Revert "Upstream merge (#228)"

This reverts commit 4094453.

Author: robert3005

.gitignore

@@ -25,7 +25,6 @@ R-unit-tests.log
 R/unit-tests.out
 R/cran-check.out
 R/pkg/vignettes/sparkr-vignettes.html
-R/pkg/tests/fulltests/Rplots.pdf
 build/*.jar
 build/apache-maven*
 build/scala*

LICENSE

@@ -263,7 +263,7 @@ The text of each license is also included at licenses/LICENSE-[project].txt.
      (New BSD license) Protocol Buffer Java API (org.spark-project.protobuf:protobuf-java:2.4.1-shaded - http://code.google.com/p/protobuf)
      (The BSD License) Fortran to Java ARPACK (net.sourceforge.f2j:arpack_combined_all:0.1 - http://f2j.sourceforge.net)
      (The BSD License) xmlenc Library (xmlenc:xmlenc:0.52 - http://xmlenc.sourceforge.net)
-     (The New BSD License) Py4J (net.sf.py4j:py4j:0.10.6 - http://py4j.sourceforge.net/)
+     (The New BSD License) Py4J (net.sf.py4j:py4j:0.10.4 - http://py4j.sourceforge.net/)
      (Two-clause BSD-style license) JUnit-Interface (com.novocode:junit-interface:0.10 - https://github.com/szeiger/junit-interface/)
      (BSD licence) sbt and sbt-launch-lib.bash
      (BSD 3 Clause) d3.min.js (https://github.com/mbostock/d3/blob/master/LICENSE)

R/pkg/NAMESPACE

@@ -429,7 +429,6 @@ export("structField",
        "structField.character",
        "print.structField",
        "structType",
-       "structType.character",
        "structType.jobj",
        "structType.structField",
        "print.structType")
@@ -466,6 +465,5 @@ S3method(print, summary.GBTRegressionModel)
 S3method(print, summary.GBTClassificationModel)
 S3method(structField, character)
 S3method(structField, jobj)
-S3method(structType, character)
 S3method(structType, jobj)
 S3method(structType, structField)

R/pkg/R/DataFrame.R

@@ -1391,10 +1391,6 @@ setMethod("summarize",
           })
 
 dapplyInternal <- function(x, func, schema) {
-  if (is.character(schema)) {
-    schema <- structType(schema)
-  }
-
   packageNamesArr <- serialize(.sparkREnv[[".packages"]],
                                connection = NULL)
 
@@ -1412,8 +1408,6 @@ dapplyInternal <- function(x, func, schema) {
   dataFrame(sdf)
 }
 
-setClassUnion("characterOrstructType", c("character", "structType"))
-
 #' dapply
 #'
 #' Apply a function to each partition of a SparkDataFrame.
@@ -1424,11 +1418,10 @@ setClassUnion("characterOrstructType", c("character", "structType"))
 #'             to each partition will be passed.
 #'             The output of func should be a R data.frame.
 #' @param schema The schema of the resulting SparkDataFrame after the function is applied.
-#'               It must match the output of func. Since Spark 2.3, the DDL-formatted string
-#'               is also supported for the schema.
+#'               It must match the output of func.
 #' @family SparkDataFrame functions
 #' @rdname dapply
-#' @aliases dapply,SparkDataFrame,function,characterOrstructType-method
+#' @aliases dapply,SparkDataFrame,function,structType-method
 #' @name dapply
 #' @seealso \link{dapplyCollect}
 #' @export
@@ -1451,17 +1444,6 @@ setClassUnion("characterOrstructType", c("character", "structType"))
 #'              y <- cbind(y, y[1] + 1L)
 #'            },
 #'            schema)
-#'
-#'   # The schema also can be specified in a DDL-formatted string.
-#'   schema <- "a INT, d DOUBLE, c STRING, d INT"
-#'   df1 <- dapply(
-#'            df,
-#'            function(x) {
-#'              y <- x[x[1] > 1, ]
-#'              y <- cbind(y, y[1] + 1L)
-#'            },
-#'            schema)
-#'
 #'   collect(df1)
 #'   # the result
 #'   #       a b c d
@@ -1470,7 +1452,7 @@ setClassUnion("characterOrstructType", c("character", "structType"))
 #' }
 #' @note dapply since 2.0.0
 setMethod("dapply",
-          signature(x = "SparkDataFrame", func = "function", schema = "characterOrstructType"),
+          signature(x = "SparkDataFrame", func = "function", schema = "structType"),
           function(x, func, schema) {
             dapplyInternal(x, func, schema)
           })
@@ -1540,7 +1522,6 @@ setMethod("dapplyCollect",
 #' @param schema the schema of the resulting SparkDataFrame after the function is applied.
 #'               The schema must match to output of \code{func}. It has to be defined for each
 #'               output column with preferred output column name and corresponding data type.
-#'               Since Spark 2.3, the DDL-formatted string is also supported for the schema.
 #' @return A SparkDataFrame.
 #' @family SparkDataFrame functions
 #' @aliases gapply,SparkDataFrame-method
@@ -1560,7 +1541,7 @@ setMethod("dapplyCollect",
 #'
 #' Here our output contains three columns, the key which is a combination of two
 #' columns with data types integer and string and the mean which is a double.
-#' schema <- structType(structField("a", "integer"), structField("c", "string"),
+#' schema <-  structType(structField("a", "integer"), structField("c", "string"),
 #'   structField("avg", "double"))
 #' result <- gapply(
 #'   df,
@@ -1569,15 +1550,6 @@ setMethod("dapplyCollect",
 #'     y <- data.frame(key, mean(x$b), stringsAsFactors = FALSE)
 #' }, schema)
 #'
-#' The schema also can be specified in a DDL-formatted string.
-#' schema <- "a INT, c STRING, avg DOUBLE"
-#' result <- gapply(
-#'   df,
-#'   c("a", "c"),
-#'   function(key, x) {
-#'     y <- data.frame(key, mean(x$b), stringsAsFactors = FALSE)
-#' }, schema)
-#'
 #' We can also group the data and afterwards call gapply on GroupedData.
 #' For Example:
 #' gdf <- group_by(df, "a", "c")