Merge pull request #65 from r-lib/RC-0.1.0

Release Candidate 0.1.0

.Rbuildignore

@@ -13,3 +13,5 @@
 ^codecov\.yml$
 ^\.vscode$
 ^debug$
+^cran-comments\.md$
+^CRAN-SUBMISSION$

DESCRIPTION

@@ -1,15 +1,17 @@
 Package: sparsevctrs
 Title: Sparse Vectors for Use in Data Frames
-Version: 0.0.0.9000
+Version: 0.1.0.9000
 Authors@R: c(
     person("Emil", "Hvitfeldt", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-0679-1945")),
+    person("Davis", "Vaughan", , "[email protected]", role = "ctb"),
     person("Posit Software, PBC", role = c("cph", "fnd"))
   )
-Description: Provides sparse vectors powered by ALTREP that behave like
-    regular vectors, and can thus be used in data frames. Also provides
-    tools to convert between sparse matrices and data frames with sparse
-    columns and functions to interact with sparse vectors.
+Description: Provides sparse vectors powered by ALTREP (Alternative
+    Representations for R Objects) that behave like regular vectors, and
+    can thus be used in data frames. Also provides tools to convert
+    between sparse matrices and data frames with sparse columns and
+    functions to interact with sparse vectors.
 License: MIT + file LICENSE
 URL: https://github.com/r-lib/sparsevctrs,
     https://r-lib.github.io/sparsevctrs/
@@ -30,7 +32,8 @@ Suggests:
     withr
 VignetteBuilder: 
     knitr
-Config/Needs/website: tidyverse/tidytemplate, rmarkdown, lobstr, ggplot2, bench, tidyr, ggbeeswarm
+Config/Needs/website: tidyverse/tidytemplate, rmarkdown, lobstr, ggplot2,
+    bench, tidyr, ggbeeswarm
 Config/testthat/edition: 3
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)

NEWS.md

@@ -0,0 +1,5 @@
+# sparsevctrs (development version)
+
+# sparsevctrs 0.1.0
+
+* Initial CRAN submission.

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)

README.md

@@ -44,7 +44,7 @@ x_dense[7] <- 1
 x_dense[15] <- 10
 
 obj_size(x_sparse)
-#> 1.24 kB
+#> 1.35 kB
 obj_size(x_dense)
 #> 8.05 kB
 
@@ -64,11 +64,11 @@ x_sparse_1000 <- sparse_double(numeric(), integer(), length = 1000)
 x_sparse_1000000 <- sparse_double(numeric(), integer(), length = 10000000)
 
 obj_size(x_sparse_0)
-#> 1.19 kB
+#> 1.30 kB
 obj_size(x_sparse_1000)
-#> 1.19 kB
+#> 1.30 kB
 obj_size(x_sparse_1000000)
-#> 1.19 kB
+#> 1.30 kB
 
 x_dense_0 <- numeric(0)
 x_dense_1000 <- numeric(1000)

cran-comments.md

@@ -0,0 +1,10 @@
+## Submission comments
+
+- Expanded ALTREP acronym in description field.
+- No known references that needs added.
+
+## R CMD check results
+
+0 errors | 0 warnings | 1 note
+
+* This is a new release.

src/altrep-sparse-double.c

@@ -58,7 +58,7 @@ SEXP alrep_sparse_double_Materialize(SEXP x) {
 // ALTVEC
 
 void* altrep_sparse_double_Dataptr(SEXP x, Rboolean writeable) {
-  return STDVEC_DATAPTR(alrep_sparse_double_Materialize(x));
+  return DATAPTR(alrep_sparse_double_Materialize(x));
 }
 
 const void* altrep_sparse_double_Dataptr_or_null(SEXP x) {
@@ -67,7 +67,7 @@ const void* altrep_sparse_double_Dataptr_or_null(SEXP x) {
   if (out == R_NilValue) {
     return NULL;
   } else {
-    return STDVEC_DATAPTR(out);
+    return DATAPTR(out);
   }
 }
 

src/altrep-sparse-integer.c

@@ -58,7 +58,7 @@ SEXP alrep_sparse_integer_Materialize(SEXP x) {
 // ALTVEC
 
 void* altrep_sparse_integer_Dataptr(SEXP x, Rboolean writeable) {
-  return STDVEC_DATAPTR(alrep_sparse_integer_Materialize(x));
+  return DATAPTR(alrep_sparse_integer_Materialize(x));
 }
 
 const void* altrep_sparse_integer_Dataptr_or_null(SEXP x) {
@@ -67,7 +67,7 @@ const void* altrep_sparse_integer_Dataptr_or_null(SEXP x) {
   if (out == R_NilValue) {
     return NULL;
   } else {
-    return STDVEC_DATAPTR(out);
+    return DATAPTR(out);
   }
 }
 

src/altrep-sparse-logical.c

@@ -58,7 +58,7 @@ SEXP alrep_sparse_logical_Materialize(SEXP x) {
 // ALTVEC
 
 void* altrep_sparse_logical_Dataptr(SEXP x, Rboolean writeable) {
-  return STDVEC_DATAPTR(alrep_sparse_logical_Materialize(x));
+  return DATAPTR(alrep_sparse_logical_Materialize(x));
 }
 
 const void* altrep_sparse_logical_Dataptr_or_null(SEXP x) {
@@ -67,7 +67,7 @@ const void* altrep_sparse_logical_Dataptr_or_null(SEXP x) {
   if (out == R_NilValue) {
     return NULL;
   } else {
-    return STDVEC_DATAPTR(out);
+    return DATAPTR(out);
   }
 }
 

src/altrep-sparse-string.c

@@ -57,7 +57,7 @@ SEXP alrep_sparse_string_Materialize(SEXP x) {
 // ALTVEC
 
 void* altrep_sparse_string_Dataptr(SEXP x, Rboolean writeable) {
-  return STDVEC_DATAPTR(alrep_sparse_string_Materialize(x));
+  return DATAPTR(alrep_sparse_string_Materialize(x));
 }
 
 const void* altrep_sparse_string_Dataptr_or_null(SEXP x) {
@@ -66,7 +66,7 @@ const void* altrep_sparse_string_Dataptr_or_null(SEXP x) {
   if (out == R_NilValue) {
     return NULL;
   } else {
-    return STDVEC_DATAPTR(out);
+    return DATAPTR(out);
   }
 }