R provides several packages to perform parallel and distributed computing to speed up the computation of large datasets. Two of the popular packages for parallel computing in R are future and rhipe.
The future package provides a simple and consistent way to work with parallelism in R using a "future" abstraction. A "future" is an R expression that will be evaluated at some point in the future. The package provides a variety of backends, including local parallelism using multi-core CPUs, remote parallelism using SSH or MPI, and cloud computing using services such as Amazon EC2 or Microsoft Azure. Here is an example of how to use the future package to perform parallel computation:
library(future)
plan(multiprocess)
x <- 1:1000000
y <- future_lapply(x, sqrt)In this example, we use plan(multiprocess) to set up a multi-core parallel processing backend. We then create a vector x and use future_lapply to apply the sqrt function to each element of x in parallel, returning a list of futures.
The rhipe package provides an interface between R and Hadoop, allowing users to process large datasets in parallel on a Hadoop cluster. The package provides functions for reading and writing data to Hadoop, as well as for performing distributed computations using MapReduce. Here is an example of how to use the rhipe package to perform a word count on a text file stored in Hadoop:
library(rhipe)
rhinit()
rhipe_input(file.path("hdfs:///path/to/input/file"))
rhipe_mapreduce(
input = "text",
output = "wordcount",
map = function(k, v) {
words <- strsplit(v, "s")[[1]]
rhcollect(list(zip(words, rep(1, length(words)))))
},
reduce = function(k, v) {
rhcollect(list(k, sum(v)))
}
)
result <- rhread("wordcount")In this example, we use rhinit() to initialize the rhipe package, and rhipe_input() to read in the text file stored in Hadoop. We then use rhipe_mapreduce() to perform a word count on the file, with the map function splitting the text into words and the reduce function summing up the counts for each word. Finally, we use rhread() to read the output of the MapReduce job into R.
Overall, the future and rhipe packages provide powerful tools for performing parallel and distributed computing in R, allowing users to take advantage of multi-core CPUs and Hadoop clusters to speed up computation on large datasets.