Release candidate 1.4.0 (#846)

* Update URLs * Rev dep checks * Update NEWS * Redocument * Increment version number to 1.4.0
rstudio · Oct 7, 2024 · ccf4575 · ccf4575
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: pins
 Title: Pin, Discover, and Share Resources
-Version: 1.3.0.9000
+Version: 1.4.0
 Authors@R: c(
     person("Julia", "Silge", , "[email protected]", role = c("cre", "aut"),
            comment = c(ORCID = "0000-0002-3671-836X")),

diff --git a/NEWS.md b/NEWS.md
@@ -1,4 +1,4 @@
-# pins (development version, to be released as 1.4.0)
+# pins 1.4.0
 
 ## Lifecycle changes
 

diff --git a/R/legacy_azure.R b/R/legacy_azure.R
@@ -5,7 +5,6 @@
 #' 
 #' To use Microsoft Azure Storage as a board, you'll need an Azure Storage
 #' account, an Azure Storage container, and an Azure Storage key.
-#' You can sign-up and create those at [portal.azure.com](https://portal.azure.com).
 #' @inheritParams legacy_datatxt
 #' @param container The name of the Azure Storage container.
 #' @param account The name of the Azure Storage account.

diff --git a/cran-comments.md b/cran-comments.md
@@ -1,6 +1,6 @@
 ## revdepcheck results
 
-We checked 4 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
+We checked 7 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
 
  * We saw 0 new problems
  * We failed to check 0 packages
diff --git a/man/legacy_azure.Rd b/man/legacy_azure.Rd
diff --git a/man/pins-package.Rd b/man/pins-package.Rd
diff --git a/revdep/README.md b/revdep/README.md
@@ -1,2 +1,25 @@
+# Platform
+
+|field    |value                                      |
+|:--------|:------------------------------------------|
+|version  |R version 4.4.0 (2024-04-24)               |
+|os       |macOS 15.0                                 |
+|system   |aarch64, darwin20                          |
+|ui       |X11                                        |
+|language |(EN)                                       |
+|collate  |en_US.UTF-8                                |
+|ctype    |en_US.UTF-8                                |
+|tz       |America/Denver                             |
+|date     |2024-10-06                                 |
+|pandoc   |3.2.1 @ /opt/homebrew/bin/ (via rmarkdown) |
+
+# Dependencies
+
+|package |old   |new        |Δ  |
+|:-------|:-----|:----------|:--|
+|pins    |1.3.0 |1.3.0.9000 |*  |
+|askpass |NA    |1.2.1      |*  |
+|sys     |NA    |3.4.3      |*  |
+
 # Revdeps
 
diff --git a/revdep/cran.md b/revdep/cran.md
@@ -1,6 +1,6 @@
 ## revdepcheck results
 
-We checked 4 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
+We checked 7 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
 
  * We saw 0 new problems
  * We failed to check 0 packages

diff --git a/vignettes/pins.Rmd b/vignettes/pins.Rmd
@@ -76,7 +76,7 @@ But you can choose another option depending on your goals:
 -   `type = "parquet"` uses `nanoparquet::write_parquet()` to create a Parquet file. [Parquet](https://parquet.apache.org/) is a modern, language-independent, column-oriented file format for efficient data storage and retrieval. Parquet is an excellent choice for storing tabular data but requires the [nanoparquet](https://nanoparquet.r-lib.org/) package.
 -   `type = "arrow"` uses `arrow::write_feather()` to create an Arrow/Feather file.
 -   `type = "json"` uses `jsonlite::write_json()` to create a JSON file. Pretty much every programming language can read json files, but they only work well for nested lists.
--   `type = "qs"` uses `qs::qsave()` to create a binary R data file, like `writeRDS()`. This format achieves faster read/write speeds than RDS, and compresses data more efficiently, making it a good choice for larger objects. Read more on the [qs package](https://github.com/traversc/qs).
+-   `type = "qs"` uses `qs::qsave()` to create a binary R data file, like `writeRDS()`. This format achieves faster read/write speeds than RDS, and compresses data more efficiently, making it a good choice for larger objects. Read more on the [qs package](https://github.com/qsbase/qs).
 
 Note that when the data lives elsewhere, pins takes care of downloading and caching so that it's only re-downloaded when needed.
 That said, most boards transmit pins over HTTP, and this is going to be slow and possibly unreliable for very large pins.