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From felixche...@apache.org
Subject spark git commit: [SPARKR][DOCS] update R API doc for subset/extract
Date Tue, 31 Jan 2017 02:47:18 GMT
Repository: spark
Updated Branches:
  refs/heads/master f9156d295 -> be7425e26


[SPARKR][DOCS] update R API doc for subset/extract

## What changes were proposed in this pull request?

With extract `[[` or replace `[[<-`, the parameter `i` is a column index, that needs to
be corrected in doc. Also a few minor updates: examples, links.

## How was this patch tested?

manual

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #16721 from felixcheung/rsubsetdoc.


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/be7425e2
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/be7425e2
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/be7425e2

Branch: refs/heads/master
Commit: be7425e26ab8248a4bfbea8cad05dd66e3427b5c
Parents: f9156d2
Author: Felix Cheung <felixcheung_m@hotmail.com>
Authored: Mon Jan 30 18:47:14 2017 -0800
Committer: Felix Cheung <felixcheung@apache.org>
Committed: Mon Jan 30 18:47:14 2017 -0800

----------------------------------------------------------------------
 R/pkg/R/DataFrame.R                  | 13 ++++++++++++-
 R/pkg/R/mllib_classification.R       |  2 +-
 R/pkg/R/mllib_clustering.R           |  6 +++---
 R/pkg/R/mllib_regression.R           |  4 ++--
 R/pkg/vignettes/sparkr-vignettes.Rmd |  4 ++--
 5 files changed, 20 insertions(+), 9 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/spark/blob/be7425e2/R/pkg/R/DataFrame.R
----------------------------------------------------------------------
diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R
index 523343e..bfec324 100644
--- a/R/pkg/R/DataFrame.R
+++ b/R/pkg/R/DataFrame.R
@@ -1831,6 +1831,8 @@ setMethod("[", signature(x = "SparkDataFrame"),
 #' Return subsets of SparkDataFrame according to given conditions
 #' @param x a SparkDataFrame.
 #' @param i,subset (Optional) a logical expression to filter on rows.
+#'                 For extract operator [[ and replacement operator [[<-, the indexing
parameter for
+#'                 a single Column.
 #' @param j,select expression for the single Column or a list of columns to select from the
SparkDataFrame.
 #' @param drop if TRUE, a Column will be returned if the resulting dataset has only one column.
 #'             Otherwise, a SparkDataFrame will always be returned.
@@ -1841,6 +1843,7 @@ setMethod("[", signature(x = "SparkDataFrame"),
 #' @export
 #' @family SparkDataFrame functions
 #' @aliases subset,SparkDataFrame-method
+#' @seealso \link{withColumn}
 #' @rdname subset
 #' @name subset
 #' @family subsetting functions
@@ -1858,6 +1861,10 @@ setMethod("[", signature(x = "SparkDataFrame"),
 #'   subset(df, df$age %in% c(19, 30), 1:2)
 #'   subset(df, df$age %in% c(19), select = c(1,2))
 #'   subset(df, select = c(1,2))
+#'   # Columns can be selected and set
+#'   df[["age"]] <- 23
+#'   df[[1]] <- df$age
+#'   df[[2]] <- NULL # drop column
 #' }
 #' @note subset since 1.5.0
 setMethod("subset", signature(x = "SparkDataFrame"),
@@ -1982,7 +1989,7 @@ setMethod("selectExpr",
 #' @aliases withColumn,SparkDataFrame,character-method
 #' @rdname withColumn
 #' @name withColumn
-#' @seealso \link{rename} \link{mutate}
+#' @seealso \link{rename} \link{mutate} \link{subset}
 #' @export
 #' @examples
 #'\dontrun{
@@ -1993,6 +2000,10 @@ setMethod("selectExpr",
 #' # Replace an existing column
 #' newDF2 <- withColumn(newDF, "newCol", newDF$col1)
 #' newDF3 <- withColumn(newDF, "newCol", 42)
+#' # Use extract operator to set an existing or new column
+#' df[["age"]] <- 23
+#' df[[2]] <- df$col1
+#' df[[2]] <- NULL # drop column
 #' }
 #' @note withColumn since 1.4.0
 setMethod("withColumn",

http://git-wip-us.apache.org/repos/asf/spark/blob/be7425e2/R/pkg/R/mllib_classification.R
----------------------------------------------------------------------
diff --git a/R/pkg/R/mllib_classification.R b/R/pkg/R/mllib_classification.R
index 8da8449..fee4a4c 100644
--- a/R/pkg/R/mllib_classification.R
+++ b/R/pkg/R/mllib_classification.R
@@ -41,7 +41,7 @@ setClass("NaiveBayesModel", representation(jobj = "jobj"))
 
 #' Logistic Regression Model
 #'
-#' Fits an logistic regression model against a Spark DataFrame. It supports "binomial": Binary
logistic regression
+#' Fits an logistic regression model against a SparkDataFrame. It supports "binomial": Binary
logistic regression
 #' with pivoting; "multinomial": Multinomial logistic (softmax) regression without pivoting,
similar to glmnet.
 #' Users can print, make predictions on the produced model and save the model to the input
path.
 #'

http://git-wip-us.apache.org/repos/asf/spark/blob/be7425e2/R/pkg/R/mllib_clustering.R
----------------------------------------------------------------------
diff --git a/R/pkg/R/mllib_clustering.R b/R/pkg/R/mllib_clustering.R
index 05bbab6..e384c73 100644
--- a/R/pkg/R/mllib_clustering.R
+++ b/R/pkg/R/mllib_clustering.R
@@ -47,7 +47,7 @@ setClass("LDAModel", representation(jobj = "jobj"))
 
 #' Bisecting K-Means Clustering Model
 #'
-#' Fits a bisecting k-means clustering model against a Spark DataFrame.
+#' Fits a bisecting k-means clustering model against a SparkDataFrame.
 #' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to
make
 #' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
 #'
@@ -189,7 +189,7 @@ setMethod("write.ml", signature(object = "BisectingKMeansModel", path
= "charact
 
 #' Multivariate Gaussian Mixture Model (GMM)
 #'
-#' Fits multivariate gaussian mixture model against a Spark DataFrame, similarly to R's
+#' Fits multivariate gaussian mixture model against a SparkDataFrame, similarly to R's
 #' mvnormalmixEM(). Users can call \code{summary} to print a summary of the fitted model,
 #' \code{predict} to make predictions on new data, and \code{write.ml}/\code{read.ml}
 #' to save/load fitted models.
@@ -314,7 +314,7 @@ setMethod("write.ml", signature(object = "GaussianMixtureModel", path
= "charact
 
 #' K-Means Clustering Model
 #'
-#' Fits a k-means clustering model against a Spark DataFrame, similarly to R's kmeans().
+#' Fits a k-means clustering model against a SparkDataFrame, similarly to R's kmeans().
 #' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to
make
 #' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
 #'

http://git-wip-us.apache.org/repos/asf/spark/blob/be7425e2/R/pkg/R/mllib_regression.R
----------------------------------------------------------------------
diff --git a/R/pkg/R/mllib_regression.R b/R/pkg/R/mllib_regression.R
index 0e07d3b..7908600 100644
--- a/R/pkg/R/mllib_regression.R
+++ b/R/pkg/R/mllib_regression.R
@@ -41,7 +41,7 @@ setClass("IsotonicRegressionModel", representation(jobj = "jobj"))
 
 #' Generalized Linear Models
 #'
-#' Fits generalized linear model against a Spark DataFrame.
+#' Fits generalized linear model against a SparkDataFrame.
 #' Users can call \code{summary} to print a summary of the fitted model, \code{predict} to
make
 #' predictions on new data, and \code{write.ml}/\code{read.ml} to save/load fitted models.
 #'
@@ -259,7 +259,7 @@ setMethod("write.ml", signature(object = "GeneralizedLinearRegressionModel",
pat
 
 #' Isotonic Regression Model
 #'
-#' Fits an Isotonic Regression model against a Spark DataFrame, similarly to R's isoreg().
+#' Fits an Isotonic Regression model against a SparkDataFrame, similarly to R's isoreg().
 #' Users can print, make predictions on the produced model and save the model to the input
path.
 #'
 #' @param data SparkDataFrame for training.

http://git-wip-us.apache.org/repos/asf/spark/blob/be7425e2/R/pkg/vignettes/sparkr-vignettes.Rmd
----------------------------------------------------------------------
diff --git a/R/pkg/vignettes/sparkr-vignettes.Rmd b/R/pkg/vignettes/sparkr-vignettes.Rmd
index 9b0ded3..36a7847 100644
--- a/R/pkg/vignettes/sparkr-vignettes.Rmd
+++ b/R/pkg/vignettes/sparkr-vignettes.Rmd
@@ -923,9 +923,9 @@ The main method calls of actual computation happen in the Spark JVM of
the drive
 
 Two kinds of RPCs are supported in the SparkR JVM backend: method invocation and creating
new objects. Method invocation can be done in two ways.
 
-* `sparkR.invokeJMethod` takes a reference to an existing Java object and a list of arguments
to be passed on to the method.
+* `sparkR.callJMethod` takes a reference to an existing Java object and a list of arguments
to be passed on to the method.
 
-* `sparkR.invokeJStatic` takes a class name for static method and a list of arguments to
be passed on to the method.
+* `sparkR.callJStatic` takes a class name for static method and a list of arguments to be
passed on to the method.
 
 The arguments are serialized using our custom wire format which is then deserialized on the
JVM side. We then use Java reflection to invoke the appropriate method.
 


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