vignettes/running_processes_examples.Rmd
running_processes_examples.RmdThe Topical Tuna Treatment (T3) aim to provide a support for the data manipulation of tropical tuna surface fisheries. This support can take many different forms and have various objectives associated. For that purpose, T3 can be processes in various ways, depending of what question(s) drive your reflexion. You will find below several examples for running process and help you regarding your personnal work. Furthermore, don’t hesitate to take a look on the issues webpage of the package (https://github.com/OB7-IRD/t3/issues) or send a mail to ob7@ird.fr if you need specific support.
Furthermore, in the following functions the “database_con” argument refers to an R object which contains connection information with an avdth database. For more detail about connections available check the function documentation. In addition, the argument “output_path” refer to the output directory on your computer. The function associated, “initiate_directory”, will create the output structure for the process.
Globaly, the T3 process have 3 differents parts that you can run independently from each other. But in some case you could want to run the entire process step by step, for example to “follow” several particular data with accuracy. In the code below, you will find an example of all the process run step by step. The parameters associated are display in the section called “setup”. You can modified them to customize the example. If you more information regarding functions arguments take à look to the R function documentation (or in the reference website section https://ob7-ird.github.io/t3/reference/index.html)
# setup ----
library(t3)
periode_reference <- as.integer(c(2006,
2007,
2008))
target_year <- as.integer(2008)
period_duration <- as.integer(2)
# countries for the France fleet
countries <- c("FRA",
"MYT")
# code 1 for the Atlantic Ocean
ocean <- as.integer(1)
# code 1 for landing data
sample_type <- as.integer(1)
# for french fleet YFT, SKJ, BET, ALB, MIX and LOT
species_rf1_fr <- as.integer(c(1, 2, 3, 4, 9, 11))
# output directories initiation
final_output_path <- initiate_directory(output_path = output_path,
level = "all")
# output format
output_format <- "eu"
# generation of the confidence interval
confidence_interval <- TRUE
# model creation ----
object_model_data <- t3:::object_model_data$new()
# object trips creation
object_model_data$trips_object_creation(data_source = "avdth_db",
periode_reference = periode_reference,
countries = countries,
oceans = ocean,
db_con = database_con)
# object activities creation
object_model_data$activities_object_creation(data_source = "avdth_db",
periode_reference = periode_reference,
countries = countries,
oceans = ocean,
db_con = database_con)
# object elementarycatches creation
object_model_data$elementarycatches_object_creation(data_source = "avdth_db",
periode_reference = periode_reference,
countries = countries,
oceans = ocean,
db_con = database_con)
# object elementarylandings creation
object_model_data$elementarylandings_object_creation(data_source = "avdth_db",
periode_reference = periode_reference,
countries = countries,
oceans = ocean,
db_con = database_con)
# object wells creation
object_model_data$wells_object_creation(data_source = "avdth_db",
periode_reference = periode_reference,
countries = countries,
oceans = ocean,
sample_type = sample_type,
db_con = database_con)
# model creation: set durations data
object_model_data$setdurationrefs_data(data_source = "csv",
data_path = system.file("setdurationrefs.csv",
package = "t3"),
periode_reference = periode_reference,
countries = countries)
# model creation: length steps data
object_model_data$lengthsteps_data(data_source = "csv",
data_path = system.file("lengthsteps.csv",
package = "t3"))
# model creation: sample sets data
object_model_data$samplesets_data(data_source = "avdth_db",
periode_reference = periode_reference,
countries = countries,
oceans = ocean,
db_con = database_con)
# model creation: length weight relationships data
object_model_data$lengthweightrelationships_data(data_source = "csv",
data_path = system.file("lengthweightrelationships.csv",
package = "t3"))
# initialisation object for full trips class
object_full_trips <- t3:::full_trips$new()
# object full_trip creation
object_full_trips$create_full_trips(object_trips = object_model_data$.__enclos_env__$private$trips)
# add activities to trips selected
object_full_trips$add_activities(object_activities = object_model_data$.__enclos_env__$private$activities)
# filter by reference year
object_full_trips$filter_by_time_period_reference(time_periode_reference = periode_reference)
# add elementarycatches to trips selected
object_full_trips$add_elementarycatches(object_elementarycatches = object_model_data$.__enclos_env__$private$elementarycatches)
# add elementarylandings to trips selected
object_full_trips$add_elementarylandings(object_elementarylandings = object_model_data$.__enclos_env__$private$elementarylandings)
# add well(s) and sample(s) to trip(s) selected
object_full_trips$add_wells_samples(object_wells = object_model_data$.__enclos_env__$private$wells)
# level 1 ---
# level 1.1: rf1
object_full_trips$rf1(species_rf1 = species_rf1_fr,
global_output_path = final_output_path,
output_format = output_format)
# level 1.2: rf2
object_full_trips$rf2(global_output_path = final_output_path,
output_format = output_format)
# level 1.3: logbook weigth categories conversion ----
object_full_trips$conversion_weigth_category(global_output_path = final_output_path,
output_format = output_format)
# level 1.4: set count
object_full_trips$set_count(global_output_path = final_output_path,
output_format = output_format)
# level 1.5: set duration
object_full_trips$set_duration(set_duration_ref = object_model_data$.__enclos_env__$private$setdurationrefs,
global_output_path = final_output_path,
output_format = output_format)
# level 1.6: time at sea
object_full_trips$time_at_sea(global_output_path = final_output_path,
output_format = output_format)
# level 1.7: fishing time
object_full_trips$fishing_time(global_output_path = final_output_path,
output_format = output_format)
# level 1.8: searching time
object_full_trips$searching_time(global_output_path = final_output_path,
output_format = output_format)
# level 2 ----
# level 2.1: sample length class ld1 to lf conversion
object_full_trips$sample_length_class_ld1_to_lf(length_step = object_model_data$.__enclos_env__$private$lengthsteps,
global_output_path = final_output_path,
output_format = output_format)
# level 2.2: sample number measured extrapolation
object_full_trips$sample_number_measured_extrapolation(global_output_path = final_output_path,
output_format = output_format)
# level 2.3: sample step length class standardisation
object_full_trips$sample_length_class_step_standardisation(global_output_path = final_output_path,
output_format = output_format)
# level 2.4: well set weight categories
object_full_trips$well_set_weigth_categories(sample_set = object_model_data$.__enclos_env__$private$samplesets,
global_output_path = final_output_path,
output_format = output_format)
# level 2.5: standardised sample creation
object_full_trips$standardised_sample_creation(global_output_path = final_output_path,
output_format = output_format)
# level 2.6: sample number standardisation
object_full_trips$standardised_sample_set_creation(length_weight_relationship_data = object_model_data$.__enclos_env__$private$lengthweightrelationships,
global_output_path = final_output_path,
output_format = output_format)
# level 2.7: raised factors determination
object_full_trips$raised_factors_determination(global_output_path = final_output_path,
output_format = output_format)
# level 2.8: samples number standardisation at set scale
object_full_trips$raised_standardised_sample_set(global_output_path = final_output_path,
output_format = output_format)
# level 3 ----
process_level3 <- object_full_trips$path_to_level3()
# level 3.1: data preparatory
process_level3 <- object_full_trips$data_preparatory(inputs_level3 = process_level3[[1]],
output_directory = final_output_path,
target_year = target_year,
period_duration = period_duration)
# level 3.2: random forest models ----
process_level3$output_level3_process2 <- object_full_trips$random_forest_models(output_level3_process1 = process_level3$output_level3_process1$data_lb_sample_screened$data4mod)
# level 3.3: models checking ----
process_level3$output_level3_process3 <- object_full_trips$models_checking(output_level3_process2 = process_level3$output_level3_process2,
output_directory = process_level3$output_directory)
# level 3.4: data formatting for predictions ----
process_level3$output_level3_process4 <- object_full_trips$data_formatting_for_predictions(inputs_level3 = process_level3$raw_inputs_level3,
output_level3_process1 = process_level3$output_level3_process1$data_lb_sample_screened$data4mod,
target_year = target_year)
# level 3.5: predictions ----
process_level3$output_level3_process5 <- object_full_trips$model_predictions(output_level3_process2 = process_level3$output_level3_process2,
output_level3_process4 = process_level3$output_level3_process4,
output_directory = process_level3$output_directory,
ci = confidence_interval)Through the following codes, you will be able to run separely each part of the treatment.
# setup ----
library(t3)
periode_reference <- as.integer(c(2006,
2007,
2008))
target_year <- as.integer(2008)
period_duration <- as.integer(2)
# countries for the France fleet
countries <- c("FRA",
"MYT")
# code 1 for the Atlantic Ocean
ocean <- as.integer(1)
# code 1 for landing data
sample_type <- as.integer(1)
# for french fleet YFT, SKJ, BET, ALB, MIX and LOT
species_rf1 <- as.integer(c(1, 2, 3, 4, 9, 11))
# output directories initiation
final_output_path <- initiate_directory(output_path = output_path,
level = "all")
# output format
output_format <- "eu"
# generation of the confidence interval
confidence_interval <- TRUE
# data model initialisation ----
t3_process <- t3::data_model_initialisation(data_source = "avdth_db",
db_con = database_con,
periode_reference = periode_reference,
countries = countries,
oceans = ocean,
sample_type = sample_type)
# level 1 ----
t3_process[[2]] <- t3::t3_level1(object_model_data = t3_process[[1]],
object_full_trips = t3_process[[2]],
species_rf1 = species_rf1,
output_path = final_output_path,
output_format = output_format)
# level 2 ----
t3_process[[2]] <- t3::t3_level2(object_model_data = t3_process[[1]],
object_full_trips = t3_process[[2]],
output_path = final_output_path,
output_format = output_format)
# level 3 ----
t3_process[[3]] <- t3_process$object_full_trips$path_to_level3()
names(t3_process)[3] <- "process_level3"
t3_process[[3]] <- t3::t3_level3(inputs_level3 = t3_process[[3]][[1]],
target_year = target_year,
period_duration = period_duration,
ci = confidence_interval,
output_path = final_output_path,
output_format = output_format)The last section presents an example of a function able to run all the t3 process through one command line. Like before, you can “play” with the arguments to customise the function, especially if you want to skip part(s) of the process.
# setup ----
library(t3)
periode_reference <- as.integer(c(2006,
2007,
2008))
target_year <- as.integer(2008)
period_duration <- as.integer(2)
# countries for the France fleet
countries <- c("FRA",
"MYT")
# code 1 for the Atlantic Ocean
ocean <- as.integer(1)
# code 1 for landing data
sample_type <- as.integer(1)
# for french fleet YFT, SKJ, BET, ALB, MIX and LOT
species_rf1 <- as.integer(c(1, 2, 3, 4, 9, 11))
# output format
output_format <- "eu"
# generation of the confidence interval
confidence_interval <- TRUE
# process ----
t3_process_all <- t3::t3_process(process = "all",
data_source = "avdth_db",
db_con = database_con,
output_path = output_path,
output_format = "eu",
periode_reference = periode_reference,
countries = countries,
oceans = ocean,
sample_type = sample_type,
species_rf1 = species_rf1,
target_year = target_year,
period_duration = period_duration,
ci = confidence_interval)