minor refresh

R/reduce.space.R

@@ -19,6 +19,7 @@
 #'      \item \code{"size"} for selecting the proportion of data points closer to the centre.
 #'      \item \code{"density"} for selecting the proportion of data points with the lower nearest neigbhour distances.
 #'      \item \code{"evenness"} for randomly selecting the proportion of data points from the regions with most density.
+#'      \item \code{"position"} for selecting the proportion of data closer to a corner of the the space.
 #' }
 #' 
 #' The parameters for each reduction type algorithms are:

README.md

@@ -3,7 +3,7 @@ Release:
 [![R-CMD-check](https://github.com/TGuillerme/dispRity/workflows/R-CMD-check/badge.svg)](https://github.com/TGuillerme/dispRity/actions)
 [![codecov](https://codecov.io/gh/TGuillerme/dispRity/branch/release/graph/badge.svg)](https://codecov.io/gh/TGuillerme/dispRity)
 [![Project Status: Active - The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](http://www.repostatus.org/#active)
-[![develVersion](https://img.shields.io/badge/devel%20version-1.9.0-green.svg?style=flat)](https://github.com/TGuillerme/dispRity)
+[![develVersion](https://img.shields.io/badge/devel%20version-1.9.1-green.svg?style=flat)](https://github.com/TGuillerme/dispRity)
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1186467.svg)](https://doi.org/10.5281/zenodo.1186467)
 
 Development (master):

inst/vignettes/select_disparity_metrics.html

@@ -11,7 +11,7 @@
 
 <meta name="author" content="Thomas Guillerme" />
 
-<meta name="date" content="2024-12-19" />
+<meta name="date" content="2025-01-03" />
 
 <title>Select your own disparity metric</title>
 
@@ -374,7 +374,7 @@
 
 <h1 class="title toc-ignore">Select your own disparity metric</h1>
 <h4 class="author">Thomas Guillerme</h4>
-<h4 class="date">2024-12-19</h4>
+<h4 class="date">2025-01-03</h4>
 
 </div>
 
@@ -383,7 +383,7 @@ <h4 class="date">2024-12-19</h4>
 tailored to your own dataset and your own biological question. We will
 be doing that using the <code>dispRity</code> package <span class="citation">Guillerme (2018)</span>. You can find the slides
 introducing this vignette <a href="https://tguillerme.github.io/Top_10_disparity_metrics.pdf">here</a>.
-<!-- And a recording of the whole workshop [here](). --></p>
+And a recording of the whole workshop <a href="https://www.youtube.com/watch?v=JKpUQbxID9A">here</a>.</p>
 <pre class="r"><code>## Installing the package
 install.packages(&quot;dispRity&quot;)
 ## Loading the package
@@ -751,9 +751,9 @@ <h1>Bonus - design your own metrics</h1>
 <pre class="r"><code>## Count the neigbhours in our dataset
 count_neighbours &lt;- dispRity(my_data, metric = counting.neighbours, radius = 8.8)
 summary(count_neighbours)</code></pre>
-<pre><code>##   subsets  n obs.median 0.25%  2.5%  7.5% 9.75%
-## 1   crown 52        7.0 0.000 0.275 1.000 1.973
-## 2    stem 54       26.5 2.398 5.325 8.975 9.168</code></pre>
+<pre><code>##   subsets  n obs.median  2.5%   25% 75% 97.5%
+## 1   crown 52        7.0 0.275  4.75   9 18.45
+## 2    stem 54       26.5 5.325 19.75  32 39.00</code></pre>
 <p>And test using the <code>test.metric</code> function (expecting it
 captures changes in density):</p>
 <pre class="r"><code>## Testing the metric