Nothing
demo/inttest-samples.R
In mfdb: MareFrame DB Querying Library
# This script demonstrates various ways you can use the data sampling functions
#
# 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
if (exists("mdb")) mfdb_disconnect(mdb)
mfdb(gsub("inttest", "inttest-samples", Sys.getenv('INTTEST_SCHEMA', 'inttest')), db_params = db_params, destroy_schema = TRUE)
#TODO: Connecting to empty database without ability to populate fails
# Rebuild database, taxonomy got populated
mdb <- mfdb(gsub("inttest", "inttest-samples", Sys.getenv('INTTEST_SCHEMA', 'inttest')), db_params = db_params, save_temp_tables = FALSE)
ok(all(mfdb:::mfdb_fetch(mdb, "SELECT name, description FROM species WHERE species_id = 9999999999")[1,] ==
mfdb::species[mfdb::species$name == 'TBX', c('name', 'description')]), "Entry for 9999999999 matches package")
ok(cmp(as.integer(mfdb:::mfdb_fetch(mdb, "SELECT count(*) FROM species")[1,1]), nrow(mfdb::species)), "Species has right number of entries")
ok_group("Unaggregated length / weight / age samples", {
# Set-up areas/divisions
mfdb_import_area(mdb, data.frame(
name = c('45G01', '45G02', '45G03'),
division = c('divA', 'divA', 'divB'),
size = c(5)))
# Set up the vessels we use in this example
mfdb_import_vessel_taxonomy(mdb, data.frame(
name = c('1.RSH', '2.RSH'),
vessel_type = c('1.RSH', '2.RSH'),
stringsAsFactors = FALSE
))
# Import a survey
mfdb_import_survey(mdb,
data_source = 'survey1',
data.frame(
Year = c('1998'), # NB: Not case-sensitive
mOnth = c(1:12),
areacell = c('45G01'),
species = c('COD'),
age = c( 1,2.1, 1,2.2, 1,2.1, 1,2.2, 1,2.1, 1,2.2),
length = c( 10, 50, 30, 10, 35, 46, 65, 62, 36, 35, 34, 22),
weight = c(100,500,300,100,350,460, 650,320,360,350,340,220)))
# Aggregate lengths
area_group <- mfdb_group(divA = c("divA"))
length_group <- mfdb_interval("len", seq(0, 50, by = 5))
ok(cmp(
mfdb_sample_meanlength(mdb, c('age'), list(
year = 1998:2000,
area = area_group,
timestep = mfdb_timestep_biannually,
data_source = "survey1",
length = length_group)),
list("0.0.0.0" = structure(
data.frame(
year = c(1998:1998),
step = c("1", "2"),
area = c("divA"),
age = c("all"),
number = c(5, 4),
mean = c(26.2, 31.75),
stringsAsFactors = FALSE),
year = as.list(structure(1998:2000, names = 1998:2000)),
step = mfdb_timestep_biannually,
area = area_group,
age = list(all = c(1, 2.2)),
generator = "mfdb_sample_meanlength"))),
"Aggregated length data")
ok(cmp(
mfdb_sample_rawdata(mdb, c('age'), list(
year = 1998:2000,
area = area_group,
timestep = mfdb_timestep_biannually,
data_source = "survey1",
age = mfdb_group(all = 1),
length = length_group))[[1]][,'raw_weight'],
c(100,300,350,360,340)), "Fetched raw weight data for age 1, length less than 50")
# mfdb_sample_meanlength_stddev is the same, but with an extra column
ok(cmp(
mfdb_sample_meanlength_stddev(mdb, c('age'), list(
year = 1998:2000,
area = area_group,
timestep = mfdb_timestep_biannually,
length = length_group)),
list("0.0.0.0" = structure(
data.frame(
year = c(1998:1998),
step = c("1", "2"),
area = c("divA"),
age = c("all"),
number = c(5, 4),
mean = c(26.2, 31.75),
stddev = c(15.880806, 6.551081),
stringsAsFactors = FALSE),
year = as.list(structure(1998:2000, names = 1998:2000)),
step = mfdb_timestep_biannually,
area = area_group,
age = list(all = c(1, 2.2)),
generator = "mfdb_sample_meanlength_stddev"))),
"Aggregated length data (with stddev)")
# mfdb_sample_totalweight aggregates weight, but still use length as a filter
length_group <- mfdb_interval("len", seq(50, 100, by = 5))
ok(cmp(
mfdb_sample_totalweight(mdb, c('age'), list(
year = 1998:2000,
area = area_group,
timestep = mfdb_timestep_biannually,
length = length_group)),
list("0.0.0.0" = structure(
data.frame(
year = c(1998:1998),
step = c("1", "2"),
area = c("divA"),
age = c("all"),
total_weight = c(500, 970),
stringsAsFactors = FALSE),
year = as.list(structure(1998:2000, names = 1998:2000)),
step = mfdb_timestep_biannually,
area = area_group,
age = list(all = c(2.1, 2.1)),
generator = "mfdb_sample_totalweight"))),
"Aggregated weight and got total")
# mfdb_sample_meanweight aggregates weight, but still use length as a filter
length_group <- mfdb_interval("len", seq(50, 100, by = 5))
ok(cmp(
mfdb_sample_meanweight(mdb, c('age'), list(
year = 1998:2000,
area = area_group,
timestep = mfdb_timestep_biannually,
length = length_group)),
list("0.0.0.0" = structure(
data.frame(
year = c(1998:1998),
step = c("1", "2"),
area = c("divA"),
age = c("all"),
number = c(1, 2),
mean = c(500, 485),
stringsAsFactors = FALSE),
year = as.list(structure(1998:2000, names = 1998:2000)),
step = mfdb_timestep_biannually,
area = area_group,
age = list(all = c(2.1, 2.1)),
generator = "mfdb_sample_meanweight"))),
"Aggregated weight data")
# mfdb_sample_meanweight_stddev works the same, but with stddev
length_group <- mfdb_interval("len", seq(50, 100, by = 5))
ok(cmp(
mfdb_sample_meanweight_stddev(mdb, c('age'), list(
year = 1998:2000,
area = area_group,
timestep = mfdb_timestep_biannually,
length = length_group)),
list("0.0.0.0" = structure(
data.frame(
year = c(1998:1998),
step = c("1", "2"),
area = c("divA"),
age = c("all"),
number = c(1, 2),
mean = c(500, 485),
stddev = c(NA, 233.3452),
stringsAsFactors = FALSE),
year = as.list(structure(1998:2000, names = 1998:2000)),
step = mfdb_timestep_biannually,
area = area_group,
age = list(all = c(2.1, 2.1)),
generator = "mfdb_sample_meanweight_stddev"))),
"Aggregated weight data (with stddev)")
# Can use step interval to produce same result
age_group <- mfdb_group(everything = c(1, 1.2, 2.2))
ok(cmp(
mfdb_sample_meanweight_stddev(mdb, c('age'), list(
year = 1998:2000,
area = area_group,
timestep = mfdb_timestep_biannually,
age = age_group,
length = mfdb_interval("len", seq(50, 100, by = 5)))),
mfdb_sample_meanweight_stddev(mdb, c('age'), list(
year = 1998:2000,
area = area_group,
step = mfdb_timestep_biannually,
age = age_group,
length = mfdb_step_interval("len", from = 50, to = 100, by = 5)))),
"Can use either mfdb_interval or mfdb_step_interval")
ok(cmp(
unattr(mfdb_sample_count(mdb, c('length'), list(
year = 1998:2000,
length = mfdb_interval("len", seq(30, 60, by = 10), open_ended = c())))[[1]]),
data.frame(
year = as.integer(1998),
step = 'all',
area = 'all',
length = c('len30', 'len40', 'len50'),
number = c(5, 1, 1),
stringsAsFactors = FALSE
)
), "Without open_ended, only 30,34,35,35,36 46 and 50 fit in bounds")
ok(cmp(
unattr(mfdb_sample_count(mdb, c('length'), list(
year = 1998:2000,
length = mfdb_interval("len", seq(30, 60, by = 10), open_ended = c("upper"))))[[1]]),
data.frame(
year = as.integer(1998),
step = 'all',
area = 'all',
length = c('len30', 'len40', 'len50'),
number = c(5, 1, 3),
stringsAsFactors = FALSE
)
), "Upper adds 62,55 to upper bound")
ok(cmp(
unattr(mfdb_sample_count(mdb, c('length'), list(
year = 1998:2000,
length = mfdb_interval("len", seq(30, 60, by = 10), open_ended = c("lower"))))[[1]]),
data.frame(
year = as.integer(1998),
step = 'all',
area = 'all',
length = c('len30', 'len40', 'len50'),
number = c(8, 1, 1),
stringsAsFactors = FALSE
)
), "Lower adds 10,10,22 to lower bound")
ok(cmp(
unattr(mfdb_sample_count(mdb, c('length'), list(
year = 1998:2000,
length = mfdb_interval("len", seq(30, 60, by = 10), open_ended = c("upper", "lower"))))[[1]]),
data.frame(
year = as.integer(1998),
step = 'all',
area = 'all',
length = c('len30', 'len40', 'len50'),
number = c(8, 1, 3),
stringsAsFactors = FALSE
)
), "Can do both at the same time")
ok(cmp(
unattr(mfdb_sample_count(mdb, c('length'), list(
year = 1998:2000,
length = mfdb_step_interval("len", 30, 60, by = 10, open_ended = c())))[[1]]),
data.frame(
year = as.integer(1998),
step = 'all',
area = 'all',
length = c('len30', 'len40', 'len50'),
number = c(5, 1, 1),
stringsAsFactors = FALSE
)
), "Without open_ended, only 30,34,35,35,36 46 and 50 fit in bounds (step_interval)")
ok(cmp(
unattr(mfdb_sample_count(mdb, c('length'), list(
year = 1998:2000,
length = mfdb_step_interval("len", 30, 60, by = 10, open_ended = c("upper"))))[[1]]),
data.frame(
year = as.integer(1998),
step = 'all',
area = 'all',
length = c('len30', 'len40', 'len50'),
number = c(5, 1, 3),
stringsAsFactors = FALSE
)
), "Upper adds 62,55 to upper bound (step_interval)")
ok(cmp(
unattr(mfdb_sample_count(mdb, c('length'), list(
year = 1998:2000,
length = mfdb_step_interval("len", 30, 60, by = 10, open_ended = c("lower"))))[[1]]),
data.frame(
year = as.integer(1998),
step = 'all',
area = 'all',
length = c('len30', 'len40', 'len50'),
number = c(8, 1, 1),
stringsAsFactors = FALSE
)
), "Lower adds 10,10,22 to lower bound (step_interval)")
ok(cmp(
unattr(mfdb_sample_count(mdb, c('length'), list(
year = 1998:2000,
length = mfdb_step_interval("len", 30, 60, by = 10, open_ended = c("upper", "lower"))))[[1]]),
data.frame(
year = as.integer(1998),
step = 'all',
area = 'all',
length = c('len30', 'len40', 'len50'),
number = c(8, 1, 3),
stringsAsFactors = FALSE
)
), "Can do both at the same time (step_interval)")
# Age / length splits by age
length_group <- mfdb_interval("len", seq(50, 100, by = 5))
age_group <- mfdb_group(age1 = c(1), age2 = c(2.1, 2.2))
ok(cmp(
mfdb_sample_count(mdb, c('age', 'length'), list(
year = 1998:2000,
area = area_group,
timestep = mfdb_timestep_biannually,
age = age_group,
length = length_group)),
list("0.0.0.0.0" = structure(
data.frame(
year = as.integer(c(1998, 1998, 1998)),
step = c("1", "2", "2"),
area = c("divA", "divA", "divA"),
age = c("age2", "age1", "age2"),
length = c("len50", "len65", "len60"),
number = c(1, 1, 1),
stringsAsFactors = FALSE),
year = as.list(structure(1998:2000, names = 1998:2000)),
step = mfdb_timestep_biannually,
area = area_group,
age = age_group,
length = agg_summary(mdb, length_group, 'col', 'out', data.frame(), 0),
generator = "mfdb_sample_count"))),
"Aggregated agelength data")
})
ok_group("Filtering of samples", {
# Set-up areas/divisions
mfdb_import_area(mdb, data.frame(id = c(1,2,3), name = c('45G01', '45G02', '45G03'), size = c(5)))
mfdb_import_division(mdb, list(divA = c('45G01', '45G02'), divB = c('45G01')))
# Set up sampling types
mfdb_import_sampling_type(mdb, data.frame(id = 1:2, name = c("SEA", "MOO")))
# Import several surveys with different metadata set
mfdb_import_survey(mdb,
data_source = 'survey1',
data.frame(
institute = 'MRI',
gear = 'GIL',
vessel = '1.RSH',
sampling_type = 'SEA',
year = c('1998'),
month = c(1:12),
areacell = c('45G01'),
species = c('COD'),
age = c( 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2),
sex = c('M','F','X','M','F','X', 'M','F','X','M','F','X'),
length = c( 10, 50, 30, 10, 35, 46, 65, 62, 36, 35, 34, 22),
weight = c(100,500,300,100,350,460, 650,320,360,350,340,220)))
mfdb_import_survey(mdb,
data_source = 'survey2',
data.frame(
institute = 'ICES',
gear = 'DSE',
vessel = '2.RSH',
sampling_type = 'SEA',
year = c('1998'),
month = c(1:12),
areacell = c('45G01'),
species = c('HAD'),
age = c( 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2),
sex = c('M','F','X','M','F','X', 'M','F','X','M','F','X'),
length = c( 35, 64, 23, 13, 99, 83, 54, 23, 65, 12, 22, 9),
weight = c(110,510,310,110,310,410, 610,310,310,310,310,230)))
# MOO sampling_type has no dat
area_group <- mfdb_group(divA = c("divA"))
ok(cmp(
mfdb_sample_meanlength(mdb, c(), list(
sampling_type = 'MOO',
area = area_group,
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000)),
list("0.0.0" = structure(
data.frame(),
year = list("1998" = 1998, "1999" = 1999, "2000" = 2000),
step = mfdb_timestep_biannually,
area = area_group,
generator = "mfdb_sample_meanlength"))),
"sampling_type MOO empty")
# Without any aggregation on, we get the whole lot
ok(cmp(
mfdb_sample_meanlength(mdb, c(), list(
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number", "mean")],
data.frame(
step = c("1", "2"),
number = c(12, 12),
mean = c(
mean(c(10,50,30,10,35,46, 35,64,23,13,99,83)),
mean(c(65,62,36,35,34,22, 54,23,65,12,22,9))),
stringsAsFactors = FALSE)),
"No filtering, got everything")
ok(cmp(
mfdb_sample_meanlength(mdb, c(), list(
sampling_type = 'SEA',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number", "mean")],
data.frame(
step = c("1", "2"),
number = c(12, 12),
mean = c(
mean(c(10,50,30,10,35,46, 35,64,23,13,99,83)),
mean(c(65,62,36,35,34,22, 54,23,65,12,22,9))),
stringsAsFactors = FALSE)),
"SEA in both, got everything")
# Filtering by gear,institute, vessel or sampling_type will break down to one or other
ok(cmp(
mfdb_sample_meanlength(mdb, c(), list(
gear = 'GIL',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number", "mean")],
data.frame(
step = c("1", "2"),
number = c(6, 6),
mean = c(
mean(c(10,50,30,10,35,46)),
mean(c(65,62,36,35,34,22))),
stringsAsFactors = FALSE)),
"GIL means survey1")
ok(cmp(
mfdb_sample_meanlength(mdb, c(), list(
vessel = '1.RSH',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number", "mean")],
data.frame(
step = c("1", "2"),
number = c(6, 6),
mean = c(
mean(c(10,50,30,10,35,46)),
mean(c(65,62,36,35,34,22))),
stringsAsFactors = FALSE)),
"1.RSH means survey1")
ok(cmp(
mfdb_sample_meanlength(mdb, c(), list(
institute = 'ICES',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number", "mean")],
data.frame(
step = c("1", "2"),
number = c(6, 6),
mean = c(
mean(c(35,64,23,13,99,83)),
mean(c(54,23,65,12,22,9))),
stringsAsFactors = FALSE)),
"ICES means survey2")
ok(cmp(
mfdb_sample_meanlength(mdb, c(), list(
vessel = '2.RSH',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number", "mean")],
data.frame(
step = c("1", "2"),
number = c(6, 6),
mean = c(
mean(c(35,64,23,13,99,83)),
mean(c(54,23,65,12,22,9))),
stringsAsFactors = FALSE)),
"2.RSH means survey2")
# Intersection gives nothing
ok(cmp(
nrow(mfdb_sample_meanlength(mdb, c(), list(
gear = 'GIL',
vessel = '2.RSH',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]]),
0),
"GIL & 2.RSH returns nothing")
# Should be able to re-import sampling types
mfdb_import_sampling_type(mdb, data.frame(name = c("SEA", "MOO"), description = c("Sea", "Seacow")))
ok(cmp(
mfdb_sample_meanlength(mdb, c(), list(
vessel = '2.RSH',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number", "mean")],
data.frame(
step = c("1", "2"),
number = c(6, 6),
mean = c(
mean(c(35,64,23,13,99,83)),
mean(c(54,23,65,12,22,9))),
stringsAsFactors = FALSE)),
"Data still exists")
# Should be able to group by species
ok(cmp(
mfdb_sample_meanlength(mdb, c('species'), list(
species = c('COD', 'HAD'),
year = 1998:2000))[["0.0.0.0"]][,c("year", "step", "area", "species", "number", "mean")],
data.frame(
year = c(1998, 1998),
step = c("all", "all"),
area = c("all", "all"),
species = c("COD", "HAD"),
number = c(12, 12),
mean = c(
mean(c( 10, 50, 30, 10, 35, 46, 65, 62, 36, 35, 34, 22)),
mean(c( 35, 64, 23, 13, 99, 83, 54, 23, 65, 12, 22, 9))),
stringsAsFactors = FALSE)),
"Can group by species (COD & HAD)")
ok(cmp(
mfdb_sample_meanlength(mdb, c('species'), list(
species = c('COD'),
year = 1998:2000))[["0.0.0.0"]][,c("year", "step", "area", "species", "number", "mean")],
data.frame(
year = c(1998),
step = c("all"),
area = c("all"),
species = c("COD"),
number = c(12),
mean = c(
mean(c( 10, 50, 30, 10, 35, 46, 65, 62, 36, 35, 34, 22))),
stringsAsFactors = FALSE)),
"Can group by species (just COD)")
ok(cmp(
mfdb_sample_meanlength(mdb, c('species'), list(
species = mfdb_group(codhad = c('COD', 'HAD')),
year = 1998:2000))[["0.0.0.0"]][,c("year", "step", "area", "species", "number", "mean")],
data.frame(
year = c(1998),
step = c("all"),
area = c("all"),
species = c("codhad"),
number = c(24),
mean = mean(c(
c( 10, 50, 30, 10, 35, 46, 65, 62, 36, 35, 34, 22),
c( 35, 64, 23, 13, 99, 83, 54, 23, 65, 12, 22, 9),
NULL)),
stringsAsFactors = FALSE)),
"Can group by species (COD & HAD combined)")
ok(cmp_error(
mfdb_sample_meanlength(mdb, c('species'), list(
species = mfdb_group(codhad = c('COD', 'HAD'), hand = c('HAND')),
year = 1998:2000)), "HAND"), "Notice and report items not in vocabulary")
})
ok_group("Invalid parameters", {
ok(cmp_error(mfdb_sample_count(mdb, c(), "camel"), "params"), "Complained about non-list params")
ok(cmp(
mfdb_sample_count(mdb, c(), list(
camelcamelcamel = "No thanks",
vessel = '2.RSH',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number")],
data.frame(
step = c("1", "2"),
number = c(6, 6),
stringsAsFactors = FALSE)),
"Useless camelcamelcamel parameter ignored")
ok(cmp_error(
mfdb_sample_count(mdb, c("camelcamelcamel"), list(
camelcamelcamel = "No thanks",
vessel = '2.RSH',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number")],
"camelcamelcamel"),
"Cannot use camelcamelcamel as a column")
})
ok_group("Invalid import", {
ok(cmp_error(
mfdb_import_survey(mdb,
data_source = 'survey1',
data.frame(
Year = c('1998'), # NB: Not case-sensitive
month = c(1:6, 13, 28, 9, 10, 0, 12),
areacell = c('45G01'),
species = c('COD'),
age = c( 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2),
length = c( 10, 50, 30, 10, 35, 46, 65, 62, 36, 35, 34, 22),
weight = c(101,500,300,100,350,460, 650,320,360,350,340,220))),
"13,28,0"), "Notice when month isn't within 1..12")
})
ok_group("Removing an import", {
# Can insert nothing without an error
mfdb_import_survey(mdb, data_source = 'empty_survey', data.frame())
ok(cmp(
unattr(mfdb_sample_count(mdb, c(), list(year = c('1998'), data_source = 'empty_survey'))[['0.0.0']]),
data.frame()),
"No data available")
# Importing data still works
mfdb_import_survey(mdb,
data_source = 'empty_survey',
data.frame(
Year = c('1998'),
month = c(1:12),
areacell = c('45G01'),
species = c('COD'),
age = c( 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2),
length = c( 10, 50, 30, 10, 35, 46, 65, 62, 36, 35, 34, 22),
weight = c(100,500,300,100,350,460, 650,320,360,350,340,220)))
ok(cmp(
unattr(mfdb_sample_count(mdb, c(), list(year = c('1998'), data_source = 'empty_survey'))[['0.0.0']]),
data.frame(year = as.integer(1998), step = 'all', area = 'all', number = 12, stringsAsFactors = FALSE)),
"empty_survey content imported")
# Gone again
mfdb_import_survey(mdb, data_source = 'empty_survey', data.frame())
ok(cmp(
unattr(mfdb_sample_count(mdb, c(), list(year = c('1998'), data_source = 'empty_survey'))[['0.0.0']]),
data.frame()),
"Data removed again")
})
mfdb_disconnect(mdb)
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# This script demonstrates various ways you can use the data sampling functions
#
# 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
if (exists("mdb")) mfdb_disconnect(mdb)
mfdb(gsub("inttest", "inttest-samples", Sys.getenv('INTTEST_SCHEMA', 'inttest')), db_params = db_params, destroy_schema = TRUE)
#TODO: Connecting to empty database without ability to populate fails
# Rebuild database, taxonomy got populated
mdb <- mfdb(gsub("inttest", "inttest-samples", Sys.getenv('INTTEST_SCHEMA', 'inttest')), db_params = db_params, save_temp_tables = FALSE)
ok(all(mfdb:::mfdb_fetch(mdb, "SELECT name, description FROM species WHERE species_id = 9999999999")[1,] ==
mfdb::species[mfdb::species$name == 'TBX', c('name', 'description')]), "Entry for 9999999999 matches package")
ok(cmp(as.integer(mfdb:::mfdb_fetch(mdb, "SELECT count(*) FROM species")[1,1]), nrow(mfdb::species)), "Species has right number of entries")
ok_group("Unaggregated length / weight / age samples", {
# Set-up areas/divisions
mfdb_import_area(mdb, data.frame(
name = c('45G01', '45G02', '45G03'),
division = c('divA', 'divA', 'divB'),
size = c(5)))
# Set up the vessels we use in this example
mfdb_import_vessel_taxonomy(mdb, data.frame(
name = c('1.RSH', '2.RSH'),
vessel_type = c('1.RSH', '2.RSH'),
stringsAsFactors = FALSE
))
# Import a survey
mfdb_import_survey(mdb,
data_source = 'survey1',
data.frame(
Year = c('1998'), # NB: Not case-sensitive
mOnth = c(1:12),
areacell = c('45G01'),
species = c('COD'),
age = c( 1,2.1, 1,2.2, 1,2.1, 1,2.2, 1,2.1, 1,2.2),
length = c( 10, 50, 30, 10, 35, 46, 65, 62, 36, 35, 34, 22),
weight = c(100,500,300,100,350,460, 650,320,360,350,340,220)))
# Aggregate lengths
area_group <- mfdb_group(divA = c("divA"))
length_group <- mfdb_interval("len", seq(0, 50, by = 5))
ok(cmp(
mfdb_sample_meanlength(mdb, c('age'), list(
year = 1998:2000,
area = area_group,
timestep = mfdb_timestep_biannually,
data_source = "survey1",
length = length_group)),
list("0.0.0.0" = structure(
data.frame(
year = c(1998:1998),
step = c("1", "2"),
area = c("divA"),
age = c("all"),
number = c(5, 4),
mean = c(26.2, 31.75),
stringsAsFactors = FALSE),
year = as.list(structure(1998:2000, names = 1998:2000)),
step = mfdb_timestep_biannually,
area = area_group,
age = list(all = c(1, 2.2)),
generator = "mfdb_sample_meanlength"))),
"Aggregated length data")
ok(cmp(
mfdb_sample_rawdata(mdb, c('age'), list(
year = 1998:2000,
area = area_group,
timestep = mfdb_timestep_biannually,
data_source = "survey1",
age = mfdb_group(all = 1),
length = length_group))[[1]][,'raw_weight'],
c(100,300,350,360,340)), "Fetched raw weight data for age 1, length less than 50")
# mfdb_sample_meanlength_stddev is the same, but with an extra column
ok(cmp(
mfdb_sample_meanlength_stddev(mdb, c('age'), list(
year = 1998:2000,
area = area_group,
timestep = mfdb_timestep_biannually,
length = length_group)),
list("0.0.0.0" = structure(
data.frame(
year = c(1998:1998),
step = c("1", "2"),
area = c("divA"),
age = c("all"),
number = c(5, 4),
mean = c(26.2, 31.75),
stddev = c(15.880806, 6.551081),
stringsAsFactors = FALSE),
year = as.list(structure(1998:2000, names = 1998:2000)),
step = mfdb_timestep_biannually,
area = area_group,
age = list(all = c(1, 2.2)),
generator = "mfdb_sample_meanlength_stddev"))),
"Aggregated length data (with stddev)")
# mfdb_sample_totalweight aggregates weight, but still use length as a filter
length_group <- mfdb_interval("len", seq(50, 100, by = 5))
ok(cmp(
mfdb_sample_totalweight(mdb, c('age'), list(
year = 1998:2000,
area = area_group,
timestep = mfdb_timestep_biannually,
length = length_group)),
list("0.0.0.0" = structure(
data.frame(
year = c(1998:1998),
step = c("1", "2"),
area = c("divA"),
age = c("all"),
total_weight = c(500, 970),
stringsAsFactors = FALSE),
year = as.list(structure(1998:2000, names = 1998:2000)),
step = mfdb_timestep_biannually,
area = area_group,
age = list(all = c(2.1, 2.1)),
generator = "mfdb_sample_totalweight"))),
"Aggregated weight and got total")
# mfdb_sample_meanweight aggregates weight, but still use length as a filter
length_group <- mfdb_interval("len", seq(50, 100, by = 5))
ok(cmp(
mfdb_sample_meanweight(mdb, c('age'), list(
year = 1998:2000,
area = area_group,
timestep = mfdb_timestep_biannually,
length = length_group)),
list("0.0.0.0" = structure(
data.frame(
year = c(1998:1998),
step = c("1", "2"),
area = c("divA"),
age = c("all"),
number = c(1, 2),
mean = c(500, 485),
stringsAsFactors = FALSE),
year = as.list(structure(1998:2000, names = 1998:2000)),
step = mfdb_timestep_biannually,
area = area_group,
age = list(all = c(2.1, 2.1)),
generator = "mfdb_sample_meanweight"))),
"Aggregated weight data")
# mfdb_sample_meanweight_stddev works the same, but with stddev
length_group <- mfdb_interval("len", seq(50, 100, by = 5))
ok(cmp(
mfdb_sample_meanweight_stddev(mdb, c('age'), list(
year = 1998:2000,
area = area_group,
timestep = mfdb_timestep_biannually,
length = length_group)),
list("0.0.0.0" = structure(
data.frame(
year = c(1998:1998),
step = c("1", "2"),
area = c("divA"),
age = c("all"),
number = c(1, 2),
mean = c(500, 485),
stddev = c(NA, 233.3452),
stringsAsFactors = FALSE),
year = as.list(structure(1998:2000, names = 1998:2000)),
step = mfdb_timestep_biannually,
area = area_group,
age = list(all = c(2.1, 2.1)),
generator = "mfdb_sample_meanweight_stddev"))),
"Aggregated weight data (with stddev)")
# Can use step interval to produce same result
age_group <- mfdb_group(everything = c(1, 1.2, 2.2))
ok(cmp(
mfdb_sample_meanweight_stddev(mdb, c('age'), list(
year = 1998:2000,
area = area_group,
timestep = mfdb_timestep_biannually,
age = age_group,
length = mfdb_interval("len", seq(50, 100, by = 5)))),
mfdb_sample_meanweight_stddev(mdb, c('age'), list(
year = 1998:2000,
area = area_group,
step = mfdb_timestep_biannually,
age = age_group,
length = mfdb_step_interval("len", from = 50, to = 100, by = 5)))),
"Can use either mfdb_interval or mfdb_step_interval")
ok(cmp(
unattr(mfdb_sample_count(mdb, c('length'), list(
year = 1998:2000,
length = mfdb_interval("len", seq(30, 60, by = 10), open_ended = c())))[[1]]),
data.frame(
year = as.integer(1998),
step = 'all',
area = 'all',
length = c('len30', 'len40', 'len50'),
number = c(5, 1, 1),
stringsAsFactors = FALSE
)
), "Without open_ended, only 30,34,35,35,36 46 and 50 fit in bounds")
ok(cmp(
unattr(mfdb_sample_count(mdb, c('length'), list(
year = 1998:2000,
length = mfdb_interval("len", seq(30, 60, by = 10), open_ended = c("upper"))))[[1]]),
data.frame(
year = as.integer(1998),
step = 'all',
area = 'all',
length = c('len30', 'len40', 'len50'),
number = c(5, 1, 3),
stringsAsFactors = FALSE
)
), "Upper adds 62,55 to upper bound")
ok(cmp(
unattr(mfdb_sample_count(mdb, c('length'), list(
year = 1998:2000,
length = mfdb_interval("len", seq(30, 60, by = 10), open_ended = c("lower"))))[[1]]),
data.frame(
year = as.integer(1998),
step = 'all',
area = 'all',
length = c('len30', 'len40', 'len50'),
number = c(8, 1, 1),
stringsAsFactors = FALSE
)
), "Lower adds 10,10,22 to lower bound")
ok(cmp(
unattr(mfdb_sample_count(mdb, c('length'), list(
year = 1998:2000,
length = mfdb_interval("len", seq(30, 60, by = 10), open_ended = c("upper", "lower"))))[[1]]),
data.frame(
year = as.integer(1998),
step = 'all',
area = 'all',
length = c('len30', 'len40', 'len50'),
number = c(8, 1, 3),
stringsAsFactors = FALSE
)
), "Can do both at the same time")
ok(cmp(
unattr(mfdb_sample_count(mdb, c('length'), list(
year = 1998:2000,
length = mfdb_step_interval("len", 30, 60, by = 10, open_ended = c())))[[1]]),
data.frame(
year = as.integer(1998),
step = 'all',
area = 'all',
length = c('len30', 'len40', 'len50'),
number = c(5, 1, 1),
stringsAsFactors = FALSE
)
), "Without open_ended, only 30,34,35,35,36 46 and 50 fit in bounds (step_interval)")
ok(cmp(
unattr(mfdb_sample_count(mdb, c('length'), list(
year = 1998:2000,
length = mfdb_step_interval("len", 30, 60, by = 10, open_ended = c("upper"))))[[1]]),
data.frame(
year = as.integer(1998),
step = 'all',
area = 'all',
length = c('len30', 'len40', 'len50'),
number = c(5, 1, 3),
stringsAsFactors = FALSE
)
), "Upper adds 62,55 to upper bound (step_interval)")
ok(cmp(
unattr(mfdb_sample_count(mdb, c('length'), list(
year = 1998:2000,
length = mfdb_step_interval("len", 30, 60, by = 10, open_ended = c("lower"))))[[1]]),
data.frame(
year = as.integer(1998),
step = 'all',
area = 'all',
length = c('len30', 'len40', 'len50'),
number = c(8, 1, 1),
stringsAsFactors = FALSE
)
), "Lower adds 10,10,22 to lower bound (step_interval)")
ok(cmp(
unattr(mfdb_sample_count(mdb, c('length'), list(
year = 1998:2000,
length = mfdb_step_interval("len", 30, 60, by = 10, open_ended = c("upper", "lower"))))[[1]]),
data.frame(
year = as.integer(1998),
step = 'all',
area = 'all',
length = c('len30', 'len40', 'len50'),
number = c(8, 1, 3),
stringsAsFactors = FALSE
)
), "Can do both at the same time (step_interval)")
# Age / length splits by age
length_group <- mfdb_interval("len", seq(50, 100, by = 5))
age_group <- mfdb_group(age1 = c(1), age2 = c(2.1, 2.2))
ok(cmp(
mfdb_sample_count(mdb, c('age', 'length'), list(
year = 1998:2000,
area = area_group,
timestep = mfdb_timestep_biannually,
age = age_group,
length = length_group)),
list("0.0.0.0.0" = structure(
data.frame(
year = as.integer(c(1998, 1998, 1998)),
step = c("1", "2", "2"),
area = c("divA", "divA", "divA"),
age = c("age2", "age1", "age2"),
length = c("len50", "len65", "len60"),
number = c(1, 1, 1),
stringsAsFactors = FALSE),
year = as.list(structure(1998:2000, names = 1998:2000)),
step = mfdb_timestep_biannually,
area = area_group,
age = age_group,
length = agg_summary(mdb, length_group, 'col', 'out', data.frame(), 0),
generator = "mfdb_sample_count"))),
"Aggregated agelength data")
})
ok_group("Filtering of samples", {
# Set-up areas/divisions
mfdb_import_area(mdb, data.frame(id = c(1,2,3), name = c('45G01', '45G02', '45G03'), size = c(5)))
mfdb_import_division(mdb, list(divA = c('45G01', '45G02'), divB = c('45G01')))
# Set up sampling types
mfdb_import_sampling_type(mdb, data.frame(id = 1:2, name = c("SEA", "MOO")))
# Import several surveys with different metadata set
mfdb_import_survey(mdb,
data_source = 'survey1',
data.frame(
institute = 'MRI',
gear = 'GIL',
vessel = '1.RSH',
sampling_type = 'SEA',
year = c('1998'),
month = c(1:12),
areacell = c('45G01'),
species = c('COD'),
age = c( 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2),
sex = c('M','F','X','M','F','X', 'M','F','X','M','F','X'),
length = c( 10, 50, 30, 10, 35, 46, 65, 62, 36, 35, 34, 22),
weight = c(100,500,300,100,350,460, 650,320,360,350,340,220)))
mfdb_import_survey(mdb,
data_source = 'survey2',
data.frame(
institute = 'ICES',
gear = 'DSE',
vessel = '2.RSH',
sampling_type = 'SEA',
year = c('1998'),
month = c(1:12),
areacell = c('45G01'),
species = c('HAD'),
age = c( 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2),
sex = c('M','F','X','M','F','X', 'M','F','X','M','F','X'),
length = c( 35, 64, 23, 13, 99, 83, 54, 23, 65, 12, 22, 9),
weight = c(110,510,310,110,310,410, 610,310,310,310,310,230)))
# MOO sampling_type has no dat
area_group <- mfdb_group(divA = c("divA"))
ok(cmp(
mfdb_sample_meanlength(mdb, c(), list(
sampling_type = 'MOO',
area = area_group,
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000)),
list("0.0.0" = structure(
data.frame(),
year = list("1998" = 1998, "1999" = 1999, "2000" = 2000),
step = mfdb_timestep_biannually,
area = area_group,
generator = "mfdb_sample_meanlength"))),
"sampling_type MOO empty")
# Without any aggregation on, we get the whole lot
ok(cmp(
mfdb_sample_meanlength(mdb, c(), list(
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number", "mean")],
data.frame(
step = c("1", "2"),
number = c(12, 12),
mean = c(
mean(c(10,50,30,10,35,46, 35,64,23,13,99,83)),
mean(c(65,62,36,35,34,22, 54,23,65,12,22,9))),
stringsAsFactors = FALSE)),
"No filtering, got everything")
ok(cmp(
mfdb_sample_meanlength(mdb, c(), list(
sampling_type = 'SEA',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number", "mean")],
data.frame(
step = c("1", "2"),
number = c(12, 12),
mean = c(
mean(c(10,50,30,10,35,46, 35,64,23,13,99,83)),
mean(c(65,62,36,35,34,22, 54,23,65,12,22,9))),
stringsAsFactors = FALSE)),
"SEA in both, got everything")
# Filtering by gear,institute, vessel or sampling_type will break down to one or other
ok(cmp(
mfdb_sample_meanlength(mdb, c(), list(
gear = 'GIL',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number", "mean")],
data.frame(
step = c("1", "2"),
number = c(6, 6),
mean = c(
mean(c(10,50,30,10,35,46)),
mean(c(65,62,36,35,34,22))),
stringsAsFactors = FALSE)),
"GIL means survey1")
ok(cmp(
mfdb_sample_meanlength(mdb, c(), list(
vessel = '1.RSH',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number", "mean")],
data.frame(
step = c("1", "2"),
number = c(6, 6),
mean = c(
mean(c(10,50,30,10,35,46)),
mean(c(65,62,36,35,34,22))),
stringsAsFactors = FALSE)),
"1.RSH means survey1")
ok(cmp(
mfdb_sample_meanlength(mdb, c(), list(
institute = 'ICES',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number", "mean")],
data.frame(
step = c("1", "2"),
number = c(6, 6),
mean = c(
mean(c(35,64,23,13,99,83)),
mean(c(54,23,65,12,22,9))),
stringsAsFactors = FALSE)),
"ICES means survey2")
ok(cmp(
mfdb_sample_meanlength(mdb, c(), list(
vessel = '2.RSH',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number", "mean")],
data.frame(
step = c("1", "2"),
number = c(6, 6),
mean = c(
mean(c(35,64,23,13,99,83)),
mean(c(54,23,65,12,22,9))),
stringsAsFactors = FALSE)),
"2.RSH means survey2")
# Intersection gives nothing
ok(cmp(
nrow(mfdb_sample_meanlength(mdb, c(), list(
gear = 'GIL',
vessel = '2.RSH',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]]),
0),
"GIL & 2.RSH returns nothing")
# Should be able to re-import sampling types
mfdb_import_sampling_type(mdb, data.frame(name = c("SEA", "MOO"), description = c("Sea", "Seacow")))
ok(cmp(
mfdb_sample_meanlength(mdb, c(), list(
vessel = '2.RSH',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number", "mean")],
data.frame(
step = c("1", "2"),
number = c(6, 6),
mean = c(
mean(c(35,64,23,13,99,83)),
mean(c(54,23,65,12,22,9))),
stringsAsFactors = FALSE)),
"Data still exists")
# Should be able to group by species
ok(cmp(
mfdb_sample_meanlength(mdb, c('species'), list(
species = c('COD', 'HAD'),
year = 1998:2000))[["0.0.0.0"]][,c("year", "step", "area", "species", "number", "mean")],
data.frame(
year = c(1998, 1998),
step = c("all", "all"),
area = c("all", "all"),
species = c("COD", "HAD"),
number = c(12, 12),
mean = c(
mean(c( 10, 50, 30, 10, 35, 46, 65, 62, 36, 35, 34, 22)),
mean(c( 35, 64, 23, 13, 99, 83, 54, 23, 65, 12, 22, 9))),
stringsAsFactors = FALSE)),
"Can group by species (COD & HAD)")
ok(cmp(
mfdb_sample_meanlength(mdb, c('species'), list(
species = c('COD'),
year = 1998:2000))[["0.0.0.0"]][,c("year", "step", "area", "species", "number", "mean")],
data.frame(
year = c(1998),
step = c("all"),
area = c("all"),
species = c("COD"),
number = c(12),
mean = c(
mean(c( 10, 50, 30, 10, 35, 46, 65, 62, 36, 35, 34, 22))),
stringsAsFactors = FALSE)),
"Can group by species (just COD)")
ok(cmp(
mfdb_sample_meanlength(mdb, c('species'), list(
species = mfdb_group(codhad = c('COD', 'HAD')),
year = 1998:2000))[["0.0.0.0"]][,c("year", "step", "area", "species", "number", "mean")],
data.frame(
year = c(1998),
step = c("all"),
area = c("all"),
species = c("codhad"),
number = c(24),
mean = mean(c(
c( 10, 50, 30, 10, 35, 46, 65, 62, 36, 35, 34, 22),
c( 35, 64, 23, 13, 99, 83, 54, 23, 65, 12, 22, 9),
NULL)),
stringsAsFactors = FALSE)),
"Can group by species (COD & HAD combined)")
ok(cmp_error(
mfdb_sample_meanlength(mdb, c('species'), list(
species = mfdb_group(codhad = c('COD', 'HAD'), hand = c('HAND')),
year = 1998:2000)), "HAND"), "Notice and report items not in vocabulary")
})
ok_group("Invalid parameters", {
ok(cmp_error(mfdb_sample_count(mdb, c(), "camel"), "params"), "Complained about non-list params")
ok(cmp(
mfdb_sample_count(mdb, c(), list(
camelcamelcamel = "No thanks",
vessel = '2.RSH',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number")],
data.frame(
step = c("1", "2"),
number = c(6, 6),
stringsAsFactors = FALSE)),
"Useless camelcamelcamel parameter ignored")
ok(cmp_error(
mfdb_sample_count(mdb, c("camelcamelcamel"), list(
camelcamelcamel = "No thanks",
vessel = '2.RSH',
area = mfdb_group(divA = c("divA")),
timestep = mfdb_timestep_biannually,
age = mfdb_group(all = 1:1000),
length = mfdb_interval("len", seq(0, 100, by = 10)),
year = 1998:2000))[["0.0.0"]][,c("step", "number")],
"camelcamelcamel"),
"Cannot use camelcamelcamel as a column")
})
ok_group("Invalid import", {
ok(cmp_error(
mfdb_import_survey(mdb,
data_source = 'survey1',
data.frame(
Year = c('1998'), # NB: Not case-sensitive
month = c(1:6, 13, 28, 9, 10, 0, 12),
areacell = c('45G01'),
species = c('COD'),
age = c( 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2),
length = c( 10, 50, 30, 10, 35, 46, 65, 62, 36, 35, 34, 22),
weight = c(101,500,300,100,350,460, 650,320,360,350,340,220))),
"13,28,0"), "Notice when month isn't within 1..12")
})
ok_group("Removing an import", {
# Can insert nothing without an error
mfdb_import_survey(mdb, data_source = 'empty_survey', data.frame())
ok(cmp(
unattr(mfdb_sample_count(mdb, c(), list(year = c('1998'), data_source = 'empty_survey'))[['0.0.0']]),
data.frame()),
"No data available")
# Importing data still works
mfdb_import_survey(mdb,
data_source = 'empty_survey',
data.frame(
Year = c('1998'),
month = c(1:12),
areacell = c('45G01'),
species = c('COD'),
age = c( 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2),
length = c( 10, 50, 30, 10, 35, 46, 65, 62, 36, 35, 34, 22),
weight = c(100,500,300,100,350,460, 650,320,360,350,340,220)))
ok(cmp(
unattr(mfdb_sample_count(mdb, c(), list(year = c('1998'), data_source = 'empty_survey'))[['0.0.0']]),
data.frame(year = as.integer(1998), step = 'all', area = 'all', number = 12, stringsAsFactors = FALSE)),
"empty_survey content imported")
# Gone again
mfdb_import_survey(mdb, data_source = 'empty_survey', data.frame())
ok(cmp(
unattr(mfdb_sample_count(mdb, c(), list(year = c('1998'), data_source = 'empty_survey'))[['0.0.0']]),
data.frame()),
"Data removed again")
})
mfdb_disconnect(mdb)
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