Package 'framrsquared'

Description

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

Usage

add_flag_text(.data)

Arguments

Examples

## Not run:  mortality_table |> add_flag_text()

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)

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)

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)

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)

Description

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

Usage

compare_databases(
  file1,
  file2,
  runid_use = NULL,
  tables_use = NULL,
  slim = FALSE,
  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 file1) and the "comparison" value (from file2). More on those below. The original and comparison 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 file1 is the reference file; the "error" measures all show how much the value in file2 changed relative to the corresponding value in file1. 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 file2 entry was five fish more or less than the file1 entry. You can confirm this by looking at the original and comparison 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. ⁠$prop.err⁠ provides this proportional error, where -0.5 means the entry in file2 was 50% less than the corresponding value in file1, and a value of 2 means the entry in file2 was 200% more than the corresponding value in file2. ' This gives error in context of the original value, and is often a good a way to look for problems. However, we sometimes find very large ⁠$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 file1, and 0 fish in file2. 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 file1.

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 file1. ⁠$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 file1, and was then 0.002 in file2, and the average of all landed_catch entries in file 1 was 1000, then the prop.err would be 1 (since file2 had double the value of file1, 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 comparison values fall on the 1:1 line, ggplot2::geom_point() can be used, with ⁠$ratios$original⁠ and ⁠$ratios$comparison⁠ 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)

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

• ⁠$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: 
file1 = "Valid2022_Round_7_1_1_11142023_REFERENCE_fixed - fork rebuild.mdb"
file2 = "Valid2022_Round_7.1.1_11142023 - green river split.mdb"
out = tables_compare(file1, file2)

## End(Not run)

Description

Compares the fishery flags of two runs

Usage

compare_fishery_input_flags(fram_db, run_ids, verbose = TRUE)

Arguments

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

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.

Examples

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

Description

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

Usage

compare_inputs_chart(.data)

Arguments

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

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

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

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

Examples

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

Description

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

Usage

connect_fram_db(
  db_path,
  enforce_type = c("full", "transfer"),
  read_only = FALSE,
  quiet = FALSE
)

Arguments

Details

Additional details...

Examples

## Not run: fram_db <- connect_fram_db('<path>')

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)

Description

Safely disconnect from FRAM database

Usage

disconnect_fram_db(fram_db, quiet = TRUE)

Arguments

Examples

## Not run: disconnect_fram_db(fram_db)

Description

Tool to streamline development. Currently this checks for use of filter() without dplyr::. This would call stats::filter(), which is usually not what I intend. As possible, add additional checks for issues that cause problems but do not give an informative error message (or any error message).

Usage

error_check_code(filepath, n = Inf)

Arguments

Examples

## Not run: 
error_check_code("R/copy.R")

## End(Not run)

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 = "WDFW",
  color = "coffee",
  overwrite = FALSE
)

Arguments

Value

Nothing.

Description

Fetches a complete table from a FRAM database. Returns a cleaned tibble.

Usage

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

Arguments

Examples

## Not run: fram_db |> fetch_table('Mortality')

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. Requires a fishery_id column name.

Usage

filter_ak(.data, species = NULL)

Arguments

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. Requires a fishery_id column name.

Usage

filter_bc(.data, species = NULL)

Arguments

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. Requires a fishery_id column name.

Usage

filter_ca(.data, species = NULL)

Arguments

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. Requires a fishery_id column name.

Usage

filter_coast(.data, species = NULL)

Arguments

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

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. Requires a fishery_id column name.

Usage

filter_marine(.data, species = NULL)

Arguments

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. Requires a fishery_id column name.

Usage

filter_net(.data, species = NULL)

Arguments

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. Requires a fishery_id column name.

Usage

filter_or(.data, species = NULL)

Arguments

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. Requires a fishery_id column name.

Usage

filter_puget_sound(.data, species = NULL)

Arguments

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. Requires a fishery_id column name.

Usage

filter_sport(.data, species = NULL)

Arguments

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. Requires a fishery_id column name.

Usage

filter_wa(.data, species = NULL)

Arguments

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 run_ids of FRAM database

Usage

get_run_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 = "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().

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

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

make_impacts_per_catch_heatmap(fram_db, run_id, stock_id)

Arguments

Value

ggplot object

Examples

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

## End(Not run)

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)

Description

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

Usage

mortality_scalers(fram_db, run_id, stock_id)

Arguments

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

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

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

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

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

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

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

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

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

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

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

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

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_marine))

## 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

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"))

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: scaler_flags_translate(sample(c(1, 2, 7, 8, 17, 18, 27, 28), 10, replace = T))

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.

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.

Description

Returns a tibble matching the Fishery 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)

Description

Returns a dataframe with fisheries defined by the TRuns report driver

Usage

truns_fisheries(fram_db)

Arguments

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

Examples

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

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

Welcome message, summarizing database information

Usage

welcome(con)

Arguments

Examples

## Not run: welcome(con)