From f421a1c435b2e98075fc1ad8bc0096a7fe1a585b Mon Sep 17 00:00:00 2001 From: Felix Cheung Date: Mon, 30 Jan 2017 18:51:36 -0800 Subject: [PATCH] doc fix --- R/pkg/R/DataFrame.R | 15 +++++++++++++-- R/pkg/R/mllib.R | 10 +++++----- R/pkg/vignettes/sparkr-vignettes.Rmd | 4 ++-- 3 files changed, 20 insertions(+), 9 deletions(-) diff --git a/R/pkg/R/DataFrame.R b/R/pkg/R/DataFrame.R index 39e8376808f6..c960b45d9997 100644 --- a/R/pkg/R/DataFrame.R +++ b/R/pkg/R/DataFrame.R @@ -936,7 +936,7 @@ setMethod("unique", #' Sample #' -#' Return a sampled subset of this SparkDataFrame using a random seed. +#' Return a sampled subset of this SparkDataFrame using a random seed. #' Note: this is not guaranteed to provide exactly the fraction specified #' of the total count of of the given SparkDataFrame. #' @@ -1825,6 +1825,8 @@ 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. @@ -1835,6 +1837,7 @@ setMethod("[", signature(x = "SparkDataFrame"), #' @export #' @family SparkDataFrame functions #' @aliases subset,SparkDataFrame-method +#' @seealso \link{withColumn} #' @rdname subset #' @name subset #' @family subsetting functions @@ -1852,6 +1855,10 @@ 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$age +#' df[[2]] <- NULL # drop column #' } #' @note subset since 1.5.0 setMethod("subset", signature(x = "SparkDataFrame"), @@ -1976,7 +1983,7 @@ setMethod("selectExpr", #' @aliases withColumn,SparkDataFrame,character-method #' @rdname withColumn #' @name withColumn -#' @seealso \link{rename} \link{mutate} +#' @seealso \link{rename} \link{mutate} \link{subset} #' @export #' @examples #'\dontrun{ @@ -1987,6 +1994,10 @@ setMethod("selectExpr", #' # Replace an existing column #' newDF2 <- withColumn(newDF, "newCol", newDF$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", diff --git a/R/pkg/R/mllib.R b/R/pkg/R/mllib.R index 1a254ad49b08..91ce669814d8 100644 --- a/R/pkg/R/mllib.R +++ b/R/pkg/R/mllib.R @@ -173,7 +173,7 @@ predict_internal <- function(object, newData) { #' 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. #' @@ -499,7 +499,7 @@ setMethod("write.ml", signature(object = "LDAModel", path = "character"), #' 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. @@ -588,7 +588,7 @@ setMethod("summary", signature(object = "IsotonicRegressionModel"), #' 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. #' @@ -712,7 +712,7 @@ setMethod("predict", signature(object = "KMeansModel"), #' 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. #' @@ -1321,7 +1321,7 @@ setMethod("predict", signature(object = "AFTSurvivalRegressionModel"), #' 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. diff --git a/R/pkg/vignettes/sparkr-vignettes.Rmd b/R/pkg/vignettes/sparkr-vignettes.Rmd index 9b0ded3b8d38..36a78477dc26 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.