test build v1.2

DESCRIPTION

@@ -59,6 +59,6 @@ VignetteBuilder: knitr
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 Config/testthat/edition: 3
 URL: https://abbiodiversity.github.io/wildRtrax/

NAMESPACE

@@ -40,6 +40,7 @@ import(pipeR)
 import(progressr)
 import(purrr)
 import(readr)
+import(rlang)
 import(seewave)
 import(sf)
 import(stringr)

R/acoustic-pre-processing.R

@@ -599,7 +599,7 @@ wt_signal_level <- function(path, fmin = 500, fmax = NA, threshold, channel = "l
 #'
 #' @description "Chops" up wav files into many smaller files of a desired duration
 #'
-#' @param input A \code{`wt_audio_scanner`} tibble of files
+#' @param input A data frame or tibble containing information about audio files
 #' @param segment_length Numeric; Segment length in seconds. Modulo recording will be exported should there be any trailing time left depending on the segment length used
 #' @param output_folder Character; output path to where the segments will be stored
 #'
@@ -608,8 +608,7 @@ wt_signal_level <- function(path, fmin = 500, fmax = NA, threshold, channel = "l
 #'
 #' @examples
 #' \dontrun{
-#' wt_chop(input = my_audio_tibble,
-#'  segment_length = 60, output_folder "/where/i/store/my/chopped/files")
+#' wt_chop(input = my_files, segment_length = 60, output_folder = "output_folder")
 #' }
 #'
 #' @return Segmented files written to the output_folder

R/analyze-data.R

@@ -19,7 +19,7 @@
 #' @param detection_id_col Defaults to `detection`. The column indicating the detection id
 #' @param start_col_det Defaults to `start_time`. The column indicating the start time of the independent detections
 #'
-#' @import dplyr lubridate tidyr
+#' @import dplyr lubridate tidyr rlang
 #' @export
 #'
 #' @examples
@@ -203,7 +203,7 @@ wt_summarise_cam <- function(detect_data, raw_data, time_interval = "day",
 #' @param remove_human Logical; Should human and human-related tags (e.g. vehicles) be removed? Defaults to TRUE.
 #' @param remove_domestic Logical; Should domestic animal tags (e.g. cows) be removed? Defaults to TRUE.
 #'
-#' @import dplyr lubridate
+#' @import dplyr lubridate rlang
 #' @export
 #'
 #' @examples
@@ -213,7 +213,7 @@ wt_summarise_cam <- function(detect_data, raw_data, time_interval = "day",
 #'
 #' @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.
 
-wt_ind_detect <- function(x, threshold, units = "minutes",  datetime_col = image_date_time, remove_human = TRUE, remove_domestic = TRUE) {
+wt_ind_detect <- function(x, threshold, units = "minutes", datetime_col = image_date_time, remove_human = TRUE, remove_domestic = TRUE) {
 
   # Check that x is a dataframe
   if (!is.data.frame(x)) {

R/convenience-functions.R

@@ -196,7 +196,8 @@ wt_tidy_species <- function(data,
 #'
 #' @param data Dataframe of WildTrax observations, for example the summary report.
 #' @param calc Character; method to convert model predictions to integer ("round", "ceiling", or "floor"). See `?round()` for details.
-#' @import dplyr
+#'
+#' @import dplyr tidyr
 #' @export
 #'
 #' @examples

docs/pkgdown.yml

@@ -9,7 +9,7 @@ articles:
   camera-data-wrangling: camera-data-wrangling.html
   introduction: introduction.html
   tutorials: tutorials.html
-last_built: 2024-03-21T17:59Z
+last_built: 2024-03-27T14:40Z
 urls:
   reference: https://abbiodiversity.github.io/wildRtrax/reference
   article: https://abbiodiversity.github.io/wildRtrax/articles

tests/testthat/test-aru.R

@@ -11,7 +11,7 @@ test_that("Authentication works correctly", {
 
 test_that("Downloading ARU report", {
   cypress_hills_aru <- wt_download_report(620, 'ARU', 'tag', FALSE)
-  expect_true(!is.null(cypress_hills))
+  expect_true(!is.null(cypress_hills_aru))
 })
 
 test_that("Testing a Private Project", {

tests/testthat/test-cam.R

@@ -12,7 +12,7 @@ test_that("Downloading CAM report", {
 
 test_that("Individual detections", {
   abmi_amph_cam <- wt_download_report(391, 'CAM', 'main', FALSE)
-  ind_detections <- wt_ind_detect(abmi_amph_cam, threshold, units = "minutes", datetime_col = image_date_time, remove_human = TRUE, remove_domestic = TRUE)
+  ind_detections <- wt_ind_detect(abmi_amph_cam, threshold = 60, units = "minutes", datetime_col = image_date_time, remove_human = TRUE, remove_domestic = TRUE)
   expect_true(nrow(abmi_amph_cam) > nrow(ind_detections))
 
 })

vignettes/tutorials.Rmd

@@ -55,34 +55,34 @@ data <- wt_download_report(620,'ARU',c('birdnet','main'),F)
 
 When downloading multiple reports, the `data` object then becomes a list of tibbles. The results from the `main` report can then be anti-joined against the `birdnet` report to determine if any species were detected than a human did not find. It's recommended to use high threshold values from BirdNET and species that should occur or be allowed in the project.
 
-```{r, eval=F, include=T, message=F, warning=F}
-library(tidyverse)
+```{r, eval=T, include=T, message=F, warning=F}
 
-birdnet_report <- data[[1]] %>%
+birdnet_report <- data[[1]] |>
   dplyr::filter(is_species_allowed_in_project == 't',
-         confidence > 50) %>%
-  tibble::add_column(observer = 'BirdNET') %>%
-  dplyr::select(location, recording_date_time, observer, species_code) %>%
+         confidence > 50) |>
+  tibble::add_column(observer = 'BirdNET') |>
+  dplyr::select(location, recording_date_time, observer, species_code) |>
   dplyr::distinct()
 
-main_report <- data[[2]] %>%
-  wt_tidy_species(remove = c("mammal", "amphibian", "abiotic", "insect"), zerofill = F) %>%
-  dplyr::select(location, recording_date_time, observer, species_code) %>%
+main_report <- data[[2]] |>
+  wt_tidy_species(remove = c("mammal", "amphibian", "abiotic", "insect"), zerofill = F) |>
+  dplyr::select(location, recording_date_time, observer, species_code) |>
   dplyr::distinct()
 
-merged_data <- dplyr::bind_rows(birdnet_report, main_report) %>% dplyr::arrange(location, recording_date_time) %>%
-  dplyr::group_by(location, recording_date_time, observer, species_code) %>%
-  dplyr::mutate(presence = dplyr::row_number()) %>%
-  dplyr::ungroup() %>%
-  tidyr::pivot_wider(names_from = species_code, values_from = presence, values_fill = 0) %>%
+merged_data <- dplyr::bind_rows(birdnet_report, main_report) |>
+  dplyr::arrange(location, recording_date_time) |>
+  dplyr::group_by(location, recording_date_time, observer, species_code) |>
+  dplyr::mutate(presence = dplyr::row_number()) |>
+  dplyr::ungroup() |>
+  tidyr::pivot_wider(names_from = species_code, values_from = presence, values_fill = 0) |>
   tidyr::pivot_longer(cols = -c(location, recording_date_time, observer), names_to = "species_code", values_to = "presence")
 
-missing_detections <- merged_data %>%
-  dplyr::filter(observer == "BirdNET" & presence == 1) %>%
-  dplyr::select(location, recording_date_time, species_code) %>%
+missing_detections <- merged_data |>
+  dplyr::filter(observer == "BirdNET" & presence == 1) |>
+  dplyr::select(location, recording_date_time, species_code) |>
   dplyr::anti_join(
-    merged_data %>%
-      dplyr::filter(observer != "BirdNET") %>%
+    merged_data |>
+      dplyr::filter(observer != "BirdNET") |>
       dplyr::select(location, recording_date_time, species_code, presence),
     by = c("location", "recording_date_time", "species_code")
   )