Hi Alek

As Burak said, you can already use the spark-csv with SparkR in the 1.4
release. So right now I use it with something like this

# Launch SparkR
./bin/sparkR --packages com.databricks:spark-csv_2.10:1.0.3
df <- read.df(sqlContext, "./nycflights13.csv", "com.databricks.spark.csv",
header="true")

You can also pass in other options to the spark csv as arguments to
`read.df`. Let us know if this works

Thanks
Shivaram


On Tue, Jun 2, 2015 at 12:03 PM, Burak Yavuz <brk...@gmail.com> wrote:

> Hi,
>
> cc'ing Shivaram here, because he worked on this yesterday.
>
> If I'm not mistaken, you can use the following workflow:
> ```./bin/sparkR --packages com.databricks:spark-csv_2.10:1.0.3```
>
> and then
>
> ```df <- read.df(sqlContext, "/data", "csv", header = "true")```
>
> Best,
> Burak
>
> On Tue, Jun 2, 2015 at 11:52 AM, Eskilson,Aleksander <
> alek.eskil...@cerner.com> wrote:
>
>>  Are there any intentions to provide first class support for CSV files
>> as one of the loadable file types in SparkR? Data brick’s spark-csv API [1]
>> has support for SQL, Python, and Java/Scala, and implements most of the
>> arguments of R’s read.table API [2], but currently there is no way to load
>> CSV data in SparkR (1.4.0) besides separating our headers from the data,
>> loading into an RDD, splitting by our delimiter, and then converting to a
>> SparkR Data Frame with a vector of the columns gathered from the header.
>>
>>  Regards,
>>  Alek Eskilson
>>
>>  [1] -- https://github.com/databricks/spark-csv
>> [2] -- http://www.inside-r.org/r-doc/utils/read.table
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