v 0.9.8 on CRAN

.Rbuildignore

@@ -10,4 +10,5 @@ tar.gz$
 .DS_Store
 ^\.github$
 ^\.DS_Store$
-^appveyor\.yml$
+^appveyor\.yml$
+^CRAN-SUBMISSION$

CRAN-SUBMISSION

@@ -0,0 +1,3 @@
+Version: 0.9.8
+Date: 2025-06-11 04:11:57 UTC
+SHA: bab66da242d793907acfe612a135d37e0aded140

DESCRIPTION

@@ -4,13 +4,14 @@ Title: BioAcoustic eveNT classifiER
 Description: Create a hierarchical acoustic event species classifier out of  
   multiple call type detectors as described in 
   Rankin et al (2017) <doi:10.1111/mms.12381>.
-Version: 0.9.6
+Version: 0.9.8
 Authors@R: c(
-  EA = person("Eric", "Archer", email = "eric.archer@noaa.gov", role = c("aut", "cre")),
-  TS = person("Taiki", "Sakai", email = "taiki.sakai@noaa.gov", role = c("aut")))
+  EA = person("Eric", "Archer", email = "eric.ivan.archer@gmail.com", role = c("aut", "cre")),
+  SR = person("Shannon", "Rankin", email = "shannon.rankin@noaa.gov", role = c("aut")))
+URL: https://github.com/SWFSC/banter
+BugReports: https://github.com/SWFSC/banter/issues
 Depends:
-  R (>= 4.1.0),
-  magrittr
+  R (>= 4.1.0)
 Imports:
   dplyr (>= 1.0.6),
   ggplot2 (>= 3.3.3),
@@ -26,7 +27,5 @@ Imports:
   tidyr (>= 1.1.1)
 Suggests: testthat
 License: GNU General Public License
-RoxygenNote: 7.2.3
-URL: https://github.com/ericarcher/banter
-BugReports: https://github.com/ericarcher/banter/issues
+RoxygenNote: 7.3.2
 Encoding: UTF-8

NAMESPACE

@@ -19,6 +19,5 @@ export(runBanterModel)
 export(subsampleDetections)
 exportMethods(predict)
 exportMethods(summary)
-importFrom(magrittr,"%>%")
 importFrom(methods,new)
 importFrom(rlang,.data)

R/addBanterDetector.R

@@ -116,11 +116,11 @@ removeBanterDetector <- function(x, name) {
   start <- Sys.time()
 
   # Combine event data with call ids in detector
-  df <- x@data %>% 
-    dplyr::select(.data$event.id, .data$species) %>% 
-    dplyr::inner_join(data, by = "event.id") %>% 
-    dplyr::mutate(species = as.character(.data$species)) %>% 
-    as.data.frame
+  df <- x@data |> 
+    dplyr::select(.data$event.id, .data$species) |> 
+    dplyr::inner_join(data, by = "event.id") |> 
+    dplyr::mutate(species = as.character(.data$species)) |> 
+    as.data.frame()
 
   # Check if any columns need to be removed because of missing data
   to.remove <- sapply(df, function(i) any(is.na(i)))
@@ -143,12 +143,12 @@ removeBanterDetector <- function(x, name) {
   if(is.null(sampsize)) return(NULL)
 
   # Remove missing species and format columns
-  df <- df %>%
-    dplyr::filter(.data$species %in% names(sampsize)) %>%
-    dplyr::mutate(species = factor(.data$species)) %>%
-    dplyr::mutate(id = paste0(.data$event.id, ".", .data$call.id)) %>%
-    tibble::column_to_rownames("id") %>%
-    as.data.frame() %>%
+  df <- df |>
+    dplyr::filter(.data$species %in% names(sampsize)) |>
+    dplyr::mutate(species = factor(.data$species)) |>
+    dplyr::mutate(id = paste0(.data$event.id, ".", .data$call.id)) |>
+    tibble::column_to_rownames("id") |>
+    as.data.frame() |>
     droplevels()
 
   # Setup number of cores
@@ -157,7 +157,7 @@ removeBanterDetector <- function(x, name) {
 
   # Create random forest parameter list
   params <- list(
-    predictors = df %>% 
+    predictors = df |> 
       dplyr::select(-.data$event.id, -.data$call.id, -.data$species),
     response = df$species,
     sampsize = sampsize,
@@ -206,4 +206,4 @@ removeBanterDetector <- function(x, name) {
     proximity = FALSE, 
     norm.votes = FALSE
   )
-}
+}

R/banter-package.R

@@ -1,6 +1,5 @@
 #' banter
 #' 
-#' @docType package
 #' @name banter-package
 #' @aliases banter
 #' @title BioAcoustic EveNT ClassifiER
@@ -9,10 +8,10 @@
 #'   of dolphins in the California Current using whistles, echolocation clicks,
 #'   and burst pulses. Marine Mammal Science 33:520-540. doi:10.1111/mms.12381
 #' @importFrom rlang .data
-#' @importFrom magrittr %>%
 #' @importFrom methods new
 #' @keywords package
 NULL
+"_PACKAGE"
 
 .onAttach <- function(libname, pkgname) {
   packageStartupMessage(
@@ -46,4 +45,4 @@ NULL
 #' @usage data(test.data)
 #' @format list
 #' @keywords datasets
-NULL
+NULL

R/getBanterModel.R

@@ -5,7 +5,7 @@
 #' @param model name of model to extract. Default is \code{"event"} 
 #'   to extract the event-level model. Can also be the name of a detector.
 #' 
-#' @return a \code{\link{randomForest}} model object.
+#' @return a \code{\link[randomForest]{randomForest}} model object.
 #' 
 #' @author Eric Archer \email{eric.archer@@noaa.gov}
 #' 

R/internals.R

@@ -95,18 +95,18 @@
 #' 
 .meanVotes <- function(x) {
   df <- sapply(names(x), function(d) {
-    x[[d]] %>% 
+    x[[d]] |> 
       tidyr::pivot_longer(
         -.data$event.id,
         names_to = "species", 
         values_to = "prob"
-      ) %>% 
-      dplyr::mutate(species = paste0(d, ".", .data$species)) %>% 
-      dplyr::group_by(.data$event.id, .data$species) %>% 
+      ) |> 
+      dplyr::mutate(species = paste0(d, ".", .data$species)) |> 
+      dplyr::group_by(.data$event.id, .data$species) |> 
       dplyr::summarize(prob.mean = mean(.data$prob), .groups = "drop") 
-  }, simplify = FALSE) %>% 
-    dplyr::bind_rows() %>% 
+  }, simplify = FALSE) |> 
+    dplyr::bind_rows() |> 
     tidyr::pivot_wider(names_from = "species", values_from = "prob.mean") 
 
   replace(df, is.na(df), 0)
-}
+}

R/modelPctCorrect.R

@@ -49,12 +49,12 @@ modelPctCorrect <- function(x) {
       model = model,
       stringsAsFactors = FALSE
     )
-  }) %>% 
-    dplyr::bind_rows() %>% 
+  }) |> 
+    dplyr::bind_rows() |> 
     dplyr::mutate(
       model = factor(.data$model, levels = c(names(x@detectors), "event"))
-    ) %>% 
-    tidyr::pivot_wider(names_from = "model", values_from = "pct.correct") %>% 
-    dplyr::mutate(species = factor(.data$species, levels = c(spp, "Overall"))) %>% 
+    ) |> 
+    tidyr::pivot_wider(names_from = "model", values_from = "pct.correct") |> 
+    dplyr::mutate(species = factor(.data$species, levels = c(spp, "Overall"))) |> 
     dplyr::arrange(.data$species)
-}
+}

R/numCalls.R

@@ -39,16 +39,16 @@ numCalls <- function(x, by = c("species", "event")) {
   if(is.null(x@detectors)) stop("no detectors loaded in model.")
   by <- switch(match.arg(by), species = "species", event = "event.id")
   df <- lapply(names(x@detectors), function(d) {
-    x@data %>% 
-      dplyr::left_join(x@detectors[[d]]@ids, by = "event.id") %>% 
-      dplyr::group_by(dplyr::across(by)) %>% 
-      dplyr::summarize(n = sum(!is.na(.data$call.id)), .groups = "drop") %>% 
+    x@data |> 
+      dplyr::left_join(x@detectors[[d]]@ids, by = "event.id") |> 
+      dplyr::group_by(dplyr::across(by)) |> 
+      dplyr::summarize(n = sum(!is.na(.data$call.id)), .groups = "drop") |> 
       dplyr::mutate(detector = paste0("num.", d))
-  }) %>%
-    dplyr::bind_rows() %>% 
+  }) |>
+    dplyr::bind_rows() |> 
     tidyr::pivot_wider(names_from = "detector", values_from = "n")
 
-  replace(df, is.na(df), 0) %>% 
+  replace(df, is.na(df), 0) |> 
     as.data.frame()
 }
 
@@ -58,17 +58,17 @@ numCalls <- function(x, by = c("species", "event")) {
 propCalls <- function(x, by = c("species", "event")) {
   df <- numCalls(x, by)
   by <- colnames(df)[1]
-  df %>% 
+  df |> 
     tidyr::pivot_longer(
       -dplyr::all_of(by),
       names_to = "detector", 
       values_to = "n"
-    ) %>% 
-    dplyr::group_by(dplyr::across(by)) %>%
-    dplyr::mutate(prop = .data$n / sum(.data$n, na.rm = TRUE)) %>% 
-    dplyr::ungroup() %>% 
-    dplyr::select(-.data$n) %>% 
-    dplyr::mutate(detector = gsub("num.", "prop.", .data$detector)) %>% 
-    tidyr::pivot_wider(names_from = "detector", values_from = "prop") %>% 
+    ) |> 
+    dplyr::group_by(dplyr::across(by)) |>
+    dplyr::mutate(prop = .data$n / sum(.data$n, na.rm = TRUE)) |> 
+    dplyr::ungroup() |> 
+    dplyr::select(-.data$n) |> 
+    dplyr::mutate(detector = gsub("num.", "prop.", .data$detector)) |> 
+    tidyr::pivot_wider(names_from = "detector", values_from = "prop") |> 
     as.data.frame()
-}
+}

R/numEvents.R

@@ -32,7 +32,6 @@
 #' # number of events in burst pulse (bp) detector model
 #' numEvents(bant.mdl, "bp")
 #' 
-#' @importFrom magrittr %>%
 #' @importFrom rlang .data
 #' 
 #' @export
@@ -42,12 +41,12 @@ numEvents <- function(x, model = "event") {
   df <- if(model == "event") {
     x@data 
   } else {
-    x@data %>% 
+    x@data |> 
       dplyr::filter(.data$event.id %in% x@detectors[[model]]@ids$event.id)
   }
   spp.fac <- factor(df$species, levels = sort(unique(x@data$species)))
-  table(species = spp.fac) %>% 
-    as.data.frame() %>% 
-    stats::setNames(c("species", "num.events")) %>% 
+  table(species = spp.fac) |> 
+    as.data.frame() |> 
+    stats::setNames(c("species", "num.events")) |> 
     dplyr::mutate(species = as.character(.data$species)) 
 }

R/predict.R

@@ -123,10 +123,10 @@ predict.banter_model <- function(object, new.data, ...) {
   # Get number of calls in each detector for each event
   detector.num <- lapply(names(object@detectors), function(d) {
     if(d %in% names(new.data$detectors)) {
-      new.data$events %>% 
-        dplyr::left_join(new.data$detectors[[d]], by = "event.id") %>% 
-        dplyr::group_by(.data$event.id) %>% 
-        dplyr::summarize(n = sum(!is.na(.data$call.id)), .groups = "drop") %>% 
+      new.data$events |> 
+        dplyr::left_join(new.data$detectors[[d]], by = "event.id") |> 
+        dplyr::group_by(.data$event.id) |> 
+        dplyr::summarize(n = sum(!is.na(.data$call.id)), .groups = "drop") |> 
         dplyr::mutate(detector = paste0("prop.", d))
     } else { # detector not present in new.data, proportion = 0
       tibble::tibble(
@@ -135,16 +135,16 @@ predict.banter_model <- function(object, new.data, ...) {
         detector = paste0("prop.", d)
       )
     }
-  }) %>%
+  }) |>
     dplyr::bind_rows()
 
   # Convert number to proportion of calls
-  detector.prop <- detector.num %>% 
-    dplyr::group_by(.data$event.id) %>% 
-    dplyr::mutate(n = .data$n / sum(.data$n, na.rm = TRUE)) %>% 
-    dplyr::ungroup() %>% 
+  detector.prop <- detector.num |> 
+    dplyr::group_by(.data$event.id) |> 
+    dplyr::mutate(n = .data$n / sum(.data$n, na.rm = TRUE)) |> 
+    dplyr::ungroup() |> 
     tidyr::pivot_wider(names_from = "detector", values_from = "n")
-  detector.prop <- replace(detector.prop, is.na(detector.prop), 0) %>% 
+  detector.prop <- replace(detector.prop, is.na(detector.prop), 0) |> 
     as.data.frame()
 
   # Calculate mean votes for each event
@@ -154,38 +154,38 @@ predict.banter_model <- function(object, new.data, ...) {
         object@detectors[[d]]@model, 
         new.data$detectors[[d]], 
         type = "prob"
-      ) %>% 
-        as.data.frame() %>% 
+      ) |> 
+        as.data.frame() |> 
         dplyr::bind_cols(event.id = new.data$detectors[[d]]$event.id)
     } else { # detector not present in model - fill with 0's
       spp <- colnames(object@detectors[[d]]@model$votes)
       vote.0 <- matrix(0, nrow = length(unique.events), ncol = length(spp))
       colnames(vote.0) <- spp
       dplyr::bind_cols(as.data.frame(vote.0), event.id = unique.events)
     }
-  }, simplify = FALSE) %>% 
+  }, simplify = FALSE) |> 
     .meanVotes()
 
   # Construct data.frame to predict
-  df <- new.data$events %>% 
-    dplyr::left_join(detector.prop, by = "event.id") %>% 
+  df <- new.data$events |> 
+    dplyr::left_join(detector.prop, by = "event.id") |> 
     dplyr::left_join(detector.votes, by = "event.id")
 
     # add call rate columns if duration is present and there is no missing data
     if("duration" %in% colnames(df)) {
       if(all(!is.na(df$duration))) {
-        df <- df %>% 
+        df <- df |> 
           dplyr::left_join(
-            detector.num %>%  
+            detector.num |>  
               dplyr::left_join(
                 dplyr::select(df, "event.id", "duration"), 
                 by = "event.id"
-              ) %>% 
+              ) |> 
               dplyr::mutate(
                 detector = gsub("prop.", "rate.", .data$detector),
                 n = .data$n / .data$duration
-              ) %>% 
-              dplyr::select(-.data$duration) %>% 
+              ) |> 
+              dplyr::select(-.data$duration) |> 
               tidyr::pivot_wider(names_from = "detector", values_from = "n"),
             by = "event.id"
           )
@@ -206,11 +206,11 @@ predict.banter_model <- function(object, new.data, ...) {
       predict(object@model, df, type = "prob"),
       stringsAsFactors = FALSE
     ),
-    detector.freq = detector.num %>% 
-      dplyr::mutate(detector = gsub("prop.", "", .data$detector)) %>% 
-      dplyr::group_by(.data$detector) %>% 
-      dplyr::summarize(num.events = sum(.data$n > 0)) %>% 
-      dplyr::ungroup() %>% 
+    detector.freq = detector.num |> 
+      dplyr::mutate(detector = gsub("prop.", "", .data$detector)) |> 
+      dplyr::group_by(.data$detector) |> 
+      dplyr::summarize(num.events = sum(.data$n > 0)) |> 
+      dplyr::ungroup() |> 
       as.data.frame(stringsAsFactors = FALSE)
   )
 
@@ -237,4 +237,4 @@ predict.banter_model <- function(object, new.data, ...) {
 #' @name predict
 #' @rdname predict
 #' @aliases predict,banter_model-method
-methods::setMethod("predict", "banter_model", predict.banter_model) 
+methods::setMethod("predict", "banter_model", predict.banter_model)