Skip to content

Commit

Permalink
Built site for gh-pages
Browse files Browse the repository at this point in the history
  • Loading branch information
Quarto GHA Workflow Runner committed Dec 5, 2023
1 parent f250013 commit c6fb7ff
Show file tree
Hide file tree
Showing 6 changed files with 5 additions and 5 deletions.
2 changes: 1 addition & 1 deletion .nojekyll
Original file line number Diff line number Diff line change
@@ -1 +1 @@
0e53788f
4fa26af0
Binary file modified 04-git_files/figure-pdf/unnamed-chunk-36-1.pdf
Binary file not shown.
4 changes: 2 additions & 2 deletions 08-products.html
Original file line number Diff line number Diff line change
Expand Up @@ -1431,7 +1431,7 @@ <h3 data-number="7.4.3" class="anchored" data-anchor-id="basic-optimization-of-s
</section>
<section id="deploying-your-shiny-app" class="level3" data-number="7.4.4">
<h3 data-number="7.4.4" class="anchored" data-anchor-id="deploying-your-shiny-app"><span class="header-section-number">7.4.4</span> Deploying your shiny app</h3>
<p>The easiest way is certainly to use shinyapps.io. I won’t go into details, but you can read more about it <a href="https://shiny.rstudio.com/articles/shinyapps.html">here</a>. You could also get a Virtual Private Server on a website like <a href="https://vultr.com">Vultr</a> or <a href="https://www.digitalocean.com/">DigitalOcean</a>. When signing up with these services you get some free credit to test things out. If you use my <a href="https://www.vultr.com/?ref=9276120-8H">Vultr referral link</a> you get 100USD to test the platform. This is more than enough to get a basic VPS with Ubuntu on it. You can then try to install everything needed to deploy Shiny apps from your VPS. You could follow <a href="https://www.marinedatascience.co/blog/2019/04/28/run-shiny-server-on-your-own-digitalocean-droplet-part-1/">this guide</a> to deploy from DigitalOcean, which should generalize well to other services like Vultr. Doing this will teach you a lot, and I would highly recommend you do it.</p>
<p>The easiest way is certainly to use shinyapps.io. I won’t go into details, but you can read more about it <a href="https://shiny.rstudio.com/articles/shinyapps.html">here</a>. You could also get a Virtual Private Server on a website like <a href="https://vultr.com">Vultr</a> or <a href="https://www.digitalocean.com/">DigitalOcean</a>. When signing up with these services you get some free credit to test things out. If you use my <a href="https://m.do.co/c/b68adc727710">Digital Ocean referral link</a> you get 200USD to test the platform. This is more than enough to get a basic VPS with Ubuntu on it. You can then try to install everything needed to deploy Shiny apps from your VPS. You could follow <a href="https://www.marinedatascience.co/blog/2019/04/28/run-shiny-server-on-your-own-digitalocean-droplet-part-1/">this guide</a> to deploy from DigitalOcean, which should generalize well to other services like Vultr. Doing this will teach you a lot, and I would highly recommend you do it.</p>
</section>
<section id="references" class="level3" data-number="7.4.5">
<h3 data-number="7.4.5" class="anchored" data-anchor-id="references"><span class="header-section-number">7.4.5</span> References</h3>
Expand All @@ -1444,7 +1444,7 @@ <h3 data-number="7.4.5" class="anchored" data-anchor-id="references"><span class
</section>
<section id="how-to-build-data-products-using-targets" class="level2" data-number="7.5">
<h2 data-number="7.5" class="anchored" data-anchor-id="how-to-build-data-products-using-targets"><span class="header-section-number">7.5</span> How to build data products using <code>{targets}</code></h2>
<p>We will now put everything together and create a <code>{targets}</code> pipeline to build a data product from start to finish. Let’s go back to one of the pipelines we wrote in Chapter 7. If you’re using RStudio, start a new project and make it <code>renv</code>-enabled by checking the required checkbox. If you’re using another editor, start with an empty folder and run <code>renv::init()</code>. Now create a new script with the following code:</p>
<p>We will now put everything together and create a <code>{targets}</code> pipeline to build a data product from start to finish. Let’s go back to one of the pipelines we wrote in Chapter 7. If you’re using RStudio, start a new project and make it <code>renv</code>-enabled by checking the required checkbox. If you’re using another editor, start with an empty folder and run <code>renv::init()</code>. Now create a new script with the following code (create the script <code>functions.R</code> and put the <code>get_data()</code> function in it, as described <a href="https://rap4mads.eu/07-targets.html#our-actual-first-pipeline">here</a>):</p>
<div class="cell">
<div class="sourceCode cell-code" id="cb33"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(targets)</span>
<span id="cb33-2"><a href="#cb33-2" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(myPackage)</span>
Expand Down
Binary file modified Building-Reproducible-Analytical-Pipelines.epub
Binary file not shown.
Binary file modified Building-Reproducible-Analytical-Pipelines.pdf
Binary file not shown.
4 changes: 2 additions & 2 deletions search.json

Large diffs are not rendered by default.

0 comments on commit c6fb7ff

Please sign in to comment.