diff --git a/R/pkg/DESCRIPTION b/R/pkg/DESCRIPTION
index 20433362459d9..c141baa51b8cb 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 8ca338f09969b..72d96151f6371 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 5ed0481f33d8f..14262e1a74ab0 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 5bc5ae07c5f03..bbb9188cd083f 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 ec83b6bd406a7..71ebe4e26ef63 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 8bc15353465d8..ff7cbd8fc9b74 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 d238ff93ed245..87a1bc991f812 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 b2228a141689b..db9f367407df3 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 6db4d5d4831dd..f82fb589bb5a5 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 b5a014b0a3cfd..f3192ee9b1382 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 7252351ebebb2..0aabceef226e3 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 a0608748696a3..3177b54dc5fac 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.