library(rmr2)
library(rhdfs)

setwd('/C:UsersDesktopNew folder' )
data<-read.csv("datafile.csv")
#head(data)
hdfs.init()
data.values <- to.dfs(data)


data.map.fn <- function(k,v)
{
p <- which((as.numeric(v[,4]) == 1) & ((as.numeric(v[,8]) >= 500) & (as.numeric(v[,8]) <= 565))
keyval(v[p,],v[p,c(4,8)])
}

data.reduce.fn <- function(k,v)
{
keyval(k,(unlist(v)))
}

# study mapreduce function and pass appropriate inputs and ouputs.

dataex <- mapreduce(input= data.values ,
                   map = data.map.fn,
                   reduce = data.reduce.fn)

totalvar<-from.dfs(dataex)
o<- unlist(totalvar[2])
l <- length(o)/2
totalvar           #AT this point return is a NULL

k <- unlist(totalvar)
l <- length(k)/2
y<-matrix(k,nrow=l,ncol=2,byrow=TRUE)



k <- rbind(as.data.frame(totalvar[2]))
j <- as.data.frame(k[!duplicated(as.data.frame(k)),])


y<-matrix(k,nrow=l,ncol=10,byrow=FALSE)
write.csv(y,'mapreducedfile.csv')