name"      "get_major_areas"       
#> [21] "get_management"         "get_management_areas"  
#> [23] "get_most_common_level"  "get_other_surveys"     
#> [25] "get_sara_dat"           "get_species"           
#> [27] "get_species_groups"     "get_ssids"             
#> [29] "get_survey_index"       "get_survey_samples"    
#> [31] "get_survey_sets"        "iphc_get_calc_plot"

Generic functions for any similarly formatted data:

sort(fns[grepl("tidy", fns)])
#>  [1] "tidy_age_precision"            "tidy_ages_raw"                
#>  [3] "tidy_ages_weighted"            "tidy_catch"                   
#>  [5] "tidy_comps"                    "tidy_comps_commercial"        
#>  [7] "tidy_comps_survey"             "tidy_cpue_historic"           
#>  [9] "tidy_cpue_historical"          "tidy_cpue_index"              
#> [11] "tidy_cpue_index_coefs"         "tidy_cpue_index_coefs_tweedie"
#> [13] "tidy_iphc_survey"              "tidy_lengths_raw"             
#> [15] "tidy_lengths_weighted"         "tidy_maturity_months"         
#> [17] "tidy_sample_avail"             "tidy_survey_index"            
#> [19] "tidy_survey_sets"
sort(fns[grepl("fit", fns)])
#> [1] "fit_cpue_index"         "fit_cpue_index_glmmtmb"
#> [3] "fit_cpue_index_tweedie" "fit_length_weight"     
#> [5] "fit_mat_ogive"          "fit_survey_sets"       
#> [7] "fit_vb"
sort(fns[grepl("plot", fns)])
#>  [1] "iphc_get_calc_plot"     "plot_age_precision"    
#>  [3] "plot_ages"              "plot_catch"            
#>  [5] "plot_cpue_index"        "plot_cpue_index_coefs" 
#>  [7] "plot_cpue_index_jk"     "plot_cpue_spatial"     
#>  [9] "plot_growth"            "plot_length_weight"    
#> [11] "plot_lengths"           "plot_mat_ogive"        
#> [13] "plot_maturity_months"   "plot_predictor_bubbles"
#> [15] "plot_qres_histogram"    "plot_qres_qq"          
#> [17] "plot_sample_avail"      "plot_survey_index"     
#> [19] "plot_survey_sets"       "plot_vb"