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| <li class="title"><span>Extending Zeppelin</span></li> |
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| |
| <h1>R Interpreter for Apache Zeppelin</h1> |
| |
| <div id="toc"></div> |
| |
| <h2>Overview</h2> |
| |
| <p><a href="https://www.r-project.org">R</a> is a free software environment for statistical computing and graphics.</p> |
| |
| <p>To run R code and visualize plots in Apache Zeppelin, you will need R on your zeppelin server node (or your dev laptop).</p> |
| |
| <ul> |
| <li>For Centos: <code>yum install R R-devel libcurl-devel openssl-devel</code></li> |
| <li>For Ubuntu: <code>apt-get install r-base</code></li> |
| </ul> |
| |
| <p>Validate your installation with a simple R command:</p> |
| <div class="highlight"><pre><code class="text language-text" data-lang="text">R -e "print(1+1)" |
| </code></pre></div> |
| <p>To enjoy plots, install additional libraries with:</p> |
| |
| <ul> |
| <li><p>devtools with </p> |
| <div class="highlight"><pre><code class="bash language-bash" data-lang="bash">R -e <span class="s2">"install.packages('devtools', repos = 'http://cran.us.r-project.org')"</span> |
| </code></pre></div></li> |
| <li><p>knitr with </p> |
| <div class="highlight"><pre><code class="bash language-bash" data-lang="bash">R -e <span class="s2">"install.packages('knitr', repos = 'http://cran.us.r-project.org')"</span> |
| </code></pre></div></li> |
| <li><p>ggplot2 with</p> |
| <div class="highlight"><pre><code class="bash language-bash" data-lang="bash">R -e <span class="s2">"install.packages('ggplot2', repos = 'http://cran.us.r-project.org')"</span> |
| </code></pre></div></li> |
| <li><p>Other visualization libraries: </p> |
| <div class="highlight"><pre><code class="bash language-bash" data-lang="bash">R -e <span class="s2">"install.packages(c('devtools','mplot', 'googleVis'), repos = 'http://cran.us.r-project.org'); </span> |
| <span class="s2">require(devtools); install_github('ramnathv/rCharts')"</span> |
| </code></pre></div></li> |
| </ul> |
| |
| <p>We recommend you to also install the following optional R libraries for happy data analytics:</p> |
| |
| <ul> |
| <li>glmnet</li> |
| <li>pROC</li> |
| <li>data.table</li> |
| <li>caret</li> |
| <li>sqldf</li> |
| <li>wordcloud</li> |
| </ul> |
| |
| <h2>Supported Interpreters</h2> |
| |
| <p>Zeppelin supports R language in 3 interpreters</p> |
| |
| <table class="table-configuration"> |
| <tr> |
| <th>Name</th> |
| <th>Class</th> |
| <th>Description</th> |
| </tr> |
| <tr> |
| <td>%r.r</td> |
| <td>RInterpreter</td> |
| <td>Vanilla r interpreter, with least dependencies, only R environment and knitr are required. |
| It is always recommended to use the fully qualified interpreter name <code>%r.r</code>, because <code>%r</code> is ambiguous, |
| it could mean <code>%spark.r</code> when current note's default interpreter is <code>%spark</code> and <code>%r.r</code> when the default interpreter is <code>%r</code></td> |
| </tr> |
| <tr> |
| <td>%r.ir</td> |
| <td>IRInterpreter</td> |
| <td>Provide more fancy R runtime via [IRKernel](https://github.com/IRkernel/IRkernel), almost the same experience like using R in Jupyter. It requires more things, but is the recommended interpreter for using R in Zeppelin.</td> |
| </tr> |
| <tr> |
| <td>%r.shiny</td> |
| <td>ShinyInterpreter</td> |
| <td>Run Shiny app in Zeppelin</td> |
| </tr> |
| </table> |
| |
| <p>If you want to use R with Spark, it is almost the same via <code>%spark.r</code>, <code>%spark.ir</code> & <code>%spark.shiny</code> . You can refer Spark interpreter docs for more details.</p> |
| |
| <h2>Configuration</h2> |
| |
| <table class="table-configuration"> |
| <tr> |
| <th>Property</th> |
| <th>Default</th> |
| <th>Description</th> |
| </tr> |
| <tr> |
| <td>zeppelin.R.cmd</td> |
| <td>R</td> |
| <td>Path of the installed R binary. You should set this property explicitly if R is not in your <code>$PATH</code>(example: /usr/bin/R). |
| </td> |
| </tr> |
| <tr> |
| <td>zeppelin.R.knitr</td> |
| <td>true</td> |
| <td>Whether to use knitr or not. It is recommended to install [knitr](https://yihui.org/knitr/)</td> |
| </tr> |
| <tr> |
| <td>zeppelin.R.image.width</td> |
| <td>100%</td> |
| <td>Image width of R plotting</td> |
| </tr> |
| <tr> |
| <td>zeppelin.R.shiny.iframe_width</td> |
| <td>100%</td> |
| <td>IFrame width of Shiny App</td> |
| </tr> |
| <tr> |
| <td>zeppelin.R.shiny.iframe_height</td> |
| <td>500px</td> |
| <td>IFrame height of Shiny App</td> |
| </tr> |
| <tr> |
| <td>zeppelin.R.shiny.portRange</td> |
| <td>:</td> |
| <td>Shiny app would launch a web app at some port, this property is to specify the portRange via format 'start':'end', e.g. '5000:5001'. By default it is ':' which means any port.</td> |
| </tr> |
| <tr> |
| <td>zeppelin.R.maxResult</td> |
| <td>1000</td> |
| <td>Max number of dataframe rows to display when using z.show</td> |
| </tr> |
| </table> |
| |
| <h2>Play R in Zeppelin docker</h2> |
| |
| <p>For beginner, we would suggest you to play R in Zeppelin docker first. In the Zeppelin docker image, we have already installed R and lots of useful R libraries including IRKernel's prerequisites, so <code>%r.ir</code> is available.</p> |
| |
| <p>Without any extra configuration, you can run most of tutorial notes under folder <code>R Tutorial</code> directly.</p> |
| <div class="highlight"><pre><code class="text language-text" data-lang="text">docker run -u $(id -u) -p 8080:8080 -p:6789:6789 --rm --name zeppelin apache/zeppelin:0.10.0 |
| </code></pre></div> |
| <p>After running the above command, you can open <code>http://localhost:8080</code> to play R in Zeppelin. |
| The port <code>6789</code> exposed in the above command is for R shiny app. You need to make the following 2 interpreter properties to enable shiny app accessible as iframe in Zeppelin docker container. </p> |
| |
| <ul> |
| <li><code>zeppelin.R.shiny.portRange</code> to be <code>6789:6789</code></li> |
| <li>Set <code>ZEPPELIN_LOCAL_IP</code> to be <code>0.0.0.0</code></li> |
| </ul> |
| |
| <p><img class="img-responsive" src="/docs/0.10.1/assets/themes/zeppelin/img/docs-img/r_shiny_app.gif" width="800px"/></p> |
| |
| <h2>Interpreter binding mode</h2> |
| |
| <p>The default <a href="../usage/interpreter/interpreter_binding_mode.html">interpreter binding mode</a> is <code>globally shared</code>. That means all notes share the same R interpreter. |
| So we would recommend you to ues <code>isolated per note</code> which means each note has own R interpreter without affecting each other. But it may run out of your machine resource if too many R |
| interpreters are created. You can <a href="../interpreter/r.html#run-r-in-yarn-cluster">run R in yarn mode</a> to avoid this problem. </p> |
| |
| <h2>How to use R Interpreter</h2> |
| |
| <p>There are two different implementations of R interpreters: <code>%r.r</code> and <code>%r.ir</code>.</p> |
| |
| <ul> |
| <li>Vanilla R Interpreter(<code>%r.r</code>) behaves like an ordinary REPL and use SparkR to communicate between R process and JVM process. It requires <code>knitr</code> to be installed.</li> |
| <li>IRKernel R Interpreter(<code>%r.ir</code>) behaves like using IRKernel in Jupyter notebook. It is based on <a href="jupyter.html">jupyter interpreter</a>. Besides jupyter interpreter's prerequisites, <a href="https://github.com/IRkernel/IRkernel">IRkernel</a> needs to be installed as well. </li> |
| </ul> |
| |
| <p>Take a look at the tutorial note <code>R Tutorial/1. R Basics</code> for how to write R code in Zeppelin.</p> |
| |
| <h3>R basic expressions</h3> |
| |
| <p>R basic expressions are supported in both <code>%r.r</code> and <code>%r.ir</code>.</p> |
| |
| <p><img class="img-responsive" src="/docs/0.10.1/assets/themes/zeppelin/img/docs-img/r_basic.png" width="800px"/></p> |
| |
| <h3>R base plotting</h3> |
| |
| <p>R base plotting is supported in both <code>%r.r</code> and <code>%r.ir</code>.</p> |
| |
| <p><img class="img-responsive" src="/docs/0.10.1/assets/themes/zeppelin/img/docs-img/r_plotting.png" width="800px"/></p> |
| |
| <h3>Other plotting</h3> |
| |
| <p>Besides R base plotting, you can use other visualization libraries in both <code>%r.r</code> and <code>%r.ir</code>, e.g. <code>ggplot</code> and <code>googleVis</code> </p> |
| |
| <p><img class="img-responsive" src="/docs/0.10.1/assets/themes/zeppelin/img/docs-img/r_ggplot.png" width="800px"/></p> |
| |
| <p><img class="img-responsive" src="/docs/0.10.1/assets/themes/zeppelin/img/docs-img/r_googlevis.png" width="800px"/></p> |
| |
| <h3>z.show</h3> |
| |
| <p><code>z.show()</code> is only available in <code>%r.ir</code> to visualize R dataframe, e.g.</p> |
| |
| <p><img class="img-responsive" src="/docs/0.10.1/assets/themes/zeppelin/img/docs-img/r_zshow.png" width="800px"/></p> |
| |
| <p>By default, <code>z.show</code> would only display 1000 rows, you can specify the maxRows via <code>z.show(df, maxRows=2000)</code></p> |
| |
| <h2>Make Shiny App in Zeppelin</h2> |
| |
| <p><a href="https://shiny.rstudio.com/tutorial/">Shiny</a> is an R package that makes it easy to build interactive web applications (apps) straight from R. |
| <code>%r.shiny</code> is used for developing R shiny app in Zeppelin notebook. It only works when IRKernel Interpreter(<code>%r.ir</code>) is enabled. |
| For developing one Shiny App in Zeppelin, you need to write at least 3 paragraphs (server type paragraph, ui type paragraph and run type paragraph)</p> |
| |
| <ul> |
| <li>Server type R shiny paragraph</li> |
| </ul> |
| <div class="highlight"><pre><code class="r language-r" data-lang="r">%r.shiny<span class="p">(</span>type<span class="o">=</span>server<span class="p">)</span> |
| |
| <span class="c1"># Define server logic to summarize and view selected dataset ----</span> |
| server <span class="o"><-</span> <span class="kr">function</span><span class="p">(</span>input<span class="p">,</span> output<span class="p">)</span> <span class="p">{</span> |
| |
| <span class="c1"># Return the requested dataset ----</span> |
| datasetInput <span class="o"><-</span> reactive<span class="p">({</span> |
| <span class="kr">switch</span><span class="p">(</span>input<span class="o">$</span>dataset<span class="p">,</span> |
| <span class="s">"rock"</span> <span class="o">=</span> rock<span class="p">,</span> |
| <span class="s">"pressure"</span> <span class="o">=</span> pressure<span class="p">,</span> |
| <span class="s">"cars"</span> <span class="o">=</span> cars<span class="p">)</span> |
| <span class="p">})</span> |
| |
| <span class="c1"># Generate a summary of the dataset ----</span> |
| output<span class="o">$</span>summary <span class="o"><-</span> renderPrint<span class="p">({</span> |
| dataset <span class="o"><-</span> datasetInput<span class="p">()</span> |
| summary<span class="p">(</span>dataset<span class="p">)</span> |
| <span class="p">})</span> |
| |
| <span class="c1"># Show the first "n" observations ----</span> |
| output<span class="o">$</span>view <span class="o"><-</span> renderTable<span class="p">({</span> |
| head<span class="p">(</span>datasetInput<span class="p">(),</span> n <span class="o">=</span> input<span class="o">$</span>obs<span class="p">)</span> |
| <span class="p">})</span> |
| <span class="p">}</span> |
| </code></pre></div> |
| <ul> |
| <li>UI type R shiny paragraph</li> |
| </ul> |
| <div class="highlight"><pre><code class="r language-r" data-lang="r">%r.shiny<span class="p">(</span>type<span class="o">=</span>ui<span class="p">)</span> |
| |
| <span class="c1"># Define UI for dataset viewer app ----</span> |
| ui <span class="o"><-</span> fluidPage<span class="p">(</span> |
| |
| <span class="c1"># App title ----</span> |
| titlePanel<span class="p">(</span><span class="s">"Shiny Text"</span><span class="p">),</span> |
| |
| <span class="c1"># Sidebar layout with a input and output definitions ----</span> |
| sidebarLayout<span class="p">(</span> |
| |
| <span class="c1"># Sidebar panel for inputs ----</span> |
| sidebarPanel<span class="p">(</span> |
| |
| <span class="c1"># Input: Selector for choosing dataset ----</span> |
| selectInput<span class="p">(</span>inputId <span class="o">=</span> <span class="s">"dataset"</span><span class="p">,</span> |
| label <span class="o">=</span> <span class="s">"Choose a dataset:"</span><span class="p">,</span> |
| choices <span class="o">=</span> c<span class="p">(</span><span class="s">"rock"</span><span class="p">,</span> <span class="s">"pressure"</span><span class="p">,</span> <span class="s">"cars"</span><span class="p">)),</span> |
| |
| <span class="c1"># Input: Numeric entry for number of obs to view ----</span> |
| numericInput<span class="p">(</span>inputId <span class="o">=</span> <span class="s">"obs"</span><span class="p">,</span> |
| label <span class="o">=</span> <span class="s">"Number of observations to view:"</span><span class="p">,</span> |
| value <span class="o">=</span> <span class="m">10</span><span class="p">)</span> |
| <span class="p">),</span> |
| |
| <span class="c1"># Main panel for displaying outputs ----</span> |
| mainPanel<span class="p">(</span> |
| |
| <span class="c1"># Output: Verbatim text for data summary ----</span> |
| verbatimTextOutput<span class="p">(</span><span class="s">"summary"</span><span class="p">),</span> |
| |
| <span class="c1"># Output: HTML table with requested number of observations ----</span> |
| tableOutput<span class="p">(</span><span class="s">"view"</span><span class="p">)</span> |
| |
| <span class="p">)</span> |
| <span class="p">)</span> |
| <span class="p">)</span> |
| </code></pre></div> |
| <ul> |
| <li>Run type R shiny paragraph</li> |
| </ul> |
| <div class="highlight"><pre><code class="r language-r" data-lang="r">%r.shiny<span class="p">(</span>type<span class="o">=</span>run<span class="p">)</span> |
| </code></pre></div> |
| <p>After executing the run type R shiny paragraph, the shiny app will be launched and embedded as iframe in paragraph. |
| Take a look at the tutorial note <code>R Tutorial/2. Shiny App</code> for how to develop R shiny app.</p> |
| |
| <p><img class="img-responsive" src="/docs/0.10.1/assets/themes/zeppelin/img/docs-img/r_shiny.png" width="800px"/></p> |
| |
| <h3>Run multiple shiny apps</h3> |
| |
| <p>If you want to run multiple shiny apps, you can specify <code>app</code> in paragraph local property to differentiate different shiny apps.</p> |
| |
| <p>e.g.</p> |
| <div class="highlight"><pre><code class="r language-r" data-lang="r">%r.shiny<span class="p">(</span>type<span class="o">=</span>ui<span class="p">,</span> app<span class="o">=</span>app_1<span class="p">)</span> |
| </code></pre></div><div class="highlight"><pre><code class="r language-r" data-lang="r">%r.shiny<span class="p">(</span>type<span class="o">=</span>server<span class="p">,</span> app<span class="o">=</span>app_1<span class="p">)</span> |
| </code></pre></div><div class="highlight"><pre><code class="r language-r" data-lang="r">%r.shiny<span class="p">(</span>type<span class="o">=</span>run<span class="p">,</span> app<span class="o">=</span>app_1<span class="p">)</span> |
| </code></pre></div> |
| <h2>Run R in yarn cluster</h2> |
| |
| <p>Zeppelin support to <a href="../quickstart/yarn.html">run interpreter in yarn cluster</a>. But there's one critical problem to run R in yarn cluster: how to manage the R environment in yarn container. |
| Because yarn cluster is a distributed cluster which is composed of many nodes, and your R interpreter can start in any node. |
| It is not practical to manage R environment in each node.</p> |
| |
| <p>So in order to run R in yarn cluster, we would suggest you to use conda to manage your R environment, and Zeppelin can ship your |
| R conda environment to yarn container, so that each R interpreter can have its own R environment without affecting each other.</p> |
| |
| <p>To be noticed, you can only run IRKernel interpreter(<code>%r.ir</code>) in yarn cluster. So make sure you include at least the following prerequisites in the below conda env:</p> |
| |
| <ul> |
| <li>python</li> |
| <li>jupyter</li> |
| <li>grpcio</li> |
| <li>protobuf</li> |
| <li>r-base</li> |
| <li>r-essentials</li> |
| <li>r-irkernel</li> |
| </ul> |
| |
| <p><code>python</code>, <code>jupyter</code>, <code>grpcio</code> and <code>protobuf</code> are required for <a href="../interpreter/jupyter.html">jupyter interpreter</a>, because IRKernel interpreter is based on <a href="../interpreter/jupyter.html">jupyter interpreter</a>. Others are for R runtime.</p> |
| |
| <p>Following are instructions of how to run R in yarn cluster. You can find all the code in the tutorial note <code>R Tutorial/3. R Conda Env in Yarn Mode</code>.</p> |
| |
| <h3>Step 1</h3> |
| |
| <p>We would suggest you to use conda pack to create archive of conda environment.</p> |
| |
| <p>Here's one example of yaml file which is used to generate a conda environment with R and some useful R libraries.</p> |
| |
| <ul> |
| <li>Create a yaml file for conda environment, write the following content into file <code>r_env.yml</code></li> |
| </ul> |
| <div class="highlight"><pre><code class="text language-text" data-lang="text">name: r_env |
| channels: |
| - conda-forge |
| - defaults |
| dependencies: |
| - python=3.7 |
| - jupyter |
| - grpcio |
| - protobuf |
| - r-base=3 |
| - r-essentials |
| - r-evaluate |
| - r-base64enc |
| - r-knitr |
| - r-ggplot2 |
| - r-irkernel |
| - r-shiny |
| - r-googlevis |
| </code></pre></div> |
| <ul> |
| <li>Create conda environment via this yaml file using either <code>conda</code> or <code>mamba</code></li> |
| </ul> |
| <div class="highlight"><pre><code class="bash language-bash" data-lang="bash">conda env create -f r_env.yml |
| </code></pre></div><div class="highlight"><pre><code class="bash language-bash" data-lang="bash">mamba env create -f r_env.yml |
| </code></pre></div> |
| <ul> |
| <li>Pack the conda environment using <code>conda</code></li> |
| </ul> |
| <div class="highlight"><pre><code class="bash language-bash" data-lang="bash">conda pack -n r_env |
| </code></pre></div> |
| <h3>Step 2</h3> |
| |
| <p>Specify the following properties to enable yarn mode for R interpreter via <a href="../usage/interpreter/overview.html#inline-generic-configuration">inline configuration</a></p> |
| <div class="highlight"><pre><code class="text language-text" data-lang="text">%r.conf |
| |
| zeppelin.interpreter.launcher yarn |
| zeppelin.yarn.dist.archives hdfs:///tmp/r_env.tar.gz#environment |
| zeppelin.interpreter.conda.env.name environment |
| </code></pre></div> |
| <p><code>zeppelin.yarn.dist.archives</code> is the R conda environment tar file which is created in step 1. This tar will be shipped to yarn container and untar in the working directory of yarn container. |
| <code>hdfs:///tmp/r_env.tar.gz</code> is the R conda archive file you created in step 2. <code>environment</code> in <code>hdfs:///tmp/r_env.tar.gz#environment</code> is the folder name after untar. |
| This folder name should be the same as <code>zeppelin.interpreter.conda.env.name</code>.</p> |
| |
| <h3>Step 3</h3> |
| |
| <p>Now you can use run R interpreter in yarn container and also use any R libraries you specify in step 1.</p> |
| |
| </div> |
| </div> |
| |
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