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.
In addition to the help available through the R console, a web version of the documentation is available.
The gfplot package can then be installed and loaded with:
# install.packages("remotes")
remotes::install_github("pbs-assess/gfplot")library(gfplot)Functions specific to PBS (now in gfdata):
library(gfdata)
fns <- ls("package:gfdata")
sort(fns[grepl("get", fns)])
#> [1] "get_active_survey_blocks" "get_age_methods"
#> [3] "get_age_precision" "get_all_stomachs"
#> [5] "get_catch" "get_catch_spatial"
#> [7] "get_comm_gear_types" "get_commercial_samples"
#> [9] "get_cpue_historical" "get_cpue_historical_hake"
#> [11] "get_cpue_historical_hl" "get_cpue_index"
#> [13] "get_cpue_index_hl" "get_cpue_spatial"
#> [15] "get_cpue_spatial_ll" "get_eulachon_specimens"
#> [17] "get_fishery_ids" "get_fishery_sectors"
#> [19] "get_gear_types" "get_hake_catch"
#> [21] "get_hake_survey_samples" "get_ll_hook_data"
#> [23] "get_major_areas" "get_management"
#> [25] "get_management_areas" "get_other_surveys"
#> [27] "get_sable_landings" "get_sablefish_surveys"
#> [29] "get_sensor_attributes" "get_sensor_data_ll_ctd"
#> [31] "get_sensor_data_ll_ctd_fe" "get_sensor_data_ll_td"
#> [33] "get_sensor_data_ll_td_fe" "get_sensor_data_trawl"
#> [35] "get_sensor_data_trawl_fe" "get_species"
#> [37] "get_species_groups" "get_ssids"
#> [39] "get_strata_areas" "get_survey_blocks"
#> [41] "get_survey_gear_types" "get_survey_ids"
#> [43] "get_survey_index" "get_survey_samples"
#> [45] "get_survey_sets" "get_survey_stomachs"
#> [47] "get_table"Generic functions for any similarly formatted data:
fns <- ls("package:gfplot")
sort(fns[grepl("tidy", fns)])
#> [1] "tidy_age_precision" "tidy_ages_raw" "tidy_ages_weighted"
#> [4] "tidy_catch" "tidy_comps" "tidy_comps_commercial"
#> [7] "tidy_comps_survey" "tidy_cpue_historical" "tidy_cpue_index"
#> [10] "tidy_lengths_raw" "tidy_lengths_weighted" "tidy_maturity_months"
#> [13] "tidy_sample_avail" "tidy_survey_index" "tidy_survey_sets"sort(fns[grepl("fit", fns)])
#> [1] "fit_cpue_index_glmmtmb" "fit_length_weight" "fit_mat_ogive"
#> [4] "fit_survey_sets" "fit_vb"sort(fns[grepl("plot", fns)])
#> [1] "plot_age_precision" "plot_ages" "plot_catch"
#> [4] "plot_catch_spatial" "plot_cpue_spatial" "plot_growth"
#> [7] "plot_length_weight" "plot_lengths" "plot_mat_annual_ogives"
#> [10] "plot_mat_ogive" "plot_maturity_months" "plot_predictor_bubbles"
#> [13] "plot_qres_histogram" "plot_qres_qq" "plot_sample_avail"
#> [16] "plot_survey_index" "plot_survey_sets" "plot_vb"