Samples can be assigned to a trip, which provides a way of including socioeconomic data into MFDB.
The following examples use the following table_string helper to succintly define tables:
# Convert a string into a data.frame
table_string <- function (text, ...) read.table(
text = text,
blank.lines.skip = TRUE,
header = TRUE,
stringsAsFactors = FALSE,
...)Then connect to a database and set up some areas/divisions:
mdb <- mfdb(tempfile(fileext = '.duckdb'))
mfdb_import_area(mdb, table_string('
name division size
45G01 divA 10
45G02 divA 200
45G03 divB 400
'))Trips can have a start_port and end_port associated
with them, before we can do this we need to define any ports we use. We
do this with mfdb_import_port_taxonomy:
mfdb_import_port_taxonomy(mdb, table_string('
name description institute latitude longitude
ISHFF Hafnarfjordur ISL 64.04 21.57
NOHVJ Hvalfjordur ISL 64.21 21.45
ISKEF Keflavik ISL 64.00 22.33
ISREK Reykjavik ISL 64.09 21.55
ISSEJ Seydisfjordur ISL 65.15 13.55
ISKJF Skerjafjordur ISL 64.07 21.54
'))We can now define trips that use these ports:
mfdb_import_trip_taxonomy(mdb, table_string('
name start_date end_date crew oil_consumption start_port end_port
T1 2019-01-21 2019-02-11 4 3000 ISKEF ISREK
T2 2019-01-24 2019-02-14 5 9000 ISKEF ISSEJ
T3 2019-04-21 2019-05-11 4 3000 ISREK ISKEF
T4 2019-04-24 2019-05-14 5 9000 ISSEJ ISKEF
'))There is no formal link between trips and tows, so you can link a sample to a tow without recording trips. But we can create tows and vessels to use:
mfdb_import_tow_taxonomy(mdb, table_string('
name latitude longitude depth length
T1a 64.1 -23.15 98.82 10
T1b 64.1 -23.15 98.82 10
T2a 64.1 -23.15 98.82 10
T2b 64.1 -23.15 98.82 10
T3a 64.1 -23.15 98.82 10
T3b 64.1 -23.15 98.82 10
T4a 64.1 -23.15 98.82 10
T4b 64.1 -23.15 98.82 10
'))And finally import data that is associated to a vessel/trip/tow:
mfdb_import_survey(mdb, data_source = "cod2000",
table_string("
year month areacell species vessel trip tow length count
2019 1 45G01 COD V1 T1 T1a 10 358
2019 1 45G01 COD V1 T1 T1a 20 320
2019 1 45G01 COD V1 T1 T1a 30 162
2019 1 45G02 COD V1 T1 T1b 10 240
2019 1 45G02 COD V1 T1 T1b 20 278
2019 1 45G02 COD V1 T1 T1b 30 122
2019 2 45G01 COD V2 T2 T2a 10 255
2019 2 45G01 COD V2 T2 T2a 20 138
2019 2 45G01 COD V2 T2 T2a 30 168
2019 2 45G02 COD V2 T2 T2b 10 349
2019 2 45G02 COD V2 T2 T2b 20 106
2019 2 45G02 COD V2 T2 T2b 30 262
2019 4 45G01 COD V1 T3 T3a 10 395
2019 4 45G01 COD V1 T3 T3a 20 214
2019 4 45G01 COD V1 T3 T3a 30 195
2019 4 45G02 COD V1 T3 T3b 10 396
2019 4 45G02 COD V1 T3 T3b 20 325
2019 4 45G02 COD V1 T3 T3b 30 269
2019 5 45G01 COD V2 T4 T4a 10 239
2019 5 45G01 COD V2 T4 T4a 20 309
2019 5 45G01 COD V2 T4 T4a 30 162
2019 5 45G02 COD V2 T4 T4b 10 342
2019 5 45G02 COD V2 T4 T4b 20 175
2019 5 45G02 COD V2 T4 T4b 30 299
"))We can now use the mfdb_sample_* functions to select
this data back out again.
We can group and filter by any of the trip attributes. For instance,
to group/filter by trip_start_date:
agg_data <- mfdb_sample_count(mdb, c('trip_start_date', 'length'), list(
trip_start_date = '2019-01-21',
length = mfdb_unaggregated()))
agg_data## $`0.0.0.0.0`
## year step area trip_start_date length number
## 1 all all all 2019-01-21 10 598
## 2 all all all 2019-01-21 20 598
## 3 all all all 2019-01-21 30 284Note that we’ve summed T1a and T1b, since their start date matches.
We can use mfdb_group() to aggregate several ports, for
instance:
agg_data <- mfdb_sample_count(mdb, c('trip_start_port', 'length'), list(
trip_start_port = mfdb_group(KEF = 'ISKEF', other = c('ISREK', 'ISSEJ')),
length = mfdb_unaggregated()))
agg_data## $`0.0.0.0.0`
## year step area trip_start_port length number
## 1 all all all KEF 10 1202
## 2 all all all KEF 20 842
## 3 all all all KEF 30 714
## 4 all all all other 10 1372
## 5 all all all other 20 1023
## 6 all all all other 30 925We can also use mfdb_dplyr_sample to re-extract data,
joining with port metadata:
# TODO: Use native pipe syntax once available
x <- mfdb_dplyr_sample(mdb, c('trip_start_port', 'trip_end_port', 'trip_start_port_latitude'))
x <- dplyr::select(x, 'year', 'month', 'trip_start_port', 'trip_end_port', 'trip_start_port_latitude', 'count')
as.data.frame(x)## year month trip_start_port trip_end_port trip_start_port_latitude count
## 1 2019 1 ISKEF ISREK 64.00 358
## 2 2019 1 ISKEF ISREK 64.00 320
## 3 2019 1 ISKEF ISREK 64.00 162
## 4 2019 1 ISKEF ISREK 64.00 240
## 5 2019 1 ISKEF ISREK 64.00 278
## 6 2019 1 ISKEF ISREK 64.00 122
## 7 2019 2 ISKEF ISSEJ 64.00 255
## 8 2019 2 ISKEF ISSEJ 64.00 138
## 9 2019 2 ISKEF ISSEJ 64.00 168
## 10 2019 2 ISKEF ISSEJ 64.00 349
## 11 2019 2 ISKEF ISSEJ 64.00 106
## 12 2019 2 ISKEF ISSEJ 64.00 262
## 13 2019 4 ISREK ISKEF 64.09 395
## 14 2019 4 ISREK ISKEF 64.09 214
## 15 2019 4 ISREK ISKEF 64.09 195
## 16 2019 4 ISREK ISKEF 64.09 396
## 17 2019 4 ISREK ISKEF 64.09 325
## 18 2019 4 ISREK ISKEF 64.09 269
## 19 2019 5 ISSEJ ISKEF 65.15 239
## 20 2019 5 ISSEJ ISKEF 65.15 309
## 21 2019 5 ISSEJ ISKEF 65.15 162
## 22 2019 5 ISSEJ ISKEF 65.15 342
## 23 2019 5 ISSEJ ISKEF 65.15 175
## 24 2019 5 ISSEJ ISKEF 65.15 299