Fixing 'no visible binding for global variable' issues

NAMESPACE

@@ -7,7 +7,6 @@ S3method(as_OpenSpecy,data.frame)
 S3method(as_OpenSpecy,default)
 S3method(as_OpenSpecy,hyperSpec)
 S3method(as_OpenSpecy,list)
-S3method(as_OpenSpecy,vector)
 S3method(conform_spec,OpenSpecy)
 S3method(conform_spec,default)
 S3method(correlate_spectra,OpenSpecy)

R/as_OpenSpecy.R

@@ -152,15 +152,8 @@ as_OpenSpecy.list <- function(x, ...) {
 #'
 #' @export
 as_OpenSpecy.hyperSpec <- function(x, ...) {
-    do.call("as_OpenSpecy", list(x = x@wavelength,
-                                 spectra = as.data.table(t(x$spc)), ...))
-}
-
-#' @rdname as_OpenSpecy
-#'
-#' @export
-as_OpenSpecy.vector <- function(x, ...) {
-    do.call("as_OpenSpecy", list(x = x, spectra = spectra, ...))
+  do.call("as_OpenSpecy", list(x = x@wavelength,
+                               spectra = as.data.table(t(x$spc)), ...))
 }
 
 #' @rdname as_OpenSpecy

R/collapse_particles.R

@@ -43,7 +43,7 @@ collapse_spectra <- function(object) {
         transpose(make.names = "id")
 
     object$metadata <- object$metadata |>
-        unique(by = c("particle_ids", "area", "feret_max", "centroid_y",
+        unique(by = c("particle_id", "area", "feret_max", "centroid_y",
                       "centroid_x"))
 
     return(object)
@@ -72,7 +72,7 @@ characterize_particles <- function(object, particles) {
         stop("Particles needs to be a character or logical vector.", call. = F)
     }
 
-    object$metadata <- particles_df[setDT(object$metadata), on = .(x, y)][,particle_ids := ifelse(is.na(particle_ids), "-88", particle_ids)][,centroid_x := mean(x), by = "particle_ids"][,centroid_y := mean(y), by = "particle_ids"]
+    object$metadata <- particles_df[setDT(object$metadata), on = .(x, y)][,particle_id := ifelse(is.na(particle_id), "-88", particle_id)][,centroid_x := mean(x), by = "particle_id"][,centroid_y := mean(y), by = "particle_id"]
 
     return(object)
 }
@@ -81,13 +81,14 @@ characterize_particles <- function(object, particles) {
 #' @importFrom stats dist
 .characterize_particles <- function(x, binary, name = NULL) {
   # Label connected components in the binary image
-  binary_matrix <- matrix(binary, ncol = max(x$metadata$y)+1, byrow = T)
-  labeled_image <- imager::label(imager::as.cimg(binary_matrix), high_connectivity = T)
+  binary_matrix <- matrix(binary, ncol = max(x$metadata$y) + 1, byrow = T)
+  labeled_image <- imager::label(imager::as.cimg(binary_matrix),
+                                 high_connectivity = T)
 
   # Create a dataframe with particle IDs for each true pixel
   particle_points_dt <- data.table(x = x$metadata$x,
                                    y = x$metadata$y,
-                                   particle_ids = as.character(as.vector(t(ifelse(binary_matrix, labeled_image, -88)))))
+                                   particle_id = as.character(as.vector(t(ifelse(binary_matrix, labeled_image, -88)))))
 
   # Apply the logic to clean components
   cleaned_components <- ifelse(binary_matrix, labeled_image, -88)
@@ -114,9 +115,13 @@ characterize_particles <- function(object, particles) {
     # Area
     area <- sum(cleaned_components == as.integer(id))
 
-    data.table(particle_ids = id, area = area, feret_max = feret_max)
-  }), fill = TRUE)
+    data.table(particle_id = id, area = area, feret_max = feret_max)
+  }), fill = T)
 
   # Join with the coordinates from the binary image
-  particle_points_dt[particles_dt[, particle_ids := if (!is.null(name)) paste0(name, "_", particle_ids) else particle_ids], on = "particle_ids"]
+  if (!is.null(name)) {
+    particles_dt$particle_id <- paste0(name, "_", particles_dt$particle_id)
+  }
+
+  particle_points_dt[particles_dt, on = "particle_id"]
 }

R/smooth_intens.R

@@ -11,7 +11,7 @@
 #' A typical good smooth can be achieved with 11 data point window and a 3rd or
 #' 4th order polynomial.
 #'
-#' @param object a list object of class \code{OpenSpecy}.
+#' @param x an object of class \code{OpenSpecy}.
 #' @param p polynomial order for the filter
 #' @param n number of data points in the window, filter length (must be odd).
 #' @param m the derivative order if you want to calculate the derivative. Zero (default) is no derivative.
@@ -40,34 +40,35 @@
 #' Simplified Least Squares Procedures.” \emph{Analytical Chemistry},
 #' \strong{36}(8), 1627--1639.
 #'
-#' @importFrom magrittr %>%
-#' @importFrom signal filter sgolay
 #' @importFrom data.table .SD
 #' @export
-smooth_intens <- function(object, ...) {
+smooth_intens <- function(x, ...) {
   UseMethod("smooth_intens")
 }
 
 #' @rdname smooth_intens
 #'
 #' @export
-smooth_intens.default <- function(object, ...) {
-  stop("object 'x' needs to be of class 'OpenSpecy'", call. = F)
+smooth_intens.default <- function(x, ...) {
+  stop("'x' needs to be of class 'OpenSpecy'", call. = F)
 }
 
 #' @rdname smooth_intens
 #'
 #' @export
-smooth_intens.OpenSpecy <- function(object, p = 3, n = 11, m = 0, abs = F, make_rel = TRUE,
-                                  ...) {
-  filt <- object$spectra[, lapply(.SD, .sgfilt, p = p, n = n, m = m, abs = abs, ...)]
-
-  if (make_rel) object$spectra <- make_rel(filt) else object$spectra <- filt
+smooth_intens.OpenSpecy <- function(x, p = 3, n = 11, m = 0, abs = FALSE,
+                                    make_rel = TRUE,
+                                    ...) {
+  filt <- x$spectra[, lapply(.SD, .sgfilt, p = p, n = n, m = m, abs = abs, ...)]
+  if (make_rel) x$spectra <- make_rel(filt) else x$spectra <- filt
 
-  return(object)
+  return(x)
 }
 
+#' @importFrom signal filter sgolay
 .sgfilt <- function(y, p, n, m, abs = F, ...) {
-  signal::filter(filt = sgolay(p = p, n = n, m = m, ...), x = y) %>%
-        {if(abs) abs(.) else .}
+  out <- signal::filter(filt = sgolay(p = p, n = n, m = m, ...), x = y)
+  if(abs) out <- abs(out)
+
+  return(out)
 }

tests/testthat/test-collapse_particles.R

@@ -4,7 +4,7 @@ test_that("check that particles are identified when given logical", {
     map$metadata$particles <- map$metadata$x == 0
     identified_map <- characterize_particles(map, map$metadata$particles)
     expect_true(is_OpenSpecy(identified_map))
-    expect_true(length(unique(identified_map$metadata$particle_ids)) == 2)
+    expect_true(length(unique(identified_map$metadata$particle_id)) == 2)
     expect_true(max(identified_map$metadata$area, na.rm = T) == 13)
     expect_true(max(identified_map$metadata$feret_max, na.rm = T) == 13)
 })
@@ -14,7 +14,7 @@ test_that("check that particles are identified when given character", {
     map$metadata$particles <- ifelse(map$metadata$x == 1, "particle", "not_particle")
     identified_map <- characterize_particles(map, map$metadata$particles)
     expect_true(is_OpenSpecy(identified_map))
-    expect_true(length(unique(identified_map$metadata$particle_ids)) == 3)
+    expect_true(length(unique(identified_map$metadata$particle_id)) == 3)
     expect_true(max(identified_map$metadata$area, na.rm = T) == 182)
     expect_true(round(max(identified_map$metadata$feret_max, na.rm = T)) == 19)
 })
@@ -61,7 +61,7 @@ identified_map <- characterize_particles(map, particles)
 test_collapsed <- collapse_spectra(identified_map)
 expect_true(is_OpenSpecy(test_collapsed))
 
-expect_equal(c("particle_ids", "area", "feret_max", "centroid_y", "centroid_x"), names(test_collapsed$metadata))
+expect_equal(c("particle_id", "area", "feret_max", "centroid_y", "centroid_x"), names(test_collapsed$metadata))
 
 })