Introduction to gfdata
Elise Keppel
2024-10-15
Source:vignettes/01-gfdata-vignette.Rmd
01-gfdata-vignette.RmdSetup
If you don’t already have the package installed, then run:
# install.packages("devtools")
devtools::install_github("pbs-assess/gfdata")First we will load the package along with dplyr since we will use it within our code later.
An overview of gfdata
Commercial and research catch, effort, and biological data for groundfish are archived by the DFO Pacific Groundfish Data Unit (Fisheries and Oceans Canada, Science Branch, Pacific Region) and housed in a number of relational databases archived on-site at the Pacific Biological Station, Nanaimo, BC).
The gfdata package was develeoped to automate data extraction from
these databases in a consistent, reproducible manner with a series of
get_*() functions. The functions extract data using SQL
queries, developed with support from the Groundfish Data Unit. The
standardized datasets are designed to feed directly into functions in
the gfplot package, or can of course also be analyzed outside of
gfplot.
The SQL code called in the get_*() functions can be viewed
here:
https://github.com/pbs-assess/gfata/tree/master/inst/sql
How the various functions fit together:
https://github.com/pbs-assess/gfplot/blob/master/inst/function-web.pdf
Detailed information on the data extraction and the get_*()
functions can be found in:
Anderson, S.C., Keppel, E.A., Edwards, A.M.. “A reproducible data
synopsis for over 100 species of British Columbia groundfish”. DFO Can.
Sci. Advis. Sec. REs. Doc. 2019/nnn. iv + 327 p.
The complete list of get_*() functions in gfdata is:
#> [1] "get_active_survey_blocks" "get_age_methods" "get_age_precision"
#> [4] "get_all_stomachs" "get_all_survey_samples" "get_all_survey_sets"
#> [7] "get_catch" "get_catch_spatial" "get_comm_gear_types"
#> [10] "get_commercial_hooks_per_fe" "get_commercial_samples" "get_cpue_historical"
#> [13] "get_cpue_historical_hake" "get_cpue_historical_hl" "get_cpue_index"
#> [16] "get_cpue_index_hl" "get_cpue_spatial" "get_cpue_spatial_ll"
#> [19] "get_eulachon_specimens" "get_fishery_ids" "get_fishery_sectors"
#> [22] "get_gear_types" "get_hake_catch" "get_hake_survey_samples"
#> [25] "get_ll_hook_data" "get_major_areas" "get_management"
#> [28] "get_management_areas" "get_other_surveys" "get_parent_level_counts"
#> [31] "get_sable_landings" "get_sablefish_surveys" "get_sample_trips"
#> [34] "get_sensor_attributes" "get_sensor_data_ll_ctd" "get_sensor_data_ll_ctd_fe"
#> [37] "get_sensor_data_ll_td" "get_sensor_data_ll_td_fe" "get_sensor_data_trawl"
#> [40] "get_sensor_data_trawl_fe" "get_skate_level_counts" "get_species"
#> [43] "get_species_groups" "get_spp_sample_length_type" "get_ssids"
#> [46] "get_strata_areas" "get_survey_blocks" "get_survey_gear_types"
#> [49] "get_survey_ids" "get_survey_index" "get_survey_samples"
#> [52] "get_survey_sets" "get_survey_stomachs" "get_table"The get_*() functions extract data by species, and some
functions have arguments for additional filtering, such as survey
series, management area, years, gear type, or environmental data type.
In all cases, the get_*() functions can extract data for
one or multiple species.
All functions can be viewed with the available arguments in the help documentation for each set of functions with:
?get_data
?get_environmental_data
?get_lookup_tablesIn addition, a number of the get functions retain many
relevant database columns that users can filter on with, for example,
dplyr::filter(dat, x = "y").
Example
As an example, we could extract Pacific cod survey sample data with the following function call if we were on a DFO laptop, with appropriate database permissions, and on the PBS network.
dat <- get_survey_samples("pacific cod")#> All or majority of length measurements are Fork_Length#> Warning in get_survey_samples("pacific cod"): Duplicate specimen IDs are present because of
#> overlapping survey stratifications. If working with the data yourelf, filter them after selecting
#> specific surveys. For example, `dat <- dat[!duplicated(dat$specimen_id), ]`. The tidying and
#> plotting functions within gfplot will do this for you.
head(dat)#> # A tibble: 6 x 40
#> trip_start_date fishing_event_id year month gear survey_series_id survey_abbrev
#> <dttm> <dbl> <int> <int> <dbl> <dbl> <chr>
#> 1 2014-10-01 00:00:00 3420684 2014 10 5 76 DOG
#> 2 2019-10-01 00:00:00 5167333 2019 10 5 76 DOG
#> 3 2003-08-13 00:00:00 309491 2003 8 5 39 HBLL INS N
#> 4 2003-08-13 00:00:00 309493 2003 8 5 39 HBLL INS N
#> 5 2003-08-13 00:00:00 309503 2003 8 5 39 HBLL INS N
#> 6 2003-08-13 00:00:00 309506 2003 8 5 39 HBLL INS N
#> # i 33 more variables: survey_series_desc <chr>, survey_id <int>, major_stat_area_code <chr>,
#> # major_stat_area_name <chr>, minor_stat_area_code <chr>, species_code <chr>,
#> # species_common_name <chr>, species_science_name <chr>, specimen_id <dbl>, sample_id <dbl>,
#> # sex <dbl>, age_specimen_collected <int>, age <dbl>, sampling_desc <chr>,
#> # ageing_method_code <dbl>, length <dbl>, weight <int>, maturity_code <dbl>, maturity_name <chr>,
#> # maturity_desc <chr>, maturity_convention_code <dbl>, maturity_convention_desc <chr>,
#> # maturity_convention_maxvalue <dbl>, trip_sub_type_code <dbl>, sample_type_code <dbl>, ...Note that there are some duplicate records in the databases due to relating a record to multiple stratification schemes for alternative analyses. If this occurs, a warning is given.
“Duplicate specimen IDs are present because of overlapping survey stratifications. If working with the data yourelf, filter them after selecting specific surveys. For example,
dat <- dat[!duplicated(dat$specimen_id), ]. The tidying and plotting functions within gfplot will do this for you.”
Either species name or species code can be given as an argument, and species name, if used, is not case-sensitive. The following all do the same thing:
get_survey_samples("pacific cod")
get_survey_samples("Pacific cod")
get_survey_samples("PaCiFiC cOD")
get_survey_samples("222")
get_survey_samples(222)To extract multiple species at once, give a list as the species argument:
get_survey_samples(c("pacific ocean perch", "pacific cod"))
get_survey_samples(c(396, 222))
get_survey_samples(c(222, "pacific cod"))We can further restrict the data extraction to a single trawl survey
series by including the ssid (survey series id) argument. For a list of
survey series id codes, run the lookup function
get_ssids().
#> # A tibble: 6 x 3
#> SURVEY_SERIES_ID SURVEY_SERIES_DESC SURVEY_ABBREV
#> <dbl> <chr> <chr>
#> 1 0 Individual survey without a series OTHER
#> 2 1 Queen Charlotte Sound Synoptic Bottom Trawl SYN QCS
#> 3 2 Hecate Strait Multispecies Assemblage Bottom Trawl HS MSA
#> 4 3 Hecate Strait Synoptic Bottom Trawl SYN HS
#> 5 4 West Coast Vancouver Island Synoptic Bottom Trawl SYN WCVI
#> 6 5 Hecate Strait Pacific Cod Monitoring Bottom Trawl OTHERSelect desired ssid and include as argument (i.e. the Queen Charlotte Sound bottom trawl survey):
dat <- get_survey_samples(222, ssid = 1)#> All or majority of length measurements are Fork_Length
head(dat)#> # A tibble: 6 x 40
#> trip_start_date fishing_event_id year month gear survey_series_id survey_abbrev
#> <dttm> <dbl> <int> <int> <dbl> <dbl> <chr>
#> 1 2003-07-03 00:00:00 308673 2003 7 1 1 SYN QCS
#> 2 2003-07-03 00:00:00 308673 2003 7 1 1 SYN QCS
#> 3 2003-07-03 00:00:00 308673 2003 7 1 1 SYN QCS
#> 4 2003-07-03 00:00:00 308673 2003 7 1 1 SYN QCS
#> 5 2003-07-03 00:00:00 308673 2003 7 1 1 SYN QCS
#> 6 2003-07-03 00:00:00 308673 2003 7 1 1 SYN QCS
#> # i 33 more variables: survey_series_desc <chr>, survey_id <int>, major_stat_area_code <chr>,
#> # major_stat_area_name <chr>, minor_stat_area_code <chr>, species_code <chr>,
#> # species_common_name <chr>, species_science_name <chr>, specimen_id <dbl>, sample_id <dbl>,
#> # sex <dbl>, age_specimen_collected <int>, age <dbl>, sampling_desc <chr>,
#> # ageing_method_code <dbl>, length <dbl>, weight <int>, maturity_code <dbl>, maturity_name <chr>,
#> # maturity_desc <chr>, maturity_convention_code <dbl>, maturity_convention_desc <chr>,
#> # maturity_convention_maxvalue <dbl>, trip_sub_type_code <dbl>, sample_type_code <dbl>, ...
glimpse(dat)#> Rows: 11,509
#> Columns: 40
#> $ trip_start_date <dttm> 2003-07-03, 2003-07-03, 2003-07-03, 2003-07-03, 2003-07-03, ~
#> $ fishing_event_id <dbl> 308673, 308673, 308673, 308673, 308673, 308673, 308673, 30867~
#> $ year <int> 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2003, 2~
#> $ month <int> 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7~
#> $ gear <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
#> $ survey_series_id <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
#> $ survey_abbrev <chr> "SYN QCS", "SYN QCS", "SYN QCS", "SYN QCS", "SYN QCS", "SYN Q~
#> $ survey_series_desc <chr> "Queen Charlotte Sound Synoptic Bottom Trawl", "Queen Charlot~
#> $ survey_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
#> $ major_stat_area_code <chr> "05", "05", "05", "05", "05", "05", "05", "05", "05", "05", "~
#> $ major_stat_area_name <chr> "5A: SOUTHERN Q.C. SOUND", "5A: SOUTHERN Q.C. SOUND", "5A: SO~
#> $ minor_stat_area_code <chr> "11", "11", "11", "11", "11", "11", "11", "11", "11", "11", "~
#> $ species_code <chr> "222", "222", "222", "222", "222", "222", "222", "222", "222"~
#> $ species_common_name <chr> "pacific cod", "pacific cod", "pacific cod", "pacific cod", "~
#> $ species_science_name <chr> "gadus macrocephalus", "gadus macrocephalus", "gadus macrocep~
#> $ specimen_id <dbl> 7607986, 7607987, 7607988, 7607989, 7607990, 7607991, 7607992~
#> $ sample_id <dbl> 233551, 233551, 233551, 233551, 233551, 233551, 233551, 23355~
#> $ sex <dbl> 2, 2, 1, 1, 1, 1, 2, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2, 2, 2, 2, 2~
#> $ age_specimen_collected <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
#> $ age <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N~
#> $ sampling_desc <chr> "UNSORTED", "UNSORTED", "UNSORTED", "UNSORTED", "UNSORTED", "~
#> $ ageing_method_code <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N~
#> $ length <dbl> 21, 21, 23, 23, 23, 23, 23, 24, 24, 25, 26, 26, 27, 28, 28, 2~
#> $ weight <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N~
#> $ maturity_code <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
#> $ maturity_name <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N~
#> $ maturity_desc <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N~
#> $ maturity_convention_code <dbl> 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9~
#> $ maturity_convention_desc <chr> "MATURITIES NOT LOOKED AT", "MATURITIES NOT LOOKED AT", "MATU~
#> $ maturity_convention_maxvalue <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
#> $ trip_sub_type_code <dbl> 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3~
#> $ sample_type_code <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
#> $ species_category_code <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
#> $ sample_source_code <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
#> $ dna_sample_type <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N~
#> $ dna_container_id <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N~
#> $ usability_code <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1~
#> $ grouping_code <dbl> 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, 1~
#> $ length_type <chr> "Fork_Length", "Fork_Length", "Fork_Length", "Fork_Length", "~
#> $ species_ageing_group <chr> "pcod_lingcod", "pcod_lingcod", "pcod_lingcod", "pcod_lingcod~Caching the data from the SQL servers
In addition to the individual get_*() functions, there
is a function cache_pbs_data() that runs all the
get_*() functions and caches the data in a folder that you
specify. This is useful to be able to have the data available for
working on later when not on the PBS network, and it saves running the
SQL queries (though the data do get updated occassionally and the most
up-to-date data should usually be extracted for analysis).
The helper function cache_pbs_data() will extract all of
the data for the given species into a series of .rds files
into whatever folder you specify to the path argument. I’ll
wrap it in a quick check just to make sure we don’t download the data
twice if we build this document again.
cache_pbs_data("pacific cod", path = "pcod-cache")#> Extracting data for#> Extracting survey samples#> All or majority of length measurements are Fork_Length#> Warning in get_survey_samples(this_sp): Duplicate specimen IDs are present because of overlapping
#> survey stratifications. If working with the data yourelf, filter them after selecting specific
#> surveys. For example, `dat <- dat[!duplicated(dat$specimen_id), ]`. The tidying and plotting
#> functions within gfplot will do this for you.#> Extracting commercial samples#> All or majority of length measurements are Fork_Length#> Extracting catch#> Extracting spatial CPUE#> Extracting spatial LL CPUE#> Extracting spatial catch#> Extracting survey indexes#> Extracting aging precision#> All data extracted and saved in the folder `pcod-cache`.And to call the list of output files:
#> $survey_samples
#> # A tibble: 77,134 x 40
#> trip_start_date fishing_event_id year month gear survey_series_id survey_abbrev
#> <dttm> <dbl> <int> <int> <dbl> <dbl> <chr>
#> 1 2014-10-01 00:00:00 3420684 2014 10 5 76 DOG
#> 2 2019-10-01 00:00:00 5167333 2019 10 5 76 DOG
#> 3 2003-08-13 00:00:00 309491 2003 8 5 39 HBLL INS N
#> 4 2003-08-13 00:00:00 309493 2003 8 5 39 HBLL INS N
#> 5 2003-08-13 00:00:00 309503 2003 8 5 39 HBLL INS N
#> 6 2003-08-13 00:00:00 309506 2003 8 5 39 HBLL INS N
#> 7 2003-08-13 00:00:00 309516 2003 8 5 39 HBLL INS N
#> 8 2003-08-13 00:00:00 309517 2003 8 5 39 HBLL INS N
#> 9 2003-08-13 00:00:00 309524 2003 8 5 39 HBLL INS N
#> 10 2003-08-13 00:00:00 309478 2003 8 5 39 HBLL INS N
#> # i 77,124 more rows
#> # i 33 more variables: survey_series_desc <chr>, survey_id <int>, major_stat_area_code <chr>,
#> # major_stat_area_name <chr>, minor_stat_area_code <chr>, species_code <chr>,
#> # species_common_name <chr>, species_science_name <chr>, specimen_id <dbl>, sample_id <dbl>,
#> # sex <dbl>, age_specimen_collected <int>, age <dbl>, sampling_desc <chr>,
#> # ageing_method_code <dbl>, length <dbl>, weight <int>, maturity_code <dbl>, maturity_name <chr>,
#> # maturity_desc <chr>, maturity_convention_code <dbl>, maturity_convention_desc <chr>, ...
#>
#> $commercial_samples
#> # A tibble: 155,282 x 54
#> trip_start_date trip_end_date trip_year year month day time_deployed
#> <dttm> <dttm> <int> <dbl> <int> <int> <dttm>
#> 1 1962-04-19 00:00:00 1962-04-30 00:00:00 1962 1962 4 2 NA
#> 2 1962-04-19 00:00:00 1962-04-30 00:00:00 1962 1962 4 2 NA
#> 3 1962-04-19 00:00:00 1962-04-30 00:00:00 1962 1962 4 2 NA
#> 4 1962-04-19 00:00:00 1962-04-30 00:00:00 1962 1962 4 2 NA
#> 5 1962-04-19 00:00:00 1962-04-30 00:00:00 1962 1962 4 2 NA
#> 6 1962-04-19 00:00:00 1962-04-30 00:00:00 1962 1962 4 2 NA
#> 7 1962-04-19 00:00:00 1962-04-30 00:00:00 1962 1962 4 2 NA
#> 8 1962-04-19 00:00:00 1962-04-30 00:00:00 1962 1962 4 2 NA
#> 9 1962-04-19 00:00:00 1962-04-30 00:00:00 1962 1962 4 2 NA
#> 10 1962-04-19 00:00:00 1962-04-30 00:00:00 1962 1962 4 2 NA
#> # i 155,272 more rows
#> # i 47 more variables: time_retrieved <dttm>, trip_id <dbl>, fishing_event_id <dbl>,
#> # latitude <dbl>, lat_start <dbl>, lat_end <dbl>, longitude <dbl>, lon_start <dbl>,
#> # lon_end <dbl>, best_depth <dbl>, gear_code <dbl>, gear_desc <chr>, species_code <chr>,
#> # species_common_name <chr>, species_science_name <chr>, sample_id <dbl>, specimen_id <dbl>,
#> # sex <dbl>, age_specimen_collected <int>, age <dbl>, ageing_method_code <dbl>, length <dbl>,
#> # weight <int>, maturity_code <dbl>, maturity_convention_code <dbl>, ...
#>
#> $catch
#> # A tibble: 333,430 x 27
#> database_name trip_id fishing_event_id fishery_sector trip_category gear best_date
#> <chr> <chr> <chr> <chr> <chr> <chr> <dttm>
#> 1 GFCatch 54000002 1 GROUNDFISH TRAWL <NA> BOTTO~ 1954-01-04 00:00:00
#> 2 GFCatch 54000054 1 GROUNDFISH TRAWL <NA> BOTTO~ 1954-01-04 00:00:00
#> 3 GFCatch 54000001 1 GROUNDFISH TRAWL <NA> BOTTO~ 1954-01-04 00:00:00
#> 4 GFCatch 54000055 1 GROUNDFISH TRAWL <NA> BOTTO~ 1954-01-04 00:00:00
#> 5 GFCatch 54000034 1 GROUNDFISH TRAWL <NA> BOTTO~ 1954-01-05 00:00:00
#> 6 GFCatch 54000003 1 GROUNDFISH TRAWL <NA> BOTTO~ 1954-01-07 00:00:00
#> 7 GFCatch 54000036 4 GROUNDFISH TRAWL <NA> BOTTO~ 1954-01-07 00:00:00
#> 8 GFCatch 54000061 1 GROUNDFISH TRAWL <NA> BOTTO~ 1954-01-07 00:00:00
#> 9 GFCatch 54000036 3 GROUNDFISH TRAWL <NA> BOTTO~ 1954-01-07 00:00:00
#> 10 GFCatch 54000037 1 GROUNDFISH TRAWL <NA> BOTTO~ 1954-01-07 00:00:00
#> # i 333,420 more rows
#> # i 20 more variables: fe_start_date <dttm>, fe_end_date <dttm>, lat <dbl>, lon <dbl>,
#> # best_depth <int>, species_code <chr>, dfo_stat_area_code <chr>, dfo_stat_subarea_code <int>,
#> # species_scientific_name <chr>, species_common_name <chr>, landed_kg <dbl>, discarded_kg <dbl>,
#> # landed_pcs <int>, discarded_pcs <int>, major_stat_area_code <chr>, minor_stat_area_code <chr>,
#> # major_stat_area_name <chr>, vessel_name <chr>, vessel_registration_number <chr>, year <dbl>
#>
#> $cpue_spatial
#> # A tibble: 77,392 x 11
#> year best_date major_stat_area_code trip_id fishing_event_id lat lon
#> <int> <dttm> <chr> <int> <int> <dbl> <dbl>
#> 1 2007 2007-05-08 09:50:00 06 83395 732412 51.4 -129.
#> 2 2007 2007-05-08 11:15:00 06 83395 732415 51.4 -129.
#> 3 2007 2007-05-07 16:30:00 06 83395 732418 51.3 -129.
#> 4 2007 2007-05-08 19:55:00 06 83395 732419 51.3 -129.
#> 5 2007 2007-05-07 09:30:00 07 83739 736980 52.4 -130.
#> 6 2007 2007-05-11 08:07:00 05 83739 736990 50.8 -129.
#> 7 2007 2007-05-08 17:26:00 08 83916 726785 54.5 -131.
#> 8 2007 2007-05-07 13:17:00 08 83916 726787 54.5 -131.
#> 9 2007 2007-05-08 11:14:00 08 83916 726788 54.5 -131.
#> 10 2007 2007-05-08 07:15:00 08 83916 726790 54.5 -131.
#> # i 77,382 more rows
#> # i 4 more variables: vessel_registration_number <int>, species_scientific_name <chr>,
#> # species_common_name <chr>, cpue <dbl>
#>
#> $cpue_spatial_ll
#> # A tibble: 8,899 x 16
#> year best_date fishery_sector vessel_registration_num~1 gear trip_id fishing_event_id
#> <int> <dttm> <chr> <int> <chr> <int> <int>
#> 1 2006 2006-03-12 12:00:00 halibut 30850 long~ 60792 512243
#> 2 2006 2006-03-06 09:30:00 halibut 22452 long~ 60809 512797
#> 3 2006 2006-03-07 10:10:00 halibut 22452 long~ 60809 512802
#> 4 2006 2006-03-07 12:00:00 halibut 22452 long~ 60809 512803
#> 5 2006 2006-03-09 20:00:00 halibut 22452 long~ 60809 512806
#> 6 2006 2006-03-10 10:45:00 halibut 23703 long~ 60944 511671
#> 7 2006 2006-03-11 14:45:00 halibut 23703 long~ 60944 511683
#> 8 2006 2006-03-12 09:15:00 halibut 23703 long~ 60944 511701
#> 9 2006 2006-03-12 17:00:00 halibut 23703 long~ 60944 511705
#> 10 2006 2006-03-13 23:59:00 halibut 22452 long~ 61099 513046
#> # i 8,889 more rows
#> # i abbreviated name: 1: vessel_registration_number
#> # i 9 more variables: lat <dbl>, lon <dbl>, species_code <chr>, species_scientific_name <chr>,
#> # species_common_name <chr>, landed_round_kg <dbl>, cpue <int>, total_released_pcs <int>,
#> # major_stat_area_code <chr>
#>
#> $catch_spatial
#> # A tibble: 77,392 x 11
#> year best_date major_stat_area_code trip_id fishing_event_id lat lon
#> <int> <dttm> <chr> <int> <int> <dbl> <dbl>
#> 1 2007 2007-12-28 14:25:00 01 99701 881231 48.9 -123.
#> 2 2007 2007-12-28 16:23:00 01 99701 881232 48.9 -123.
#> 3 2007 2007-12-28 18:13:00 01 99701 881233 48.9 -123.
#> 4 2007 2007-12-28 20:10:00 01 99701 881234 48.9 -123.
#> 5 2007 2007-12-29 08:20:00 01 99701 881235 48.9 -123.
#> 6 2007 2007-12-29 10:01:00 01 99701 881236 48.9 -123.
#> 7 2007 2007-12-28 08:35:00 01 99705 881398 49.3 -123.
#> 8 2007 2007-12-29 15:01:00 01 99705 881402 49.2 -123.
#> 9 2007 2007-12-29 10:48:00 01 99711 881395 49.3 -123.
#> 10 2007 2007-04-05 17:50:00 01 82028 880764 48.9 -123.
#> # i 77,382 more rows
#> # i 4 more variables: vessel_registration_number <int>, species_scientific_name <chr>,
#> # species_common_name <chr>, catch <dbl>And to call one object/dataframe (i.e. our survey sample data) from the list:
dat <- dat$survey_samples
head(dat)#> # A tibble: 6 x 40
#> trip_start_date fishing_event_id year month gear survey_series_id survey_abbrev
#> <dttm> <dbl> <int> <int> <dbl> <dbl> <chr>
#> 1 2014-10-01 00:00:00 3420684 2014 10 5 76 DOG
#> 2 2019-10-01 00:00:00 5167333 2019 10 5 76 DOG
#> 3 2003-08-13 00:00:00 309491 2003 8 5 39 HBLL INS N
#> 4 2003-08-13 00:00:00 309493 2003 8 5 39 HBLL INS N
#> 5 2003-08-13 00:00:00 309503 2003 8 5 39 HBLL INS N
#> 6 2003-08-13 00:00:00 309506 2003 8 5 39 HBLL INS N
#> # i 33 more variables: survey_series_desc <chr>, survey_id <int>, major_stat_area_code <chr>,
#> # major_stat_area_name <chr>, minor_stat_area_code <chr>, species_code <chr>,
#> # species_common_name <chr>, species_science_name <chr>, specimen_id <dbl>, sample_id <dbl>,
#> # sex <dbl>, age_specimen_collected <int>, age <dbl>, sampling_desc <chr>,
#> # ageing_method_code <dbl>, length <dbl>, weight <int>, maturity_code <dbl>, maturity_name <chr>,
#> # maturity_desc <chr>, maturity_convention_code <dbl>, maturity_convention_desc <chr>,
#> # maturity_convention_maxvalue <dbl>, trip_sub_type_code <dbl>, sample_type_code <dbl>, ...