The creelutils package facilitates working with
freshwater creel data by providing a toolkit that streamlines accessing
data and performing common tasks in R.
Accessing public data
Freshwater recreational creel data is made publicly available at Washington’s official open data portal, data.wa.gov. Creel datasets are comprised of multiple elements (e.g., effort, interview, and catch data) that are grouped in space and time by a “Fishery Name” identifier. This identifier is an easy way to view a given creel’s full dataset.
For example, the Skagit winter steelhead 2021 dataset contains effort counts, angler interviews, etc. conducted on sections of the Skagit and Sauk rivers from 2021-02-01 to 2021-04-15.
By Fishery Name
The function fetch_dwg downloads creel data from
data.wa.gov by Fishery Name and is the primary route for accessing a
complete dataset from a fishery.
library(creelutils)
fishery_name <- "Skagit winter steelhead 2021"
dat <- fetch_dwg(fishery_name)
#> Warning: One or more parsing issues, call `problems()` on your data frame for details,
#> e.g.:
#> dat <- vroom(...)
#> problems(dat)
#> # A tibble: 6 × 2
#> `Data Component` Records
#> <chr> <int>
#> 1 effort 4864
#> 2 ll 2
#> 3 interview 1860
#> 4 catch 683
#> 5 closures 60
#> 6 fishery_manager 22
#ll stands for latitude and longitude
names(dat)
#> [1] "effort" "ll" "interview" "catch"
#> [5] "closures" "fishery_manager"
colnames(dat$effort)
#> [1] "creel_event_id" "event_date" "water_body"
#> [4] "project_name" "fishery_name" "effort_event_id"
#> [7] "location" "location_id" "tie_in_indicator"
#> [10] "count_sequence" "effort_start_time" "effort_end_time"
#> [13] "no_count_reason" "comments" "count_type"
#> [16] "count_quantity" "location_type" "survey_type"
#> [19] "location_season_name" "section_num" "surveyor_num"
#> [22] "p_census_bank" "p_census_boat" "indirect_census_bank"
#> [25] "direct_census_bank"What if I don’t know the Fishery Name?
The function fetch_fishery_names(), which has no
arguments, returns a list of all fishery names in the creel
database.
fetch_fishery_names() |> head(n = 10)
#> [1] "Baker summer sockeye 2022" "Baker summer sockeye 2023"
#> [3] "Cascade fall salmon 2021" "Cascade fall salmon 2022"
#> [5] "Cascade fall salmon 2023" "Cascade fall salmon 2024"
#> [7] "Cascade spring Chinook 2021" "Cascade spring Chinook 2022"
#> [9] "Cascade spring Chinook 2023" "Cascade spring Chinook 2024"What if I know part of the Fishery Name but not the specific syntax?
The function search_fishery_name() can accept a vector
of characters or numbers and returns the fishery names with those
values.
# by water body
search_fishery_name("Humptulips") |> head(n = 5)
#> [1] "Humptulips salmon 2019" "Humptulips salmon 2020" "Humptulips salmon 2021"
#> [4] "Humptulips salmon 2022" "Humptulips salmon 2023"
# by run / species
search_fishery_name("winter steelhead") |> head(n = 5)
#> [1] "Chehalis winter steelhead 2024-25" "Clearwater winter steelhead 2024-25"
#> [3] "Hoh winter steelhead 2022-23" "Hoh winter steelhead 2023-24"
#> [5] "Hoh winter steelhead 2024-25"
# by year
search_fishery_name("2025") |> head(n = 5)
#> [1] "Quillayute spring chinook 2025" "Skagit spring Chinook 2025 lower"
#> [3] "Skagit spring Chinook 2025 upper" "Skagit spring gamefish 2025"
#> [5] "Skagit winter gamefish 2025"Accessing internal data
This section covers operations that require the appropriate credentials and permissions for WDFW’s Postgres creel database. Selected ‘views’ from the relational database are mirrored on Washington’s official open data portal, data.wa.gov, for public access.
Connecting to database
An internal connection to the Postgres creel database can be made
using the establish_db_con function. When called a RStudio
UI prompt will open for the user to enter their password. This requires
the proper user credentials and setup on local computer. If this is your
first time using this method, contact the package maintainer for
assistance.
# con <- establish_db_con()