- #LINKING RSTUDIO TO GITHUB MAC OS X MAC OS X#
- #LINKING RSTUDIO TO GITHUB MAC OS X INSTALL#
- #LINKING RSTUDIO TO GITHUB MAC OS X SOFTWARE#
- #LINKING RSTUDIO TO GITHUB MAC OS X DOWNLOAD#
#LINKING RSTUDIO TO GITHUB MAC OS X DOWNLOAD#
Open internet browser (Firefox) and download the following file (link below):Ĭlick on link for file: kernel-devel-2.6.32-220.23.1.el6.x86_64.rpmĭownload and save file to folder ‘Downloads’ under ‘home/cloudera’ (Either create new folder using ‘Save’ dialog box or use console: mkdir /home/cloudera/Downloads There are some prerequisites to installation of ‘Guest additions’.
#LINKING RSTUDIO TO GITHUB MAC OS X INSTALL#
For close integration and better performance we need to install “Guest additions” in the VM. Virtualization software: VirtualBox 4.2.6ħ.
#LINKING RSTUDIO TO GITHUB MAC OS X MAC OS X#
Platforms used in this tutorial: Guest OS : Mac OS X 10.7.5 (Lion) This offers a great way to get familiarized with Hadoop. The packages have been implemented and tested in Cloudera’s distribution of Hadoop (CDH3) & (CDH4). Cloudera Hadoop’s Demo VM provides everything you need to run small jobs in a virtual environment. Cloudera created a set of virtual machines (VM) with everything we need to make it easy to get started with Apache Hadoop. Rhbase – functions providing database management for the HBase distributed database from within RĬloudera Hadoop Demo VM CDH is Cloudera’s 100% open source distribution of Hadoop and related projects, built specifically to meet enterprise demands. Rhdfs – functions providing file management of the HDFS from within R Rmr – functions providing Hadoop MapReduce functionality in R RHadoop consists of the following packages: RHadoop is a collection of three R packages that allow users to manage and analyze data with Hadoop. Revolution Analytics released RHadoop allowing integration of R and Hadoop. Data analysts can then perform complex modeling exercises on a subset of prepared data in R. R and Hadoop The most common way to link R and Hadoop is to use HDFS (potentially managed by Hive or HBase) as the long-term store for all data, and use MapReduce jobs (potentially submitted from Hive, Pig, or Oozie) to encode, enrich, and sample data sets from HDFS into R. Both map/reduce and the distributed file system are designed so that node failures are automatically handled by the framework. In addition, it provides a distributed file system that stores data on the compute nodes, providing very high aggregate bandwidth across the cluster.
Hadoop implements a computational paradigm named map/reduce, where the application is divided into many small fragments of work, each of which may be executed or re-executed on any node in the cluster. The Hadoop framework transparently provides applications both reliability and data motion. It supports the running of applications on large clusters of commodity hardware.
#LINKING RSTUDIO TO GITHUB MAC OS X SOFTWARE#
Hadoop Apache Hadoop is an open-source software framework that supports data-intensive distributed applications, licensed under the Apache v2 license. I thought it would be useful to self-taught enthusiasts like me if I lay out the steps in a comprehensive manner, since I have spent some time dealing with the quirks in the process.
I did manage to clear these hurdles and went on to installing R and RStudio along with RHadoop packages. Although there are solutions, the resources are scattered and obscure. I came across different hurdles when it came to addition of VirtualBox Guest Additions, which is intended to spruce up the virtual machine by offering such features as a shared folder with the host OS. Most of the trouble started after a hassle free installation of VirtualBox and creation of the cloudera’s demo VM.
VirtualBox offers an open-source alternative and thenceforth, I chose this. I know most of the people including me like to hear the words open-source and free, especially when it is a smooth ride. One downside to using VMware is that it’s not free. However, this tutorial describes the implementation using VMware’s application. I was inspired by Revolution’s blog and step-by-step tutorial from Jeffrey Breen on the set up of a local virtual instance of Hadoop with R.
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