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# Setting up the RHadoop working place

Here we summarize all steps needed to set up the RHadoop working place:

- running virtual machine and Hadoop;
- running R, Rstudio and Rhadoop.
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In the third step we run Hadoop. First, we start the terminal window.
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We run Hadoop with two commands: Start-dfs.sh is used to run the hadoop distributed file system. This establishes one namenode and the related datanodes, in our case only one datanode. Next we run start-yarn.sh to start master and node resource managers and map reduce. Now Hadoop is running. In the fourth step we run R, which we will use to create and submit map-reduce tasks to Hadoop. We decided to use RStudio, which is a free and open-source, integrated development environment for R. We run R through RStudio from the terminal window. Note that if the running of the script ‘rstudio’ reports some warnings then they are probably related to missing fonts. We ignore them and just press enter.
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In the last step we set up RStudio for data analysis with RHadoop. We open a new script file and save it to your local folder. It the beginning, we must set the system environment for Hadoop. These lines define the system variables. We copy them into the script file, mark them and execute by pressing ctrl+enter. Finally, we load the basic RHadoop libraries. We establish our connectivity to the Hadoop Distributed File System by loading the library rhdfs. To perform a statistical analysis in R with Hadoop MapReduce we also need to load library rmr2, where the scripts for the map and reduce operations are defined. We close this last step with the execution hdfs.init().
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Now RHadoop is ready and we can start writing scripts for the big-data analysis.
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When we want to close the RHadoop session we: Save all the script files and if needed also the workspace; Close RStudio by clicking the close button; Stop Hadoop by typing stop-yarn.sh and stop-dfs.sh; Stop the terminal window and the mint_hadoop virtual machine by clicking the close button.