[SPARK-12204][SPARKR] Implement drop method for DataFrame in SparkR.

Author: Sun Rui <rui.sun@intel.com>

Closes #10201 from sun-rui/SPARK-12204.

1 files changed, 13 insertions, 0 deletions

diff --git a/docs/sql-programming-guide.md b/docs/sql-programming-guide.md
index bc89c78156..fddc513794 100644
--- a/docs/sql-programming-guide.md
+++ b/docs/sql-programming-guide.md
@@ -2150,6 +2150,8 @@ options.
      --conf spark.sql.hive.thriftServer.singleSession=true \
      ...
    {% endhighlight %}
+ - Since 1.6.1, withColumn method in sparkR supports adding a new column to or replacing existing columns
+   of the same name of a DataFrame.
 
  - From Spark 1.6, LongType casts to TimestampType expect seconds instead of microseconds. This
    change was made to match the behavior of Hive 1.2 for more consistent type casting to TimestampType
@@ -2183,6 +2185,7 @@ options.
    users can use `REFRESH TABLE` SQL command or `HiveContext`'s `refreshTable` method
    to include those new files to the table. For a DataFrame representing a JSON dataset, users need to recreate
    the DataFrame and the new DataFrame will include new files.
+ - DataFrame.withColumn method in pySpark supports adding a new column or replacing existing columns of the same name.
 
 ## Upgrading from Spark SQL 1.3 to 1.4
 
@@ -2262,6 +2265,16 @@ sqlContext.setConf("spark.sql.retainGroupColumns", "false")
 </div>
 
 
+#### Behavior change on DataFrame.withColumn
+
+Prior to 1.4, DataFrame.withColumn() supports adding a column only. The column will always be added
+as a new column with its specified name in the result DataFrame even if there may be any existing
+columns of the same name. Since 1.4, DataFrame.withColumn() supports adding a column of a different
+name from names of all existing columns or replacing existing columns of the same name.
+
+Note that this change is only for Scala API, not for PySpark and SparkR.
+
+
 ## Upgrading from Spark SQL 1.0-1.2 to 1.3
 
 In Spark 1.3 we removed the "Alpha" label from Spark SQL and as part of this did a cleanup of the