There are many programming languages that integrate with R:
1. R with Hadoop
Hadoop is an open source framework for large-scale datasets in a distributed computing environment. In distributed file systems such as Hadoop are missing strong statistical techniques that why Hadoop and R programming are quite compatible in terms of visualization and analytics of big data.
3 Different ways of using R and Hadoop work together
• R and Streaming
Allows users to run and develop Map/Reduce programs with any script or executable that can access standard input/standard output.
• RHadoop
RHadoop is provided by Revolution Analytics. rmr2, rhdfs, and rhbase are the three packages that intended to help manage the distribution and analysis of data with Hadoop.
• RHive
Rhive is an R library which allows running a Map Reduce job within R programming language
2. R with Python
R developed as a statistical programming language with a large ecosystem of user-contributed packages (over 4500) aimed at a variety of statistical and data mining tasks. Python is a general programming language with an increasingly mature set of packages for data manipulation and analysis.
RPy is a package that is used to integrate R with Python.
3. R with Java
RJava is a simple R-to-Java interface. It provides a low level bridge between R and Java using JNI (Java Native Interface). It allows to create objects, call methods and access fields of Java objects from R.
4. R with MongoDB
MongoDB is a NoSQL database. In R programming language, there are two packages that is used to providing the interface with MongoDB, namely
• RMongo
• Rmongodb
5. R with C++
C++ is a middle level programming language. In R there is package called Rcpp that provided the seamless integration of R with C++.
The Rcpp package provides C++ classes that greatly facilitate interfacing C or C++ code in R packages using the .Call() interface provided by R.
R is integrated with many other language due to its open source property and its great visualization.
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