Github user shivaram commented on a diff in the pull request:
https://github.com/apache/spark/pull/6668#discussion_r32038867
--- Diff: examples/src/main/r/data-manipulation.R ---
@@ -0,0 +1,101 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+# Load SparkR library into your R session
+library(SparkR)
+
+## Initialize SparkContext
+sc <- sparkR.init(appName = "SparkR-data-manipulation-example")
+
+## Initialize SQLContext
+sqlContext <- SparkRSQL.init(sc)
+
+# For this example, we shall use the "flights" dataset
+# The dataset consists of every flight departing Houston in 2011.
+# The data set is made up of 227,496 rows x 14 columns.
+
+
+args <- commandArgs(trailing = TRUE)
+if (length(args) != 1) {
+ print("Usage: data-manipulation.R <path-to-flights.csv")
+ print("The data can be downloaded from:
http://s3-us-west-2.amazonaws.com/sparkr-data/flights.csv ")
+ q("no")
+}
+
+flightsCsvPath <- args[[1]]
+
+
+# # Option 1: Create a local R data frame and then convert it to a SparkR
DataFrame -------
+
+# ## Create a local R dataframe
+flights_df <- read.csv(flightsCsvPath, header = TRUE)
+flights_df$date <- as.Date(flights_df$date)
+
+## Convert the local data frame into a SparkR DataFrame
+flightsDF <- createDataFrame(sqlContext, flights_df)
--- End diff --
So I tried to run this locally and this step is very slow for the dataset
we are using here (I filed https://issues.apache.org/jira/browse/SPARK-8277)
due to the way we convert local data frames to lists.
I see two options here: (1) Use fewer rows in the example file, so that
this runs fast or (2) use a different dataset to demonstrate creating a SparkR
DataFrame from a local dataframe (the CSV reader is fine)
Let me know which you think is better.
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