The existing algorithms operating on R data.frame can't simply operate on 
SparkR DataFrame. They have to be re-implemented to be based on SparkR 
DataFrame API.

-----Original Message-----
From: ekraffmiller [mailto:ellen.kraffmil...@gmail.com] 
Sent: Thursday, September 17, 2015 3:30 AM
To: user@spark.apache.org
Subject: SparkR - calling as.vector() with rdd dataframe causes error

Hi,
I have a library of clustering algorithms that I'm trying to run in the SparkR 
interactive shell. (I am working on a proof of concept for a document 
classification tool.) Each algorithm takes a term document matrix in the form 
of a dataframe.  When I pass the method a local dataframe, the clustering 
algorithm works correctly, but when I pass it a spark rdd, it gives an error 
trying to coerce the data into a vector.  Here is the code, that I'm calling 
within SparkR:

# get matrix from a file
file <-
"/Applications/spark-1.5.0-bin-hadoop2.6/examples/src/main/resources/matrix.csv"

#read it into variable
 raw_data <- read.csv(file,sep=',',header=FALSE)

#convert to a local dataframe
localDF = data.frame(raw_data)

# create the rdd
rdd  <- createDataFrame(sqlContext,localDF)

#call the algorithm with the localDF - this works result <- galileo(localDF, 
model='hclust',dist='euclidean',link='ward',K=5)

#call with the rdd - this produces error result <- galileo(rdd, 
model='hclust',dist='euclidean',link='ward',K=5)

Error in as.vector(data) : 
  no method for coercing this S4 class to a vector


I get the same error if I try to directly call as.vector(rdd) as well.

Is there a reason why this works for localDF and not rdd?  Should I be doing 
something else to coerce the object into a vector?

Thanks,
Ellen



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