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Posted to commits@spark.apache.org by gu...@apache.org on 2021/01/07 04:59:20 UTC
[spark] branch branch-3.1 updated: [SPARK-34021][R] Fix hyper links
in SparkR documentation for CRAN submission
This is an automated email from the ASF dual-hosted git repository.
gurwls223 pushed a commit to branch branch-3.1
in repository https://gitbox.apache.org/repos/asf/spark.git
The following commit(s) were added to refs/heads/branch-3.1 by this push:
new ddaa677 [SPARK-34021][R] Fix hyper links in SparkR documentation for CRAN submission
ddaa677 is described below
commit ddaa677a9e4b19d3b61ca157ec236e27e298133a
Author: HyukjinKwon <gu...@apache.org>
AuthorDate: Thu Jan 7 13:58:13 2021 +0900
[SPARK-34021][R] Fix hyper links in SparkR documentation for CRAN submission
### What changes were proposed in this pull request?
3.0.1 CRAN submission was failed as the reason below:
```
Found the following (possibly) invalid URLs:
URL: http://jsonlines.org/ (moved to https://jsonlines.org/)
From: man/read.json.Rd
man/write.json.Rd
Status: 200
Message: OK
URL: https://dl.acm.org/citation.cfm?id=1608614 (moved to
https://dl.acm.org/doi/10.1109/MC.2009.263)
From: inst/doc/sparkr-vignettes.html
Status: 200
Message: OK
```
The links were being redirected now. This PR checked all hyperlinks in the docs such as `href{...}` and `url{...}`, and fixed all in SparkR:
- Fix two problems above.
- Fix http to https
- Fix `https://www.apache.org/ https://spark.apache.org/` -> `https://www.apache.org https://spark.apache.org`.
### Why are the changes needed?
For CRAN submission.
### Does this PR introduce _any_ user-facing change?
Virtually no because it's just cleanup that CRAN requires.
### How was this patch tested?
Manually tested by clicking the links
Closes #31058 from HyukjinKwon/SPARK-34021.
Authored-by: HyukjinKwon <gu...@apache.org>
Signed-off-by: HyukjinKwon <gu...@apache.org>
(cherry picked from commit 0ba3ab4c23ee1cd3785caa0fde76862dce478530)
Signed-off-by: HyukjinKwon <gu...@apache.org>
---
R/pkg/DESCRIPTION | 2 +-
R/pkg/R/DataFrame.R | 2 +-
R/pkg/R/SQLContext.R | 2 +-
R/pkg/R/install.R | 6 +++---
R/pkg/R/mllib_classification.R | 4 ++--
R/pkg/R/mllib_clustering.R | 4 ++--
R/pkg/R/mllib_recommendation.R | 2 +-
R/pkg/R/mllib_regression.R | 2 +-
R/pkg/R/mllib_stat.R | 2 +-
R/pkg/R/mllib_tree.R | 12 ++++++------
R/pkg/R/stats.R | 3 ++-
R/pkg/vignettes/sparkr-vignettes.Rmd | 2 +-
12 files changed, 22 insertions(+), 21 deletions(-)
diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION
index ae32d59..2a8b8a5 100644
--- a/R/pkg/DESCRIPTION
+++ b/R/pkg/DESCRIPTION
@@ -11,7 +11,7 @@ Authors@R: c(person("Shivaram", "Venkataraman", role = c("aut", "cre"),
email = "felixcheung@apache.org"),
person(family = "The Apache Software Foundation", role = c("aut", "cph")))
License: Apache License (== 2.0)
-URL: https://www.apache.org/ https://spark.apache.org/
+URL: https://www.apache.org https://spark.apache.org
BugReports: https://spark.apache.org/contributing.html
SystemRequirements: Java (>= 8, < 12)
Depends:
diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 8ca338f..72d9615 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -880,7 +880,7 @@ setMethod("toJSON",
#' Save the contents of SparkDataFrame as a JSON file
#'
-#' Save the contents of a SparkDataFrame as a JSON file (\href{http://jsonlines.org/}{
+#' Save the contents of a SparkDataFrame as a JSON file (\href{https://jsonlines.org/}{
#' JSON Lines text format or newline-delimited JSON}). Files written out
#' with this method can be read back in as a SparkDataFrame using read.json().
#'
diff --git a/R/pkg/R/SQLContext.R b/R/pkg/R/SQLContext.R
index 5ed0481..14262e1 100644
--- a/R/pkg/R/SQLContext.R
+++ b/R/pkg/R/SQLContext.R
@@ -374,7 +374,7 @@ setMethod("toDF", signature(x = "RDD"),
#' Create a SparkDataFrame from a JSON file.
#'
#' Loads a JSON file, returning the result as a SparkDataFrame
-#' By default, (\href{http://jsonlines.org/}{JSON Lines text format or newline-delimited JSON}
+#' By default, (\href{https://jsonlines.org/}{JSON Lines text format or newline-delimited JSON}
#' ) is supported. For JSON (one record per file), set a named property \code{multiLine} to
#' \code{TRUE}.
#' It goes through the entire dataset once to determine the schema.
diff --git a/R/pkg/R/install.R b/R/pkg/R/install.R
index 5bc5ae0..bbb9188 100644
--- a/R/pkg/R/install.R
+++ b/R/pkg/R/install.R
@@ -39,11 +39,11 @@
#' version number in the format of "x.y" where x and y are integer.
#' If \code{hadoopVersion = "without"}, "Hadoop free" build is installed.
#' See
-#' \href{http://spark.apache.org/docs/latest/hadoop-provided.html}{
+#' \href{https://spark.apache.org/docs/latest/hadoop-provided.html}{
#' "Hadoop Free" Build} for more information.
#' Other patched version names can also be used, e.g. \code{"cdh4"}
#' @param mirrorUrl base URL of the repositories to use. The directory layout should follow
-#' \href{http://www.apache.org/dyn/closer.lua/spark/}{Apache mirrors}.
+#' \href{https://www.apache.org/dyn/closer.lua/spark/}{Apache mirrors}.
#' @param localDir a local directory where Spark is installed. The directory contains
#' version-specific folders of Spark packages. Default is path to
#' the cache directory:
@@ -64,7 +64,7 @@
#'}
#' @note install.spark since 2.1.0
#' @seealso See available Hadoop versions:
-#' \href{http://spark.apache.org/downloads.html}{Apache Spark}
+#' \href{https://spark.apache.org/downloads.html}{Apache Spark}
install.spark <- function(hadoopVersion = "2.7", mirrorUrl = NULL,
localDir = NULL, overwrite = FALSE) {
sparkHome <- Sys.getenv("SPARK_HOME")
diff --git a/R/pkg/R/mllib_classification.R b/R/pkg/R/mllib_classification.R
index ec83b6bd..71ebe4e 100644
--- a/R/pkg/R/mllib_classification.R
+++ b/R/pkg/R/mllib_classification.R
@@ -425,7 +425,7 @@ setMethod("write.ml", signature(object = "LogisticRegressionModel", path = "char
#' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
#' Only categorical data is supported.
#' For more details, see
-#' \href{http://spark.apache.org/docs/latest/ml-classification-regression.html}{
+#' \href{https://spark.apache.org/docs/latest/ml-classification-regression.html}{
#' Multilayer Perceptron}
#'
#' @param data a \code{SparkDataFrame} of observations and labels for model fitting.
@@ -574,7 +574,7 @@ setMethod("write.ml", signature(object = "MultilayerPerceptronClassificationMode
#' @rdname spark.naiveBayes
#' @aliases spark.naiveBayes,SparkDataFrame,formula-method
#' @name spark.naiveBayes
-#' @seealso e1071: \url{https://cran.r-project.org/package=e1071}
+#' @seealso e1071: \url{https://cran.r-project.org/web/packages/e1071/index.html}
#' @examples
#' \dontrun{
#' data <- as.data.frame(UCBAdmissions)
diff --git a/R/pkg/R/mllib_clustering.R b/R/pkg/R/mllib_clustering.R
index 8bc1535..ff7cbd8 100644
--- a/R/pkg/R/mllib_clustering.R
+++ b/R/pkg/R/mllib_clustering.R
@@ -204,7 +204,7 @@ setMethod("write.ml", signature(object = "BisectingKMeansModel", path = "charact
#' @return \code{spark.gaussianMixture} returns a fitted multivariate gaussian mixture model.
#' @rdname spark.gaussianMixture
#' @name spark.gaussianMixture
-#' @seealso mixtools: \url{https://cran.r-project.org/package=mixtools}
+#' @seealso mixtools: \url{https://cran.r-project.org/web/packages/mixtools/index.html}
#' @examples
#' \dontrun{
#' sparkR.session()
@@ -483,7 +483,7 @@ setMethod("write.ml", signature(object = "KMeansModel", path = "character"),
#' @return \code{spark.lda} returns a fitted Latent Dirichlet Allocation model.
#' @rdname spark.lda
#' @aliases spark.lda,SparkDataFrame-method
-#' @seealso topicmodels: \url{https://cran.r-project.org/package=topicmodels}
+#' @seealso topicmodels: \url{https://cran.r-project.org/web/packages/topicmodels/index.html}
#' @examples
#' \dontrun{
#' text <- read.df("data/mllib/sample_lda_libsvm_data.txt", source = "libsvm")
diff --git a/R/pkg/R/mllib_recommendation.R b/R/pkg/R/mllib_recommendation.R
index d238ff9..87a1bc9 100644
--- a/R/pkg/R/mllib_recommendation.R
+++ b/R/pkg/R/mllib_recommendation.R
@@ -30,7 +30,7 @@ setClass("ALSModel", representation(jobj = "jobj"))
#' to make predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
#'
#' For more details, see
-#' \href{http://spark.apache.org/docs/latest/ml-collaborative-filtering.html}{MLlib:
+#' \href{https://spark.apache.org/docs/latest/ml-collaborative-filtering.html}{MLlib:
#' Collaborative Filtering}.
#'
#' @param data a SparkDataFrame for training.
diff --git a/R/pkg/R/mllib_regression.R b/R/pkg/R/mllib_regression.R
index b2228a1..db9f367 100644
--- a/R/pkg/R/mllib_regression.R
+++ b/R/pkg/R/mllib_regression.R
@@ -475,7 +475,7 @@ setMethod("write.ml", signature(object = "IsotonicRegressionModel", path = "char
#' @param ... additional arguments passed to the method.
#' @return \code{spark.survreg} returns a fitted AFT survival regression model.
#' @rdname spark.survreg
-#' @seealso survival: \url{https://cran.r-project.org/package=survival}
+#' @seealso survival: \url{https://cran.r-project.org/web/packages/survival/index.html}
#' @examples
#' \dontrun{
#' df <- createDataFrame(ovarian)
diff --git a/R/pkg/R/mllib_stat.R b/R/pkg/R/mllib_stat.R
index 6db4d5d..f82fb58 100644
--- a/R/pkg/R/mllib_stat.R
+++ b/R/pkg/R/mllib_stat.R
@@ -49,7 +49,7 @@ setClass("KSTest", representation(jobj = "jobj"))
#' @rdname spark.kstest
#' @aliases spark.kstest,SparkDataFrame-method
#' @name spark.kstest
-#' @seealso \href{http://spark.apache.org/docs/latest/mllib-statistics.html#hypothesis-testing}{
+#' @seealso \href{https://spark.apache.org/docs/latest/mllib-statistics.html#hypothesis-testing}{
#' MLlib: Hypothesis Testing}
#' @examples
#' \dontrun{
diff --git a/R/pkg/R/mllib_tree.R b/R/pkg/R/mllib_tree.R
index b5a014b..f3192ee 100644
--- a/R/pkg/R/mllib_tree.R
+++ b/R/pkg/R/mllib_tree.R
@@ -127,9 +127,9 @@ print.summary.decisionTree <- function(x) {
#' \code{write.ml}/\code{read.ml} to save/load fitted models.
#' For more details, see
# nolint start
-#' \href{http://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-regression}{
+#' \href{https://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-regression}{
#' GBT Regression} and
-#' \href{http://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-classifier}{
+#' \href{https://spark.apache.org/docs/latest/ml-classification-regression.html#gradient-boosted-tree-classifier}{
#' GBT Classification}
# nolint end
#'
@@ -343,9 +343,9 @@ setMethod("write.ml", signature(object = "GBTClassificationModel", path = "chara
#' save/load fitted models.
#' For more details, see
# nolint start
-#' \href{http://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-regression}{
+#' \href{https://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-regression}{
#' Random Forest Regression} and
-#' \href{http://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-classifier}{
+#' \href{https://spark.apache.org/docs/latest/ml-classification-regression.html#random-forest-classifier}{
#' Random Forest Classification}
# nolint end
#'
@@ -568,9 +568,9 @@ setMethod("write.ml", signature(object = "RandomForestClassificationModel", path
#' save/load fitted models.
#' For more details, see
# nolint start
-#' \href{http://spark.apache.org/docs/latest/ml-classification-regression.html#decision-tree-regression}{
+#' \href{https://spark.apache.org/docs/latest/ml-classification-regression.html#decision-tree-regression}{
#' Decision Tree Regression} and
-#' \href{http://spark.apache.org/docs/latest/ml-classification-regression.html#decision-tree-classifier}{
+#' \href{https://spark.apache.org/docs/latest/ml-classification-regression.html#decision-tree-classifier}{
#' Decision Tree Classification}
# nolint end
#'
diff --git a/R/pkg/R/stats.R b/R/pkg/R/stats.R
index 7252351..0aabcee 100644
--- a/R/pkg/R/stats.R
+++ b/R/pkg/R/stats.R
@@ -109,7 +109,8 @@ setMethod("corr",
#'
#' Finding frequent items for columns, possibly with false positives.
#' Using the frequent element count algorithm described in
-#' \url{https://doi.org/10.1145/762471.762473}, proposed by Karp, Schenker, and Papadimitriou.
+#' \url{https://dl.acm.org/doi/10.1145/762471.762473}, proposed by Karp, Schenker,
+#' and Papadimitriou.
#'
#' @param x A SparkDataFrame.
#' @param cols A vector column names to search frequent items in.
diff --git a/R/pkg/vignettes/sparkr-vignettes.Rmd b/R/pkg/vignettes/sparkr-vignettes.Rmd
index a060874..3177b54 100644
--- a/R/pkg/vignettes/sparkr-vignettes.Rmd
+++ b/R/pkg/vignettes/sparkr-vignettes.Rmd
@@ -1007,7 +1007,7 @@ perplexity
#### Alternating Least Squares
-`spark.als` learns latent factors in [collaborative filtering](https://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering) via [alternating least squares](https://dl.acm.org/citation.cfm?id=1608614).
+`spark.als` learns latent factors in [collaborative filtering](https://en.wikipedia.org/wiki/Recommender_system#Collaborative_filtering) via [alternating least squares](https://dl.acm.org/doi/10.1109/MC.2009.263).
There are multiple options that can be configured in `spark.als`, including `rank`, `reg`, and `nonnegative`. For a complete list, refer to the help file.
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