First, list_projects returns the project page, which is
useful to figure out what URLs are associated with each project. You do
not need MATOS permissions in order to view this page.
all_projects <- list_projects()
head(all_projects)
#> name number
#> 1 ACK Array 168
#> 2 APG Atlantic and Shortnose Sturgeon 176
#> 3 ASI - White Shark Study, Montauk, NY 211
#> 4 ASI Acoustic Array 100
#> 5 ASI Spinner Shark Study 227
#> 6 ASI White Shark Study, Southern NE 232
#> url
#> 1 https://matos.asascience.com/project/detail/168
#> 2 https://matos.asascience.com/project/detail/176
#> 3 https://matos.asascience.com/project/detail/211
#> 4 https://matos.asascience.com/project/detail/100
#> 5 https://matos.asascience.com/project/detail/227
#> 6 https://matos.asascience.com/project/detail/232I can also view the files that I’ve uploaded to my projects using
list_project_files, but that requires logging in first. The
family of list_ functions in this package will prompt you
to log in before moving on. Note that I’ll be entering my MATOS username
and password behind the scenes here.
project_files <- list_project_files(project = 'umces boem offshore wind energy')
#> ! Please log in.
#> ✔ Login successful!
head(project_files)
#> project file_type upload_date
#> 1 87 Deployed Receivers – Deployment Metadata 2020-03-30
#> 2 87 Tag Detections - .vfl file 2020-05-28
#> 3 87 Tag Detections - .vfl file 2020-05-28
#> 4 87 Tag Detections - .vfl file 2020-05-28
#> 5 87 Tag Detections - .vfl file 2020-05-28
#> 6 87 Tag Detections - .vfl file 2020-05-28
#> file_name
#> 1 BOEM_metadata_deployment.xls
#> 2 VR2AR_546455_20170328_1.vrl
#> 3 VR2AR_546456_20170328_1.vrl
#> 4 VR2AR_546457_20170329_1.vrl
#> 5 VR2AR_546458_20170329_1.vrl
#> 6 VR2AR_546459_20170328_1.vrl
#> url
#> 1 https://matos.asascience.com/projectfile/download/375
#> 2 https://matos.asascience.com/projectfile/download/1810
#> 3 https://matos.asascience.com/projectfile/download/1811
#> 4 https://matos.asascience.com/projectfile/download/1812
#> 5 https://matos.asascience.com/projectfile/download/1813
#> 6 https://matos.asascience.com/projectfile/download/1814A side note on your MATOS username and password: matos
defaults to asking you for your login credentials every time you start a
new session. To skirt around this you can use
set_matos_credentials(), which installs your username and
password in your .Renviron file.
You will be automatically logged in every time you use your current
computer after doing this, but beware: someone else could theoretically
access your username and password if they gain access to your
computer.
I can also list any of my OTN node Data Extraction Files.
ACT_MATOS_files <- list_extract_files(project = 'umces boem offshore wind energy',
detection_type = 'all')
head(ACT_MATOS_files)
#> project file_type detection_type detection_year
#> 1 87 Data Extraction File matched 2017
#> 2 87 Data Extraction File matched 2018
#> 3 87 Data Extraction File matched 2019
#> 4 87 Data Extraction File matched 2020
#> 5 87 Data Extraction File matched 2021
#> 6 87 Data Extraction File matched 2022
#> upload_date file_name
#> 1 2023-07-06 mdwea_matched_detections_2017.zip
#> 2 2023-07-06 mdwea_matched_detections_2018.zip
#> 3 2023-07-06 mdwea_matched_detections_2019.zip
#> 4 2023-07-06 mdwea_matched_detections_2020.zip
#> 5 2023-07-06 mdwea_matched_detections_2021.zip
#> 6 2023-07-06 mdwea_matched_detections_2022.zip
#> url
#> 1 https://matos.asascience.com/projectfile/downloadExtraction/87_1
#> 2 https://matos.asascience.com/projectfile/downloadExtraction/87_2
#> 3 https://matos.asascience.com/projectfile/downloadExtraction/87_3
#> 4 https://matos.asascience.com/projectfile/downloadExtraction/87_4
#> 5 https://matos.asascience.com/projectfile/downloadExtraction/87_5
#> 6 https://matos.asascience.com/projectfile/downloadExtraction/87_6There are a few ways to download the different types of files held by MATOS. I can download directly if I know the URL of the file:
project_files$url[1]
#> [1] "https://matos.asascience.com/projectfile/download/375"
get_project_file(url = project_files$url[1])
#>
#> ── Downloading files ──────────────────────────────────────────────
#> ✔ File(s) saved to:
#> C:\Users\darpa2\Analysis\matos\vignettes\BOEM_metadata_deployment.xls
#>
#> ── Unzipping files ────────────────────────────────────────────────
#> [1] "C:\\Users\\darpa2\\Analysis\\matos\\vignettes\\BOEM_metadata_deployment.xls"I can download by using an index from the
ACT_MATOS_files table above, here the file on the second
row.
get_extract_file(file = 2, project = 'umces boem offshore wind energy')
#>
#> ── Downloading files ──────────────────────────────────────────────
#> ✔ File(s) saved to:
#> C:\Users\darpa2\Analysis\matos\vignettes\mdwea_matched_detections_2018.zip
#>
#> ── Unzipping files ────────────────────────────────────────────────
#> ✔ File(s) unzipped to:
#> C:/Users/darpa2/Analysis/matos/vignettes/mdwea_matched_detections_2018.csv
#> C:/Users/darpa2/Analysis/matos/vignettes/data_description.txt
#> [1] "C:/Users/darpa2/Analysis/matos/vignettes/mdwea_matched_detections_2018.csv"
#> [2] "C:/Users/darpa2/Analysis/matos/vignettes/data_description.txt"Using the tag_search function, I can interface with
MATOS’ tag search
page. Be very careful with this function – it can take a
very, VERY long time to return your files.
This function downloads the requested CSV into your working directory,
and, if import = T is used, reads it into your R
session.
There are times when you want to upload new data to MATOS. The currently accepted data types and formats are:
A few data types use designated Ocean Tracking Network templates:
If you don’t have one of these templates downloaded, you can download it through the package. For example:
Then, get to uploading!