Preparing for CRAN submission

.Rbuildignore

@@ -9,3 +9,4 @@ extra/*
 ^_pkgdown\.yml$
 ^docs$
 ^pkgdown$
+^CRAN-SUBMISSION$

CRAN-SUBMISSION

@@ -0,0 +1,3 @@
+Version: 1.0.0
+Date: 2024-07-14 16:18:21 UTC
+SHA: cf16688021606b21a9f4ec274e2dd4ebd51210dc

LICENSE

@@ -1,2 +1,2 @@
-YEAR: 2020
+YEAR: 2024
 COPYRIGHT HOLDER: Pablo Paccioretti

LICENSE.md

@@ -1,6 +1,6 @@
 # MIT License
 
-Copyright (c) 2020 Pablo Paccioretti
+Copyright (c) 2024 Pablo Paccioretti
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

R/compare_zones.R → R/compare_zone.R

@@ -10,26 +10,15 @@
 #' @param grid_dim \code{numeric} grid dimentins to estimate spatial variance
 #'
 #' @return \code{list} with differences and descriptive_stat
+#' @references
+#' Paccioretti, P., Córdoba, M., & Balzarini, M. (2020).
+#' FastMapping: Software to create field maps and identify management zones
+#' in precision agriculture. Computers and Electronics in Agriculture, 175,
+#' 105556 https://doi.org/10.1016/j.compag.2020.105556.
 #' @export
 #'
-#' @examples
+#' @example inst/examples/compare_zone.R
 #'
-#' library(sf)
-#' data(wheat, package = "paar")
-#'
-#' ##Convert to an sf object
-#' wheat <- sf::st_as_sf(wheat,
-#'                       coords = c("x", "y"),
-#'                       crs = 32720)
-#' clusters <- paar::kmspc(
-#'   wheat,
-#'   variables = c('CE30', 'CE90', 'Elev', 'Pe', 'Tg'),
-#'   number_cluster = 3:4
-#' )
-#' data_clusters <- cbind(wheat, clusters$cluster)
-#' compare_zone(data_clusters,
-#'              "Elev",
-#'              "Cluster_3")
 
 compare_zone <- function(data,
                          variable,
@@ -317,7 +306,6 @@ CalcLetras <- function(myMeans, pvalue, alpha = 0.05) {
     if (chequeo > n)
       break
     for (i in j:n) {
-      # browser()
       stest <- Qm[q[i], q[j]] > alpha
       if (stest) {
         if (lastC(M[i]) != letras[k]) {
@@ -330,7 +318,7 @@ CalcLetras <- function(myMeans, pvalue, alpha = 0.05) {
         cambio1 <- 0
         ja <- j
         for (jj in cambio:n)
-          M[jj] <- paste(M[jj], "", sep = "") # El espacio
+          M[jj] <- paste(M[jj], "", sep = "")
         M[cambio] <- paste(M[cambio], letras[k], sep = "")
         for (v in ja:cambio) {
           if (Qm[q[v], q[cambio]] <= alpha) {
@@ -346,15 +334,14 @@ CalcLetras <- function(myMeans, pvalue, alpha = 0.05) {
     if (cambio1 == 0)
       j <- j + 1
   }
-  #-----------
-  # browser()
+
   w <- data.frame(w, stat = M)
   if (k > 81
   )
     cat(
       "\n",
       k,
-      "groups are estimated.The number of groups exceeded the maximum of 81 labels. change to group=FALSE.\n"
+      "groups are estimated.The number of groups exceeded the maximum of 81 labels. Change to group=FALSE.\n"
     )
   invisible(w)
 }

R/depurate.R

@@ -4,12 +4,33 @@
 #'  outliers and spatial outliers or local defective observations. Default
 #'  values are optimized for precision agricultural data.
 #'
-#' @inheritParams remove_border
-#' @inheritParams remove_outlier
-#' @inheritParams remove_inlier
+#' @param x an \code{sf} points object
+#' @param y \code{character} with the name of the variable to use for
+#'   depuration/filtering process
 #' @param toremove \code{character} vector specifying the procedure to
 #'   implement for errors removal. Default 'edges', 'outlier', 'inlier'.
 #'   See Details.
+#' @param crs coordinate reference system: integer with the EPSG code,
+#'   or character with proj4string to convert coordinates if \code{x} has
+#'   longitude/latitude data
+#' @param buffer \code{numeric} distance in meters to be removed. Negative
+#'   values are recommended
+#' @param ylimitmax \code{numeric} of length 1 indicating the maximum limit
+#'   for the \code{y} variable. If \code{NA} \code{Inf} is assumed
+#' @param ylimitmin \code{numeric} of length 1 indicating the minimum limit
+#'   for the \code{y} variable. If \code{NA} \code{-Inf} is assumed
+#' @param sdout \code{numeric} values outside the interval
+#'  \eqn{mean ± sdout × sdout} values will be removed
+#' @param ldist \code{numeric} lower distance bound to identify neighbors
+#' @param udist \code{numeric} upper distance bound to identify neighbors
+#' @param criteria \code{character} with "LM" and/or "MP" for methods to
+#'    identify spatial outliers
+#' @param zero.policy default NULL, use global option value;
+#'   if FALSE stop with error for any empty neighbors sets,
+#'   if TRUE permit the weights list to be formed with zero-length
+#'   weights vectors
+#' @param poly_border \code{sf} object with one polygon or NULL. Can be
+#' the result of \code{concaveman::concaveman}
 #'
 #' @details
 #' Possible values for \code{toremove} are one or more elements of:
@@ -26,7 +47,13 @@
 #' automating error removal from yield maps. Precision Agric 20, 1030–1044
 #' (2019). https://doi.org/10.1007/s11119-018-09632-8
 #'
-#' @return an object of class \code{paar}
+#' @return an object of class \code{paar} with two elements:
+#' \describe{
+#'  \item{depurated_data}{\code{sf} object with the data after the removal
+#'  process}
+#'  \item{condition}{\code{character} vector with the condition of each
+#'  observation}
+#'  }
 #' @export
 #' @example inst/examples/depurate.R
 
@@ -141,7 +168,8 @@ depurate <- function(x,
 #'   values are recommended
 #' @param poly_border \code{sf} object with one polygon or NULL. Can be
 #' the result of \code{concaveman::concaveman}
-#'
+#' @noRd
+#' @keywords internal
 remove_border <- function(x,
                           crs = NULL,
                           buffer,
@@ -282,8 +310,8 @@ remove_border <- function(x,
 #'   for the \code{y} variable. If \code{NA} \code{-Inf} is assumed
 #' @param sdout \code{numeric} values outside the interval
 #'  \eqn{mean ± sdout × sdout} values will be removed
-#'
-#'
+#' @noRd
+#' @keywords internal
 remove_outlier <- function(x,
                            y,
                            ylimitmax = NA,
@@ -351,7 +379,8 @@ remove_outlier <- function(x,
 #'   if FALSE stop with error for any empty neighbors sets,
 #'   if TRUE permit the weights list to be formed with zero-length
 #'   weights vectors
-#'
+#' @noRd
+#' @keywords internal
 remove_inlier <- function(x,
                           y,
                           ldist = 0,
@@ -442,6 +471,7 @@ remove_inlier <- function(x,
 #' @param is_error internal object
 #' @param remove_result internal results from remove_* functions
 #' @noRd
+#' @keywords internal
 is_error_update <- function(is_error, remove_result) {
   # Keeps NA, others are conditions
   is_error_no_na <- is_error[is.na(is_error$because),]

R/fuzzy_k_means.R

@@ -9,6 +9,7 @@
 #' @return a list with classification results and indices to select best number of
 #' clusters.
 #' @export
+#' @example inst/examples/fuzzy_k_means.R
 #'
 fuzzy_k_means <- function(data,
                           variables,

R/kmspc.R

@@ -1,7 +1,7 @@
 #' MULTISPATI-PCA clustering
 #'
 #'
-#' @inheritParams remove_inlier
+#' @inheritParams depurate
 #' @param data sf object
 #' @param variables variables to use for clustering, if missing, all numeric
 #' variables will be used

R/spatial_t_test.R

@@ -8,15 +8,16 @@
 #' columns \code{matrix} or \code{data.frame} specifying coordinates.
 #'
 #' @param variables \code{character} vector with column names to perform ttest
-#'
+#' @return a data.frame with the correlation and p-value for each pair
+#'  of variables
 #' @export
 
 
 spatial_t_test <- function(data, variables) {
 
   if (!requireNamespace("SpatialPack", quietly = TRUE)) {
     stop(paste0("SpatialPack package is needed to perform spatial_t_test", "\n",
-               "Install it with 'install.packages(SpatialPack)'"),
+               "Install it with 'install.packages(\'SpatialPack\')'"),
                .call = FALSE)
   }
 

README.Rmd

@@ -27,11 +27,11 @@ precision agriculture spatial data depuration.
 
 ## Installation
 
-<!-- You can install the released version of paar from [CRAN](https://CRAN.R-project.org) with: -->
+You can install the released version of paar from [CRAN](https://CRAN.R-project.org) with (not-yet):
 
-<!-- ``` r -->
-<!-- install.packages("paar") -->
-<!-- ``` -->
+``` r
+# install.packages("paar")
+```
 You can install the development version from [GitHub](https://github.com/PPaccioretti/paar) with:
 
 ``` r

README.md

@@ -16,10 +16,12 @@ agriculture spatial data depuration.
 
 ## Installation
 
-<!-- You can install the released version of paar from [CRAN](https://CRAN.R-project.org) with: -->
-<!-- ``` r -->
-<!-- install.packages("paar") -->
-<!-- ``` -->
+You can install the released version of paar from
+[CRAN](https://CRAN.R-project.org) with (not-yet):
+
+``` r
+# install.packages("paar")
+```
 
 You can install the development version from
 [GitHub](https://github.com/PPaccioretti/paar) with:

inst/examples/compare_zone.R

@@ -0,0 +1,16 @@
+library(sf)
+data(wheat, package = "paar")
+
+##Convert to an sf object
+wheat <- sf::st_as_sf(wheat,
+                      coords = c("x", "y"),
+                      crs = 32720)
+clusters <- paar::kmspc(
+  wheat,
+  variables = c('CE30', 'CE90', 'Elev', 'Pe', 'Tg'),
+  number_cluster = 3:4
+)
+data_clusters <- cbind(wheat, clusters$cluster)
+compare_zone(data_clusters,
+             "Elev",
+             "Cluster_3")

inst/examples/fuzzy_k_means.R

@@ -0,0 +1,27 @@
+library(sf)
+data(wheat, package = 'paar')
+
+# Transform the data.frame into a sf object
+wheat_sf <- st_as_sf(wheat,
+                     coords = c('x', 'y'),
+                     crs = 32720)
+
+# Run the fuzzy_k_means function
+fuzzy_k_means_results <- fuzzy_k_means(wheat_sf,
+                               variables = 'Tg',
+                               number_cluster = 2:4)
+
+# Print the summaryResults
+fuzzy_k_means_results$summaryResults
+
+# Print the indices
+fuzzy_k_means_results$indices
+
+# Print the cluster
+head(fuzzy_k_means_results$cluster, 5)
+
+# Combine the results in a single object
+wheat_clustered <- cbind(wheat_sf, fuzzy_k_means_results$cluster)
+
+# Plot the results
+plot(wheat_clustered[, "Cluster_2"])

inst/examples/spatial_t_test.R

@@ -0,0 +1,20 @@
+if (requireNamespace("SpatialPack", quietly = TRUE)) {
+  library(sf)
+  data(wheat, package = 'paar')
+
+  # Transform the data.frame into a sf object
+  wheat_sf <- st_as_sf(wheat,
+                       coords = c('x', 'y'),
+                       crs = 32720)
+
+  # Run spatial t test
+  t_test_results <-
+    spatial_t_test(
+      wheat_sf,
+      variables = c('CE30', 'CE90'))
+
+  # Print the t_test_results
+  t_test_results
+
+
+}