rebuild

NEWS.md

@@ -4,7 +4,7 @@
 
 * Addition of five new functions: 
   * `wt_dd_summary()` for querying data from Data Discover. See [APIs](https://abbiodiversity.github.io/wildrtrax/articles/apis.html#data-discover) for more information
-  * `wt_evaluate_classifier()`, `wt_get_threshold()`, and `wt_additional_species()` for wrangling acoustic automated classification results
+  * `wt_evaluate_classifier()`, `wt_get_threshold()`, and `wt_additional_species()` for wrangling acoustic automated classification results. See [Acoustic classifiers](https://abbiodiversity.github.io/wildrtrax/articles/acoustic-classifiers.html) for more information.
   * `wt_add_grts()` to intersect locations with GRTS IDs from [NABat](https://www.nabatmonitoring.org/)
 * `wt_download_tags()` now becomes `wt_download_media()` to support broader media downloads in batch from WildTrax
 * Deprecated `wt_report()`
@@ -13,6 +13,7 @@
 
 * Switch to `curl::curl_download()` for media and assets
 * Removed dependencies `pipeR`, `progressr`, `jsonlite`, `future`, `furrr`, `tools`, `magrittr`, `markdown`, `rmarkdown` to increase package stability but reduces speed for functions such as `wt_audio_scanner()`, `wt_run_ap()`. Moved `vembedr` to suggests for vignettes
+* Switched `wt_download_report()` to POST requests
 * Lowercase package name
 
 ---

R/acoustic-pre-processing.R

@@ -59,7 +59,7 @@ wt_audio_scanner <- function(path, file_type, extra_cols = F) {
   }
 
   # Create the main tibble
-  df <- z %>%
+  df <- df %>%
     tidyr::unnest(file_path) %>%
     dplyr::mutate(size_Mb = round(purrr::map_dbl(.x = file_path, .f = ~ fs::file_size(.x)) / 10e5, digits = 2), # Convert file sizes to megabytes
                   file_path = as.character(file_path)) %>%

R/analyze-data.R

@@ -213,7 +213,7 @@ wt_summarise_cam <- function(detect_data, raw_data, time_interval = "day",
 #' detections <- wt_ind_detect(x = df, threshold = 30, units = "minutes")
 #' }
 #'
-#' @return A dataframe of independent detections in your camera data, based on the threshold you specified. The df wil include information about the duration of each detection, the number of images, the average number of individual animals per image, and the max number of animals in the detection.
+#' @return A dataframe of independent detections in your camera data, based on the threshold you specified. The df will include information about the duration of each detection, the number of images, the average number of individual animals per image, and the max number of animals in the detection.
 
 wt_ind_detect <- function(x, threshold, units = "minutes", datetime_col = image_date_time, remove_human = TRUE, remove_domestic = TRUE) {
 

R/api.R

@@ -450,13 +450,12 @@ wt_download_media <- function(input, output, type = c("recording","image", "tag_
         purrr::map2_chr(.$spectrogram_url, .$clip_file_name_spec, ~ curl::curl_download(.x, .y, mode = "wb"))
         purrr::map2_chr(.$clip_url, .$clip_file_name_audio, ~ curl::curl_download(.x, .y, mode = "wb"))
       }
-  } else if ("media_url" %in% colnames(data)){
+  } else if ("media_url" %in% colnames(input_data)){
     output_data <- input_data %>%
-      mutate(image_name = file.path(output, paste0(location, "_", format(parse_date_time(recording_date_time, "%Y-%m-%d %H:%M:%S"), "%Y%m%d_%H%M%S"),".jpeg"))) %>%
+      mutate(image_name = file.path(output, "/", paste0(location, "_", format(parse_date_time(image_date_time, "%Y-%m-%d %H:%M:%S"), "%Y%m%d_%H%M%S"),".jpeg"))) %>%
       {
-        purrr::map2_chr(.$spectrogram_url, .$image_name, ~ curl::curl_download(.x, .y, mode = "wb"))
+        purrr::map2_chr(.$media_url, .$image_name, ~ curl::curl_download(.x, .y, mode = "wb"))
       }
-
   } else {
     stop("Required columns are either 'recording_url', 'media_url', 'spectrogram_url', or 'clip_url'. Use wt_download_report(reports = 'recording', 'image_report' or 'tag') to get the correct media.")
   }

R/classifier-functions.R

@@ -20,7 +20,7 @@
 #' remove_species = TRUE, thresholds = c(10, 99))
 #' }
 #'
-#' @return A tibble containing columsn for precision, recall, and F-score for each of the requested thresholds.
+#' @return A tibble containing columns for precision, recall, and F-score for each of the requested thresholds.
 
 wt_evaluate_classifier <- function(data, resolution = "recording", remove_species = TRUE,  species = NULL, thresholds = c(10, 99)){
 

R/convenience-functions.R

@@ -664,6 +664,7 @@ wt_add_grts <- function(data, group_locations_in_cell = FALSE) {
 #' Format data for a specified portal
 #'
 #' @description This function takes the WildTrax reports and converts them to the desired format
+#' `r lifecycle::badge("experimental")`
 #'
 #' @param input The report from `wt_download_report()`
 #' @param format A format i.e. 'FWMIS'

README.md

@@ -7,6 +7,7 @@
 [![R-CMD-check](https://github.com/ABbiodiversity/wildrtrax/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/ABbiodiversity/wildrtrax/actions/workflows/R-CMD-check.yaml)
 [![codecov](https://codecov.io/gh/ABbiodiversity/wildrtrax/branch/main/graph/badge.svg)](https://app.codecov.io/gh/ABbiodiversity/wildrtrax)
 [![CRAN status](https://www.r-pkg.org/badges/version/wildrtrax)](https://CRAN.R-project.org/package=wildrtrax)
+[![Codecov test coverage](https://codecov.io/gh/ABbiodiversity/wildRtrax/branch/main/graph/badge.svg)](https://app.codecov.io/gh/ABbiodiversity/wildRtrax?branch=main)
 <!-- badges: end -->
 
 ## Overview

codecov.yml

@@ -1,60 +1,19 @@
-# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
-on:
-  push:
-    branches: [main]
-  pull_request:
-    branches: [main]
-
-name: test-coverage
-
-permissions: read-all
-
-jobs:
-  test-coverage:
-    runs-on: ubuntu-latest
-    env:
-      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - uses: r-lib/actions/setup-r@v2
-        with:
-          use-public-rspm: true
-
-      - uses: r-lib/actions/setup-r-dependencies@v2
-        with:
-          extra-packages: any::covr, any::xml2
-          needs: coverage
-
-      - name: Test coverage
-        run: |
-          cov <- covr::package_coverage(
-            quiet = FALSE,
-            clean = FALSE,
-            install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package")
-          )
-          covr::to_cobertura(cov)
-        shell: Rscript {0}
-
-      - uses: codecov/codecov-action@v4
-        with:
-          fail_ci_if_error: ${{ github.event_name != 'pull_request' && true || false }}
-          file: ./cobertura.xml
-          plugin: noop
-          disable_search: true
-          token: ${{ secrets.CODECOV_TOKEN }}
-
-      - name: Show testthat output
-        if: always()
-        run: |
-          ## --------------------------------------------------------------------
-          find '${{ runner.temp }}/package' -name 'testthat.Rout*' -exec cat '{}' \; || true
-        shell: bash
-
-      - name: Upload test results
-        if: failure()
-        uses: actions/upload-artifact@v4
-        with:
-          name: coverage-test-failures
-          path: ${{ runner.temp }}/package
+comment: false
+
+coverage:
+  status:
+    project:
+      default:
+        target: auto
+        threshold: 1%
+        informational: true
+    patch:
+      default:
+        target: auto
+        threshold: 1%
+        informational: true
+
+- name: Upload coverage reports to Codecov
+  uses: codecov/codecov-action@v4.0.1
+  with:
+    token: ${{ secrets.CODECOV_TOKEN }}

docs/pkgdown.yml

@@ -10,7 +10,7 @@ articles:
   classifiers-tutorial: classifiers-tutorial.html
   introduction: introduction.html
   tutorials: tutorials.html
-last_built: 2024-07-22T21:47Z
+last_built: 2024-07-23T20:54Z
 urls:
   reference: https://abbiodiversity.github.io/wildrtrax/reference
   article: https://abbiodiversity.github.io/wildrtrax/articles

vignettes/classifiers-tutorial.Rmd

@@ -217,7 +217,7 @@ eval[eval$threshold==threshold_use,]
 
 ```
 
-A precision at our chosen score threshold of approximately `r round(eval$precision,2)` means that more than half of these detections are likely still false positives, we should probably visually verify to remove those false positives. Given that the overall recall rate of BirdNET is < 10% for precision values above 0.7, the detections should be used with caution in ecological analyses. From a detectability perspective, a recall rate of 10% means that your detection probability with BirdNET is 10% of what it would be with a human listener.
+A precision at our chosen score threshold of approximately `round(eval[eval$threshold==threshold_use,]$precision,3)` means that ~1/3 of detections are likely still false positives, we should probably visually verify to remove those false positives. Given that the overall recall rate of BirdNET is < 10% for precision values above 0.7, the detections should be used with caution in ecological analyses. From a detectability perspective, a recall rate of 10% means that your detection probability with BirdNET is 10% of what it would be with a human listener.
 
 ## Check for additional species detected
 

vignettes/introduction.Rmd

@@ -23,7 +23,7 @@ library(wildrtrax)
 
 ### What is `wildrtrax`?
 
-`wildrtrax`, pronounced *'wild-R-tracks'*, is an R package for ecologists and advanced users who work with environmental sensors such as autonomous recording units (ARUs) and remote cameras. It contains functions designed to meet most needs in order to organize, analyze and standardize data with the [WildTrax](https://wwww.wildtrax.ca) infrastructure. `wildrtrax` iis self-contained and must be run under an R statistical environment, and it also depends on many other R packages. `wildrtrax` is free software and distributed under [MIT License (c) 2023](https://github.com/ABbiodiversity/wildrtrax/blob/master/LICENSE).
+`wildrtrax`, pronounced *'wild-R-tracks'*, is an R package for ecologists and advanced users who work with environmental sensors such as autonomous recording units (ARUs) and remote cameras. It contains functions designed to meet most needs in order to organize, analyze and standardize data with the [WildTrax](https://wwww.wildtrax.ca) infrastructure. `wildrtrax` is self-contained and must be run under an R statistical environment, and it also depends on many other R packages. `wildrtrax` is free software and distributed under [MIT License (c) 2023](https://github.com/ABbiodiversity/wildrtrax/blob/master/LICENSE).
 
 ### What is **WildTrax**?