Package index • framrsquared

Connecting to FRAM database

Functions to connect a disconnect to a FRAM database

connect_fram_db(): This is a connection object to a FRAM database. Returns a list used throughout the rest of this package which carries meta data.

disconnect_fram_db(): Safely disconnect from FRAM database

Importing FRAM tables

Functions to import a FRAM database table into R

fetch_table(): Fetches a complete table from a FRAM database. Returns a cleaned tibble.

fetch_table_bkchin(): Safely fetch Chinook BackwardsFRAM table

FRAM filtering

Functions to filter FRAM tables by fishery

filter_ak(): Filters a dataframe to Alaska fisheries. Will automatically detect whether it's working with a Chinook or Coho dataset if the tables were generated within this package. Requires a fishery_id column name.

filter_bc(): Filters a dataframe to Canadian (BC) fisheries. Will automatically detect whether it's working with a Chinook or Coho dataset if the tables were generated within this package. Requires a fishery_id column name.

filter_ca(): Filters a dataframe to California fisheries. Will automatically detect whether it's working with a Chinook or Coho dataset if the tables were generated within this package. Requires a fishery_id column name.

filter_coast(): Filters a dataframe to Coastal fisheries. Will automatically detect whether it's working with a Chinook or Coho dataset if the tables were generated within this package. Requires a fishery_id column name.

filter_flag(): NA's all the information in the FisheryScalers that's not being used e.g Flag 1 only NS Scalers will be returned

filter_marine(): Filters a dataframe to marine fisheries. Will automatically detect whether it's working with a Chinook or Coho dataset if the tables were generated within this package. Requires a fishery_id column name.

filter_net(): Filters a dataframe to net fisheries. Will automatically detect whether it's working with a Chinook or Coho dataset if the tables were generated within this package. Requires a fishery_id column name.

filter_or(): Filters a dataframe to Oregon fisheries. Will automatically detect whether it's working with a Chinook or Coho dataset if the tables were generated within this package. Requires a fishery_id column name.

filter_puget_sound(): Filters a dataframe to Puget Sound fisheries. Will automatically detect whether it's working with a Chinook or Coho dataset if the tables were generated within this package. Requires a fishery_id column name.

filter_sport(): Filters a dataframe to sport fisheries. Will automatically detect whether it's working with a Chinook or Coho dataset if the tables were generated within this package. Requires a fishery_id column name.

filter_wa(): Filters a dataframe to Washington State fisheries. Will automatically detect whether it's working with a Chinook or Coho dataset if the tables were generated within this package. Requires a fishery_id column name.

Comparing FRAM runs

Functions that compare two FRAM runs within the same database

compare_databases(): Compare tables in two equivalent FRAM databases

compare_fishery_input_flags(): Compares the fishery flags of two runs

compare_fishery_inputs(): Compares the fishery inputs of two runs

compare_inputs(): Generates a dataframe that compares fishery scalers table for two runs identified by run_id's

compare_inputs_chart(): Generates a heat map of changed between two run inputs. Can be a very busy chart if not filtered down. Consider using a filter.

compare_non_retention_input_flags(): Compares the non retention flags of two runs

compare_non_retention_inputs(): Compares the non retention inputs of two runs

compare_recruits(): Compares the recruit scalers of two runs

compare_runs(): Generates a report to the console of changes to inputs between two runs

compare_stock_fishery_rate_scalers(): Compares the stock fishery rate scalers of two runs

FRAM Screen Reports

Functions that replicate FRAM UI screen reports

msf_encounters(): Produces the MSF screen report numbers for encounters. Returns different format depending database.

msf_encounters_chinook_(): Returns a tibble matching the MSF screen report encounters for Chinook. This is specific for Chinook and in most cases msf_encounters() is preferable.

msf_encounters_coho_(): Returns a tibble matching the MSF screen report encounters for Coho This is specific for Coho and in most cases msf_encounters() is preferable.

msf_landed_catch(): Produces the MSF screen report numbers for landed catch. Returns different format depending database.

msf_landed_catch_chinook_(): Returns a tibble matching the MSF screen report landed catch for Chinook. This is specific for Chinook and in most cases msf_landed_catch() is preferable.

msf_landed_catch_coho_(): Returns a tibble matching the MSF screen report landed catch for Coho This is specific for Coho and in most cases msf_landed_catch() is preferable.

msf_mortalities(): Produces the MSF screen report numbers for mortalities. Returns different format depending database.

msf_mortalities_chinook_(): Returns a tibble matching the MSF screen report mortalities for Chinook. This is specific for Chinook and in most cases msf_mortalities() is preferable.

msf_mortalities_coho_(): Returns a tibble matching the MSF screen report mortalities for Coho This is specific for Coho and in most cases msf_mortalities() is preferable.

population_statistics(): Returns a tibble matching the Population Statistics screen.

fishery_mortality(): Returns a tibble matching the Fishery Mortality screen.

stock_mortality(): Returns a tibble matching the Fishery Mortality screen.

Stock impact visualizations

Functions that visualize impacts of stocks in fisheries

plot_stock_mortality(): Creates an ordered bar chart with the top number of mortalities per fishery.

plot_stock_mortality_time_step(): Creates an ordered bar chart with the top number of mortalities per fishery and time step.

make_impacts_per_catch_heatmap(): Make plots to show the amount of landed catch_per_impact

Initializing R FRAM Projects

Creates a standardized workspace for fram related R projects

initialize_project(): Initializes a FRAM project

fetch_quarto_templates(): Creates quarto template files

Database altering functions

Functions that alter FRAM database tables, use with caution

copy_fishery_scalers(): Experimental copying scaler inputs from one run to another DANGEROUS

change_run_id(): Changes a run's ID number in a FRAM database

remove_run(): Removes a run in a FRAM database

Week calculations

Functions calculate a week number

management_week(): Vectorized approach to calculating the management week, returns an integer

statistical_week(): Vectorized approach to calculating the statistical week, returns an integer

Validations

Functions that validate FRAM data

validate_fram_db(): Convenience function to check fram_db input

validate_run_id(): Convenience function to check run_id input

Terminal Runs

Functions that unpack the TRuns table in the FRAM database

truns_fisheries(): Returns a dataframe with fisheries defined by the TRuns report driver

truns_stocks(): Returns a dataframe with stocks defined by the TRuns report driver

Post-season FRAM

Functions that focus on post-season runs and backwards FRAM

bkfram_checks_coho(): Performs error checks of a backwards FRAM run Returns nested tibble with diagnostics

post_season_abundance(): Generates post-season January age 3 abundances by stock from post-season databases. Used for forecasting.

Describing the FRAM Database

Functions that describe the current FRAM database

fram_clean_tables(): Cleans the names of FRAM tables and coverts to a tibble

fram_database_species(): Identifies the FRAM database species focus - Chinook or Coho

fram_database_type(): Identifies the FRAM database type - Full or Transfer

get_run_ids(): Gets all run_ids of FRAM database

find_tables_by_column_(): Finds tables that contain a specific column name

provide_table_names(): List names of FRAM table

run_info(): Provides a print out of Run ID information

Data checking

Functions that provide checks for the current data

addstock_check(): Check FRAM database after adding new stock

bkfram_checks_coho(): Performs error checks of a backwards FRAM run Returns nested tibble with diagnostics

error_check_code(): Check code for common errors

stock_age_checker(): Helper function to check that all stock x age combinations are present

stock_check_helper(): Helper function to check that stock id make sense

frs_stylecheck_assignment() frs_stylecheck_snakecase(): Framrsquared style guide

Misc.

Miscellaneous functions

add_flag_text(): Adds a column with a text version of flags for either non-retention or fishery scalers

aeq_mortality(): Extract AEQ mortality from Chinook FRAM database. Refactored and stripped down from the framr package written by Dan Auerbach. https://github.com/FRAMverse/framr/

coho_mark_rates() experimental: Returns a tibble displaying predicted FRAMencounter mark rates by fishery, fishery type, and time-step.

input_summary_(): Generates an input summary based on a FisheryScalers dataframe. Probably end up streamlining / revising this.

mortality_scalers(): Guestimate the impact on a particular stock by multiplying its mortalities by the stock_mortality_ratio produced by these functions.

msp_mortality(): Expand Chinook mortality table using Model-Stock Proportion

NR_flag_translate(): Provides English translation of numeric non-retention flags

scalers_flag_translate(): Provides English translation of numeric scalers flags

stock_id_comp(): Helper function to check that stock id exist in the Stock database

welcome(): Welcome message, summarizing database information