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	<title><![CDATA[BOL: Related items]]></title>
	<link>https://bioinformaticsonline.com/related/19820?offset=1340</link>
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	<description><![CDATA[]]></description>
	
	<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/31574/biostats-class-tutorial</guid>
	<pubDate>Thu, 16 Mar 2017 01:50:50 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/31574/biostats-class-tutorial</link>
	<title><![CDATA[BioStats class tutorial]]></title>
	<description><![CDATA[<p>Nice biostat turorial by&nbsp;<strong>Ingo Ruczinski</strong></p><p>Address of the bookmark: <a href="http://www.biostat.jhsph.edu/~iruczins/teaching/" rel="nofollow">http://www.biostat.jhsph.edu/~iruczins/teaching/</a></p>]]></description>
	<dc:creator>Shruti Paniwala</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34041/r-tuorial</guid>
	<pubDate>Mon, 31 Jul 2017 08:41:40 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34041/r-tuorial</link>
	<title><![CDATA[R tuorial]]></title>
	<description><![CDATA[<p>R learning resources</p>
<p>https://flowingdata.com/</p><p>Address of the bookmark: <a href="https://flowingdata.com/" rel="nofollow">https://flowingdata.com/</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/34585/r-googlevis-examples</guid>
	<pubDate>Sun, 10 Dec 2017 06:13:42 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/34585/r-googlevis-examples</link>
	<title><![CDATA[R googleVis examples]]></title>
	<description><![CDATA[<p>It may take a little while to load all charts. Please be patient. All charts require an Internet connection.</p>
<p>These examples are taken from the googleVis demo. You can execute the demo via</p>
<pre><code><span>library</span><span>(</span><span>googleVis</span><span>)</span>
<span>demo</span><span>(</span><span>googleVis</span><span>)</span>
</code></pre>
<p>For more details about the charts and further examples see the helpfiles of the individual googleVis function and review the&nbsp;<a href="https://developers.google.com/chart/interactive/docs/gallery">Google Charts API documentation</a>&nbsp;and&nbsp;<a href="https://developers.google.com/terms">Terms of Service</a>.</p><p>Address of the bookmark: <a href="https://cran.r-project.org/web/packages/googleVis/vignettes/googleVis_examples.html" rel="nofollow">https://cran.r-project.org/web/packages/googleVis/vignettes/googleVis_examples.html</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/news/view/36585/custom-r-charts-coming-to-excel</guid>
	<pubDate>Sat, 12 May 2018 07:30:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/news/view/36585/custom-r-charts-coming-to-excel</link>
	<title><![CDATA[Custom R charts coming to Excel !]]></title>
	<description><![CDATA[<p>This week at the BUILD conference, Microsoft&nbsp;<a href="https://dev.office.com/blogs/azure-machine-learning-javascript-custom-functions-and-power-bi-custom-visuals-further-expand-developers-capabilities-with-excel" target="_blank">announced</a>&nbsp;that Power BI custom visuals will soon be available as charts with Excel. You'll be able to choose a range of data within an Excel workbook, and pass those data to one of the built-in Power BI custom visuals, or one you've&nbsp;<a href="https://github.com/Microsoft/PowerBI-Visuals/" target="_blank">created yourself using the API</a>.</p><p><a href="http://a0.typepad.com/6a0105360ba1c6970c0224e038fa08200d-pi" target="_blank"><img src="https://www.r-bloggers.com/wp-content/plugins/lazy-load/images/1x1.trans.gif" alt="Excel custom visuals" title="Excel custom visuals" style="border: 0px; border: 0px;"></a></p><p>Since you can&nbsp;<a href="https://docs.microsoft.com/en-us/power-bi/desktop-r-visuals?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">create Power BI custom visuals using R</a>, that means you'll be able to design a custom R-based chart, and make it available to people using Excel &mdash; even if they don't know how to use R themselves. There also many&nbsp;<a href="https://appsource.microsoft.com/en-us/marketplace/apps?product=power-bi-visuals&amp;page=1&amp;src=office" target="_blank">pre-defined custom visuals available</a>, including some familiar R charts like&nbsp;<a href="https://appsource.microsoft.com/en-us/product/power-bi-visuals/WA104380817?tab=Overview" target="_blank">decision trees</a>,&nbsp;<a href="https://appsource.microsoft.com/en-us/product/power-bi-visuals/WA104380905?tab=Overview" target="_blank">calendar heatmaps</a>, and&nbsp;<a href="https://appsource.microsoft.com/en-us/product/power-bi-visuals/WA104381492?tab=Overview" target="_blank">hexbin scatterplots</a>.</p><p>For more details on how you'll be able to use custom R visuals in Excel, check out the blog post linked below.</p><p>PowerBI Blog:&nbsp;<a href="https://powerbi.microsoft.com/en-us/blog/excel-announces-new-data-visualization-capabilities-with-power-bi-custom-visuals/" target="_blank">Excel announces new data visualization capabilities with Power BI custom visuals</a></p>]]></description>
	<dc:creator>Surabhi Chaudhary</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/37732/making-2d-hilbert-curve</guid>
	<pubDate>Mon, 17 Sep 2018 05:43:35 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/37732/making-2d-hilbert-curve</link>
	<title><![CDATA[Making 2D Hilbert Curve]]></title>
	<description><![CDATA[<p><a href="https://en.wikipedia.org/wiki/Hilbert_curve">Hilbert curve</a>&nbsp;is a type of space-filling curves that folds one dimensional axis into a two dimensional space, but still keeps the locality. It has advantages to visualize data with long axis in following two aspects:</p>
<ol>
<li>greatly improve resolution of the visualization fron n to&nbsp;<span><span><span><span><span><span><span>&radic;</span></span><span><span><span><span>n</span></span></span></span></span></span></span></span><span>n</span></span>;</li>
<li>easy to visualize clusters because generally data points in the axis will also be close in the 2D space.</li>
</ol>
<p>This package aims to provide an easy and flexible way to visualize data through Hilbert curve. The implementation and example figures are based on following sources:</p>
<ul>
<li><a href="http://mkweb.bcgsc.ca/hilbert/">http://mkweb.bcgsc.ca/hilbert/</a></li>
<li><a href="http://corte.si/posts/code/hilbert/portrait/index.html">http://corte.si/posts/code/hilbert/portrait/index.html</a></li>
<li><a href="http://bioconductor.org/packages/devel/bioc/html/HilbertVis.html">http://bioconductor.org/packages/devel/bioc/html/HilbertVis.html</a></li>
</ul><p>Address of the bookmark: <a href="https://bioconductor.org/packages/devel/bioc/vignettes/HilbertCurve/inst/doc/HilbertCurve.html" rel="nofollow">https://bioconductor.org/packages/devel/bioc/vignettes/HilbertCurve/inst/doc/HilbertCurve.html</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/38420/regioner-an-r-package-for-the-management-and-comparison-of-genomic-regions</guid>
	<pubDate>Tue, 11 Dec 2018 08:43:51 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/38420/regioner-an-r-package-for-the-management-and-comparison-of-genomic-regions</link>
	<title><![CDATA[regioneR: an R package for the management and comparison of genomic regions]]></title>
	<description><![CDATA[<p><span>Regioner is an R package for the management and comparison of genomic regions. It offers a set of function for basic manipulation of region sets extending the functionality of GenomicRanges and a powerful and customizable permutation test framework. With it, it's possible to study the association of a set of regions with other sets of regions, functions defined over the genome or essentially any user defined function.</span></p>
<p><span>http://gattaca.imppc.org/regioner/</span></p><p>Address of the bookmark: <a href="http://gattaca.imppc.org/regioner/" rel="nofollow">http://gattaca.imppc.org/regioner/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39114/plumberan-r-package-that-converts-your-existing-r-code-to-a-web-api</guid>
	<pubDate>Wed, 13 Mar 2019 19:20:10 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39114/plumberan-r-package-that-converts-your-existing-r-code-to-a-web-api</link>
	<title><![CDATA[plumber:An R package that converts your existing R code to a web API]]></title>
	<description><![CDATA[<p>plumber allows you to create a REST API by merely decorating your existing R source code with special comments. Take a look at an example.</p>
<pre><code><span># plumber.R
</span><span>
</span><span>#* Echo back the input
#* @param msg The message to echo
#* @get /echo
</span><span>function</span><span>(</span><span>msg</span><span>=</span><span>""</span><span>){</span><span>
  </span><span>list</span><span>(</span><span>msg</span><span> </span><span>=</span><span> </span><span>paste0</span><span>(</span><span>"The message is: '"</span><span>,</span><span> </span><span>msg</span><span>,</span><span> </span><span>"'"</span><span>))</span><span>
</span><span>}</span><span>

</span><span>#* Plot a histogram
#* @png
#* @get /plot
</span><span>function</span><span>(){</span><span>
  </span><span>rand</span><span> </span><span>&lt;-</span><span> </span><span>rnorm</span><span>(</span><span>100</span><span>)</span><span>
  </span><span>hist</span><span>(</span><span>rand</span><span>)</span><span>
</span><span>}</span><span>

</span><span>#* Return the sum of two numbers
#* @param a The first number to add
#* @param b The second number to add
#* @post /sum
</span><span>function</span><span>(</span><span>a</span><span>,</span><span> </span><span>b</span><span>){</span><span>
  </span><span>as.numeric</span><span>(</span><span>a</span><span>)</span><span> </span><span>+</span><span> </span><span>as.numeric</span><span>(</span><span>b</span><span>)</span><span>
</span><span>}</span></code></pre><p>Address of the bookmark: <a href="https://www.rplumber.io/" rel="nofollow">https://www.rplumber.io/</a></p>]]></description>
	<dc:creator>BioJoker</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/39947/radar-charts-with-ggplot2</guid>
	<pubDate>Tue, 17 Sep 2019 23:01:28 -0500</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/39947/radar-charts-with-ggplot2</link>
	<title><![CDATA[radar charts with ggplot2]]></title>
	<description><![CDATA[<p><code>ggradar</code>&nbsp;allows you to build radar charts with ggplot2. This package is based on&nbsp;<a href="http://rstudio-pubs-static.s3.amazonaws.com/5795_e6e6411731bb4f1b9cc7eb49499c2082.html">Paul Williamson&rsquo;s</a>&nbsp;code, with new aesthetics and compatibility with ggplot2 2.0.</p>
<p>It was inspired by&nbsp;<a href="http://www.buildingwidgets.com/blog/2015/12/9/week-49-d3radarr">d3radaR</a>, an htmlwidget built by&nbsp;<a href="https://github.com/timelyportfolio">timelyportfolio</a>.</p><p>Address of the bookmark: <a href="https://github.com/ricardo-bion/ggradar" rel="nofollow">https://github.com/ricardo-bion/ggradar</a></p>]]></description>
	<dc:creator>Rahul Nayak</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/40721/efs-an-ensemble-feature-selection-tool-implemented-as-r-package-and-web-application</guid>
	<pubDate>Tue, 28 Jan 2020 05:12:23 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/40721/efs-an-ensemble-feature-selection-tool-implemented-as-r-package-and-web-application</link>
	<title><![CDATA[EFS: an ensemble feature selection tool implemented as R-package and web-application]]></title>
	<description><![CDATA[<p><span>The software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble.</span></p>
<p><a href="https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0142-8">https://biodatamining.biomedcentral.com/articles/10.1186/s13040-017-0142-8</a></p><p>Address of the bookmark: <a href="http://efs.heiderlab.de/" rel="nofollow">http://efs.heiderlab.de/</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>
<item>
	<guid isPermaLink="true">https://bioinformaticsonline.com/bookmarks/view/41169/instructions-for-creating-your-own-r-package</guid>
	<pubDate>Wed, 19 Feb 2020 01:22:48 -0600</pubDate>
	<link>https://bioinformaticsonline.com/bookmarks/view/41169/instructions-for-creating-your-own-r-package</link>
	<title><![CDATA[Instructions for Creating Your Own R Package]]></title>
	<description><![CDATA[<p>The following is a step-by-step guide to creating your own R package.&nbsp; Even beyond this course, youmay find this useful for storing functions you create for your own research or for editing existingR packages to suit your needs.</p>
<p>This guide contains three different sets of instructions.&nbsp; If you use RStudio, you can follow the &ldquo;Ba-sic Instructions&rdquo; in Section 2 which involve using RStudio&rsquo;s interface.&nbsp; </p><p>Address of the bookmark: <a href="http://web.mit.edu/insong/www/pdf/rpackage_instructions.pdf" rel="nofollow">http://web.mit.edu/insong/www/pdf/rpackage_instructions.pdf</a></p>]]></description>
	<dc:creator>Jit</dc:creator>
</item>

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