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demo/inttest-vessel.R
In mfdb: MareFrame DB Querying Library
# Excerise storing / grouping by vessel information
#
# ok and ok_group are there so we can run this code and verify it is correct,
# and not needed every-day use.
#
# NB: if you want to run this, you must be using mfdb-workspace, also be warned
# any data stored in your database will be destroyed.
library(unittest)
library(mfdb)
source('tests/utils/helpers.R')
source('tests/utils/inttest-helpers.R')
# Empty database & rebuild
if (exists("mdb")) mfdb_disconnect(mdb)
mfdb(gsub("inttest", "inttest-vessel", Sys.getenv('INTTEST_SCHEMA', 'inttest')), db_params = db_params, destroy_schema = TRUE)
mdb <- mfdb(gsub("inttest", "inttest-vessel", Sys.getenv('INTTEST_SCHEMA', 'inttest')), db_params = db_params, save_temp_tables = FALSE)
# Set-up areas/divisions
mfdb_import_area(mdb, data.frame(
name = c('45G01', '45G02', '45G03'),
division = c('divA', 'divA', 'divB'),
size = c(10,200,400)))
ok_group("Vessel metadata example", {
# Create a temporary gadget directory
gd <- gadget_directory(tempfile())
mfdb_import_vessel_owner_taxonomy(mdb, table_string('
name full_name t_group
ISL "Iceland" ISL
GBR "Great Britain" GBR
ITA "Italy" ITA
MFRI "Marine and Freshwater Research Institute (Iceland)" ISL
CEFAS "Centre for Environment, Fisheries and Aquaculture Science" GBR
CNR-IAMC "Consiglio Nazionale delle Ricerche" ITA
COMM "Combined commercial fisheries" COMM
COMM1 "Commercial fishery 1" COMM
COMM2 "Commercial fishery 2" COMM
'))
# Set up the vessels we use in this example
mfdb_import_vessel_taxonomy(mdb, table_string('
name full_name vessel_type vessel_owner length power tonnage
A "Arni Fridriksson" 1.RSH MFRI 70 50 900
B "Bjarni Saemundsson" 1.RSH MFRI 56 100 800
C "Cefas Endeavour" 1.RSH CEFAS 65.50 150 700
D "Dallaporta" 1.RSH CNR-IAMC 24 900 600
S "Commercial vessel 1a" 2.COM COMM1 10 50 900
T "Commercial vessel 1b" 2.COM COMM1 30 50 900
U "Commercial vessel 2a" 2.COM COMM2 50 50 900
V "Commercial vessel 2b" 2.COM COMM2 70 50 900
'))
# Import a survey for the data we are interested in
mfdb_import_survey(mdb, data_source = "x",
table_string('
year month areacell species vessel length age weight
2000 1 45G01 COD A 21 2 210
2000 1 45G02 COD A 34 3 220
2000 1 45G03 COD A 34 3 230
2000 1 45G01 COD B 62 1 320
2000 1 45G02 COD B 53 1 330
2000 1 45G03 COD B 54 2 430
2000 1 45G01 COD C 28 2 210
2000 1 45G02 COD C 34 3 220
2000 1 45G03 COD C 24 3 230
2000 1 45G01 COD D 12 1 320
2000 1 45G02 COD D 44 1 330
2000 1 45G03 COD D 14 2 430
2000 1 45G01 COD S 47 2 383
2000 1 45G02 COD S 68 3 114
2000 1 45G03 COD S 15 3 101
2000 1 45G01 COD T 92 1 251
2000 1 45G02 COD T 85 1 289
2000 1 45G03 COD T 64 2 322
2000 1 45G01 COD U 28 2 140
2000 1 45G02 COD U 87 3 170
2000 1 45G03 COD U 63 3 197
2000 1 45G01 COD V 61 1 160
2000 1 45G02 COD V 45 1 201
2000 1 45G03 COD V 32 2 313
'))
# Group the data by vessel length
agg_data <- mfdb_sample_meanlength(mdb, c('vessel_type', 'vessel_length'), list(
step = mfdb_timestep_yearly,
vessel_type = mfdb_unaggregated(),
vessel_length = mfdb_interval('veslen', c(0, 10, 20, 50, 100)),
null = NULL))
ok(ut_cmp_equal(unattr(agg_data[[1]]), table_string('
year step area vessel_type vessel_length number mean
all "1" all 1.RSH veslen20 3 23.33333
all "1" all 1.RSH veslen50 9 38.22222
all "1" all 2.COM veslen10 3 43.33333
all "1" all 2.COM veslen20 3 80.33333
all "1" all 2.COM veslen50 6 52.66667
', colClasses = c(NA, "character", NA, NA, NA, "numeric", NA)), tolerance = 1e-7), "Grouped by vessel_type, vessel_length")
# Group the data by vessel type and area
agg_data <- mfdb_sample_meanlength(mdb, c('vessel_type'), list(
step = mfdb_timestep_yearly,
area = mfdb_group(x = 'divA', y = 'divB'),
vessel_type = mfdb_group(RSH = '1.RSH', COM = '1.COM'),
null = NULL))
ok(cmp(unattr(agg_data[[1]]), table_string('
year step area vessel_type number mean
all "1" x RSH 8 36.0
all "1" y RSH 4 31.5
', colClasses = c(NA, "character", NA, NA, "numeric", NA))), "Grouped by vessel_type/area")
# Show each vessel separately, with full name, power, tonnage
agg_data <- mfdb_sample_meanlength(mdb, c('vessel', 'vessel_full_name', 'vessel_power', 'vessel_tonnage'), list(
step = mfdb_timestep_yearly,
vessel = mfdb_unaggregated(),
vessel_full_name = mfdb_unaggregated(),
vessel_power = mfdb_unaggregated(),
vessel_tonnage = mfdb_unaggregated(),
null = NULL))
ok(cmp(unattr(agg_data[[1]]), table_string('
year step area vessel vessel_full_name vessel_power vessel_tonnage number mean
all 1 all A "Arni Fridriksson" 50 900 3 29.66667
all 1 all B "Bjarni Saemundsson" 100 800 3 56.33333
all 1 all C "Cefas Endeavour" 150 700 3 28.66667
all 1 all D "Dallaporta" 900 600 3 23.33333
all 1 all S "Commercial vessel 1a" 50 900 3 43.33333
all 1 all T "Commercial vessel 1b" 50 900 3 80.33333
all 1 all U "Commercial vessel 2a" 50 900 3 59.33333
all 1 all V "Commercial vessel 2b" 50 900 3 46.00000
', colClasses = c(NA, "character", NA, NA, NA, "numeric", "numeric", "numeric", NA))), "Dissaggregated vessels")
# Can group vessels by owner, which aggregates multiple vessels together
agg_data <- mfdb_sample_meanlength(mdb, c('vessel_owner'), list(
step = mfdb_timestep_yearly,
vessel_owner = mfdb_unaggregated(),
null = NULL))
ok(cmp(unattr(agg_data[[1]]), table_string('
year step area vessel_owner number mean
all 1 all CEFAS 3 28.66667
all 1 all CNR-IAMC 3 23.33333
all 1 all COMM1 6 61.83333
all 1 all COMM2 6 52.66667
all 1 all MFRI 6 43.00000
', colClasses = c(NA, "character", NA, NA, "numeric", NA))), "Vessels grouped by owner")
})
mfdb_disconnect(mdb)
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mfdb documentation built on June 21, 2022, 5:07 p.m.
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# Excerise storing / grouping by vessel information
#
# ok and ok_group are there so we can run this code and verify it is correct,
# and not needed every-day use.
#
# NB: if you want to run this, you must be using mfdb-workspace, also be warned
# any data stored in your database will be destroyed.
library(unittest)
library(mfdb)
source('tests/utils/helpers.R')
source('tests/utils/inttest-helpers.R')
# Empty database & rebuild
if (exists("mdb")) mfdb_disconnect(mdb)
mfdb(gsub("inttest", "inttest-vessel", Sys.getenv('INTTEST_SCHEMA', 'inttest')), db_params = db_params, destroy_schema = TRUE)
mdb <- mfdb(gsub("inttest", "inttest-vessel", Sys.getenv('INTTEST_SCHEMA', 'inttest')), db_params = db_params, save_temp_tables = FALSE)
# Set-up areas/divisions
mfdb_import_area(mdb, data.frame(
name = c('45G01', '45G02', '45G03'),
division = c('divA', 'divA', 'divB'),
size = c(10,200,400)))
ok_group("Vessel metadata example", {
# Create a temporary gadget directory
gd <- gadget_directory(tempfile())
mfdb_import_vessel_owner_taxonomy(mdb, table_string('
name full_name t_group
ISL "Iceland" ISL
GBR "Great Britain" GBR
ITA "Italy" ITA
MFRI "Marine and Freshwater Research Institute (Iceland)" ISL
CEFAS "Centre for Environment, Fisheries and Aquaculture Science" GBR
CNR-IAMC "Consiglio Nazionale delle Ricerche" ITA
COMM "Combined commercial fisheries" COMM
COMM1 "Commercial fishery 1" COMM
COMM2 "Commercial fishery 2" COMM
'))
# Set up the vessels we use in this example
mfdb_import_vessel_taxonomy(mdb, table_string('
name full_name vessel_type vessel_owner length power tonnage
A "Arni Fridriksson" 1.RSH MFRI 70 50 900
B "Bjarni Saemundsson" 1.RSH MFRI 56 100 800
C "Cefas Endeavour" 1.RSH CEFAS 65.50 150 700
D "Dallaporta" 1.RSH CNR-IAMC 24 900 600
S "Commercial vessel 1a" 2.COM COMM1 10 50 900
T "Commercial vessel 1b" 2.COM COMM1 30 50 900
U "Commercial vessel 2a" 2.COM COMM2 50 50 900
V "Commercial vessel 2b" 2.COM COMM2 70 50 900
'))
# Import a survey for the data we are interested in
mfdb_import_survey(mdb, data_source = "x",
table_string('
year month areacell species vessel length age weight
2000 1 45G01 COD A 21 2 210
2000 1 45G02 COD A 34 3 220
2000 1 45G03 COD A 34 3 230
2000 1 45G01 COD B 62 1 320
2000 1 45G02 COD B 53 1 330
2000 1 45G03 COD B 54 2 430
2000 1 45G01 COD C 28 2 210
2000 1 45G02 COD C 34 3 220
2000 1 45G03 COD C 24 3 230
2000 1 45G01 COD D 12 1 320
2000 1 45G02 COD D 44 1 330
2000 1 45G03 COD D 14 2 430
2000 1 45G01 COD S 47 2 383
2000 1 45G02 COD S 68 3 114
2000 1 45G03 COD S 15 3 101
2000 1 45G01 COD T 92 1 251
2000 1 45G02 COD T 85 1 289
2000 1 45G03 COD T 64 2 322
2000 1 45G01 COD U 28 2 140
2000 1 45G02 COD U 87 3 170
2000 1 45G03 COD U 63 3 197
2000 1 45G01 COD V 61 1 160
2000 1 45G02 COD V 45 1 201
2000 1 45G03 COD V 32 2 313
'))
# Group the data by vessel length
agg_data <- mfdb_sample_meanlength(mdb, c('vessel_type', 'vessel_length'), list(
step = mfdb_timestep_yearly,
vessel_type = mfdb_unaggregated(),
vessel_length = mfdb_interval('veslen', c(0, 10, 20, 50, 100)),
null = NULL))
ok(ut_cmp_equal(unattr(agg_data[[1]]), table_string('
year step area vessel_type vessel_length number mean
all "1" all 1.RSH veslen20 3 23.33333
all "1" all 1.RSH veslen50 9 38.22222
all "1" all 2.COM veslen10 3 43.33333
all "1" all 2.COM veslen20 3 80.33333
all "1" all 2.COM veslen50 6 52.66667
', colClasses = c(NA, "character", NA, NA, NA, "numeric", NA)), tolerance = 1e-7), "Grouped by vessel_type, vessel_length")
# Group the data by vessel type and area
agg_data <- mfdb_sample_meanlength(mdb, c('vessel_type'), list(
step = mfdb_timestep_yearly,
area = mfdb_group(x = 'divA', y = 'divB'),
vessel_type = mfdb_group(RSH = '1.RSH', COM = '1.COM'),
null = NULL))
ok(cmp(unattr(agg_data[[1]]), table_string('
year step area vessel_type number mean
all "1" x RSH 8 36.0
all "1" y RSH 4 31.5
', colClasses = c(NA, "character", NA, NA, "numeric", NA))), "Grouped by vessel_type/area")
# Show each vessel separately, with full name, power, tonnage
agg_data <- mfdb_sample_meanlength(mdb, c('vessel', 'vessel_full_name', 'vessel_power', 'vessel_tonnage'), list(
step = mfdb_timestep_yearly,
vessel = mfdb_unaggregated(),
vessel_full_name = mfdb_unaggregated(),
vessel_power = mfdb_unaggregated(),
vessel_tonnage = mfdb_unaggregated(),
null = NULL))
ok(cmp(unattr(agg_data[[1]]), table_string('
year step area vessel vessel_full_name vessel_power vessel_tonnage number mean
all 1 all A "Arni Fridriksson" 50 900 3 29.66667
all 1 all B "Bjarni Saemundsson" 100 800 3 56.33333
all 1 all C "Cefas Endeavour" 150 700 3 28.66667
all 1 all D "Dallaporta" 900 600 3 23.33333
all 1 all S "Commercial vessel 1a" 50 900 3 43.33333
all 1 all T "Commercial vessel 1b" 50 900 3 80.33333
all 1 all U "Commercial vessel 2a" 50 900 3 59.33333
all 1 all V "Commercial vessel 2b" 50 900 3 46.00000
', colClasses = c(NA, "character", NA, NA, NA, "numeric", "numeric", "numeric", NA))), "Dissaggregated vessels")
# Can group vessels by owner, which aggregates multiple vessels together
agg_data <- mfdb_sample_meanlength(mdb, c('vessel_owner'), list(
step = mfdb_timestep_yearly,
vessel_owner = mfdb_unaggregated(),
null = NULL))
ok(cmp(unattr(agg_data[[1]]), table_string('
year step area vessel_owner number mean
all 1 all CEFAS 3 28.66667
all 1 all CNR-IAMC 3 23.33333
all 1 all COMM1 6 61.83333
all 1 all COMM2 6 52.66667
all 1 all MFRI 6 43.00000
', colClasses = c(NA, "character", NA, NA, "numeric", NA))), "Vessels grouped by owner")
})
mfdb_disconnect(mdb)
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