[SPARK-19828][R] Support array type in from_json in R

## What changes were proposed in this pull request?

Since we could not directly define the array type in R, this PR proposes to support array types in R as string types that are used in `structField` as below:

```R
jsonArr <- "[{\"name\":\"Bob\"}, {\"name\":\"Alice\"}]"
df <- as.DataFrame(list(list("people" = jsonArr)))
collect(select(df, alias(from_json(df$people, "array<struct<name:string>>"), "arrcol")))
```

prints

```R
      arrcol
1 Bob, Alice
```

## How was this patch tested?

Unit tests in `test_sparkSQL.R`.

Author: hyukjinkwon <gurwls223@gmail.com>

Closes #17178 from HyukjinKwon/SPARK-19828.

R/pkg/R/functions.R

@@ -2437,6 +2437,7 @@ setMethod("date_format", signature(y = "Column", x = "character"),
 #'
 #' @param x Column containing the JSON string.
 #' @param schema a structType object to use as the schema to use when parsing the JSON string.
+#' @param asJsonArray indicating if input string is JSON array of objects or a single object.
 #' @param ... additional named properties to control how the json is parsed, accepts the same
 #'            options as the JSON data source.
 #'
@@ -2452,11 +2453,18 @@ setMethod("date_format", signature(y = "Column", x = "character"),
 #'}
 #' @note from_json since 2.2.0
 setMethod("from_json", signature(x = "Column", schema = "structType"),
-          function(x, schema, ...) {
+          function(x, schema, asJsonArray = FALSE, ...) {
+            if (asJsonArray) {
+              jschema <- callJStatic("org.apache.spark.sql.types.DataTypes",
+                                     "createArrayType",
+                                     schema$jobj)
+            } else {
+              jschema <- schema$jobj
+            }
             options <- varargsToStrEnv(...)
             jc <- callJStatic("org.apache.spark.sql.functions",
                               "from_json",
-                              x@jc, schema$jobj, options)
+                              x@jc, jschema, options)
             column(jc)
           })
 

R/pkg/inst/tests/testthat/test_sparkSQL.R

@@ -1364,6 +1364,18 @@ test_that("column functions", {
   # check for unparseable
   df <- as.DataFrame(list(list("a" = "")))
   expect_equal(collect(select(df, from_json(df$a, schema)))[[1]][[1]], NA)
+
+  # check if array type in string is correctly supported.
+  jsonArr <- "[{\"name\":\"Bob\"}, {\"name\":\"Alice\"}]"
+  df <- as.DataFrame(list(list("people" = jsonArr)))
+  schema <- structType(structField("name", "string"))
+  arr <- collect(select(df, alias(from_json(df$people, schema, asJsonArray = TRUE), "arrcol")))
+  expect_equal(ncol(arr), 1)
+  expect_equal(nrow(arr), 1)
+  expect_is(arr[[1]][[1]], "list")
+  expect_equal(length(arr$arrcol[[1]]), 2)
+  expect_equal(arr$arrcol[[1]][[1]]$name, "Bob")
+  expect_equal(arr$arrcol[[1]][[2]]$name, "Alice")
 })
 
 test_that("column binary mathfunctions", {

sql/core/src/main/scala/org/apache/spark/sql/api/r/SQLUtils.scala

@@ -81,7 +81,7 @@ private[sql] object SQLUtils extends Logging {
     new JavaSparkContext(spark.sparkContext)
   }
 
-  def createStructType(fields : Seq[StructField]): StructType = {
+  def createStructType(fields: Seq[StructField]): StructType = {
     StructType(fields)
   }