Fix linting

R/stitch.R

@@ -12,23 +12,23 @@
 #' do not overlap. Defaults to `TRUE`.
 #'
 #' @export
-#' 
+#'
 #' @examples
 #'
-#' # Simulate a UV-VIS and NIR reflectance spectrum whose wavelength regions 
+#' # Simulate a UV-VIS and NIR reflectance spectrum whose wavelength regions
 #' # slightly overlap then stitch them together, with the overlapping
 #' # regions being averaged.
-#' 
+#'
 #' # Simulate specs
 #' reflect1 <- simulate_spec(wl_peak = 550, xlim = c(300, 700))
 #' reflect2 <- simulate_spec(wl_inflect = 1100, xlim = c(650, 1200))
-#' 
+#'
 #' # Ensure the names of the spectra match
 #' names(reflect1) <- names(reflect2) <- c('wl', 'sample_1')
-#' 
+#'
 #' # Stitch the spectra together by their wavelength column
 #' full_spec <- stitch(reflect1, reflect2)
-#' 
+#'
 #' # Plot the resulting spectrum
 #' plot(full_spec)
 #'
@@ -39,21 +39,21 @@
 #' # the spectra match, but are in a different order in the two rspec objects. This isn't
 #' # an issue, as the function can match up the spectra by name irrespective of their
 #' # ordering
-#' 
+#'
 #' # Simulate UV-VIS and NIR spectra
 #' reflect_vis <- merge(simulate_spec(wl_peak = 550, xlim = c(300, 700)),
 #'                      simulate_spec(wl_peak = 550, xlim = c(300, 700)))
 #' reflect_nir <- merge(simulate_spec(wl_inflect = 1000, xlim = c(800, 1250)),
 #'                      simulate_spec(wl_inflect = 1100, xlim = c(800, 1250)))
-#' 
+#'
 #' # Ensure the names of the spectra exist in each, albeit in a different order
 #' names(reflect_vis) <- c('wl', 'sample_1', 'sample_2')
 #' names(reflect_nir) <- c('wl', 'sample_2', 'sample_1')
-#' 
+#'
 #' # Stitch together by their wavelength column, with missing regions being
 #' # interpolated
 #' reflect_vis_nir <- stitch(reflect_vis, reflect_nir)
-#' 
+#'
 #' # Plot the resulting spectrum
 #' plot(reflect_vis_nir)
 #'
@@ -63,67 +63,67 @@
 #' @seealso [as.rspec()], [merge.rspec()]
 
 stitch <- function(rspec1, rspec2,
-                   overlap_method = c('average', 'minimum', 'maximum'),
+                   overlap_method = c("average", "minimum", "maximum"),
                    interp = TRUE) {
-  
+
   # Class check
-  if (!inherits(rspec1, 'rspec') || !inherits(rspec2, 'rspec')) {
+  if (!inherits(rspec1, "rspec") || !inherits(rspec2, "rspec")) {
     stop("Both inputs must be of class 'rspec'")
   }
-  
+
   # Valied overlap_method
   overlap_method <- match.arg(overlap_method)
-  
+
   # Check that at least one spectrum has a matching name in both objects
   common_cols <- intersect(names(rspec1), names(rspec2))
   if (length(common_cols) <= 1) {
     stop("At least one spectrum in both rspec objects must have a matching name")
   }
-  
+
   # Identify unique spectra in both objects
   unique_rspec1 <- setdiff(names(rspec1), common_cols)
   unique_rspec2 <- setdiff(names(rspec2), common_cols)
-  
+
   # Create NA-filled columns in each rspec for unique spectra in the other
   rspec1[, unique_rspec2] <- NA
   rspec2[, unique_rspec1] <- NA
-  
+
   # Reorder columns of rspec2 to match rspec1
   rspec2 <- rspec2[, names(rspec1)]
-  
+
   # Merge by wl
   res <- rbind(rspec1, rspec2)
-  
+
   # Handle overlapping wl values
-  if (any(duplicated(res$wl))) {
+  if (anyDuplicated(res$wl) > 0) {
     overlap_wl <- unique(res$wl[duplicated(res$wl)])
-    
+
     for (wl in overlap_wl) {
       idx <- which(res$wl == wl)
-      
+
       # Replace with a switch statement
       switch(overlap_method,
-             average = res[idx[1],-1] <- colMeans(as.matrix(res[idx,-1]), na.rm = TRUE),
-             minimum = res[idx[1],-1] <- apply(as.matrix(res[idx,-1]), 2, min, na.rm = TRUE),
-             maximum = res[idx[1],-1] <- apply(as.matrix(res[idx,-1]), 2, max, na.rm = TRUE)
+             average = res[idx[1], -1] <- colMeans(as.matrix(res[idx, -1]), na.rm = TRUE),
+             minimum = res[idx[1], -1] <- apply(as.matrix(res[idx, -1]), 2, min, na.rm = TRUE),
+             maximum = res[idx[1], -1] <- apply(as.matrix(res[idx, -1]), 2, max, na.rm = TRUE)
       )
-      
+
       # Remove extra rows
       if (length(idx) > 1) {
         res <- res[-idx[-1], ]
       }
     }
   }
-  
+
   # Interpolate missing values
   if (interp) {
     full_wl_range <- min(res$wl):max(res$wl)
     missing_wl <- setdiff(full_wl_range, res$wl)
-    
-    if(length(missing_wl) > 0) {
+
+    if (length(missing_wl) > 0) {
       new_rows <- data.frame(wl = missing_wl, matrix(rep(NA, length(names(res)) - 1), ncol = length(names(res)) - 1))
       names(new_rows)[-1] <- names(res)[-1]
-      
+
       # Interpolate only common spectra
       for (col in common_cols[-1]) {
         new_values <- approx(res$wl, res[, col], xout = missing_wl)$y
@@ -132,12 +132,12 @@ stitch <- function(rspec1, rspec2,
       res <- rbind(res, new_rows)
     }
   }
-  
+
   # Sort stitched spec by wl
   res <- res[order(res$wl), ]
-  
+
   # Classes
   class(res) <- c("rspec", "data.frame")
-  
+
   res
 }