Мне удалось добиться этого с помощью jvmr. Приведенный ниже код является примером приложения Apache Spark, которое я запускаю из консоли Scala.
package org.scala.rtest
import org.ddahl.jvmr.RInScala
object RIntegration {
def main(args: Array[String]) {
val R = RInScala()
R>"""
require(sparkR)
score.sentiment = function(sentences, pos.words, neg.words, .progress='none')
{
require(plyr)
require(stringr)
scores = laply(sentences, function(sentence, pos.words, neg.words) {
# clean up sentences with R's regex-driven global substitute, gsub():
sentence = gsub('[[:punct:]]', '', sentence, ignore.case=T)
sentence = gsub('[[:cntrl:]]', '', sentence, ignore.case=T)
sentence = gsub('\\d+', '', sentence, ignore.case=T)
# and convert to lower case:
sentence = tolower(sentence)
# split into words. str_split is in the stringr package
word.list = str_split(sentence, '\\s+')
# sometimes a list() is one level of hierarchy too much
words = unlist(word.list)
# compare our words to the dictionaries of positive & negative terms
pos.matches = match(words, pos.words)
neg.matches = match(words, neg.words)
# match() returns the position of the matched term or NA
# we just want a TRUE/FALSE:
pos.matches = !is.na(pos.matches)
neg.matches = !is.na(neg.matches)
# and conveniently enough, TRUE/FALSE will be treated as 1/0 by sum():
score = sum(pos.matches) - sum(neg.matches)
return(score)
}, pos.words, neg.words, .progress=.progress )
scores.df = data.frame(score=scores, text=sentences)
return(scores.df)
}
"""
R(" x <- scan('positive-words.txt',what='character',comment.char=';')")
R(" y <- scan('negative-words.txt',what='character',comment.char=';')")
R(" z <- scan('twitterstream1.txt', what='character' )")
R.eval("df <- score.sentiment(z,x,y)")
println(R.capture("df"))
}
}
Надеюсь, это поможет.