Repository: spark
Updated Branches:
  refs/heads/branch-2.3 56cfbd932 -> 8fe20e151


[SPARKR][DOC] fix link in vignettes

## What changes were proposed in this pull request?

Fix doc link that was changed in 2.3

shivaram

Author: Felix Cheung <felixcheun...@hotmail.com>

Closes #20711 from felixcheung/rvigmean.

(cherry picked from commit 0b6ceadeb563205cbd6bd03bc88e608086273b5b)
Signed-off-by: Felix Cheung <felixche...@apache.org>


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/8fe20e15
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/8fe20e15
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/8fe20e15

Branch: refs/heads/branch-2.3
Commit: 8fe20e15196b4ddbd80828ad3a91cf06c5dbea84
Parents: 56cfbd9
Author: Felix Cheung <felixcheun...@hotmail.com>
Authored: Fri Mar 2 09:23:39 2018 -0800
Committer: Felix Cheung <felixche...@apache.org>
Committed: Fri Mar 2 09:24:10 2018 -0800

----------------------------------------------------------------------
 R/pkg/vignettes/sparkr-vignettes.Rmd | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)
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http://git-wip-us.apache.org/repos/asf/spark/blob/8fe20e15/R/pkg/vignettes/sparkr-vignettes.Rmd
----------------------------------------------------------------------
diff --git a/R/pkg/vignettes/sparkr-vignettes.Rmd 
b/R/pkg/vignettes/sparkr-vignettes.Rmd
index feca617..d4713de 100644
--- a/R/pkg/vignettes/sparkr-vignettes.Rmd
+++ b/R/pkg/vignettes/sparkr-vignettes.Rmd
@@ -46,7 +46,7 @@ Sys.setenv("_JAVA_OPTIONS" = paste("-XX:-UsePerfData", 
old_java_opt, sep = " "))
 
 ## Overview
 
-SparkR is an R package that provides a light-weight frontend to use Apache 
Spark from R. With Spark `r packageVersion("SparkR")`, SparkR provides a 
distributed data frame implementation that supports data processing operations 
like selection, filtering, aggregation etc. and distributed machine learning 
using [MLlib](http://spark.apache.org/mllib/).
+SparkR is an R package that provides a light-weight frontend to use Apache 
Spark from R. With Spark `r packageVersion("SparkR")`, SparkR provides a 
distributed data frame implementation that supports data processing operations 
like selection, filtering, aggregation etc. and distributed machine learning 
using [MLlib](https://spark.apache.org/mllib/).
 
 ## Getting Started
 
@@ -132,7 +132,7 @@ sparkR.session.stop()
 
 Different from many other R packages, to use SparkR, you need an additional 
installation of Apache Spark. The Spark installation will be used to run a 
backend process that will compile and execute SparkR programs.
 
-After installing the SparkR package, you can call `sparkR.session` as 
explained in the previous section to start and it will check for the Spark 
installation. If you are working with SparkR from an interactive shell (eg. R, 
RStudio) then Spark is downloaded and cached automatically if it is not found. 
Alternatively, we provide an easy-to-use function `install.spark` for running 
this manually. If you don't have Spark installed on the computer, you may 
download it from [Apache Spark Website](http://spark.apache.org/downloads.html).
+After installing the SparkR package, you can call `sparkR.session` as 
explained in the previous section to start and it will check for the Spark 
installation. If you are working with SparkR from an interactive shell (eg. R, 
RStudio) then Spark is downloaded and cached automatically if it is not found. 
Alternatively, we provide an easy-to-use function `install.spark` for running 
this manually. If you don't have Spark installed on the computer, you may 
download it from [Apache Spark 
Website](https://spark.apache.org/downloads.html).
 
 ```{r, eval=FALSE}
 install.spark()
@@ -147,7 +147,7 @@ sparkR.session(sparkHome = "/HOME/spark")
 ### Spark Session {#SetupSparkSession}
 
 
-In addition to `sparkHome`, many other options can be specified in 
`sparkR.session`. For a complete list, see [Starting up: 
SparkSession](http://spark.apache.org/docs/latest/sparkr.html#starting-up-sparksession)
 and [SparkR API 
doc](http://spark.apache.org/docs/latest/api/R/sparkR.session.html).
+In addition to `sparkHome`, many other options can be specified in 
`sparkR.session`. For a complete list, see [Starting up: 
SparkSession](https://spark.apache.org/docs/latest/sparkr.html#starting-up-sparksession)
 and [SparkR API 
doc](https://spark.apache.org/docs/latest/api/R/sparkR.session.html).
 
 In particular, the following Spark driver properties can be set in 
`sparkConfig`.
 
@@ -169,7 +169,7 @@ sparkR.session(spark.sql.warehouse.dir = 
spark_warehouse_path)
 
 
 #### Cluster Mode
-SparkR can connect to remote Spark clusters. [Cluster Mode 
Overview](http://spark.apache.org/docs/latest/cluster-overview.html) is a good 
introduction to different Spark cluster modes.
+SparkR can connect to remote Spark clusters. [Cluster Mode 
Overview](https://spark.apache.org/docs/latest/cluster-overview.html) is a good 
introduction to different Spark cluster modes.
 
 When connecting SparkR to a remote Spark cluster, make sure that the Spark 
version and Hadoop version on the machine match the corresponding versions on 
the cluster. Current SparkR package is compatible with
 ```{r, echo=FALSE, tidy = TRUE}
@@ -177,7 +177,7 @@ paste("Spark", packageVersion("SparkR"))
 ```
 It should be used both on the local computer and on the remote cluster.
 
-To connect, pass the URL of the master node to `sparkR.session`. A complete 
list can be seen in [Spark Master 
URLs](http://spark.apache.org/docs/latest/submitting-applications.html#master-urls).
+To connect, pass the URL of the master node to `sparkR.session`. A complete 
list can be seen in [Spark Master 
URLs](https://spark.apache.org/docs/latest/submitting-applications.html#master-urls).
 For example, to connect to a local standalone Spark master, we can call
 
 ```{r, eval=FALSE}
@@ -317,7 +317,7 @@ A common flow of grouping and aggregation is
 
 2. Feed the `GroupedData` object to `agg` or `summarize` functions, with some 
provided aggregation functions to compute a number within each group.
 
-A number of widely used functions are supported to aggregate data after 
grouping, including `avg`, `countDistinct`, `count`, `first`, `kurtosis`, 
`last`, `max`, `mean`, `min`, `sd`, `skewness`, `stddev_pop`, `stddev_samp`, 
`sumDistinct`, `sum`, `var_pop`, `var_samp`, `var`. See the [API doc for 
`mean`](http://spark.apache.org/docs/latest/api/R/mean.html) and other 
`agg_funcs` linked there.
+A number of widely used functions are supported to aggregate data after 
grouping, including `avg`, `countDistinct`, `count`, `first`, `kurtosis`, 
`last`, `max`, `mean`, `min`, `sd`, `skewness`, `stddev_pop`, `stddev_samp`, 
`sumDistinct`, `sum`, `var_pop`, `var_samp`, `var`. See the [API doc for 
aggregate 
functions](https://spark.apache.org/docs/latest/api/R/column_aggregate_functions.html)
 linked there.
 
 For example we can compute a histogram of the number of cylinders in the 
`mtcars` dataset as shown below.
 
@@ -935,7 +935,7 @@ 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](http://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/citation.cfm?id=1608614).
 
 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.
 
@@ -1171,11 +1171,11 @@ env | map
 
 ## References
 
-* [Spark Cluster Mode 
Overview](http://spark.apache.org/docs/latest/cluster-overview.html)
+* [Spark Cluster Mode 
Overview](https://spark.apache.org/docs/latest/cluster-overview.html)
 
-* [Submitting Spark 
Applications](http://spark.apache.org/docs/latest/submitting-applications.html)
+* [Submitting Spark 
Applications](https://spark.apache.org/docs/latest/submitting-applications.html)
 
-* [Machine Learning Library Guide 
(MLlib)](http://spark.apache.org/docs/latest/ml-guide.html)
+* [Machine Learning Library Guide 
(MLlib)](https://spark.apache.org/docs/latest/ml-guide.html)
 
 * [SparkR: Scaling R Programs with 
Spark](https://people.csail.mit.edu/matei/papers/2016/sigmod_sparkr.pdf), 
Shivaram Venkataraman, Zongheng Yang, Davies Liu, Eric Liang, Hossein Falaki, 
Xiangrui Meng, Reynold Xin, Ali Ghodsi, Michael Franklin, Ion Stoica, and Matei 
Zaharia. SIGMOD 2016. June 2016.
 


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