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<!DOCTYPE html>

<head>

<title>Package Introduction</title>

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<a class="navbar-brand" href="index.html">GEO 503: Spatial Data Science</a>

</div>

<li>

</li>

<li>

<a href="Syllabus.html">Syllabus</a>

</li>

<li>

<a href="Schedule.html">Schedule</a>

</li>

Content

</a>

<li>

<a href="CourseContent.html">About Course Content</a>

</li>

<li class="dropdown-header">Module 1: Introduction to R</li>

<li>

<a href="00_CourseIntroductionFrame.html">00 Course Introduction</a>

</li>

<li>

<a href="01_Rintro.html">01 First Steps</a>

</li>

<li>

<a href="02_graphics.html">02 Graphics</a>

</li>

<li>

<a href="03_DataWrangling.html">03 Data Wrangling</a>

</li>

<li>

<a href="03b_DataWrangling.html">03 Data Wrangling 2</a>

</li>

<li class="dropdown-header">Module 2: Spatial Analyses</li>

<li>

<a href="04_Spatial_with_sf.html">04 Spatial Data with sf</a>

</li>

<li>

<a href="05_Raster.html">05 Spatial Raster Data</a>

</li>

<li class="dropdown-header">Module 3: Advanced Programming</li>

<li>

<a href="06_CreatingWorkflows.html">06 Creating Workflows</a>

</li>

<li>

<a href="07_Reproducibile.html">07 Reproducible Research</a>

</li>

<li>

<a href="08_WeatherData.html">08 Weather Data Processing</a>

</li>

<li>

<a href="09_RemoteSensing_appeears.html">09 Satellite Data Processing</a>

</li>

<li>

<a href="11_ParallelProcessing.html">11 Parallel Processing</a>

</li>

<li>

<a href="12_DynamicVisualization.html">12 Dynamic Visualization</a>

</li>

<li>

<a href="13_SDM_Exercise.html">13 Species Distribution Modeling</a>

</li>

</ul>

</li>

Assignments

</a>

<li>

</li>

<li>

<a href="DataCamp.html">DataCamp</a>

</li>

<li>

<a href="PackageIntro.html">Package Introduction</a>

</li>

<li>

<a href="Project.html">Final Project</a>

</li>

</ul>

</li>

Resources

</a>

<li>

<a href="Provenance.html">Provenance</a>

</li>

<li>

<a href="Projects_2017.html">2017 Final Projects</a>

</li>

<li>

<a href="Resources.html">Resources</a>

</li>

<li>

<a href="GitSSHNotes.html">Setting up Github</a>

</li>

</ul>

</li>

</ul>

<li>

</a>

</li>

</ul>

</div>

</div>

</div>

<h1 class="title toc-ignore">Package Introduction</h1>

</div>

<h1>Description</h1>

<p>Each student will be expected to introduce a package (or two) that is relevant to their research interests in a 5 minute presentation during a class session. The objectives are:</p>

<ul>

<li>Learn how to find/download/install a new package and learn how to use it</li>

<li>Teach your peers about existing R packages that may be useful in their research</li>

</ul>

<p>The presentation must include:</p>

<li>Brief introduction: what does the package do and why is it useful? (<strong>1-2 slides, 1 minute</strong>)</li>

<li>Author introduction: a short background (affiliation and other packages, etc.) on at least one of the package authors (<strong>1 slide, 1 minute</strong>)</li>

<li>Simple demonstration of package code: example input/output from the examples or custom coded examples (<strong>2-3 slides, 3 minutes</strong>)</li>

</ol>

<p>There will not be time to actually run any code <em>on-the-fly</em> during your presentation. Instead, copy the code and output into a presentation (powerpoint, etc.) so that you can simply display it. See an <a href="assets/PackagePresentation.pptx">example presentation here</a>.</p>

<p>To select a package, I recommend starting with the <a href="https://cran.r-project.org/web/views/">views on CRAN</a> for a topic of interest. Then read the narrative in the task view for something interesting to you and install the package in R with <code>install.package("packagename")</code> in R and take a look at what it can do. Most package functions include sample code that performs a function. For example, if I was introducing the <code>dplyr</code> package, I might choose the <code>filter()</code> function. If you look in the help you will find a section called “Examples” that you can use for your example code in your presentation. All you have to do is copy-paste it from the help into the R console and them summarize what it’s doing in your presentation. For example, I might demonstrate (from the <code>filter()</code> documentation) that the following code will select all characters from Star Wars that are human.</p>

<pre class="r"><code>library(dplyr)

filter(starwars, species == "Human")</code></pre>

<pre><code>## # A tibble: 35 x 13

## name height mass hair_color skin_color eye_color birth_year gender

## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>

## 1 Luke… 172 77 blond fair blue 19 male

## 2 Dart… 202 136 none white yellow 41.9 male

## 3 Leia… 150 49 brown light brown 19 female

## 4 Owen… 178 120 brown, gr… light blue 52 male

## 5 Beru… 165 75 brown light blue 47 female

## 6 Bigg… 183 84 black light brown 24 male

## 7 Obi-… 182 77 auburn, w… fair blue-gray 57 male

## 8 Anak… 188 84 blond fair blue 41.9 male

## 9 Wilh… 180 NA auburn, g… fair blue 64 male

## 10 Han … 180 80 brown fair brown 29 male

## # ... with 25 more rows, and 5 more variables: homeworld <chr>,

## # species <chr>, films <list>, vehicles <list>, starships <list></code></pre>

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