FishSET R package functions

##Getting started The FishSET R package includes functions that can be used in the R console or that can be run through the FishSET app (a guided user interface built u sing the Shiny R package). Calling function through the R console allows for greater flexibility and ease in troubleshooting but requires knowledge of R and the FishSET R package. The FishSET app is meant to allow for users to use functions and models with limited knowledge of R. The FishSET app is opened by typing run_fishset() in the R console. This tutorial does not use the FishSET app but walks through examples of FishSET R package functions in the R console.

##Load the package.

install.packages('devtools')
library(devtools)
install.packages("PATH/TO/Directory/Containing/FishSET", repos=NULL, type='source')
library(FishSET)

##Upload data When the data is loaded into the FishSET database a series of checks are performed. The output of these checks will be printed to the R console. Check these messages to identify if any corrections should be made. In the box below, we load both the main data set and the port data. We will also need a spatial data set. We will load that with the read_dat function. The read_dat function recognizes most data formats, including spss, json, and stata, and reads the data into the working environment. The functions does not save the data into the FishSET database.

#Upload data
load_maindata(dat, over_write = TRUE, project = 'EXAMP') 
load_port(dat, port_name = port, over_write = TRUE, project = 'EXAMP')
spatdat <- read_dat('')

Data are now loaded into the FishSET database. The first line of code below returns a list of all the data tables in the FishSET database. The second line of code returns the first six lines of data from the data table you just loaded into the database.

tables_database()
head(table_view('projectMainDataTable'))

Additional codes for working within the FishSET database:

#Check whether a table exists in the database.
  table_exists()      
#Remove a table from the database.
 table_remove()     
table_fields()      View column names of data table.

##Data Quality FishSET has built-in functions to check for NAs, NaNs, outliers, unique observations, empty variables, and whether latitude and longitude variables are in the correct format. The data_verification function checks for these potential issues. All messages returned to R console are also saved in the log file.

  data_verification()
  # The following functions are contained in `data_verification` but can be run on their own too.
    outlier_check()
    nan_identify()

Functions also exist to view table or plot output of data quality assessments. All plots and tables are automatically saved with project name to the output folder in the FishSET R package.

#Table output functions
  summary_stats()
  outlier_table() 
  
#Plots output functions
  outlier_plot()

Functions to correct data quality issues.

  na_filter()
  nan_filter()

  outlier_remove()

  unique_filter()

  empty_vars_filter()

  degree()

Data tables can be filtered using the filter_table and filter_dat functions. The filter statements are saved as a table in the FishSET database and can be applied to any dataset using the filter_dat function. No function has been written to remove variables from the working data set. Users should use base R functions for this. Within the FishSET app variables can be removed from the working data set in the Data Exploration tab. Use the add_vars function to add variables from the raw data set back into the working data set.

#Filter
  filter_table()
  filter_dat()

#Add variables back into the working data set
  add_vars()

##Data Exploration
View the spatial (map_plot) and temporal (temp_plot) distribution of data and the relationship between variables (xy_plot). Plots and tables to the output folder. The map_kernel, getis_ord_stats, and moran_stats functions provide measures of clustering, or hot spot analysis. The density_plot creates a density or cumulative distribution function plot of the selected variable.

#Map
  map_plot()
#map kernel
  map_kernel()
#Getis Ord
  getis_ord_stats()
#Moran's I
  moran_stats()
#Temporal Plots
  temp_plot()
#x-y plots
  xy_plot(dat, project, var1, var2, regress = FALSE) 
  density_plot()

##Fleet Analyses Fleet functions focus on functions that allow users to assign vessels to fleet and view fleet characteristics (vessel count) and fleet effort (trip length, weekly effort), and catch as a fleet (species catch, by catch, weekly catch). All functions return output as plots and/or tables that are output to the R console and saved to the output folder.

#Define and store fleet expressions 
  fleet_assign(dat, project, cond = NULL, fleet_val = NULL, table = NULL, save = TRUE)
#Number of unique vessels by time period
  vessel_count(dat, project, v, t, period = "month", group = NULL, 
                         year = NULL, position = "stack", output = c("table", "plot"))
#Total species catch by period
  species_catch(dat, project, species, date, period = "month_abv", fun = "sum", group = NULL, year = NULL, 
                          convert_to_tons = TRUE, value = c("count", "percent"), 
                          output = c("table", "plot"), position = "stack", format_tab = c("wide", "long"))
#Compare bycatch to other species caught
  bycatch(dat, project, cpue, catch = NULL , date, names = NULL, group = NULL, 
                    year = NULL, period = "year", value = c("count", "stc"), 
                    output = c("table", "plot"), format_tab = "wide")
#Aggregate species catch by unique vessel ID
  sum_catch("MainDataTable", "myProject", "catch", "species == 'cod' & catch > .5", val = "per", out = "logical")
#Catch total by week
  weekly_catch(dat, project, species, date, year = NULL, group = NULL, fun = "sum", position = "stack",
                         convert_to_tons = FALSE, value ="percent", output = c("plot", "table"))
#Average CPUE by week
  weekly_effort(dat, project, cpue, date, group = NULL, year = NULL, plot_type = "line_point", 
                          output = c("plot", "table"), format_tab = "wide")
#Create a plot or table of vessel trip length
  trip_length(dat, project, start, end, units = "days", catch = NULL, 
                        hauls = NULL, output = c("table", "plot"), haul_to_trip = FALSE)

##Simple Analyses
View and quantify relationship between variables. The correlation function, corr_out returns a plot and table of correlations values between all numberic variables supplied. The regression function, xy_plot', returns a plot with fitted simple linear regression line and test statitistics whne regress is set to TRUE. Whenregress` is set to FALSE, points are plotted without a best fit line.

#correlation
  corr_out(dat, project, variables='all')
#regression
  xy_plot(dat, project, var1, var2, regress = TRUE)

##Compute New Variables
Modify or create variables such as cpue and trip centroid. Functions focus on data transformations, generating ID variables (for haul or fishery season), computing simple arithmetic functions such as catch per unit effort or sum of hauls, generate dummy variablesm, create a variety of spatial variables such as distance between hauls or the latitude and longitude of the haul midpoint, and create trip level functions such collapsing haul to trip and identfiying trip centroid.

#Data Transformations
  ##Change time unit
  temporal_mod
  ##Coded variable based on quantiles
  set_quants()
#Nominal ID
  #Haul or Trip ID
  ID_var()
  #Fishery Seasonal Identifier
  create_seasonal_ID()
#Arithmetic and temporal
  #Numeric functions
  create_var_num()
  #CPUE
  cpue()
#Dummy
  #From variable
  dummy_num()
  dummy_matrix()
#spatial
  #Distance between two points
  create_dist_between()
  #Haul midpoint 
  create_mid_haul()
  #Duration of time
  create_duration()
  #Zone when choice where to go next was made
  create_startingloc()
#Trip-level Functions
  #Collapse Haul to trip
  haul_to_trip()
  #Trip distance
  create_trip_distance()
  #Trip centroid
  create_trip_centroid()

##Zonal Definition
Functions in this group require a spatial data frame defining fishing or regulatory zone. Both the find_centroid and assignment_column can be used on their own but are called as necessary in other function. The find_centroid function identifies the centroid of a zone and the assignment_column function assigns observations in the primary data set to zones. The create_alternative_choice function defines alternative fishing choices and is required.

  find_centroid()
  assignment_column()
  create_alternative_choice()

##Expected Catch/Revenue
Compute expected catch or expected revenue. The function creates the expected catch/revenue matrix based on user-provided parameters, and three default cases: Near-term: Movng window size of two days. In this case, vessels are grouped based on defineGroup parameter. Medium-term: Moving window size of seven days. In this case, there is no grouping and catch for entire fleet is used. Long-term: Moving window size of seven days from the previous year. In this case, there is no grouping and catch for entire fleet is used. Output is saved in the FishSET database and called in the make_model_design function.

The sparstable function provides a table of data sparsity by time periods. It should be used to assess the availability of data for defining the temporal moving window size in the create_expectations function. Sparse data is not suited for shorter moving window sizes.

  sparsetable()  
  create_expectations()

##Model design file and run The make_model_design function creates a list with data and parameters required to run the models, which are called in the discretefish_subroutine function. The discretefish_subroutine requires inputs of initial parameters, optimization options (maximum iterations, the relative tolerance of x, report frequency, and whether to report iterations), the optimization method chosen, which must be one of the base R optim options, and a name for the model to allow for easier tracking and comparison of models. The subroutine also requires catch, choice, and distance data, plus any other data otherdat needed to run the chosen likelihood, such as expected catches or harvester data. These data are defined in the make_model_design file. Expected catches is pulled by the make_model_design function from the saved table generated by the create_expectations function. make_model_design calls output from create_alternative_choice to generate distance data.

make_model_design()
discretefish_subroutine()

###Likelihood functions FishSET includes six functions. User-created functions can be saved and logged using the log_func-model function. logit_c Conditional logit likelihood logit_avgcat Average catch multinomial logit procedure logit_correction Full information model with Dahl’s correction function epm_normal Expected profit model normal catch function epm_lognormal Expected profit model lognormal catch function epm_weibull Expected profit model Weibull catch function

###Model run output The discretefish_subroutine function can take 30 or minutes to run. Model run status is returned to the R console. The subroutine function outputs model results in a list that can be summarized as:

errorExplain: If it exists, a description of the model error.
OutLogit: A matrix of coefficients, standard errors, and t-statistics
optoutput: Optimization information (such as number of function iterations)
seoutmat2: Standard errors
MCM: Model comparison metrics (e.g. AIC, BIC)
H1: The inverse hessian

The output can be viewed using:

#Display errorExplain output
globalcheck_view('pcodldglobalcheck20190604')
#Display all other model output.
model_out_view('pcod')

###Selecting models Model comparison metrics are conveniently saved in a table in the FishSET database. To access the table use model_fit with the project name. Identifying the best model for future reference requires select_model. This function utilized R Shiny to open an interactive window where best models can be selected and saved. Model selection can also be accomplished from the discretefish_subroutine function by setting the select.model parameter to TRUE. When the parameter is set to TRUE, a interactive window opens after the model has compiled and output has saved.

model_fit('pollock')
select_model()

##Interactivity R Shiny is used to provide interactive interfaces. All FishSET functions described here can be run through a user interface with run_fishset_gui. The user interace guides users through loading data to model evaluation. In addition, users can use the select_vars function to open an interactive interface for selecting a subset of variables to keep in the working data set and the add_vars function to interactively select vars to add back into the working data set from the raw dataset.

run_fishset_gui()
select_vars()
select_model()
add_vars()

filter_table(dat = 'pcodmaindatatable', project = 'pcod', x = 'PERFORMANCE_Code', exp = 'PERFORMANCE_Code==1')

 {
          "functionID": "filter_table",
          "args": [
            "pcodMainDataTable",
            "pcod",
            "PERFORMANCE_Code",
            "PERFORMANCE_Code==1"
          ],
          "kwargs": [],
          "output": "",
          "msg": [
            {
              "dataframe": "pcodMainDataTable",
              "vector": "PERFORMANCE_Code",
              "FilterFunction": "PERFORMANCE_Code==1"
            }
          ]
        }

`log_reset`
`log_func_model`