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 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 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
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.