Package 'framrsquared'

Description

Convenience function for combining the separate mortality columns of the Mortality table. Note: this does not account for AEQ for Chinook.

Usage

add_total_mortality(.data)

Arguments

Value

.data with additional ⁠$total_mortality⁠ column just before ⁠$landed_catch⁠.

Description

Either provides the step by step process of adding new stock to a FRAM database, or walks through fram database run and checks the tables for potential errors associated with adding new stock.

Usage

addstock_check(
  file_name = NULL,
  run_id = NULL,
  old_stockcount = 78,
  override_db_checks = FALSE
)

Arguments

Examples

## Not run: 
## review process
addstock_check()
## check database for additional stock
addstock_check("2024 Pre-Season Chinook DB - first test.mdb",
run_id = 138)

## End(Not run)

Description

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

Usage

aeq_mortality(fram_db, run_id = NULL, msp = TRUE, label = TRUE)

Arguments

Examples

## Not run: 
fram_db |> aeq_mortality(run_id = 132)

## End(Not run)

Description

Performs error checks of a backwards FRAM run Returns nested tibble with diagnostics

Usage

bkfram_checks_coho(fram_db, backward_run_id = NULL, forward_run_id = NULL)

Arguments

Examples

## Not run: 
fram_db |> bkfram_checks_coho(backward_run_id = 132, forward_run_id = 133)

## End(Not run)

Calculate match/replace df based on scaling

Description

Uses a match/replace-style table like in modify_table(), but allows user to specify scaling factors for individual columns rather than absolute values, and returns the corresponding match/replace df to be used in modify_table(). This is intended to support sensitivity analyses structured as "carry out 100 runs, with stock recruit scalers for stock X running from 5% to 500% of the current value" (calc_fram_scaling() is only one part of the pipeline for this). See modify_table() for details of setting up a match/replace dataframe; the only difference here is that the columns to be scaled should start with "scale_" instead of "replace_", and should contain the scalers.

Usage

calc_fram_scaling(fram_db, table_name, df)

Arguments

Details

Note: In the StockRecruit table, RecruitScaleFactor and RecruitCohortSize should have a fixed relationship (scale factor = cohort size / base period size). For this reason, if applying scaling to only one of those columns, calc_fram_scaling will automatically apply the same scaling to the other. If the scaling for both is provided and they do not match, calc_fram_scaling will error out.

Value

A match/replace df for use in modify_table(), with "replace_" values generated by scaling the corresponding values in the FRAM database. Includes additional "match_" columns for all columns except "PrimaryKey"

Examples

## Not run: 
## in run 31, decrease stock 1's recruit numbers by 50% and double 2's recruit numbers
library(here)
fram_db <- connect_fram_db(here("example_fram_db.mdb"))

df <- data.frame(match_RunID = c(31, 31), match_StockID = 1:2, scale_RecruitScaleFactor = c(.5, 2))

df_scaled <- calc_fram_scaling(fram_db, "StockRecruit", df)
## here's what the values become:
df_scaled

## we can then modify the database
modified <- modify_table(fram_db, "StockRecruit", df_scaled)

disconnect_fram_db(fram_db)

## End(Not run)

Description

Changes a run's ID number in a FRAM database

Usage

change_run_id(fram_db, old_run_id, new_run_id)

Arguments

Examples

## Not run: fram_db |> change_run_id(old_run_id = 132, new_run_id = 300)

Description

Reads a package NAMESPACE file to extract exported functions and checks whether each function appears in a specified documentation file (e.g., a Quarto document or vignette). Reports coverage statistics to the console using the cli package.

Usage

check_demo_coverage(namespace_path, doc_path)

Arguments

Details

The function searches for function calls in the form ⁠function_name(⁠ to determine if a function is present in the documentation. It prints a formatted report to the console showing which functions are documented and which are missing.

Value

Invisibly returns a list with three elements:

found

Character vector of function names found in the document

missing

Character vector of function names not found in the document

coverage

Numeric percentage of functions documented

Examples

## Not run: 
# Check if all exported functions appear in a vignette
check_demo_coverage("NAMESPACE", "vignettes/function_demo.qmd")

# Store results for further analysis
results <- check_demo_coverage("NAMESPACE", "README.md")
results$missing

## End(Not run)

Returns a tibble displaying predicted FRAMencounter mark rates by fishery, fishery type, and time-step.

Description

Returns a tibble displaying predicted FRAMencounter mark rates by fishery, fishery type, and time-step.

Usage

coho_mark_rates(fram_db, run_id = NULL)

Arguments

Examples

## Not run: 
fram_db |> coho_mark_rates(run_id)

## End(Not run)

Calculate starting cohort abundance

Description

The starting cohort abundance listed in the database can be wrong. This function calculates the value by multipying the Stock Recruit Scalar by the base period abundance.

Usage

cohort_abundance(fram_db, run_id = NULL)

Arguments

Examples

## Not run: 
fram_db |>  cohort_abundance(run_id = 145)

## End(Not run)

Compare tables in two equivalent FRAM databases

Description

Function supports QAQC practices by comparing the tables of two FRAM databases and identifying (and quantifying) differences.

Usage

compare_databases(
  fram_db_1,
  fram_db_2,
  runid_use = NULL,
  tables_use = NULL,
  slim = TRUE,
  quiet = TRUE
)

Arguments

Details

The key output is the ⁠$ratios⁠ tibble, which contains every comparison of relevant table entries in long-form. These comparisons are implemented by first aligning corresponding table rows using appropriate key columns (e.g. run_id, fishery_id, stock_id, age, time_step, etc).

In ⁠$ratios⁠, the table and variable columns specify the table column being compared, respectively. prop_err, abs_err, and scale_err provide measures of the changes between the "original" value (from fram_db_1) and the "comparison" value (from fram_db_2). More on those below. The original and new columns give the actual values being compared. run_id through time_step specify the rows being compared. bkfram_off_by_fish and bkfram_off_by_prop provide the context for the comparison (more on that below).

Quantifying error

Because FRAM involves numerical solvers, we expect some small differences in table entries even when comparing two effectively equivalent databases. compare_databases() provides three metrics for these changes. In each case, it is assumed that fram_db_1 is the reference file; the "error" measures all show how much the value in fram_db_2 changed relative to the corresponding value in fram_db_1. The simplest measure of error is the abs_err. This is the absolute value of the difference between the two values. If we're looking at an entry with table = "Mortality" and variable = "landed_catch", then an abs.err of 5 means that the fram_db_2 entry was five fish more or less than the fram_db_1 entry. You can confirm this by looking at the original and new columns. While abs_err is the most easily interpreted, it is often not very meaningful when looking across tables and variables. After all, an abs_err value of 5 could mean a a relatively meaningless change of five fish for a landed catch entry that was originally thousands of fish, but the same value of 5 would be a huge change in fishing effort if it were for a fishery scaler entry.

One way to make error comparable across tables and variables is to calculate the proportional error. If an entry changed by 0.01%, that's not meaningful, while if it changed by 10%, that is. ⁠$prop_err⁠ provides this proportional error, where -0.5 means the entry in fram_db_2 was 50% less than the corresponding value in fram_db_1, and a value of 2 means the entry in fram_db_2 was 200% more than the corresponding value in fram_db_1. ' This gives error in context of the original value, and is often a good a way to look for problems. However, we sometimes find very large ⁠$prop_err⁠ values for changes that aren't concerning. For example, we may have an entry for landed catch in the mortality table that was 0.00001 fish in fram_db_1, and 0 fish in fram_db_2. In all practicality these two values are identical, and the 0.00001 fish difference is likely one of random jitter in the numerical solver or rounding differences. However, our ⁠$prop_err⁠ value for this cell is -1, the most extreme negative change we can get. We can jointly look at ⁠$abs_err⁠ and ⁠$prop_err⁠ to address the potential for misleadingly large errors ⁠$prop_err⁠, but it would be nice to have a single error metric that provides error in context without being sensitive to very small entries in fram_db_1.

scale_err is an elaboration on ⁠$prop_err⁠ that provides broader context. ⁠$prop_err⁠ takes the absolute error and scales by the original value in fram_db_1. ⁠$scale_err⁠ generalizes this idea, first calculating the average error for each table-variable combination, and then scaling the absolute error by the corresponding table-variable average. That is, if an entry for landed_catch in the Mortality table was 0.001 in fram_db_1, and was then 0.002 in fram_db_2, and the average of all landed_catch entries in fram_db_1 was 1000, then the prop_err would be 1 (since fram_db_2 had double the value of fram_db_1, or (0.002-0.001)/0.001), and the scale.err would be 0.000001 ((0.002-0.001)/1000). This better captures our intuition that a difference of 0.001 fish in the landed catch isn't a big deal, since those values are typically huge. scale_err is thus a measure of error that is comparable across variables and tables, essentially answer the question "Has this entry changed a lot for this kind of variable and table?".

While scale_err is frequently the most useful error metric, compare_databases() provides all three. There may be contexts in which it's important to focus on the proportional error. For example, large proportional errors landed catch for the catch rare stocks can be important, but the much larger catch from other stock could water down the scale_err metric.

Addressing the backwardsFRAM wiggle

For post-season runs, the backwards FRAM algorithm is employed; its solver stops when the estimated fish numbers are within 1 fish of the target size. This means that there is the potential for substantial "wiggle" in bkFRAm values when comparing two databases. This wiggle can propagate to other tables, especially for stock-age-timesteps in which the target values were quite small (so a wiggle of +/- 1 fish would be a proportionally large amount). For this reason, it can be useful to see how our measures of error correspond to the errors in the corresponding bk fram table. For every table entry for which this makes sense (e.g., has a stock id, age, and timestep), ⁠$bkfram_off_by_fish⁠ gives the absolute error in the corresponding row of the BackwardsFram table, and bkfram_off_by_prop give the relative error (as a proportion) in the corresponding row of the BackwardsFram table. If this bkfram wiggle were the cause of observed errors, we would expect the largest errors to correspond to the largest ⁠$bkfram_off_by_fish⁠ or ⁠$bkfram_off_by_prop⁠ values.

Suggestions

For simple plotting to see if the original and new values fall on the 1:1 line, ggplot2::geom_point() can be used, with ⁠$ratios$original⁠ and ⁠$ratios$new⁠ for x and y, and a facet_wrap by table (and perhaps variable) to make plots readable. For identifying meaningful change, scale.err is likely the best measure of error. It can be helpful to plot scale.err against bkfram.off.by.fish or bkfram.off.by.prop to see if the table entries with the largest error correspond to the stock-fishery-age-timestep in which there's the largest wiggle in the backwards fram solutions.

When digging into individual tables, it can sometimes be helpful to look at the comparisons in ⁠$ratios_detailed⁠, which contains additional columns which did not fit into the standardized formatting of ⁠$ratios⁠.

Value

List of lists and tibbles containing comparison information:

• ⁠$ratios⁠ tibble comparing every entry of every relevant column of every table. See "Details" for column descriptions.

• ⁠$ratios_detailed⁠ list of tibbles showing the contents of ⁠$ratios⁠ broken into tables, with additional non-compared columns present (e.g., stock_name in ⁠$ratios_detailed$Stock⁠). Not shown if slim is TRUE.

• ⁠$nrow_tracker⁠ dataframe providing the number of rows in each table of file1 (⁠$nrow_original⁠), file2 (⁠$nrow_new⁠), and the joined comparison (⁠$nrow_comparison⁠). Useful to track down cause of many-to-many join warnings that can result from duplicated table entries; unless there are duplicate entries, ⁠$nrow_comparison⁠ should be less than or equal to the minimum of ⁠$nrow_original⁠ and ⁠$nrow_new⁠.

• ⁠$tabs_file1⁠ List containing the original fetched tables from file1. Not returned if argument slim is TRUE

• ⁠$tabs_file2⁠ List containing the original fetched tables from file2. Not returned if argument slim is TRUE.

Examples

## Not run: 
fram_db_1 = connect_fram_db("Valid2022_Round_7_1_1_11142023_REFERENCE_fixed - fork rebuild.mdb")
fram_db_2 = connect_fram_db("Valid2022_Round_7.1.1_11142023 - green river split.mdb")
out = tables_compare(fram_db_1, fram_db_2)

## End(Not run)

Description

Compares the fishery flags of two runs

Usage

compare_fishery_input_flags(fram_db, run_ids, verbose = TRUE)

Arguments

See Also

compare_runs()

Examples

## Not run: fram_db |> compare_fishery_input_flags(c(55, 56))

Description

Compares the fishery inputs of two runs

Usage

compare_fishery_inputs(fram_db, run_ids, tolerance = 0.01, verbose = TRUE)

Arguments

See Also

compare_runs()

Examples

## Not run: fram_db |> compare_fishery_inputs(c(55, 56))

Description

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

Usage

compare_inputs(fram_db, run_ids)

Arguments

Details

Comparisons assume the first run provided is the baseline, and provide relative changes from that. This includes percent changes ($percent.diff)include percent changes (infinite when)

Value

Data frame of differences. ⁠$percentdiff⁠ = change in quota (comparing the appropriate quotas based on fishery flags), ⁠$regulation_comparison⁠ = change in regulation (NS, MSF, NS + MSF). Columns present in the FisheriesScalers database are included, with ⁠_original⁠ and ⁠_comparison⁠ suffixes identifying entries associated with the first and second entries of run_ids, respectively.

See Also

compare_inputs_chart()

Examples

## Not run: fram_db |> compare_inputs(c(100,101))

Description

Can be a very busy chart if not filtered down. Consider using a filter on the dataframe before piping into compare_input_chart.

Usage

compare_inputs_chart(.data)

Arguments

See Also

compare_inputs()

Examples

## Not run: fram_db |> compare_inputs(c(100, 101)) |> compare_inputs_chart()

Description

Compares the non retention flags of two runs

Usage

compare_non_retention_input_flags(fram_db, run_ids, verbose = TRUE)

Arguments

See Also

compare_runs()

Examples

## Not run: fram_db |> compare_non_retention_inputs(c(55, 56))

Description

Compares the non retention inputs of two runs

Usage

compare_non_retention_inputs(fram_db, run_ids, verbose = TRUE)

Arguments

See Also

compare_runs()

Examples

## Not run: fram_db |> compare_non_retention_inputs(c(55, 56))

Description

Compares the recruit scalers of two runs

Usage

compare_recruits(fram_db, run_ids, tolerance = 0.01, verbose = TRUE)

Arguments

See Also

compare_runs()

Examples

## Not run: fram_db |> compare_recruits()

Description

Generates a report to the console of changes to inputs between two runs

Usage

compare_runs(fram_db, run_ids, tolerance = 0.01)

Arguments

Examples

## Not run: fram_db |> compare_runs(c(55, 56))

Description

Compares the stock fishery rate scalers of two runs

Usage

compare_stock_fishery_rate_scalers(fram_db, run_ids)

Arguments

See Also

compare_runs()

Examples

## Not run: fram_db |> compare_stock_fishery_rate_scalers(c(55, 56))

Description

This produces a connection object to a FRAM database, which is a necessary precursor for almost all framrsquared functions. For most users, can just treat the connection object as a black box that's used as an argument in other functions. See details for an explanation of the returned object.

Usage

connect_fram_db(db_path, read_only = FALSE, quiet = FALSE)

Arguments

Details

The returned object of connect_fram_db() is a list of useful objects for other framrsquared functions.

⁠$fram_db_connection⁠

connection object, used for SQL calls.

⁠$fram_db_connection_id⁠

connection name in .fram_connections, used for orphan cleanup via disconnect_all_fram_connections().

⁠$fram_db_type⁠

"full" or "transfer", useful for validation for specific functions.

⁠$fram_db_species⁠

"COHO" or "CHINOOK"

⁠$fram_db_species⁠

filetype, typeically "mdb"

⁠$fram_read_only⁠

Optional user-specified safety measure, TRUE or FALSE. Functions that modify the fram database should error out if TRUE.

framrsquared creates a special environment in the global environment, .fram_connections. Whenever a new connection is made with connect_fram_db, it is added to that environment as well; whenever the connection is closed with disconnect_fram_db(), the connection is removed from that environment. This tracking allows disconnect_all_fram_connections() to clear out any orphan connections made by assigning a connection to an existing connection object.

See Also

disconnect_fram_db(), disconnect_all_fram_connections()

Examples

## Not run: fram_db <- connect_fram_db('<path>')
fram_db |> fetch_table("Mortality")
## End(Not run)

Description

Experimental copying scaler inputs from one run to another DANGEROUS

Usage

copy_fishery_scalers(fram_db, from_run, to_run, fishery_id = NULL)

Arguments

Examples

## Not run: framdb |> copy_fishery_scalers(132, 133, 87)

Copies a run a number of times

Description

FRAM is stored in an access database; these have hard size limits of 2GB. Chinook and Coho are expected to reach this limit with ~540 runs. This function includes a failsafe to prevent databases from exceeding 500 runs. This can be overridden with optional force_many_runs argument.

Usage

copy_run(
  fram_db,
  target_run,
  times = 1,
  label = "copy",
  force_many_runs = FALSE,
  verbose = TRUE
)

Arguments

Value

Invisibly returns the run ids of the copied runs, for use in other functions.

Examples

## Not run: framdb |> copy_run(target_run = 141, times = 1)

Copy TAMM for FRAM batch runs

Description

Preps a folder for batch running in the 'Run Multiple Runs' screen of the FRAM automation fork (https://github.com/FRAMverse/FRAM_automation), for use with the advanced approach to identify multiple runs. One TAMM file will be copied multiple times in the target_folder with suffixes that identify each of the run_ids. The "Use folder" button on the "Run Multiple Runs" screen can then use the target folder to set up large batch runs. Typically users should use make_batch_runs() instead (this first copies runs and then uses copy_tamms to create tamms that match the new runs).

Usage

copy_tamms(tamm_name, target_folder, run_id_vec, overwrite = FALSE)

Arguments

Value

invisibly returns logical vector of file.copy() success.

Examples

## Not run: copy_tamms(tamm_name = "C:/TAMMs/Chin2020.xlsx",
target_folder = "C:/Batch_run_5", run_id_vec = 10:20)
## End(Not run)

Description

It is relatively easy to create an orphan connection using framrsquared by assigning a connection to fram_db, and then assigning another connection to fram_db without disconnecting the first connection using disconnect_fram_db(). Orphaned connections can make it frustrating to work with database files (moving, deleting, etc) without restarting rstudio or rebooting your computer. disconnect_all_fram_connections() disconnects any existing connections made by framrsquared in this R session.

Usage

disconnect_all_fram_connections()

Value

nothing

Examples

## Not run: 
fram_db = connect_fram_db("Chin2025.mdb")
fram_db = connect_fram_db("Chin2025.mdb")
disconnect_fram_db(fram_db)

list_extant_fram_connections()
## oops, still a connection left.

disconnect_all_fram_connections()

list_extant_fram_connections

## End(Not run)

Description

Safely disconnect from FRAM database

Usage

disconnect_fram_db(fram_db, quiet = TRUE)

Arguments

See Also

connect_fram_db()

Examples

## Not run: disconnect_fram_db(fram_db)

Description

Creates template files from specified organization in the specified path. Generally initialize_project() will be more useful for new R projects, while fetch_quarto_templates() can be helpful when working with existing projects. See initialize_project() for details on adding template files for new organizations.

Usage

fetch_quarto_templates(
  to.path,
  organization = c("WDFW"),
  color = "coffee",
  overwrite = FALSE
)

Arguments

Value

Nothing.

See Also

initialize_project()

Description

Returns a cleaned tibble, with column labels that were camel case (e.g., TimeStep) converted to snake case (e.g., time_step). WARNING: the Chinook "BackwardsFRAM" table uses a different stock_id numbering system from every other table. To avoid errors when joining that with other tables, instead fetch with fetch_table_bkchin() fetch_table_() is an alias for fetch_table() with the optional argument label set to FALSE.

Usage

fetch_table(fram_db, table_name = NULL, label = TRUE, warn = TRUE)

fetch_table_(fram_db, table_name = NULL, warn = TRUE)

Arguments

Examples

## Not run: 
fram_db <- connect_fram_db("validat2024.mdb")
fram_db |> fetch_table('Mortality')
## End(Not run)

Description

The BackwardsFRAM table uses a stock_id different numbering system from all other tables, and includes sums of joint stocks (e.g. for a marked and unmarked pair of stocks, BackwardsFRAM will typically have an additional stock which represents the sum of those two). Because the numbering is different but the column name is the same, joining the Chinook BackwardsFRAM table with other tables can cause problems.

Usage

fetch_table_bkchin(fram_db)

Arguments

Details

This function augments fetch_table by renaming the stock_id column to bk_stock_id, and then adding on the associated stock_id (with NAs when the bkfram stock is one of these new "sum" stocks and the associated bkfram stock names). This function only works for Chinook databases.

The resulting dataframe will necessarily NOT be an exact match to the BackwardsFRAM table in the FRAM database. The stock_id column will differ (containing normal stock ID values instead of bk stock ID values), and there will be two additional columns.

Examples

#' @examples
## Not run: 
##Potentially problematic stock_id won't align with other tables
fram_db |> fetch_table('BackwardsFRAM')
## "safe" version of the table; stock_id WILL align with other tables
fram_db |> fetch_table_bkchin()

## End(Not run)

Description

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. .data must have a fishery_id column name.

Usage

filter_ak(.data, species = NULL)

Arguments

See Also

Other fishery_filters: filter_bc(), filter_ca(), filter_coast(), filter_commercial_wa_nt(), filter_marine(), filter_net(), filter_or(), filter_puget_sound(), filter_sport(), filter_wa()

Examples

## Not run: 
fram_dataframe |> filter_ak()

## End(Not run)
framrosetta::fishery_chinook_fram |> filter_ak(species = "CHINOOK")
framrosetta::fishery_coho_fram |> filter_ak(species = "COHO")

Description

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. .data must have a fishery_id column name.

Usage

filter_bc(.data, species = NULL)

Arguments

See Also

Other fishery_filters: filter_ak(), filter_ca(), filter_coast(), filter_commercial_wa_nt(), filter_marine(), filter_net(), filter_or(), filter_puget_sound(), filter_sport(), filter_wa()

Examples

## Not run: 
fram_dataframe |> filter_bc()

## End(Not run)
framrosetta::fishery_chinook_fram |> filter_bc(species = "CHINOOK")
framrosetta::fishery_coho_fram |> filter_bc(species = "COHO")

Description

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. .data must have a fishery_id column name.

Usage

filter_ca(.data, species = NULL)

Arguments

See Also

Other fishery_filters: filter_ak(), filter_bc(), filter_coast(), filter_commercial_wa_nt(), filter_marine(), filter_net(), filter_or(), filter_puget_sound(), filter_sport(), filter_wa()

Examples

## Not run: 
fram_dataframe |> filter_ca()

## End(Not run)
framrosetta::fishery_chinook_fram |> filter_ca(species = "CHINOOK")
framrosetta::fishery_coho_fram |> filter_ca(species = "COHO")

Description

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. .data must have a fishery_id column name.

Usage

filter_coast(.data, species = NULL)

Arguments

See Also

Other fishery_filters: filter_ak(), filter_bc(), filter_ca(), filter_commercial_wa_nt(), filter_marine(), filter_net(), filter_or(), filter_puget_sound(), filter_sport(), filter_wa()

Examples

## Not run: 
fram_dataframe |> filter_coast()

## End(Not run)
framrosetta::fishery_chinook_fram |> filter_coast(species = "CHINOOK")
framrosetta::fishery_coho_fram |> filter_coast(species = "COHO")

Description

Will automatically detect whether it's working with a Chinook or Coho dataset if the tables were generated within this package. .data must have a fishery_id column name.

Usage

filter_commercial_wa_nt(.data, species = NULL)

Arguments

See Also

Other fishery_filters: filter_ak(), filter_bc(), filter_ca(), filter_coast(), filter_marine(), filter_net(), filter_or(), filter_puget_sound(), filter_sport(), filter_wa()

Examples

## Not run: 
fram_dataframe |> filter_commercial_wa_NT()

## End(Not run)
framrosetta::fishery_chinook_fram |> filter_commercial_wa_nt(species = "CHINOOK")
framrosetta::fishery_coho_fram |> filter_commercial_wa_nt(species = "COHO")

Description

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

Usage

filter_flag(.data)

Arguments

Examples

## Not run:  fishery_scalers_table |> filter_flag()

Description

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. .data must have a fishery_id column name.

Usage

filter_marine(.data, species = NULL)

Arguments

See Also

Other fishery_filters: filter_ak(), filter_bc(), filter_ca(), filter_coast(), filter_commercial_wa_nt(), filter_net(), filter_or(), filter_puget_sound(), filter_sport(), filter_wa()

Examples

## Not run: 
fram_dataframe |> filter_marine()

## End(Not run)
framrosetta::fishery_chinook_fram |> filter_marine(species = "CHINOOK")
framrosetta::fishery_coho_fram |> filter_marine(species = "COHO")

Description

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. .data must have a fishery_id column name.

Usage

filter_net(.data, species = NULL)

Arguments

See Also

Other fishery_filters: filter_ak(), filter_bc(), filter_ca(), filter_coast(), filter_commercial_wa_nt(), filter_marine(), filter_or(), filter_puget_sound(), filter_sport(), filter_wa()

Examples

## Not run: 
fram_dataframe |> filter_net()

## End(Not run)
framrosetta::fishery_chinook_fram |> filter_net(species = "CHINOOK")
framrosetta::fishery_coho_fram |> filter_net(species = "COHO")

Description

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. .data must have a fishery_id column name.

Usage

filter_or(.data, species = NULL)

Arguments

See Also

Other fishery_filters: filter_ak(), filter_bc(), filter_ca(), filter_coast(), filter_commercial_wa_nt(), filter_marine(), filter_net(), filter_puget_sound(), filter_sport(), filter_wa()

Examples

## Not run: 
fram_dataframe |> filter_or()

## End(Not run)
framrosetta::fishery_chinook_fram |> filter_or(species = "CHINOOK")
framrosetta::fishery_coho_fram |> filter_or(species = "COHO")

Description

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. .data must have a fishery_id column name.

Usage

filter_puget_sound(.data, species = NULL)

Arguments

See Also

Other fishery_filters: filter_ak(), filter_bc(), filter_ca(), filter_coast(), filter_commercial_wa_nt(), filter_marine(), filter_net(), filter_or(), filter_sport(), filter_wa()

Examples

## Not run: 
fram_dataframe |> filter_puget_sound()

## End(Not run)
framrosetta::fishery_chinook_fram |> filter_puget_sound(species = "CHINOOK")
framrosetta::fishery_coho_fram |> filter_puget_sound(species = "COHO")

Description

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. .data must have a fishery_id column name.

Usage

filter_sport(.data, species = NULL)

Arguments

See Also

Other fishery_filters: filter_ak(), filter_bc(), filter_ca(), filter_coast(), filter_commercial_wa_nt(), filter_marine(), filter_net(), filter_or(), filter_puget_sound(), filter_wa()

Examples

## Not run: 
fram_dataframe |> filter_sport()

## End(Not run)
framrosetta::fishery_chinook_fram |> filter_sport(species = "CHINOOK")
framrosetta::fishery_coho_fram |> filter_sport(species = "COHO")

Description

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. .data must have a fishery_id column name.

Usage

filter_wa(.data, species = NULL)

Arguments

See Also

Other fishery_filters: filter_ak(), filter_bc(), filter_ca(), filter_coast(), filter_commercial_wa_nt(), filter_marine(), filter_net(), filter_or(), filter_puget_sound(), filter_sport()

Examples

## Not run: 
fram_dataframe |> filter_wa()

## End(Not run)
framrosetta::fishery_chinook_fram |> filter_wa(species = "CHINOOK")
framrosetta::fishery_coho_fram |> filter_wa(species = "COHO")

Description

Finds tables that contain a specific column name

Usage

find_tables_by_column_(fram_db, column_name)

Arguments

Examples

## Not run: fram_db |> find_tables_by_column_('RunID')

Description

Returns a tibble matching the Fishery Mortality screen.

Usage

fishery_mortality(fram_db, run_id = NULL, msp = TRUE)

Arguments

Examples

## Not run: 
fram_db |> fishery_mortality(run_id = 101)

## End(Not run)

Description

Cleans the names of FRAM tables and coverts to a tibble

Usage

fram_clean_tables(.data)

Arguments

Examples

## Not run: fram_dataframe |> fram_clean_tables()

Description

Identifies the FRAM database species focus - Chinook or Coho

Usage

fram_database_species(con)

Arguments

Examples

## Not run: fram_database_species(con)

Description

Identifies the FRAM database type - Full or Transfer

Usage

fram_database_type(con)

Arguments

Examples

## Not run: fram_database_type(con)

Description

Gets all fishery_ids of FRAM database

Usage

get_fishery_ids(fram_db)

Arguments

Examples

## Not run: fram_dataframe |> get_run_ids()

Description

Gets all run_ids of FRAM database

Usage

get_run_ids(fram_db)

Arguments

Examples

## Not run: fram_dataframe |> get_run_ids()

Description

Gets all stock_id of FRAM database

Usage

get_stock_ids(fram_db)

Arguments

Examples

## Not run: fram_dataframe |> get_run_ids()

Initializes a FRAM project

Description

By default, creates suggested folder structure from best coding practices, and adds WDFW-style yaml and style.css files to give quarto files consistent appearance. If you belong to another organization and want this function to support your own organization-specific quarto styling, reach out to the developers with a ⁠_quarto.yml⁠ and (optionally) style.css file.

Usage

initialize_project(
  folders = c("scripts", "original_data", "cleaned_data", "figures", "results",
    "results/quarto_output"),
  quarto = TRUE,
  organization = c("WDFW"),
  renv = FALSE,
  template_overwrite = TRUE,
  color = "coffee",
  quiet = TRUE
)

Arguments

Details

Dev note: new template files for additional organizations should be added to ⁠inst/⁠ in a subfolder matching an R-friendly organization name, and the same name should be added to the organization parameter description here and the supported_organizations in fetch_quarto_templates().

See Also

fetch_quarto_templates()

Examples

## Not run: 
framrsquared::initialize_project()

## End(Not run)

Description

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

Usage

input_summary_(.data, run_id)

Arguments

Examples

## Not run: fishery_scalers_dataframe |> input_summary()

Description

Like label_fisheries(), but uses an active FRAM database to label fisheries, rather than the look-up table present in the framrosetta package. Primarily used in fetch_table(), useful if working with databases with unusual stock or fishery tables.

Usage

label_fisheries_db(.data, fram_db)

Arguments

Value

.data with additional column, ⁠$fishery_label⁠

See Also

label_fisheries(), label_stocks_db()

Description

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

Usage

label_flags(.data, species = NULL, warn = TRUE)

Arguments

Examples

## Not run:  mortality_table |> add_flag_text()

Description

Like label_stocks(), but uses an active FRAM database to label stocks, rather than the look-up table present in the framrosetta package. Primarily used in fetch_table(), useful if working with databases with unusual stock or fishery tables.

Usage

label_stocks_db(.data, fram_db)

Arguments

Value

.data with additional column, ⁠$stock_label⁠

See Also

label_stocks(), label_fisheries_db()

Description

Provides information in the terminal, including the number of existing FRAM database connections created in this R session using framrsquared, as well as the files those connections connect to. Note that there may be multiple connections to the same file.

Usage

list_extant_fram_connections()

Description

Make multiple copies of a FRAM run, make copies of specified TAMM in target_folder with run_id suffixes matching newly created runs. Intended to streamline using the multi-run fork of FRAM. Primarily for use internally, in the sensitivity analysis functions

Usage

make_batch_runs(
  fram_db,
  target_run,
  tamm_name,
  target_folder,
  times = 1,
  label = "copy",
  force_many_runs = FALSE,
  verbose = TRUE
)

Arguments

See Also

sensitivity_exact(), sensitivity_scaled(), sensitivity_custom()

Description

Vectorized approach to calculating the management week, returns an integer

Usage

management_week(date)

Arguments

Examples

## Not run: 
data_fram |>
  mutate(mngmt_week = management_week(date_field))

## End(Not run)

Modify FRAM database based on match/replace dataframe

Description

Uses a special match/replace dataframe to modify values in a FRAM table.

Usage

modify_table(fram_db, table_name, df)

Arguments

Details

At a high level, modifying a FRAM table requires identify which rows to change, and then replacing the values of one or more of the columns of those row with new values. We often want to make multiple changes at once, and modify_table is written around using a dataframe to define the matching and replacing, so that it is relatively easy to check all of the changes being made. This dataframe (hereafter the "match/replace dataframe") should have column names starting with "match_" and "replace_", and ending with the exact match of column names in the FRAM table identified with argument table_name. For each row of argument df, modify_table() will use columns starting with "match_" as conditions to identify rows in the FRAM database to modify, and then for those rows will replace the values of columns identified with "replace_" with the corresponding values in the df columns.

As a simple example, imagine we want to see how modifying the size limits for Area 7 Sport (chinook fishery id 36) affect our ERs. We would probably start by using copy_run to create multiple duplicate runs, and then we can use modify_table to change just the MinimumSize values of the "SizeLimits" table for just those rows for which fishery id was 36. If our run ids were 100, 101, and 102, and we wanted to look at minimum sizes of 450, 550, and 650, our df argument might look like data.frame(match_RunID = c(100, 101, 102), match_FisheryID = c(36, 36, 36), replace_MinimumSize = c(450, 550, 650)). Notably, we might create df programmatically to combine different run ids with multiple changes at once or to apply some kind of randomized parameter sampling scheme. Or we could even use an excel sheet to write out the experiment in a df format and then read in the sheet and feed it into modify_table.

Examples

## Not run: 
df_total <- tibble(match_Age = c(3, 4, 5))
df_total$match_StockID <- 100
df_total$match_RunID <- 396
df_total$replace_RecruitScaleFactor <- 1:3
df_total$replace_RecruitCohortSize <- 100:102
fram_db |> modify_db(table_name = "StockRecruit", df = df_total)

## End(Not run)

Description

Supports guestimating the impact of making changes to a fishery on a particular stock (or group of stocks) by multiplying its mortalities by the stock_mortality_ratio produced by mortality_scalers().

Usage

mortality_scalers(fram_db, run_id, stock_id, msp = FALSE)

Arguments

See Also

plot_impacts_per_catch_heatmap()

Examples

## Not run: fram_db |> mortality_scalers(run_id = 101, stock_id = c(17:18))

Description

Produces the MSF screen report numbers for encounters. Returns different format depending database.

Usage

msf_encounters(fram_db, run_id = NULL)

Arguments

See Also

msf_mortalities(), msf_landed_catch()

Examples

## Not run: fram_db |> msf_encounters(run_id = 101)

Description

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

Usage

msf_encounters_chinook_(fram_db)

Arguments

See Also

msf_encounters(), msf_mortalities_chinook_(), msf_landed_catch_chinook_()

Examples

## Not run: fram_db |> msf_encounters_chinook_(run_id = 101)

Description

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

Usage

msf_encounters_coho_(fram_db)

Arguments

See Also

msf_encounters(), msf_landed_catch_coho_(), msf_mortalities_coho_()

Examples

## Not run: fram_db |> msf_encounters_coho_()

Description

Produces the MSF screen report numbers for landed catch. Returns different format depending database.

Usage

msf_landed_catch(fram_db, run_id = NULL)

Arguments

See Also

msf_encounters(), msf_mortalities()

Examples

## Not run: fram_db |> msf_landed_catch(run_id = 101)

Description

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.

Usage

msf_landed_catch_chinook_(fram_db)

Arguments

See Also

msf_landed_catch(), msf_mortalities_chinook_(), msf_encounters_chinook_()

Examples

## Not run: fram_db |> msf_landed_catch_chinook_(run_id = 101)

Description

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.

Usage

msf_landed_catch_coho_(fram_db)

Arguments

See Also

msf_landed_catch(), msf_encounters_coho_(), msf_mortalities_coho_()

Examples

## Not run: fram_db |> msf_landed_catch_coho_()

Description

Produces the MSF screen report numbers for mortalities. Returns different format depending database.

Usage

msf_mortalities(fram_db, run_id = NULL)

Arguments

See Also

msf_encounters(), msf_landed_catch()

Examples

## Not run: fram_db |> msf_mortalities_coho_(run_id = 101)

Description

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

Usage

msf_mortalities_chinook_(fram_db)

Arguments

See Also

msf_mortalities(), msf_encounters_chinook_(), msf_landed_catch_chinook_()

Examples

## Not run: fram_db |> msf_mortalities_chinook_(run_id = 101)

Description

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

Usage

msf_mortalities_coho_(fram_db)

Arguments

See Also

msf_mortalities(), msf_encounters_coho_(), msf_landed_catch_coho_()

Examples

## Not run: fram_db |> msf_mortalities_coho_()

Description

See https://framverse.github.io/fram_doc/calcs_data_chin.html#46_Model-Stock_Proportion.

Usage

msp_mortality(fram_db, run_id = NULL)

Arguments

Value

Mortality table with mortality values expanded by msp

See Also

aeq_mortality()

Examples

## Not run: 
fram_db |> msp_mortality(run_id = 132)

## End(Not run)

Description

Provides English translation of numeric non-retention flags

Usage

NR_flag_translate(vec)

Arguments

Examples

## Not run: NR_flag_translate(sample(1:4, 10, replace = T))

Description

Identify how much reduction in landed catch at each fishery that would be needed to reduce the impacts on a focal stock by 1 fish.

Usage

plot_impacts_per_catch_heatmap(
  fram_db,
  run_id,
  stock_id,
  filters_list = list(filter_wa, filter_sport),
  msp = TRUE
)

Arguments

Value

ggplot object

See Also

plot_stock_mortality()

Examples

## Not run: 
path = "FRAM compilations - readonly/2024-Pre-Season-Chinook-DB/2024 Pre-Season Chinook DB.mdb"
run_id = 132
stock_id = 3
plot_impacts_per_catch_heatmap(path,
                               run_id = 132,
                               stock_id = 5)

## End(Not run)

Description

Produces a stock composition chart, low frequency stocks are grouped into geographic area.

Usage

plot_stock_comp(fram_db, run_id, fishery_id, time_step, group_threshold = 0.01)

Arguments

Examples

## Not run: 
fram_db |> stock_comp(run_id = 132)

## End(Not run)

Description

Creates an ordered bar chart with the top number of mortalities per fishery.

Usage

plot_stock_mortality(
  fram_db,
  run_id,
  stock_id,
  top_n = 10,
  filters_list = NULL,
  msp = TRUE
)

Arguments

See Also

plot_impacts_per_catch_heatmap(), plot_stock_mortality_time_step()

Examples

## Not run: 
fram_db |> plot_stock_mortality(run_id = 132, stock_id = 17)
fram_db |> plot_stock_mortality(run_id = 132, stock_id = 17,
        filters_list = list(filter_wa, filter_sport))

## End(Not run)

Description

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

Usage

plot_stock_mortality_time_step(
  fram_db,
  run_id,
  stock_id,
  top_n = 10,
  filters_list = NULL,
  msp = TRUE
)

Arguments

See Also

plot_stock_mortality(), plot_impacts_per_catch_heatmap()

Examples

## Not run: 
fram_db |> stock_mortality_time_step(run_id = 132, stock_id = 17)

## End(Not run)

Description

Returns a tibble matching the Population Statistics screen.

Usage

population_statistics(fram_db, run_id = NULL)

Arguments

Examples

## Not run: fram_db |> population_statistics(run_id = 101)

Description

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

Usage

post_season_abundance(fram_db, units = c("ja3", "oa3"), run_ids = NULL)

Arguments

Examples

## Not run: framdb |> post_season_abundance()

Description

Provides list of FRAm database names, typically useful for internal functions.

Usage

provide_table_names(is_full = TRUE)

Arguments

Value

Character string of the names of FRAM tables

Examples

provide_table_names(is_full = FALSE)

Description

Removes a run in a FRAM database

Usage

remove_run(fram_db, run_id)

Arguments

Examples

## Not run: fram_db |> delete_run(run_id = 132)

Description

Provides a print out of Run ID information

Usage

run_info(fram_db, run_id)

Arguments

Examples

## Not run: fram_db |> run_info(run_id = 132)

Description

Provides English translation of numeric scalers flags

Usage

scalers_flag_translate(vec)

Arguments

Examples

## Not run: scalers_flag_translate(sample(c(1, 2, 7, 8, 17, 18, 27, 28), 10, replace = T))

Generate sensitivity analyses runs based on a list of match/replace dataframes

Description

For complex sensitivity analyses, it may be easiest to programmatically create a series of match/replace dataframes (?modify_table), one for each sensitivity run. sensitivity_custom() uses a list of these dataframes to create a series of sensitivity analyses runs. Otherwise behaves as sensitivity_exact() or sensitivity_scaled(). Saved log is a .rds file that contains scenario_list but with each list item named with the matching RunID.

Usage

sensitivity_custom(
  fram_db,
  template_run,
  table_name,
  scenario_list,
  tamm_template = NULL,
  tamm_target_folder = NULL,
  label = "sensitivity",
  save_log = TRUE
)

Arguments

Details

Current framework does not support automating creation of sensitivity analyses in which changes are being made to multiple tables for a single run.

Value

Invisibly returns object scenario_list, but with list items named with the corresponding RunID.

See Also

sensitivity_scaled(), sensitivity_exact()

Examples

## Not run: 
## silly quick-and-dirty example: try these
## two scenarios: mark release rates of 0.05 and 0.01 for fisheries 1 and 2
## for timestep 1, or flipping those. Modifications to FisheryScalers table
fram_db <- connect_fram_db(here("Valid2024_sens_test.mdb"))

custom_scenarios = list(data.frame(match_FisheryID = c(1, 2),
                                  match_TimeStep = c(1, 1),
                                  replace_MarkReleaseRate = c(.05, .01)),
                       data.frame(match_FisheryID = c(1, 2),
                                  match_TimeStep = c(1, 1),
                                  replace_MarkReleaseRate = c(.01, .05))
)

tamm_template = here("Coho2513NOF-165.xlsx")
tamm_target_folder = here("sens_test_custom/")
fram_db |>
 sensitivity_custom(template_run = 28,
                    table_name = 'FisheryScalers',
                    scenario_list = custom_scenarios,
                    tamm_template = tamm_template,
                    tamm_target_folder = tamm_target_folder,
                    label = "markrelease custom")
disconnect_fram_db(fram_db)

## End(Not run)

Generate sensitivity analyses runs based on exact values

Description

As sensitivity_scaled, but provide exact values for the sensitivity analyses (in argument exact_values) instead of scaling factors.

Usage

sensitivity_exact(
  fram_db,
  template_run,
  table_name,
  match_df,
  exact_values,
  cols_to_vary,
  tamm_template = NULL,
  tamm_target_folder = NULL,
  label = "sensitivity",
  save_log = TRUE
)

Arguments

Value

Invisibly returns a list of dataframes. $values_by_run contains a row for each sensitivity run and maps the values used to run ids. $full_df is the full match/scale factor used by calc_fram_scaling, and shows the match conditions and scaling used for each run. If cols_to_vary has length 1, the two dataframes will contain the same information.

Examples

## Not run: 
fram_db <- connect_fram_db(here("Valid2024_sens_test.mdb"))

tamm_template <- here("Coho2513NOF-165.xlsx")
tamm_target_folder <- here("sens_test_exact/")
fram_db |>
  sensitivity_exact(
    template_run = 28,
    table_name = "StockRecruit",
    match_df = data.frame(match_StockID = 1:2),
    exact_values = seq(0.5, 5, by = 0.5),
    cols_to_vary = c("RecruitScaleFactor"),
    tamm_template = tamm_template,
    tamm_target_folder = tamm_target_folder,
    label = "Stilly sensitivity exact"
  )
disconnect_fram_db(fram_db)

## End(Not run)

Generate sensitivity analyses runs scaled by template values

Description

From a template FRAM run, for a single vector of scaling factors (e.g., c(0.5, 2) would test halving and doubling), generate sensitivity analyses which rescale the columns specied in (cols_to_vary) for rows which match the conditions specified in match_df. Optionally creates corresponding tamms from a template TAMM, labeled to work with folder loading option in the FRAM multi-run fork.

Usage

sensitivity_scaled(
  fram_db,
  template_run,
  table_name,
  match_df,
  scale_values,
  cols_to_vary,
  tamm_template = NULL,
  tamm_target_folder = NULL,
  label = "sensitivity",
  save_log = TRUE
)

Arguments

Details

Dev note: update to allow match_df to NOT start with "match_" – it's implied.

Value

Invisibly returns a list of dataframes. $scales_by_runs contains a row for each sensitivity run and maps the scaling factors to run ids. $full_df is the full match/scale factor used by calc_fram_scaling, and shows the match conditions and scaling used for each run.

See Also

sensitivity_exact(), sensitivity_custom()

Examples

## Not run: 
# Testing sensitivity_scaled
library(here)
fram_db <- connect_fram_db(here("Valid2024_sens_test.mdb"))

tamm_template <- "ChinValidrunTest.xlsx"
tamm_target_folder <- here("sens_test/")
fram_db |>
  sensitivity_scaled(
    template_run = 28,
    table_name = "StockRecruit",
    match_df = data.frame(match_StockID = c(17, 19)),
    scale_values = seq(0.1, 2, length = 10),
    cols_to_vary = c("RecruitScaleFactor", "RecruitCohortSize"),
    tamm_template = tamm_template,
    tamm_target_folder = tamm_target_folder
  )
disconnect_fram_db(fram_db)

## End(Not run)

Description

Statistical weeks start on mondays, so the first statistical week of the year starts on the first monday of the year. (Contrast with management weeks which start on Sundays).

Usage

statistical_week(date)

Arguments

Examples

## Not run: 
data_fram |>
  mutate(mngmt_week = statistical_week(date_field))

## End(Not run)

Description

#' Intended for internal use, makes some assumptions about inputs.

Usage

stock_age_checker(table_name, NumStk, old_stockcount, df, min_age, max_age)

Arguments

Value

numeri; 0 if no warning, 1 if warning.

Description

More thorough checking than stock_id_comp. Checks that the number of stock IDs makes sense given NumStk, that Stock IDs are sequential (in the sense that if NumStk = n, every integer up to n is represented). Optionally, can check that each stock ID is unique.

Usage

stock_check_helper(table_name, NumStk, stock_vec, uniques_only = FALSE)

Arguments

Value

Numeric, returning number of warnings detected.

See Also

addstock_check()

Summarizes the three true outcomes of a stocks abundance, where it dies to fishery related mortality, natural mortality, or reaches some sort of escapement. When run against the coho database spawning escapement will be displayed, when run against the Chinook database escapement to the river will be dislpayed along with recruits to the next year 'age_up'

Description

Summarizes the three true outcomes of a stocks abundance, where it dies to fishery related mortality, natural mortality, or reaches some sort of escapement. When run against the coho database spawning escapement will be displayed, when run against the Chinook database escapement to the river will be dislpayed along with recruits to the next year 'age_up'

Usage

stock_fate(fram_db, run_id = NULL, units = c("fish", "percentage"))

Arguments

Examples

## Not run: 
fram_db |> stock_fate(run_id = 145)

## End(Not run)

Chinook flavor of the stock fate function

Description

Chinook flavor of the stock fate function

Usage

stock_fate_chinook(fram_db, run_id = NULL, units = c("fish", "percentage"))

Arguments

See Also

stock_fate()

Examples

## Not run: 
fram_db |> stock_fate_chinook(run_id = 145)

## End(Not run)

Coho flavor of the stock fate function

Description

Coho flavor of the stock fate function

Usage

stock_fate_coho(fram_db, run_id = NULL, units = c("fish", "percentage"))

Arguments

See Also

stock_fate()

Examples

## Not run: 
fram_db |> stock_fate_coho(run_id = 145)

## End(Not run)

Description

Intended for internal use, makes some assumptions about inputs.

Usage

stock_id_comp(table_name, df, stock_ref)

Arguments

Value

numeric; 0 if no warning, 1 if warning.

See Also

addstock_check()

Description

Returns a tibble matching the Stock Mortality screen.

Usage

stock_mortality(fram_db, run_id = NULL)

Arguments

Examples

## Not run: 
fram_db |>
 stock_mortality(run_id=132) |>
 filter(stock_id == 17, fishery_id == 36)


## End(Not run)

Description

For easy readability, we want to use consistent coding style when developing code for framrsquared. Presently this includes (a) using ⁠<-⁠ for assignment, and (b) using snakecase for variable and function names. The functions here streamlining checking R code for consistency with this style.

Usage

frs_stylecheck_assignment(filepath, n = Inf)

frs_stylecheck_snakecase(filepath, n = Inf)

Arguments

Details

frs_stylecheck_assignment() takes the path to an R file, and prints (and returns) any rows that may be mis-using the = for assignment. Note that it will give false positives for arguments defined in function calls if the call spans multiple lines, as well as = signs included in character strings.

frs_stylecheck_snakecase takes the path to an R file and prints (and returns) the names of any variables assigned using ⁠<-⁠ that do not include underscores. This will identify variables that do not use snakecase, but will also give false positive matches for variables that are single words and thus do not need snakecase.

Examples

## Not run: 
frs_stylecheck_assignment("R/copy.R")
frs_stylecheck_snakecase("R/copy.R")

## End(Not run)

List terminal stock information

Description

For each TAA, lists the associated fisheries

Usage

terminal_fisheries(fram_db, species = NULL)

Arguments

Value

tibble of taa fisheries

Examples

## Not run: fram_db |> terminal_fisheries()

Parse TAAETRS table

Description

Terminal run information used by FRAM is stored in the TAAETRSList and (soon) the TAAETRSListChinook tables, but stored in a way that is not very human readable. parse_terminal_info() translates this to human-readable form, primarily to then be used by terminal_stocks() and terminal_fisheries().

Usage

terminal_info(fram_db, old_table_name = TRUE, species = NULL)

Arguments

Value

tibble of TAAETRSList or TAAETRSListChinook tables translated to long form.

See Also

terminal_stocks(), terminal_fisheries()

Examples

## Not run: fram_db |> parse_terminal_info()

List terminal stock information

Description

For each TAA, lists the associated FRAM stocks and timesteps.

Usage

terminal_stocks(fram_db, species = NULL)

Arguments

Value

taa of taa stocks and timesteps

Examples

## Not run: fram_db |> terminal_stocks()

Description

Returns a dataframe with fisheries defined by the TRuns report driver

Usage

truns_fisheries(fram_db)

Arguments

See Also

truns_stocks()

Examples

## Not run: truns <- truns_fisheries(fram_db)

Description

Returns a dataframe with stocks defined by the TRuns report driver

Usage

truns_stocks(fram_db)

Arguments

See Also

truns_fisheries()

Examples

## Not run: truns <- truns_stocks(fram_db)

Description

No error checking for transfer databases

Usage

validate_fishery_ids(fram_db, fishery_id, call = rlang::caller_env())

Arguments

Description

Convenience function to check fram_db input

Usage

validate_fram_db(
  fram_db,
  db_type = NULL,
  db_species = NULL,
  call = rlang::caller_env()
)

Arguments

Description

Convenience function to check run_id input

Usage

validate_run_id(fram_db, run_id, call = rlang::caller_env())

Arguments

Description

No error checking for transfer databases

Usage

validate_stock_ids(fram_db, stock_id, call = rlang::caller_env())

Arguments

Description

Welcome message, summarizing database information

Usage

welcome(con)

Arguments

Examples

## Not run: welcome(con)