R Language Spark API (SparkR) Create RDDs (Resilient Distributed Datasets)
Example
From dataframe:
mtrdd <- createDataFrame(sqlContext, mtcars)
From csv:
For csv's, you need to add the csv package to the environment before initiating the Spark context:
Sys.setenv('SPARKR_SUBMIT_ARGS'='"--packages" "com.databricks:spark-csv_2.10:1.4.0" "sparkr-shell"') # context for csv import read csv ->
sc <- sparkR.init()
sqlContext <- sparkRSQL.init(sc)
Then, you can load the csv either by infering the data schema of the data in the columns:
train <- read.df(sqlContext, "/train.csv", header= "true", source = "com.databricks.spark.csv", inferSchema = "true")
Or by specifying the data schema beforehand:
customSchema <- structType(
structField("margin", "integer"),
structField("gross", "integer"),
structField("name", "string"))
train <- read.df(sqlContext, "/train.csv", header= "true", source = "com.databricks.spark.csv", schema = customSchema)
