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install.html
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<html>
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<body>
<div align="center">
<div id="header" align=left style="display:inline-block">
<h1>Machine Learning with R</h1>
<span style="white-space: nowrap">
<a href='http://therbootcamp.github.io/'><i class='fas fa-home'></i></a>
<a href='http://therbootcamp.github.io/ML_2023ARC'><i class='fas fa-clock'></i></a>
<a href='mailto:[email protected]'><i class='fas fa-envelope'></i></a>
<a href='https://www.linkedin.com/company/27090302'><i class='fab fa-linkedin'></i></a>
<a href='http://therbootcamp.github.io/'>
<font style='font-style:normal;font-size:20px;padding-left:5px'>The R Bootcamp @ ARC</font>
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<br>
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<img src='https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/by-sa.png' style= 'height:2rem; vertical-align:baseline'/>
<font style='font-size:2rem;font-style:normal;padding-left:8px'>May 2023</font>
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<h2>1 Install latest Base-R und RStudio versions on your computer</h2>
Please install Base-R and RStudio prior to the course.<br><br>
<b>Step 1: Install Base-R</b><br>
<a href="https://cran.r-project.org/bin/windows/base/R-4.3.0-win.exe">Windows</a>, <a href="https://cran.r-project.org/bin/macosx/big-sur-x86_64/base/R-4.3.0-x86_64.pkg">Mac</a>, <a href="https://cran.r-project.org/bin/macosx/big-sur-arm64/base/R-4.3.0-arm64.pkg">Mac (M1)</a><br><br>
<b>Step 2: Install RStudio</b><br>
<a href="https://download1.rstudio.org/electron/windows/RStudio-2023.03.0-386.exe">Windows</a>, <a href="https://download1.rstudio.org/electron/macos/RStudio-2023.03.0-386.dmg">Mac</a>
<h2>2 Install packages</h2>
You will be using several R “packages” in this course that you need to install in your R session. To make sure that there aren’t any installation issues, please run the following line of code in R — If you don’t know how to run code in R yet, ask a friend, a member of your IT staff, or me :)<br><b>Note:</b> If you R asks you whether to install from source and don't understand what this means, just choose “no”; this will avoid potential problems.<br><br>
<font style="font-family: Monospace;color: black">install.packages(c("tidyverse", "tidymodels", "rpart", "rpart.plot","doParallel", "ranger", "randomForest", "glmnet", "keras"), dependencies = TRUE)</font><br><br>
This may take a bit of time! You may receive some warnings (which is fine in most cases) or errors when running the code. If so, please see next point on optional software!
<h3>Optional: Install additional Software</h2>
If you run into problems with the package installation, try installing these Software and then rerun package installation. <br><br>
<b>Windows</b>: <a href="https://cran.r-project.org/bin/windows/Rtools/rtools43/rtools.html">Rtools</a> plus <a href="https://cran.r-project.org/bin/windows/Rtools/">installation guide</a><br>
<b>Mac</b>: <a href="https://cran.r-project.org/bin/macosx/tools/gfortran-6.1.pkg">gfortran</a>, <a href="https://cran.r-project.org/bin/macosx/tools/clang-8.0.0.pkg">clang-7.0.0</a>
<h2>3 Install tensorflow</h2>
You will be using the Python library tensorflow to run neural networks in R. One after the other, run the following commands in the R console. If the final command returns an error, reporting that the “tf” Python is already in use, restart R using the menu item “Session/Restart R” and try again. If R asks "Is Anaconda installed?" first run <font style="font-family: Monospace;color: black">reticulate::install_miniconda()</font> and then the lines below. Note that the installation may fail for Mac M1 users, when not using the arm64 R distribution.
<br><br><font style="font-family: Monospace;color: black">reticulate::conda_create("tf")</br>keras::install_keras(envname = "tf")</br>reticulate::use_condaenv("tf")</font>
<h2>4 Project</h2>
Please download this <a href="https://www.dropbox.com/s/0rglm44ju1hjdzb/TheRBootcamp.zip?dl=1">R project</a>, unpack its contents, and move it to some suitable location on your local harddrive.
<br><br><br><br><br>
<a href="https://therbootcamp.github.io/ML_2022DHLab/" style="color:#626870;border:2px solid #626870;padding:8px;border-radius:10px;background:white;font-weight:500;font-size:16px">
return to course page
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