orcas

The goal of orcas is to scrape orca sighting data from the web and visualize it in maps and tables.

R Ladies Seattle invited me to give a talk for the ‘R in the Outdoors’ meetup. This was my first in-person talk of my professional career! I used this as an opportunity to learn new skills through a personal project. I’ve always had an affinity for the Southern Resident Killer Whales in the Salish Sea. The Center for Whale Research does a lot of really fascinating and important work monitoring their population. They post their survey data on their website; each encounter with the orcas is a separate webpage. Lately, I’ve been curious and intimidated by web scraping so I decided this would make a great case study and personal project.

I ended up also going to the Seattle useR Group lightning talks meetup afterwards and spontaneously gave the same presentation there!

orcas is still a work in progress, as the cwr_tidy dataset is mostly tidy but not completely clean. There are still missing values and typos, as evident from some encounters having a negative duration.

All photos and data belong to the Center for Whale Research, a 501c3 nonprofit organization registered in Washington State.

Installation

You can install the development version of orcas from GitHub with:

# install.packages("devtools")
devtools::install_github("jadeynryan/orcas")

R in the Outdoors Presentation

While there is no recording of the talk, you can view the slides.

Examples

Scrape the two most recent encounters from 2022 and 2023:

orcas::make_encounter_df(years = 2022:2023, max_urls = 2)
#> Scraped: https://www.whaleresearch.com/20223-36
#>  ■■■■■■■■■                         25% |  ETA:  7sScraped: https://www.whaleresearch.com/2022-82
#>  ■■■■■■■■■■■■■■■■                  50% |  ETA:  5sScraped: https://www.whaleresearch.com/2023-66
#>  ■■■■■■■■■■■■■■■■■■■■■■■           75% |  ETA:  3sScraped: https://www.whaleresearch.com/2023-65
#>                                                    
#> # A tibble: 4 × 17
#>   nmfs_permit   link  enc_date enc_seq enc_number observ_begin observ_end vessel
#>   <chr>         <chr> <chr>    <chr>   <chr>      <chr>        <chr>      <chr> 
#> 1 " 21238/ DFO… http… "07/07/… 1       36         11:25 AM     01:50 PM   Mike 1
#> 2  <NA>         http… "27/12/… 1       82         03:20 PM     04:19 PM   Mike 1
#> 3 " 27038/ DFO… http… "07/10/… 1       66         04:20 PM     07:04 PM   Mike 1
#> 4 " 27038/ DFO… http… "06/10/… 1       65         11:58 AM     12:50 PM   Orcin…
#> # ℹ 9 more variables: staff <chr>, other_observers <chr>, pods <chr>,
#> #   location_descr <chr>, start_latitude <chr>, start_longitude <chr>,
#> #   end_latitude <chr>, end_longitude <chr>, enc_summary <chr>

Make an interactive DT of 2023 encounters:

orcas::cwr_tidy |> 
  subset(year == 2023) |> 
  orcas::make_dt()

Screenshot of datatable with two rows and columns: “Date”, “Encounter number”, “Begin time”, “End time”, “Duration (min)”, “Vessel”, “Observers”, “Pods or ecotype”, “Location”, and “Encounter summary”.

Make an interactive leaflet map of last two 2023 encounters:

orcas::cwr_tidy |> 
  subset(year == 2023) |> 
  head(2) |> 
  orcas::make_leaflet()

Screenshot of leaflet map with whale icons representing the start and end of the encounters.