README.md
In AdamCottrill/GLFishStockR: Fetch Data From The Great Lakes Fish Stocking API
output: github_document
GLFishStockR
The package provides a number of low level functions that fetch data
from the Great Lakes fish stocking database for further analayis
and reporting in R.
Example
GLFishStocker provides a number of functions that can be use to fetch
data from the Great Lakes Fish Stocking Database API (www.fsis.glfc.org)
The functions in GLFishSocker expose methods that allow us to easily
get data into R, including the lakes, contributing agencies, and
associated management/spatial units.
library(GLFishStockR)
## basic example code
get_lakes()
#> abbrev lake_name
#> 1 ER Lake Erie
#> 2 HU Lake Huron
#> 3 MI Lake Michigan
#> 4 ON Lake Ontario
#> 5 SC Lake St. Clair
#> 6 SU Lake Superior
agencies <- get_agencies()
head(agencies)
#> abbrev agency_name
#> 1 CORA Chippewa-Ottawa Resource Authority, includes ITFAP
#> 2 CORA-LTBB ODAWA CORA-LTBB ODAWA
#> 3 GLFC Great Lakes Fisheries Commission
#> 4 ILDNR Illinois Department of Natural Resources
#> 5 INDNR Indiana Department of Natural Resources
#> 6 KBB Keweenaw Bay Band, Chippewas
mus <- get_management_units()
head(mus)
#> id label description mu_type slug primary lake.abbrev
#> 1 75 4-1 main basin Ontario Waters qma hu_qma_4-1 FALSE HU
#> 2 76 4-2 main basin Ontario Waters qma hu_qma_4-2 FALSE HU
#> 3 77 4-3 main basin Ontario Waters qma hu_qma_4-3 FALSE HU
#> 4 78 4-5 main basin Ontario Waters qma hu_qma_4-5 FALSE HU
#> 5 79 5-1 Georgian Bay Ontario Waters qma hu_qma_5-1 FALSE HU
#> 6 80 5-2 Georgian Bay Ontario Waters qma hu_qma_5-2 FALSE HU
#> lake.lake_name
#> 1 Lake Huron
#> 2 Lake Huron
#> 3 Lake Huron
#> 4 Lake Huron
#> 5 Lake Huron
#> 6 Lake Huron
filters <- list(
"lake" = "MI"
)
mus <- get_management_units(filters)
head(mus)
#> id label description mu_type slug primary lake.abbrev
#> 1 5 ILL Illinois waters stat_dist mi_stat_dist_ill TRUE MI
#> 2 6 IND Indiana waters stat_dist mi_stat_dist_ind TRUE MI
#> 3 17 MM1 Michigan waters stat_dist mi_stat_dist_mm1 TRUE MI
#> 4 18 MM2 Michigan waters stat_dist mi_stat_dist_mm2 TRUE MI
#> 5 19 MM3 Michigan waters stat_dist mi_stat_dist_mm3 TRUE MI
#> 6 21 MM4 Michigan waters stat_dist mi_stat_dist_mm4 TRUE MI
#> lake.lake_name
#> 1 Lake Michigan
#> 2 Lake Michigan
#> 3 Lake Michigan
#> 4 Lake Michigan
#> 5 Lake Michigan
#> 6 Lake Michigan
jurisdictions <- get_jurisdictions()
#> Warning in refresh_filters(endpoint): Filters could not be found for the
#> 'jursidiction' endpoint
head(jurisdictions)
#> id slug name description lake.abbrev
#> 1 28 er_mi Erie-Michigan Michigan waters of Lake Erie ER
#> 2 32 er_ny Erie-New York New York waters of Lake Erie ER
#> 3 33 er_oh Erie-Ohio Ohio waters of Lake Erie ER
#> 4 36 er_on Erie-Ontario Ontario waters of Lake Erie ER
#> 5 39 er_pa Erie-Pennsylvania Pennsylvania waters of Lake Erie ER
#> 6 27 hu_mi Huron-Michigan Michigan waters of Lake Huron HU
#> lake.lake_name stateprov.id stateprov.abbrev stateprov.name stateprov.country
#> 1 Lake Erie 3 MI Michigan USA
#> 2 Lake Erie 5 NY New York USA
#> 3 Lake Erie 6 OH Ohio USA
#> 4 Lake Erie 7 ON Ontario CAN
#> 5 Lake Erie 8 PA Pennsylvania USA
#> 6 Lake Huron 3 MI Michigan USA
#> stateprov.description
#> 1
#> 2
#> 3
#> 4
#> 5
#> 6
filters <- list(
"lake" = "MI"
)
jurisdictions <- get_jurisdictions(filters)
#> Warning in refresh_filters(endpoint): Filters could not be found for the
#> 'jursidiction' endpoint
#> Warning in check_filters("jursidiction", filter_list): Unknown filters provided. These will be ignored:
#> + lake
head(jurisdictions)
#> id slug name description lake.abbrev
#> 1 23 mi_il Michigan-Illinois Illinois waters of Lake Michigan MI
#> 2 24 mi_in Michigan-Indiana Indiana waters of Lake Michigan MI
#> 3 25 mi_mi Michigan-Michigan Michigan waters of Lake Michigan MI
#> 4 41 mi_wi Michigan-Wisconsin Wisconsin waters of Lake Michigan MI
#> lake.lake_name stateprov.id stateprov.abbrev stateprov.name stateprov.country
#> 1 Lake Michigan 1 IL Illinois USA
#> 2 Lake Michigan 2 IN Indiana USA
#> 3 Lake Michigan 3 MI Michigan USA
#> 4 Lake Michigan 9 WI Wisconsin USA
#> stateprov.description
#> 1
#> 2
#> 3
#> 4
get_state_provinces()
#> id abbrev name country description
#> 1 1 IL Illinois USA
#> 2 2 IN Indiana USA
#> 3 3 MI Michigan USA
#> 4 4 MN Minnesota USA
#> 5 5 NY New York USA
#> 6 6 OH Ohio USA
#> 7 7 ON Ontario CAN
#> 8 8 PA Pennsylvania USA
#> 9 9 WI Wisconsin USA
get_state_provinces(list("country" = "USA"))
#> id abbrev name country description
#> 1 1 IL Illinois USA
#> 2 2 IN Indiana USA
#> 3 3 MI Michigan USA
#> 4 4 MN Minnesota USA
#> 5 5 NY New York USA
#> 6 6 OH Ohio USA
#> 7 8 PA Pennsylvania USA
#> 8 9 WI Wisconsin USA
head(get_grid10s(filters))
#> id grid centroid slug
#> 1 722 1001 SRID=4326;POINT (-87.92416166819176 44.6130719635301) mi_1001
#> 2 723 1004 SRID=4326;POINT (-87.3926502376415 44.56850774809312) mi_1004
#> 3 724 1005 SRID=4326;POINT (-87.25000526031992 44.58333333933837) mi_1005
#> 4 725 1006 SRID=4326;POINT (-87.08333732407132 44.58333334704986) mi_1006
#> 5 726 1007 SRID=4326;POINT (-86.91667320247747 44.58333335476073) mi_1007
#> 6 727 1008 SRID=4326;POINT (-86.75000526614392 44.58333336247123) mi_1008
#> lake.abbrev lake.lake_name
#> 1 MI Lake Michigan
#> 2 MI Lake Michigan
#> 3 MI Lake Michigan
#> 4 MI Lake Michigan
#> 5 MI Lake Michigan
#> 6 MI Lake Michigan
Most of the functions accept a list of filters that can be used to
select the data that is returned from the Great Lakes Stocking
Database. The list forms a key-value pair that specified the filter
to be applied and the value of the filter.
A helper function ~show_filters()~ is included in the package that
will display the names of the filterable endpoints, if no endpoint is
provided, and provide all of the available filters if an valid
endpoint is supplied:
show_filters()
#> An endpoint name needs to be provided. Currently avaliable endpoint names are:
#> [1] "grid10" "jurisdiction" "management_unit"
#> [4] "mark" "state_province" "strain"
#> [7] "strainraw" "stocking_events" "get_cwt_events"
#> [10] "yearling_equivalent"
show_filters("strain")
#> name description
#> 1 strain_code
#> 2 strain_species
#> 3 species
show_filters("stocking_events", filter_like="clip")
#> name description
#> 39 clip_code Multiple values may be separated by commas.
#> 40 finclips Multiple values may be separated by commas.
We can get all of the species, strains and raw (detailed) strains too:
species <- get_species()
head(species)
#> abbrev common_name scientific_name species_code speciescommon
#> 1 ATS Atlantic Salmon Salmo salar 77 1230100401
#> 2 BKT Brook Trout Salvelinus fontinalis 80 1230101002
#> 3 BLG Bluegill Lepomis macrochirus 314 1701001302
#> 4 BLO Bloater Coregonus hoyi 94 1231200122
#> 5 BNT Brown Trout Salmo trutta 78 1230100402
#> 6 CHS Chinook Salmon Oncorhynchus tshawytscha 75 1230100907
strains <- get_strains()
head(strains)
#> id strain_code strain_label slug strain_species.abbrev
#> 1 180 GDL Grand Lake - ME ats-gdl ATS
#> 2 151 GL Green Lake Atlantic Salmon ats-gl ATS
#> 3 1 GU Gullspang ats-gu ATS
#> 4 202 LCH Lake Champlain lat-lch LAT
#> 5 152 LCL Little Clear Lake ats-lcl ATS
#> 6 181 LHR Lahave River ats-lhr ATS
#> strain_species.common_name strain_species.scientific_name
#> 1 Atlantic Salmon Salmo salar
#> 2 Atlantic Salmon Salmo salar
#> 3 Atlantic Salmon Salmo salar
#> 4 Lake Trout Salvelinus namaycush
#> 5 Atlantic Salmon Salmo salar
#> 6 Atlantic Salmon Salmo salar
#> strain_species.species_code strain_species.speciescommon
#> 1 77 1230100401
#> 2 77 1230100401
#> 3 77 1230100401
#> 4 81 1230101098
#> 5 77 1230100401
#> 6 77 1230100401
raw_strains <- get_raw_strains()
head(raw_strains)
#> id raw_strain description species.abbrev species.common_name
#> 1 582 Atlantic Salmon Unknown ATS Atlantic Salmon
#> 2 527 GDL Grand Lake - ME ATS Atlantic Salmon
#> 3 620 GL Green Lake ATS Atlantic Salmon
#> 4 4 GLW Undocumented ATS Atlantic Salmon
#> 5 599 GRD Grand Lake ATS Atlantic Salmon
#> 6 621 GRD,PEN Unknown ATS Atlantic Salmon
#> species.scientific_name species.species_code species.speciescommon strain.id
#> 1 Salmo salar 77 1230100401 5
#> 2 Salmo salar 77 1230100401 180
#> 3 Salmo salar 77 1230100401 151
#> 4 Salmo salar 77 1230100401 5
#> 5 Salmo salar 77 1230100401 180
#> 6 Salmo salar 77 1230100401 5
#> strain.strain_code strain.strain_label strain.slug
#> 1 UNKN Unknown ats-unkn
#> 2 GDL Grand Lake - ME ats-gdl
#> 3 GL Green Lake Atlantic Salmon ats-gl
#> 4 UNKN Unknown ats-unkn
#> 5 GDL Grand Lake - ME ats-gdl
#> 6 UNKN Unknown ats-unkn
We can fetch all of the values used for lifestages, marks, and yearling equivalent factors:
lifestages <- get_lifestages()
head(lifestages)
#> abbrev description
#> 1 a age-2 or older
#> 2 e egg
#> 3 f fingerling, age-0
#> 4 ff fall fingerling, age-0
#> 5 fry fry, age-0
#> 6 sf spring fingerling, age-0
marks <- get_marks()
head(marks)
#> clip_code mark_code mark_type description
#> 1 X OX chemical Oxytetracyclene
#> 2 5 AD finclip Adipose Fin Clip
#> 3 7 DO finclip Anterior Dorsal Fin Clip
#> 4 F LM finclip Left Maxilla Fin Clip
#> 5 2 LP finclip Left Pectoral Fin Clip
#> 6 4 LV finclip Left Ventral Fin Clip
yreq <- get_yreq_factors()
head(yreq)
#> yreq_factor species.abbrev species.common_name lifestage.abbrev
#> 1 1.000 ATS Atlantic Salmon a
#> 2 0.001 ATS Atlantic Salmon e
#> 3 0.100 ATS Atlantic Salmon f
#> 4 0.100 ATS Atlantic Salmon ff
#> 5 0.010 ATS Atlantic Salmon fry
#> 6 0.100 ATS Atlantic Salmon sf
#> lifestage.description
#> 1 age-2 or older
#> 2 egg
#> 3 fingerling, age-0
#> 4 fall fingerling, age-0
#> 5 fry, age-0
#> 6 spring fingerling, age-0
# we can get them for a single species or lifestage too
get_yreq_factors(list(species = "LAT"))
#> yreq_factor species.abbrev species.common_name lifestage.abbrev
#> 1 1.000 LAT Lake Trout a
#> 2 0.001 LAT Lake Trout e
#> 3 0.100 LAT Lake Trout f
#> 4 0.010 LAT Lake Trout fry
#> 5 0.100 LAT Lake Trout sf
#> 6 1.000 LAT Lake Trout u
#> 7 1.000 LAT Lake Trout y
#> 8 0.330 LAT Lake Trout ff
#> 9 0.100 LAT Lake Trout suf
#> lifestage.description
#> 1 age-2 or older
#> 2 egg
#> 3 fingerling, age-0
#> 4 fry, age-0
#> 5 spring fingerling, age-0
#> 6 unknown
#> 7 yearling, age-1
#> 8 fall fingerling, age-0
#> 9 summer fingerling, age-0
We can get stocking events based on a large number of attributes
including (but not limited to lake, species, strain, agency,
lifestage, stocking method, jursidiction, managment unit, or year.
filters <- list(
"lake" = "HU",
"species" = c("RBT", "LAT", "BNT"),
"first_year" = 2012
)
events <- get_stocking_events(filters)
nrow(events)
#> [1] 1613
head(events)
#> stock_id day month year site st_site
#> 1 20220075 15 4 2021 Lexington Harbor Lexington Harbor
#> 2 20220252 18 5 2021 Detour
#> 3 20220253 18 5 2021 Detour
#> 4 20220254 23 4 2021 Bois Blanc Reef Summit
#> 5 20220255 24 4 2021 Adams Point
#> 6 20220256 23 4 2021 Bois Blanc Reef Summit
#> geom no_stocked year_class
#> 1 SRID=4326;POINT (-82.52659482999999 43.26750588) 30000 2020
#> 2 SRID=4326;POINT (-83.90000000000001 45.8669) 40010 2020
#> 3 SRID=4326;POINT (-83.90000000000001 45.8669) 39982 2020
#> 4 SRID=4326;POINT (-84.4181 45.8177) 68009 2020
#> 5 SRID=4326;POINT (-83.65000000000001 45.5) 69385 2020
#> 6 SRID=4326;POINT (-84.4181 45.8177) 69538 2020
#> agemonth tag_no mark yreq_stocked agency.abbrev
#> 1 15 <NA> NA 30000 MIDNR
#> 2 17 641293 NA 40010 USFWS
#> 3 17 641341 NA 39982 USFWS
#> 4 16 641319 NA 68009 USFWS
#> 5 16 641321 NA 69385 USFWS
#> 6 16 641315 NA 69538 USFWS
#> agency.agency_name condition.condition
#> 1 Michigan Department of Natural Resources 1
#> 2 U.S. Fish and Wildlife Service 1
#> 3 U.S. Fish and Wildlife Service 1
#> 4 U.S. Fish and Wildlife Service 1
#> 5 U.S. Fish and Wildlife Service 1
#> 6 U.S. Fish and Wildlife Service 1
#> condition.description grid_10.id grid_10.grid
#> 1 <1% mortality observed, "excellent" 126 1915
#> 2 <1% mortality observed, "excellent" 156 306
#> 3 <1% mortality observed, "excellent" 156 306
#> 4 <1% mortality observed, "excellent" 178 403
#> 5 <1% mortality observed, "excellent" 226 608
#> 6 <1% mortality observed, "excellent" 178 403
#> grid_10.centroid grid_10.slug
#> 1 SRID=4326;POINT (-82.42790326846431 43.25209597110957) hu_1915
#> 2 SRID=4326;POINT (-83.91524345779918 45.90396859168997) hu_306
#> 3 SRID=4326;POINT (-83.91524345779918 45.90396859168997) hu_306
#> 4 SRID=4326;POINT (-84.40808691659738 45.73980324819702) hu_403
#> 5 SRID=4326;POINT (-83.58011249322665 45.42859111583507) hu_608
#> 6 SRID=4326;POINT (-84.40808691659738 45.73980324819702) hu_403
#> grid_10.lake.abbrev grid_10.lake.lake_name latlong_flag.value
#> 1 HU Lake Huron 1
#> 2 HU Lake Huron 1
#> 3 HU Lake Huron 1
#> 4 HU Lake Huron 1
#> 5 HU Lake Huron 1
#> 6 HU Lake Huron 1
#> latlong_flag.description lifestage.abbrev lifestage.description
#> 1 Reported y yearling, age-1
#> 2 Reported y yearling, age-1
#> 3 Reported y yearling, age-1
#> 4 Reported y yearling, age-1
#> 5 Reported y yearling, age-1
#> 6 Reported y yearling, age-1
#> species.abbrev species.common_name species.scientific_name
#> 1 RBT Rainbow Trout Oncorhynchus mykiss
#> 2 LAT Lake Trout Salvelinus namaycush
#> 3 LAT Lake Trout Salvelinus namaycush
#> 4 LAT Lake Trout Salvelinus namaycush
#> 5 LAT Lake Trout Salvelinus namaycush
#> 6 LAT Lake Trout Salvelinus namaycush
#> species.species_code species.speciescommon stocking_method.stk_meth
#> 1 76 1230100909 s
#> 2 81 1230101098 b
#> 3 81 1230101098 b
#> 4 81 1230101098 b
#> 5 81 1230101098 b
#> 6 81 1230101098 b
#> stocking_method.description jurisdiction.id jurisdiction.slug
#> 1 shore stocking (truck, atv, snowmobile) 27 hu_mi
#> 2 boat, offshore stocking 27 hu_mi
#> 3 boat, offshore stocking 27 hu_mi
#> 4 boat, offshore stocking 27 hu_mi
#> 5 boat, offshore stocking 27 hu_mi
#> 6 boat, offshore stocking 27 hu_mi
#> jurisdiction.name jurisdiction.description jurisdiction.lake.abbrev
#> 1 Huron-Michigan Michigan waters of Lake Huron HU
#> 2 Huron-Michigan Michigan waters of Lake Huron HU
#> 3 Huron-Michigan Michigan waters of Lake Huron HU
#> 4 Huron-Michigan Michigan waters of Lake Huron HU
#> 5 Huron-Michigan Michigan waters of Lake Huron HU
#> 6 Huron-Michigan Michigan waters of Lake Huron HU
#> jurisdiction.lake.lake_name jurisdiction.stateprov.id
#> 1 Lake Huron 3
#> 2 Lake Huron 3
#> 3 Lake Huron 3
#> 4 Lake Huron 3
#> 5 Lake Huron 3
#> 6 Lake Huron 3
#> jurisdiction.stateprov.abbrev jurisdiction.stateprov.name
#> 1 MI Michigan
#> 2 MI Michigan
#> 3 MI Michigan
#> 4 MI Michigan
#> 5 MI Michigan
#> 6 MI Michigan
#> jurisdiction.stateprov.country jurisdiction.stateprov.description
#> 1 USA
#> 2 USA
#> 3 USA
#> 4 USA
#> 5 USA
#> 6 USA
We can also find stocking events that were associatd with specific coded
wire tags, tag attriutes, or attributes of the stocked fish or stocking
event.
filters <- list(
cwt_number = "630157"
)
cwt_events <- get_cwt_events(filters)
head(cwt_events)
#> cwt_number tag_type seq_lower seq_upper manufacturer tag_reused
#> 1 630157 sequential 15285 15810 nmt True
#> 2 630157 sequential 15820 16380 nmt True
#> 3 630157 sequential 14014 14621 nmt True
#> 4 630157 sequential 16385 17699 nmt True
#> 5 630157 sequential 14629 15280 nmt True
#> 6 630157 sequential 11740 12800 nmt True
#> multiple_lakes multiple_species multiple_strains multiple_yearclasses
#> 1 False False True True
#> 2 False False True True
#> 3 False False True True
#> 4 False False True True
#> 5 False False True True
#> 6 False False True True
#> multiple_agencies stock_id agency_stock_id agency_code lake state jurisd
#> 1 False 20196154 12683 OMNR HU ON hu_on
#> 2 False 20196155 12684 OMNR HU ON hu_on
#> 3 False 20196156 12687 OMNR HU ON hu_on
#> 4 False 20196157 12691 OMNR HU ON hu_on
#> 5 False 20196158 12695 OMNR HU ON hu_on
#> 6 False 20196159 12696 OMNR HU ON hu_on
#> man_unit grid10 primary_location secondary_location latitude longitude year
#> 1 GB4 1124 Owen Sound Owen Sound East Wall 44.5789 -80.93954 2006
#> 2 GB4 1124 Owen Sound Owen Sound East Wall 44.5789 -80.93954 2006
#> 3 GB4 1124 Owen Sound Owen Sound East Wall 44.5789 -80.93954 2006
#> 4 GB4 1124 Owen Sound Os Harbour-Goodyear 44.6016 -80.9208 2006
#> 5 GB4 1124 Owen Sound Os Harbour-Goodyear 44.6016 -80.9208 2006
#> 6 GB4 1124 Owen Sound Owen Sound East Wall 44.5789 -80.93954 2006
#> month day spc strain year_class mark clipcode stage
#> 1 2 10 LAT Seneca Lake 2001 NA AD age-2 or older
#> 2 2 10 LAT Seneca Lake 2002 NA AD age-2 or older
#> 3 2 10 LAT Seneca Lake 2003 NA AD age-2 or older
#> 4 2 27 LAT Huron - Iroquois Bay 2001 NA AD age-2 or older
#> 5 2 27 LAT Huron - Iroquois Bay 2002 NA AD age-2 or older
#> 6 1 6 LAT Superior - Slate Island 2004 NA AD age-2 or older
#> method no_stocked event_tag_numbers
#> 1 unknown 267 630157
#> 2 unknown 311 630157
#> 3 unknown 358 630157
#> 4 unknown 402 630157
#> 5 unknown 279 630157
#> 6 unknown 638 630157
AdamCottrill/GLFishStockR documentation built on Sept. 3, 2022, 10:26 p.m.
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output: github_document
GLFishStockR
The package provides a number of low level functions that fetch data from the Great Lakes fish stocking database for further analayis and reporting in R.
Example
GLFishStocker provides a number of functions that can be use to fetch data from the Great Lakes Fish Stocking Database API (www.fsis.glfc.org)
The functions in GLFishSocker expose methods that allow us to easily get data into R, including the lakes, contributing agencies, and associated management/spatial units.
library(GLFishStockR)
## basic example code
get_lakes()
#> abbrev lake_name
#> 1 ER Lake Erie
#> 2 HU Lake Huron
#> 3 MI Lake Michigan
#> 4 ON Lake Ontario
#> 5 SC Lake St. Clair
#> 6 SU Lake Superior
agencies <- get_agencies()
head(agencies)
#> abbrev agency_name
#> 1 CORA Chippewa-Ottawa Resource Authority, includes ITFAP
#> 2 CORA-LTBB ODAWA CORA-LTBB ODAWA
#> 3 GLFC Great Lakes Fisheries Commission
#> 4 ILDNR Illinois Department of Natural Resources
#> 5 INDNR Indiana Department of Natural Resources
#> 6 KBB Keweenaw Bay Band, Chippewas
mus <- get_management_units()
head(mus)
#> id label description mu_type slug primary lake.abbrev
#> 1 75 4-1 main basin Ontario Waters qma hu_qma_4-1 FALSE HU
#> 2 76 4-2 main basin Ontario Waters qma hu_qma_4-2 FALSE HU
#> 3 77 4-3 main basin Ontario Waters qma hu_qma_4-3 FALSE HU
#> 4 78 4-5 main basin Ontario Waters qma hu_qma_4-5 FALSE HU
#> 5 79 5-1 Georgian Bay Ontario Waters qma hu_qma_5-1 FALSE HU
#> 6 80 5-2 Georgian Bay Ontario Waters qma hu_qma_5-2 FALSE HU
#> lake.lake_name
#> 1 Lake Huron
#> 2 Lake Huron
#> 3 Lake Huron
#> 4 Lake Huron
#> 5 Lake Huron
#> 6 Lake Huron
filters <- list(
"lake" = "MI"
)
mus <- get_management_units(filters)
head(mus)
#> id label description mu_type slug primary lake.abbrev
#> 1 5 ILL Illinois waters stat_dist mi_stat_dist_ill TRUE MI
#> 2 6 IND Indiana waters stat_dist mi_stat_dist_ind TRUE MI
#> 3 17 MM1 Michigan waters stat_dist mi_stat_dist_mm1 TRUE MI
#> 4 18 MM2 Michigan waters stat_dist mi_stat_dist_mm2 TRUE MI
#> 5 19 MM3 Michigan waters stat_dist mi_stat_dist_mm3 TRUE MI
#> 6 21 MM4 Michigan waters stat_dist mi_stat_dist_mm4 TRUE MI
#> lake.lake_name
#> 1 Lake Michigan
#> 2 Lake Michigan
#> 3 Lake Michigan
#> 4 Lake Michigan
#> 5 Lake Michigan
#> 6 Lake Michigan
jurisdictions <- get_jurisdictions()
#> Warning in refresh_filters(endpoint): Filters could not be found for the
#> 'jursidiction' endpoint
head(jurisdictions)
#> id slug name description lake.abbrev
#> 1 28 er_mi Erie-Michigan Michigan waters of Lake Erie ER
#> 2 32 er_ny Erie-New York New York waters of Lake Erie ER
#> 3 33 er_oh Erie-Ohio Ohio waters of Lake Erie ER
#> 4 36 er_on Erie-Ontario Ontario waters of Lake Erie ER
#> 5 39 er_pa Erie-Pennsylvania Pennsylvania waters of Lake Erie ER
#> 6 27 hu_mi Huron-Michigan Michigan waters of Lake Huron HU
#> lake.lake_name stateprov.id stateprov.abbrev stateprov.name stateprov.country
#> 1 Lake Erie 3 MI Michigan USA
#> 2 Lake Erie 5 NY New York USA
#> 3 Lake Erie 6 OH Ohio USA
#> 4 Lake Erie 7 ON Ontario CAN
#> 5 Lake Erie 8 PA Pennsylvania USA
#> 6 Lake Huron 3 MI Michigan USA
#> stateprov.description
#> 1
#> 2
#> 3
#> 4
#> 5
#> 6
filters <- list(
"lake" = "MI"
)
jurisdictions <- get_jurisdictions(filters)
#> Warning in refresh_filters(endpoint): Filters could not be found for the
#> 'jursidiction' endpoint
#> Warning in check_filters("jursidiction", filter_list): Unknown filters provided. These will be ignored:
#> + lake
head(jurisdictions)
#> id slug name description lake.abbrev
#> 1 23 mi_il Michigan-Illinois Illinois waters of Lake Michigan MI
#> 2 24 mi_in Michigan-Indiana Indiana waters of Lake Michigan MI
#> 3 25 mi_mi Michigan-Michigan Michigan waters of Lake Michigan MI
#> 4 41 mi_wi Michigan-Wisconsin Wisconsin waters of Lake Michigan MI
#> lake.lake_name stateprov.id stateprov.abbrev stateprov.name stateprov.country
#> 1 Lake Michigan 1 IL Illinois USA
#> 2 Lake Michigan 2 IN Indiana USA
#> 3 Lake Michigan 3 MI Michigan USA
#> 4 Lake Michigan 9 WI Wisconsin USA
#> stateprov.description
#> 1
#> 2
#> 3
#> 4
get_state_provinces()
#> id abbrev name country description
#> 1 1 IL Illinois USA
#> 2 2 IN Indiana USA
#> 3 3 MI Michigan USA
#> 4 4 MN Minnesota USA
#> 5 5 NY New York USA
#> 6 6 OH Ohio USA
#> 7 7 ON Ontario CAN
#> 8 8 PA Pennsylvania USA
#> 9 9 WI Wisconsin USA
get_state_provinces(list("country" = "USA"))
#> id abbrev name country description
#> 1 1 IL Illinois USA
#> 2 2 IN Indiana USA
#> 3 3 MI Michigan USA
#> 4 4 MN Minnesota USA
#> 5 5 NY New York USA
#> 6 6 OH Ohio USA
#> 7 8 PA Pennsylvania USA
#> 8 9 WI Wisconsin USA
head(get_grid10s(filters))
#> id grid centroid slug
#> 1 722 1001 SRID=4326;POINT (-87.92416166819176 44.6130719635301) mi_1001
#> 2 723 1004 SRID=4326;POINT (-87.3926502376415 44.56850774809312) mi_1004
#> 3 724 1005 SRID=4326;POINT (-87.25000526031992 44.58333333933837) mi_1005
#> 4 725 1006 SRID=4326;POINT (-87.08333732407132 44.58333334704986) mi_1006
#> 5 726 1007 SRID=4326;POINT (-86.91667320247747 44.58333335476073) mi_1007
#> 6 727 1008 SRID=4326;POINT (-86.75000526614392 44.58333336247123) mi_1008
#> lake.abbrev lake.lake_name
#> 1 MI Lake Michigan
#> 2 MI Lake Michigan
#> 3 MI Lake Michigan
#> 4 MI Lake Michigan
#> 5 MI Lake Michigan
#> 6 MI Lake Michigan
Most of the functions accept a list of filters that can be used to select the data that is returned from the Great Lakes Stocking Database. The list forms a key-value pair that specified the filter to be applied and the value of the filter.
A helper function ~show_filters()~ is included in the package that will display the names of the filterable endpoints, if no endpoint is provided, and provide all of the available filters if an valid endpoint is supplied:
show_filters()
#> An endpoint name needs to be provided. Currently avaliable endpoint names are:
#> [1] "grid10" "jurisdiction" "management_unit"
#> [4] "mark" "state_province" "strain"
#> [7] "strainraw" "stocking_events" "get_cwt_events"
#> [10] "yearling_equivalent"
show_filters("strain")
#> name description
#> 1 strain_code
#> 2 strain_species
#> 3 species
show_filters("stocking_events", filter_like="clip")
#> name description
#> 39 clip_code Multiple values may be separated by commas.
#> 40 finclips Multiple values may be separated by commas.
We can get all of the species, strains and raw (detailed) strains too:
species <- get_species()
head(species)
#> abbrev common_name scientific_name species_code speciescommon
#> 1 ATS Atlantic Salmon Salmo salar 77 1230100401
#> 2 BKT Brook Trout Salvelinus fontinalis 80 1230101002
#> 3 BLG Bluegill Lepomis macrochirus 314 1701001302
#> 4 BLO Bloater Coregonus hoyi 94 1231200122
#> 5 BNT Brown Trout Salmo trutta 78 1230100402
#> 6 CHS Chinook Salmon Oncorhynchus tshawytscha 75 1230100907
strains <- get_strains()
head(strains)
#> id strain_code strain_label slug strain_species.abbrev
#> 1 180 GDL Grand Lake - ME ats-gdl ATS
#> 2 151 GL Green Lake Atlantic Salmon ats-gl ATS
#> 3 1 GU Gullspang ats-gu ATS
#> 4 202 LCH Lake Champlain lat-lch LAT
#> 5 152 LCL Little Clear Lake ats-lcl ATS
#> 6 181 LHR Lahave River ats-lhr ATS
#> strain_species.common_name strain_species.scientific_name
#> 1 Atlantic Salmon Salmo salar
#> 2 Atlantic Salmon Salmo salar
#> 3 Atlantic Salmon Salmo salar
#> 4 Lake Trout Salvelinus namaycush
#> 5 Atlantic Salmon Salmo salar
#> 6 Atlantic Salmon Salmo salar
#> strain_species.species_code strain_species.speciescommon
#> 1 77 1230100401
#> 2 77 1230100401
#> 3 77 1230100401
#> 4 81 1230101098
#> 5 77 1230100401
#> 6 77 1230100401
raw_strains <- get_raw_strains()
head(raw_strains)
#> id raw_strain description species.abbrev species.common_name
#> 1 582 Atlantic Salmon Unknown ATS Atlantic Salmon
#> 2 527 GDL Grand Lake - ME ATS Atlantic Salmon
#> 3 620 GL Green Lake ATS Atlantic Salmon
#> 4 4 GLW Undocumented ATS Atlantic Salmon
#> 5 599 GRD Grand Lake ATS Atlantic Salmon
#> 6 621 GRD,PEN Unknown ATS Atlantic Salmon
#> species.scientific_name species.species_code species.speciescommon strain.id
#> 1 Salmo salar 77 1230100401 5
#> 2 Salmo salar 77 1230100401 180
#> 3 Salmo salar 77 1230100401 151
#> 4 Salmo salar 77 1230100401 5
#> 5 Salmo salar 77 1230100401 180
#> 6 Salmo salar 77 1230100401 5
#> strain.strain_code strain.strain_label strain.slug
#> 1 UNKN Unknown ats-unkn
#> 2 GDL Grand Lake - ME ats-gdl
#> 3 GL Green Lake Atlantic Salmon ats-gl
#> 4 UNKN Unknown ats-unkn
#> 5 GDL Grand Lake - ME ats-gdl
#> 6 UNKN Unknown ats-unkn
We can fetch all of the values used for lifestages, marks, and yearling equivalent factors:
lifestages <- get_lifestages()
head(lifestages)
#> abbrev description
#> 1 a age-2 or older
#> 2 e egg
#> 3 f fingerling, age-0
#> 4 ff fall fingerling, age-0
#> 5 fry fry, age-0
#> 6 sf spring fingerling, age-0
marks <- get_marks()
head(marks)
#> clip_code mark_code mark_type description
#> 1 X OX chemical Oxytetracyclene
#> 2 5 AD finclip Adipose Fin Clip
#> 3 7 DO finclip Anterior Dorsal Fin Clip
#> 4 F LM finclip Left Maxilla Fin Clip
#> 5 2 LP finclip Left Pectoral Fin Clip
#> 6 4 LV finclip Left Ventral Fin Clip
yreq <- get_yreq_factors()
head(yreq)
#> yreq_factor species.abbrev species.common_name lifestage.abbrev
#> 1 1.000 ATS Atlantic Salmon a
#> 2 0.001 ATS Atlantic Salmon e
#> 3 0.100 ATS Atlantic Salmon f
#> 4 0.100 ATS Atlantic Salmon ff
#> 5 0.010 ATS Atlantic Salmon fry
#> 6 0.100 ATS Atlantic Salmon sf
#> lifestage.description
#> 1 age-2 or older
#> 2 egg
#> 3 fingerling, age-0
#> 4 fall fingerling, age-0
#> 5 fry, age-0
#> 6 spring fingerling, age-0
# we can get them for a single species or lifestage too
get_yreq_factors(list(species = "LAT"))
#> yreq_factor species.abbrev species.common_name lifestage.abbrev
#> 1 1.000 LAT Lake Trout a
#> 2 0.001 LAT Lake Trout e
#> 3 0.100 LAT Lake Trout f
#> 4 0.010 LAT Lake Trout fry
#> 5 0.100 LAT Lake Trout sf
#> 6 1.000 LAT Lake Trout u
#> 7 1.000 LAT Lake Trout y
#> 8 0.330 LAT Lake Trout ff
#> 9 0.100 LAT Lake Trout suf
#> lifestage.description
#> 1 age-2 or older
#> 2 egg
#> 3 fingerling, age-0
#> 4 fry, age-0
#> 5 spring fingerling, age-0
#> 6 unknown
#> 7 yearling, age-1
#> 8 fall fingerling, age-0
#> 9 summer fingerling, age-0
We can get stocking events based on a large number of attributes including (but not limited to lake, species, strain, agency, lifestage, stocking method, jursidiction, managment unit, or year.
filters <- list(
"lake" = "HU",
"species" = c("RBT", "LAT", "BNT"),
"first_year" = 2012
)
events <- get_stocking_events(filters)
nrow(events)
#> [1] 1613
head(events)
#> stock_id day month year site st_site
#> 1 20220075 15 4 2021 Lexington Harbor Lexington Harbor
#> 2 20220252 18 5 2021 Detour
#> 3 20220253 18 5 2021 Detour
#> 4 20220254 23 4 2021 Bois Blanc Reef Summit
#> 5 20220255 24 4 2021 Adams Point
#> 6 20220256 23 4 2021 Bois Blanc Reef Summit
#> geom no_stocked year_class
#> 1 SRID=4326;POINT (-82.52659482999999 43.26750588) 30000 2020
#> 2 SRID=4326;POINT (-83.90000000000001 45.8669) 40010 2020
#> 3 SRID=4326;POINT (-83.90000000000001 45.8669) 39982 2020
#> 4 SRID=4326;POINT (-84.4181 45.8177) 68009 2020
#> 5 SRID=4326;POINT (-83.65000000000001 45.5) 69385 2020
#> 6 SRID=4326;POINT (-84.4181 45.8177) 69538 2020
#> agemonth tag_no mark yreq_stocked agency.abbrev
#> 1 15 <NA> NA 30000 MIDNR
#> 2 17 641293 NA 40010 USFWS
#> 3 17 641341 NA 39982 USFWS
#> 4 16 641319 NA 68009 USFWS
#> 5 16 641321 NA 69385 USFWS
#> 6 16 641315 NA 69538 USFWS
#> agency.agency_name condition.condition
#> 1 Michigan Department of Natural Resources 1
#> 2 U.S. Fish and Wildlife Service 1
#> 3 U.S. Fish and Wildlife Service 1
#> 4 U.S. Fish and Wildlife Service 1
#> 5 U.S. Fish and Wildlife Service 1
#> 6 U.S. Fish and Wildlife Service 1
#> condition.description grid_10.id grid_10.grid
#> 1 <1% mortality observed, "excellent" 126 1915
#> 2 <1% mortality observed, "excellent" 156 306
#> 3 <1% mortality observed, "excellent" 156 306
#> 4 <1% mortality observed, "excellent" 178 403
#> 5 <1% mortality observed, "excellent" 226 608
#> 6 <1% mortality observed, "excellent" 178 403
#> grid_10.centroid grid_10.slug
#> 1 SRID=4326;POINT (-82.42790326846431 43.25209597110957) hu_1915
#> 2 SRID=4326;POINT (-83.91524345779918 45.90396859168997) hu_306
#> 3 SRID=4326;POINT (-83.91524345779918 45.90396859168997) hu_306
#> 4 SRID=4326;POINT (-84.40808691659738 45.73980324819702) hu_403
#> 5 SRID=4326;POINT (-83.58011249322665 45.42859111583507) hu_608
#> 6 SRID=4326;POINT (-84.40808691659738 45.73980324819702) hu_403
#> grid_10.lake.abbrev grid_10.lake.lake_name latlong_flag.value
#> 1 HU Lake Huron 1
#> 2 HU Lake Huron 1
#> 3 HU Lake Huron 1
#> 4 HU Lake Huron 1
#> 5 HU Lake Huron 1
#> 6 HU Lake Huron 1
#> latlong_flag.description lifestage.abbrev lifestage.description
#> 1 Reported y yearling, age-1
#> 2 Reported y yearling, age-1
#> 3 Reported y yearling, age-1
#> 4 Reported y yearling, age-1
#> 5 Reported y yearling, age-1
#> 6 Reported y yearling, age-1
#> species.abbrev species.common_name species.scientific_name
#> 1 RBT Rainbow Trout Oncorhynchus mykiss
#> 2 LAT Lake Trout Salvelinus namaycush
#> 3 LAT Lake Trout Salvelinus namaycush
#> 4 LAT Lake Trout Salvelinus namaycush
#> 5 LAT Lake Trout Salvelinus namaycush
#> 6 LAT Lake Trout Salvelinus namaycush
#> species.species_code species.speciescommon stocking_method.stk_meth
#> 1 76 1230100909 s
#> 2 81 1230101098 b
#> 3 81 1230101098 b
#> 4 81 1230101098 b
#> 5 81 1230101098 b
#> 6 81 1230101098 b
#> stocking_method.description jurisdiction.id jurisdiction.slug
#> 1 shore stocking (truck, atv, snowmobile) 27 hu_mi
#> 2 boat, offshore stocking 27 hu_mi
#> 3 boat, offshore stocking 27 hu_mi
#> 4 boat, offshore stocking 27 hu_mi
#> 5 boat, offshore stocking 27 hu_mi
#> 6 boat, offshore stocking 27 hu_mi
#> jurisdiction.name jurisdiction.description jurisdiction.lake.abbrev
#> 1 Huron-Michigan Michigan waters of Lake Huron HU
#> 2 Huron-Michigan Michigan waters of Lake Huron HU
#> 3 Huron-Michigan Michigan waters of Lake Huron HU
#> 4 Huron-Michigan Michigan waters of Lake Huron HU
#> 5 Huron-Michigan Michigan waters of Lake Huron HU
#> 6 Huron-Michigan Michigan waters of Lake Huron HU
#> jurisdiction.lake.lake_name jurisdiction.stateprov.id
#> 1 Lake Huron 3
#> 2 Lake Huron 3
#> 3 Lake Huron 3
#> 4 Lake Huron 3
#> 5 Lake Huron 3
#> 6 Lake Huron 3
#> jurisdiction.stateprov.abbrev jurisdiction.stateprov.name
#> 1 MI Michigan
#> 2 MI Michigan
#> 3 MI Michigan
#> 4 MI Michigan
#> 5 MI Michigan
#> 6 MI Michigan
#> jurisdiction.stateprov.country jurisdiction.stateprov.description
#> 1 USA
#> 2 USA
#> 3 USA
#> 4 USA
#> 5 USA
#> 6 USA
We can also find stocking events that were associatd with specific coded wire tags, tag attriutes, or attributes of the stocked fish or stocking event.
filters <- list(
cwt_number = "630157"
)
cwt_events <- get_cwt_events(filters)
head(cwt_events)
#> cwt_number tag_type seq_lower seq_upper manufacturer tag_reused
#> 1 630157 sequential 15285 15810 nmt True
#> 2 630157 sequential 15820 16380 nmt True
#> 3 630157 sequential 14014 14621 nmt True
#> 4 630157 sequential 16385 17699 nmt True
#> 5 630157 sequential 14629 15280 nmt True
#> 6 630157 sequential 11740 12800 nmt True
#> multiple_lakes multiple_species multiple_strains multiple_yearclasses
#> 1 False False True True
#> 2 False False True True
#> 3 False False True True
#> 4 False False True True
#> 5 False False True True
#> 6 False False True True
#> multiple_agencies stock_id agency_stock_id agency_code lake state jurisd
#> 1 False 20196154 12683 OMNR HU ON hu_on
#> 2 False 20196155 12684 OMNR HU ON hu_on
#> 3 False 20196156 12687 OMNR HU ON hu_on
#> 4 False 20196157 12691 OMNR HU ON hu_on
#> 5 False 20196158 12695 OMNR HU ON hu_on
#> 6 False 20196159 12696 OMNR HU ON hu_on
#> man_unit grid10 primary_location secondary_location latitude longitude year
#> 1 GB4 1124 Owen Sound Owen Sound East Wall 44.5789 -80.93954 2006
#> 2 GB4 1124 Owen Sound Owen Sound East Wall 44.5789 -80.93954 2006
#> 3 GB4 1124 Owen Sound Owen Sound East Wall 44.5789 -80.93954 2006
#> 4 GB4 1124 Owen Sound Os Harbour-Goodyear 44.6016 -80.9208 2006
#> 5 GB4 1124 Owen Sound Os Harbour-Goodyear 44.6016 -80.9208 2006
#> 6 GB4 1124 Owen Sound Owen Sound East Wall 44.5789 -80.93954 2006
#> month day spc strain year_class mark clipcode stage
#> 1 2 10 LAT Seneca Lake 2001 NA AD age-2 or older
#> 2 2 10 LAT Seneca Lake 2002 NA AD age-2 or older
#> 3 2 10 LAT Seneca Lake 2003 NA AD age-2 or older
#> 4 2 27 LAT Huron - Iroquois Bay 2001 NA AD age-2 or older
#> 5 2 27 LAT Huron - Iroquois Bay 2002 NA AD age-2 or older
#> 6 1 6 LAT Superior - Slate Island 2004 NA AD age-2 or older
#> method no_stocked event_tag_numbers
#> 1 unknown 267 630157
#> 2 unknown 311 630157
#> 3 unknown 358 630157
#> 4 unknown 402 630157
#> 5 unknown 279 630157
#> 6 unknown 638 630157
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