add return roxygen to all exported functions

R/coerce-vector.R

@@ -8,6 +8,8 @@
 #' 
 #' The values of `x` must be double or integer. It must not contain any `Inf` or
 #' `NaN` values.
+#' 
+#' @return sparse vectors
 #'
 #' @examples
 #' x_dense <- c(3, 0, 2, 0, 0, 0, 4, 0, 0, 0)

R/coerce.R

@@ -10,6 +10,8 @@
 #' columns or not. Thus it works with any data frame. Needless to say, creating
 #' a sparse matrix out of a dense data frame is not ideal.
 #' 
+#' @return sparse matrix
+#' 
 #' @seealso [coerce_to_sparse_data_frame()] [coerce_to_sparse_tibble()]
 #' @examplesIf rlang::is_installed("Matrix")
 #' sparse_tbl <- lapply(1:10, function(x) sparse_double(x, x, length = 10))
@@ -78,6 +80,8 @@ coerce_to_sparse_matrix <- function(x) {
 #' @details
 #' The only requirement from the sparse matrix is that it contains column names.
 #' 
+#' @return tibble with sparse columns
+#' 
 #' @seealso [coerce_to_sparse_data_frame()] [coerce_to_sparse_matrix()]
 #' @examplesIf rlang::is_installed("tibble")
 #' set.seed(1234)
@@ -126,6 +130,8 @@ coerce_to_sparse_tibble <- function(x) {
 #' @details
 #' The only requirement from the sparse matrix is that it contains column names.
 #' 
+#' @return data.frame with sparse columns
+#' 
 #' @seealso [coerce_to_sparse_tibble()] [coerce_to_sparse_matrix()]
 #' @examplesIf rlang::is_installed("Matrix")
 #' set.seed(1234)

R/extractors.R

@@ -13,6 +13,8 @@
 #' @details
 #' for ease of use, these functions also works on non-sparse variables.
 #' 
+#' @return vectors of requested attributes
+#' 
 #' @examples
 #' x_sparse <- sparse_double(c(pi, 5, 0.1), c(2, 5, 10), 10)
 #' x_dense <- c(0, pi, 0, 0, 0.5, 0, 0, 0, 0, 0.1)

R/sparse_character.R

@@ -29,6 +29,8 @@
 #' setting `options("sparsevctrs.verbose_materialize" = TRUE)` will print a 
 #' message each time a sparse vector has been forced to materialize.
 #'
+#' @return sparse character vector
+#' 
 #' @seealso [sparse_double()] [sparse_integer()]
 #' 
 #' @examples

R/sparse_double.R

@@ -29,6 +29,8 @@
 #' setting `options("sparsevctrs.verbose_materialize" = TRUE)` will print a 
 #' message each time a sparse vector has been forced to materialize.
 #'
+#' @return sparse double vector
+#' 
 #' @seealso [sparse_integer()] [sparse_character()]
 #'  
 #' @examples

R/sparse_integer.R

@@ -30,6 +30,8 @@
 #' setting `options("sparsevctrs.verbose_materialize" = TRUE)` will print a 
 #' message each time a sparse vector has been forced to materialize.
 #'
+#' @return sparse integer vector
+#' 
 #' @seealso [sparse_double()] [sparse_character()]
 #' 
 #' @examples

R/sparse_logical.R

@@ -28,6 +28,8 @@
 #' setting `options("sparsevctrs.verbose_materialize" = TRUE)` will print a 
 #' message each time a sparse vector has been forced to materialize.
 #'
+#' @return sparse logical vector
+#' 
 #' @seealso [sparse_double()] [sparse_integer()] [sparse_character()] 
 #'  
 #' @examples

R/type-predicates.R

@@ -11,6 +11,8 @@
 #' more specific functions that only detects the type. `is_sparse_numeric()`
 #' matches both sparse integers and doubles.
 #'
+#' @return single logical value
+#' 
 #' @examples
 #' x_sparse <- sparse_double(c(pi, 5, 0.1), c(2, 5, 10), 10)
 #' x_dense <- c(0, pi, 0, 0, 0.5, 0, 0, 0, 0, 0.1)