improves docs (related to #41)

R/supercells.R

@@ -9,7 +9,7 @@
 #' @param compactness A compactness value. Larger values cause clusters to be more compact/even (square).
 #' A compactness value depends on the range of input cell values and selected distance measure.
 #' @param dist_fun A distance function. Currently implemented distance functions are `"euclidean"`, `"jsd"`, `"dtw"` (dynamic time warping), name of any distance function from the `philentropy` package (see [philentropy::getDistMethods()]; "log2" is used in this case), or any user defined function accepting two vectors and returning one value. Default: `"euclidean"`
-#' @param avg_fun An averaging function - how the values of the supercells' centers are calculated? The algorithm internally implements common functions `"mean"` and `"median"` (provided with quotation marks), but also accepts any fitting R function (e.g., `base::mean()` or `stats::median()`, provided as plain function name: `mean`). Default: `"mean"`.
+#' @param avg_fun An averaging function - how the values of the supercells' centers are calculated? The algorithm internally implements common functions `"mean"` and `"median"` (provided with quotation marks), but also accepts any fitting R function (e.g., `base::mean()` or `stats::median()`, provided as plain function name: `mean`). Default: `"mean"`. See details for more information.
 #' @param clean Should connectivity of the supercells be enforced?
 #' @param iter The number of iterations performed to create the output.
 #' @param minarea Specifies the minimal size of a supercell (in cells). Only works when `clean = TRUE`.
@@ -22,6 +22,9 @@
 #' @param future Should the future package be used for parallelization of the calculations? Default: `FALSE`. If `TRUE`, you also need to specify `future::plan()`.
 #' @param verbose An integer specifying the level of text messages printed during calculations. 0 means no messages (default), 1 provides basic messages (e.g., calculation stage).
 #'
+#' @details
+#' If you want to use additional arguments for the averaging function (`avg_fun`), you can create a custom function. For example, if you want to calculate the mean by removing missing values, you can use the following code: `my_mean = function(x) mean(x, na.rm = TRUE)` and then provide `avg_fun = my_mean.`
+#'
 #' @return An sf object with several columns: (1) supercells - an id of each supercell, (2) y and x coordinates, (3) one or more columns with average values of given variables in each supercell
 #'
 #' @references Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., & Süsstrunk, S. (2012). SLIC Superpixels Compared to State-of-the-Art Superpixel Methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2274–2282. https://doi.org/10.1109/tpami.2012.120
@@ -146,7 +149,7 @@ run_slic_chunks = function(ext, x, step, compactness, dist_name,
   if (is.character(x)){
     x = terra::rast(x)
   }
-  
+
   # crops the input to the chunk extent
   x = x[ext[1]:ext[2], ext[3]:ext[4], drop = FALSE]
   # gets the number of rows and columns, and the values of the input