assorted fixes (#248)

- fix compatibility issues with matrix pkg updates - fix cran warnings - update pkg docs to provide instructions for installing cplexAPI (fix #214) - fix #247 - improve code coverage - fix #218 - fix broken Urls - update cran-comments.md Co-authored-by: Richard Schuster <ricschuster@users.noreply.github.com>
prioritizr · Sep 6, 2022 · 4b6a18d · 4b6a18d
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diff --git a/.Rbuildignore b/.Rbuildignore
@@ -9,6 +9,7 @@
 ^appveyor\.yml$
 .gitkeep
 ^Makefile$
+^data-raw$
 cran-comments.md
 CRAN-RELEASE
 _pkgdown.yml

diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: prioritizr
 Type: Package
-Version: 7.2.0.8
+Version: 7.2.1
 Title: Systematic Conservation Prioritization in R
 Description:
     Systematic conservation prioritization using mixed integer linear
@@ -15,10 +15,12 @@ Description:
     multiple stakeholders. To solve large-scale or complex conservation
     planning problems, users should install the Gurobi optimization software
     (available from <https://www.gurobi.com/>) and the 'gurobi' R package (see
-    Gurobi Installation Guide vignette for details). Additionally, the 'rcbc' R
-    package (available at <https://github.com/dirkschumacher/rcbc>) can be used
-    to generate solutions using the CBC optimization software
-    (<https://github.com/coin-or/Cbc>).
+    Gurobi Installation Guide vignette for details). Users can also install the
+    IBM CPLEX software (<https://www.ibm.com/analytics/cplex-optimizer>) and
+    the 'cplexAPI' R package (available at <https://github.com/cran/cplexAPI>).
+    Additionally, the 'rcbc' R package (available at
+    <https://github.com/dirkschumacher/rcbc>) can be used to generate solutions
+    using the CBC optimization software (<https://github.com/coin-or/Cbc>).
 Authors@R:
     c(person(c('Jeffrey', 'O'), 'Hanson',
         email='jeffrey.hanson@uqconnect.edu.au', role = c('aut'),
@@ -55,7 +57,7 @@ Imports:
     slam (>= 0.1-48),
     igraph (>= 1.2.9),
     ape (>= 5.5),
-    rgeos (>= 0.5-8),
+    geos (>= 0.2),
     plyr (>= 1.8.6),
     doParallel (>= 1.0.16),
     magrittr (>= 2.0.1),
@@ -68,16 +70,15 @@ Suggests:
     knitr (>= 1.36),
     roxygen2 (>= 7.1.2),
     scales (>= 1.1.1),
-    maptools (>= 1.1-2),
-    PBSmapping (>= 2.73.0),
     gurobi (>= 8.0-1),
     rcbc (>= 0.1.0.9001),
     cplexAPI (>= 1.4.0),
     lpsymphony (>= 1.17.0),
     Rsymphony (>= 0.1-31),
     rmarkdown (>= 2.11),
     prioritizrdata (>= 0.2.4),
-    data.table (>= 1.14.2)
+    data.table (>= 1.14.2),
+    fields (>= 14.0)
 Depends:
     R (>= 3.5.0),
     sp,

diff --git a/Makefile b/Makefile
@@ -10,7 +10,7 @@ initc:
 docs: man readme vigns site
 
 data:
-	Rscript --slave inst/extdata/simulate_data.R
+	Rscript --slave inst/scripts/builtin-data.R
 
 man:
 	R --slave -e "devtools::document()"
@@ -77,7 +77,7 @@ quickcheck:
 
 check:
 	echo "\n===== R CMD CHECK =====\n" > check.log 2>&1
-	R --slave -e "devtools::check(build_args = '--no-build-vignettes', args = '--no-build-vignettes', run_dont_test = TRUE, vignettes = FALSE)" >> check.log 2>&1
+	R --slave -e "devtools::check(remote = TRUE, build_args = '--no-build-vignettes', args = '--no-build-vignettes', run_dont_test = TRUE, vignettes = FALSE)" >> check.log 2>&1
 	cp -R doc inst/
 	touch inst/doc/.gitkeep
 
@@ -106,7 +106,7 @@ install:
 	R --slave -e "devtools::install_local('../prioritizr')"
 
 examples:
-	R --slave -e "devtools::run_examples(test = TRUE, run = TRUE);warnings()"  >> examples.log
+	R --slave -e "devtools::run_examples(run_donttest = TRUE, run_dontrun = TRUE);warnings()" > examples.log 2>&1
 	rm -f Rplots.pdf
 
 .PHONY: initc clean data docs readme contrib site test check checkwb build install man spellcheck examples purl_vigns check_vigns urlcheck
diff --git a/NEWS.md b/NEWS.md
@@ -1,3 +1,24 @@
+# prioritizr 7.2.1
+
+- Release candidate for CRAN.
+
+# prioritizr 7.2.0.9
+
+- Fix compatibility with upcoming _Matrix_ package version (version 1.5-0).
+- Update package documentation to provide details for obtaining and installing
+  the _cplexAPI_ package since it has been archived on CRAN (#214).
+- Fix bug that caused the `add_cbc_solver()` to throw a segfault when solving
+  a problem wherein the `rij_matrix(x)` has a zero amount for the last feature
+  in the last planning unit (#247).
+- Update `simulate_data()`, `simulate_cost()` and `simulate_species()`
+  functions to improve performance using the _fields_ package.
+- Update `boundary_matrix()` to use STR query trees by default.
+- Remove _maptools_, _PBSmapping_, and _rgeos_ packages as dependencies.
+  This involved updating the unit tests to hard-code correct results,
+  updating examples to use the _sf_ package, and updating the
+  `boundary_matrix()` to use the _geos_ package (#218).
+- Fix broken URLs in package documentation.
+
 # prioritizr 7.2.0.8
 
 - Update `simulate_cost()` and `simulate_species()` so that they no longer

diff --git a/R/add_asym_connectivity_penalties.R b/R/add_asym_connectivity_penalties.R
@@ -90,7 +90,7 @@ NULL
 #' require(Matrix)
 #'
 #' # load data
-#' data(sim_pu_polygons, sim_pu_zones_stack, sim_features, sim_features_zones)
+#' data(sim_pu_sf, sim_pu_zones_stack, sim_features, sim_features_zones)
 #'
 #' # define function to rescale values between zero and one so that we
 #' # can compare solutions from different connectivity matrices
@@ -99,7 +99,7 @@ NULL
 #' }
 #'
 #' # create basic problem
-#' p1 <- problem(sim_pu_polygons, sim_features, "cost") %>%
+#' p1 <- problem(sim_pu_sf, sim_features, "cost") %>%
 #'       add_min_set_objective() %>%
 #'       add_relative_targets(0.2) %>%
 #'       add_default_solver(verbose = FALSE)
@@ -108,21 +108,21 @@ NULL
 #' # adjacent planning units and, due to rivers flowing southwards
 #' # through the study area, connectivity from northern planning units to
 #' # southern planning units is ten times stronger than the reverse.
-#' acm1 <- matrix(0, length(sim_pu_polygons), length(sim_pu_polygons))
+#' acm1 <- matrix(0, nrow(sim_pu_sf), nrow(sim_pu_sf))
 #' acm1 <- as(acm1, "Matrix")
-#' centroids <- rgeos::gCentroid(sim_pu_polygons, byid = TRUE)
-#' adjacent_units <- rgeos::gIntersects(sim_pu_polygons, byid = TRUE)
-#' for (i in seq_len(length(sim_pu_polygons))) {
-#'   for (j in seq_len(length(sim_pu_polygons))) {
+#' centroids <- sf::st_coordinates(suppressWarnings(sf::st_centroid(sim_pu_sf)))
+#' adjacent_units <- sf::st_intersects(sim_pu_sf, sparse = FALSE)
+#' for (i in seq_len(nrow(sim_pu_sf))) {
+#'   for (j in seq_len(nrow(sim_pu_sf))) {
 #'     # find if planning units are adjacent
 #'     if (adjacent_units[i, j]) {
 #'       # find if planning units lay north and south of each other
 #'       # i.e., they have the same x-coordinate
-#'       if (centroids@coords[i, 1] == centroids@coords[j, 1]) {
-#'         if (centroids@coords[i, 2] > centroids@coords[j, 2]) {
+#'       if (centroids[i, 1] == centroids[j, 1]) {
+#'         if (centroids[i, 2] > centroids[j, 2]) {
 #'           # if i is north of j add 10 units of connectivity
 #'           acm1[i, j] <- acm1[i, j] + 10
-#'         } else if (centroids@coords[i, 2] < centroids@coords[j, 2]) {
+#'         } else if (centroids[i, 2] < centroids[j, 2]) {
 #'           # if i is south of j add 1 unit of connectivity
 #'           acm1[i, j] <- acm1[i, j] + 1
 #'         }
@@ -131,7 +131,7 @@ NULL
 #'   }
 #' }
 #'
-#' # standardize matrix values to lay between zero and one
+#' # linearly re-scale matrix values to range between zero and one
 #' acm1[] <- rescale(acm1[])
 #'
 #' # visualize asymmetric connectivity matrix
@@ -214,7 +214,7 @@ methods::setMethod("add_asym_connectivity_penalties",
   methods::signature("ConservationProblem", "ANY", "ANY", "matrix"),
   function(x, penalty, zones, data) {
      add_asym_connectivity_penalties(x, penalty, zones,
-       methods::as(data, "dgCMatrix"))
+      as_Matrix(data, "dgCMatrix"))
 })
 
 #' @name add_asym_connectivity_penalties
@@ -224,7 +224,7 @@ methods::setMethod("add_asym_connectivity_penalties",
   methods::signature("ConservationProblem", "ANY", "ANY", "Matrix"),
   function(x, penalty, zones, data) {
      add_asym_connectivity_penalties(x, penalty, zones,
-       methods::as(data, "dgCMatrix"))
+      as_Matrix(data, "dgCMatrix"))
 })
 
 #' @name add_asym_connectivity_penalties
@@ -308,8 +308,7 @@ methods::setMethod("add_asym_connectivity_penalties",
     for (z1 in seq_len(dim(data)[3])) {
       m[[z1]] <- list()
       for (z2 in seq_len(dim(data)[4])) {
-        m[[z1]][[z2]] <-
-          methods::as(data[indices, indices, z1, z2], "dgCMatrix")
+        m[[z1]][[z2]] <- as_Matrix(data[indices, indices, z1, z2], "dgCMatrix")
       }
     }
     # add penalties

diff --git a/R/add_boundary_penalties.R b/R/add_boundary_penalties.R
@@ -306,8 +306,9 @@ internal_prepare_planning_unit_boundary_data <- function(x, data)  {
     if (inherits(data, c("matrix", "Matrix"))) {
       # if it is matrix coerce to sparse matrix
       bm <- data
-      if (!inherits(data, c("dsCMatrix", "dgCMatrix")))
-        bm <- methods::as(data, "dgCMatrix")
+      if (!inherits(data, c("dsCMatrix", "dgCMatrix"))) {
+        bm <- as_Matrix(data, "dgCMatrix")
+      }
       # check that matrix properties are correct
       assertthat::assert_that(
         ncol(bm) == nrow(bm),

diff --git a/R/add_cbc_solver.R b/R/add_cbc_solver.R
@@ -40,8 +40,8 @@ NULL
 #' such as the
 #' [Rtools software](https://cran.r-project.org/bin/windows/Rtools/)
 #' or system libraries -- prior to installing the \pkg{rcbc} package.
-#' For further details on installing this package, please consult
-#' [official installation instructions for the package](https://dirkschumacher.github.io/rcbc/).
+#' For further details on installing this package, please consult the
+#' [online package documentation](https://dirkschumacher.github.io/rcbc/).
 #'
 #' @inheritSection add_gurobi_solver Start solution format
 #'
@@ -176,11 +176,23 @@ add_cbc_solver <- function(x, gap = 0.1,
         max = identical(x$modelsense(), "max"),
         obj = x$obj(),
         is_integer = x$vtype() == "B",
-        mat = x$A(),
+        mat = as_Matrix(x$A(), "dgTMatrix"),
         col_lb = x$lb(),
         col_ub = x$ub(),
         row_lb = row_lb,
         row_ub = row_ub)
+      # if needed, insert dummy row to ensure non-zero value in last rij cell
+      if (abs(model$mat[nrow(model$mat), ncol(model$mat)]) < 1e-300) {
+        model$mat <- as_Matrix(
+          rbind(
+            model$mat,
+            Matrix::sparseMatrix(i = 1, j = ncol(model$mat), x = 1, repr = "T")
+          ),
+          "dgTMatrix"
+        )
+        model$row_lb <- c(model$row_lb, -Inf)
+        model$row_ub <- c(model$row_ub, Inf)
+      }
       # add starting solution if specified
       start <- self$get_data("start")
       if (!is.null(start) && !is.Waiver(start)) {

diff --git a/R/add_connectivity_penalties.R b/R/add_connectivity_penalties.R
@@ -167,7 +167,7 @@ NULL
 #' require(Matrix)
 #'
 #' # load data
-#' data(sim_pu_polygons, sim_pu_zones_stack, sim_features, sim_features_zones)
+#' data(sim_pu_sf, sim_pu_zones_stack, sim_features, sim_features_zones)
 #'
 #' # define function to rescale values between zero and one so that we
 #' # can compare solutions from different connectivity matrices
@@ -176,14 +176,14 @@ NULL
 #' }
 #'
 #' # create basic problem
-#' p1 <- problem(sim_pu_polygons, sim_features, "cost") %>%
+#' p1 <- problem(sim_pu_sf, sim_features, "cost") %>%
 #'       add_min_set_objective() %>%
 #'       add_relative_targets(0.2) %>%
 #'       add_default_solver(verbose = FALSE)
 #'
 #' # create a symmetric connectivity matrix where the connectivity between
 #' # two planning units corresponds to their shared boundary length
-#' b_matrix <- boundary_matrix(sim_pu_polygons)
+#' b_matrix <- boundary_matrix(sim_pu_sf)
 #'
 #' # standardize matrix values to lay between zero and one
 #' b_matrix[] <- rescale(b_matrix[])
@@ -195,8 +195,8 @@ NULL
 #' # create a symmetric connectivity matrix where the connectivity between
 #' # two planning units corresponds to their spatial proximity
 #' # i.e., planning units that are further apart share less connectivity
-#' centroids <- rgeos::gCentroid(sim_pu_polygons, byid = TRUE)
-#' d_matrix <- (1 / (as(dist(centroids@coords), "Matrix") + 1))
+#' centroids <- sf::st_coordinates(suppressWarnings(sf::st_centroid(sim_pu_sf)))
+#' d_matrix <- (1 / (Matrix::Matrix(as.matrix(dist(centroids))) + 1))
 #'
 #' # standardize matrix values to lay between zero and one
 #' d_matrix[] <- rescale(d_matrix[])
@@ -216,7 +216,7 @@ NULL
 #' # each planning unit and we could use connectivity penalties to identify
 #' # solutions that cluster planning units together that both contain large
 #' # amounts of native vegetation
-#' c_matrix <- connectivity_matrix(sim_pu_polygons, "cost")
+#' c_matrix <- connectivity_matrix(sim_pu_sf, "cost")
 #'
 #' # standardize matrix values to lay between zero and one
 #' c_matrix[] <- rescale(c_matrix[])
@@ -402,8 +402,9 @@ methods::setGeneric("add_connectivity_penalties",
 methods::setMethod("add_connectivity_penalties",
   methods::signature("ConservationProblem", "ANY", "ANY", "matrix"),
   function(x, penalty, zones, data) {
-     add_connectivity_penalties(x, penalty, zones,
-       methods::as(data, "dgCMatrix"))
+    add_connectivity_penalties(
+      x, penalty, zones, as_Matrix(data, "dgCMatrix")
+   )
 })
 
 #' @name add_connectivity_penalties
@@ -413,7 +414,7 @@ methods::setMethod("add_connectivity_penalties",
   methods::signature("ConservationProblem", "ANY", "ANY", "Matrix"),
   function(x, penalty, zones, data) {
      add_connectivity_penalties(x, penalty, zones,
-       methods::as(data, "dgCMatrix"))
+       as_Matrix(data, "dgCMatrix"))
 })
 
 #' @name add_connectivity_penalties
@@ -497,7 +498,7 @@ methods::setMethod("add_connectivity_penalties",
       m[[z1]] <- list()
       for (z2 in seq_len(dim(data)[4])) {
         m[[z1]][[z2]] <-
-          methods::as(data[indices, indices, z1, z2], "dgCMatrix")
+          as_Matrix(data[indices, indices, z1, z2], "dgCMatrix")
       }
     }
     # add penalties
@@ -523,7 +524,7 @@ internal_add_connectivity_penalties <- function(x, penalty, data) {
         # coerce to symmetric connectivity data
         cm <- self$get_data("data")
         cm <- lapply(cm, function(x) {
-          lapply(x, function(y) methods::as(Matrix::tril(y), "dgCMatrix"))
+          lapply(x, function(y) as_Matrix(Matrix::tril(y), "dgCMatrix"))
         })
         # apply penalties
         rcpp_apply_connectivity_penalties(

diff --git a/R/add_contiguity_constraints.R b/R/add_contiguity_constraints.R
@@ -224,7 +224,7 @@ methods::setMethod("add_contiguity_constraints",
      inherits(data, c("NULL", "Matrix")))
     if (!is.null(data)) {
       # check argument to data if not NULL
-      data <- methods::as(data, "dgCMatrix")
+      data <- as_Matrix(data, "dgCMatrix")
       assertthat::assert_that(all(data@x %in% c(0, 1)),
         ncol(data) == nrow(data), number_of_total_units(x) == ncol(data),
         all(is.finite(data@x)), Matrix::isSymmetric(data))
@@ -261,7 +261,7 @@ methods::setMethod("add_contiguity_constraints",
           # create matrix
           data <- adjacency_matrix(x$data$cost)
           # coerce matrix to full matrix
-          data <- methods::as(data, "dgCMatrix")
+          data <- as_Matrix(data, "dgCMatrix")
           # store data
           self$set_data("matrix", data)
         }
@@ -314,5 +314,5 @@ methods::setMethod("add_contiguity_constraints",
   methods::signature("ConservationProblem", "ANY", "matrix"),
   function(x, zones, data) {
     # add constraints
-    add_contiguity_constraints(x, zones, methods::as(data, "dgCMatrix"))
+    add_contiguity_constraints(x, zones, as_Matrix(data, "dgCMatrix"))
 })
diff --git a/R/add_cplex_solver.R b/R/add_cplex_solver.R
@@ -29,19 +29,29 @@ NULL
 #' (see <https://www.ibm.com/products/ilog-cplex-optimization-studio>).
 #'
 #' @section Installation:
-#' The pkg{cplexAPI} package is used to interface with *IBM CPLEX*. To install
-#' this package, the `CPLEX_BIN` variable must be set (similar to
-#' the `GUROBI_HOME` variable for the *Gurobi* software) to specify
-#' the file path for the *CPLEX* software. For example, on a Linux system,
+#' The \pkg{cplexAPI} package is used to interface with *IBM CPLEX* software.
+#' To install the package, the *IBM CPLEX* software must be installed
+#' (see <https://www.ibm.com/analytics/cplex-optimizer>). Next, the `CPLEX_BIN`
+#' environmental variable must be set to specify the file path for the
+#' *IBM CPLEX* software. For example, on a Linux system,
 #' this variable can be specified by adding the following text to the
 #' `~/.bashrc` file:
 #' ```
 #'   export CPLEX_BIN="/opt/ibm/ILOG/CPLEX_Studio128/cplex/bin/x86-64_linux/cplex"
 #' ```
-#' Note that you may need to change the version
-#' number in the file path (i.e., `"CPLEX_Studio128"`). For more information
-#' on installing the pkg{cplexAPI} package, please see the
-#' [official installation instructions for the package](https://github.com/cran/cplexAPI/blob/master/inst/INSTALL).
+#' Please Note that you may need to change the version number in the file path
+#' (i.e., `"CPLEX_Studio128"`). After specifying the `CPLEX_BIN`
+#' environmental variable, the \pkg{cplexAPI} package can be installed.
+#' Since the \pkg{cplexAPI} package is not available on the
+#' the Comprehensive R Archive Network (CRAN), it must be installed from
+#' [its GitHub repository](https://github.com/cran/cplexAPI). To
+#' install the \pkg{cplexAPI} package, please use the following code:
+#' ```
+#' if (!require(remotes)) install.packages("remotes")
+#' remotes::install_github("cran/cplexAPI")
+#' ```
+#' For further details on installing this package, please consult the
+#' [installation instructions](https://github.com/cran/cplexAPI/blob/master/inst/INSTALL).
 #'
 #' @inherit add_gurobi_solver return
 #'