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<li><a href="#spatial-in-r-history-and-evolutions" id="toc-spatial-in-r-history-and-evolutions" class="nav-link active" data-scroll-target="#spatial-in-r-history-and-evolutions"> <span class="header-section-number">1.1</span> Spatial in R : History and evolutions</a></li>
<li><a href="#the-package-sf" id="toc-the-package-sf" class="nav-link" data-scroll-target="#the-package-sf"> <span class="header-section-number">1.2</span> The package <code>sf</code></a>
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<li><a href="#format-of-spatial-objects-sf" id="toc-format-of-spatial-objects-sf" class="nav-link" data-scroll-target="#format-of-spatial-objects-sf"> <span class="header-section-number">1.2.1</span> Format of spatial objects <code>sf</code></a></li>
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<li><a href="#package-mapsf" id="toc-package-mapsf" class="nav-link" data-scroll-target="#package-mapsf"> <span class="header-section-number">1.3</span> Package <code>mapsf</code></a></li>
<li><a href="#the-package-terra" id="toc-the-package-terra" class="nav-link" data-scroll-target="#the-package-terra"> <span class="header-section-number">1.4</span> The package <code>terra</code></a></li>
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<section id="spatial-in-r-history-and-evolutions" class="level2" data-number="1.1">
<h2 data-number="1.1" class="anchored" data-anchor-id="spatial-in-r-history-and-evolutions"><span class="header-section-number">1.1</span> Spatial in R : History and evolutions</h2>
<p>Historically, 4 packages make it possible to import, manipulate and transform spatial data:</p>
<ul>
<li>The package <code>rgdal</code> <span class="citation" data-cites="rgdal">(<a href="references.html#ref-rgdal" role="doc-biblioref">Bivand, Keitt, and Rowlingson 2022</a>)</span> which is an interface between R and the <a href="http://www.gdal.org/">GDAL</a> <span class="citation" data-cites="GDAL">(<a href="references.html#ref-GDAL" role="doc-biblioref">GDAL/OGR contributors, n.d.</a>)</span> and <a href="https://proj.org/">PROJ</a> <span class="citation" data-cites="PROJ">(<a href="references.html#ref-PROJ" role="doc-biblioref">PROJ contributors 2021</a>)</span> libraries allow you to import and export spatial data (shapefiles for example) and also to manage cartographic projections<br>
</li>
<li>The package <code>sp</code> <span class="citation" data-cites="sp">(<a href="references.html#ref-sp" role="doc-biblioref">E. J. Pebesma and Bivand 2005</a>)</span> provides class and methods for vector spatial data in R. It allows displaying background maps, inspectiong an attribute table etc.<br>
</li>
<li>The package <code>rgeos</code> <span class="citation" data-cites="rgeos">(<a href="references.html#ref-rgeos" role="doc-biblioref">Bivand and Rundel 2021</a>)</span> gives access to the <a href="http://trac.osgeo.org/geos/">GEOS</a> spatial operations library and therefore makes classic GIS operations available: calculation of surfaces or perimeters, calculation of distances, spatial aggregations, buffer zones, intersections, etc.<br>
</li>
<li>The package <code>raster</code> <span class="citation" data-cites="raster">(<a href="references.html#ref-raster" role="doc-biblioref">Hijmans 2022a</a>)</span> is dedicated to the import, manipulation and modeling of raster data.</li>
</ul>
<p>Today, the main developments concerning vector data have moved away from the old 3 (<code>sp</code>, <code>rgdal</code>, <code>rgeos</code>) to rely mainly on the package <code>sf</code> (<span class="citation" data-cites="sf">(<a href="references.html#ref-sf" role="doc-biblioref">E. Pebesma 2018a</a>)</span>, <span class="citation" data-cites="pebesma2018">(<a href="references.html#ref-pebesma2018" role="doc-biblioref">E. Pebesma 2018b</a>)</span>). In this manual we will rely exclusively on this package to manipulate vector data.</p>
<p>The packages <code>stars</code> <span class="citation" data-cites="stars">(<a href="references.html#ref-stars" role="doc-biblioref">E. Pebesma 2021</a>)</span> and <code>terra</code> <span class="citation" data-cites="terra">(<a href="references.html#ref-terra" role="doc-biblioref">Hijmans 2022b</a>)</span> come to replace the package <code>raster</code> for processing raster data. We have chosen to use the package here <code>terra</code> for its proximity to the <code>raster</code>.</p>
</section>
<section id="the-package-sf" class="level2" data-number="1.2">
<h2 data-number="1.2" class="anchored" data-anchor-id="the-package-sf"><span class="header-section-number">1.2</span> The package <code>sf</code></h2>
<p><img src="img/sf.gif" align="right" width="150"> The package <code>sf</code> was released in late 2016 by Edzer Pebesma (also author of <code>sp</code>). Its goal is to combine the feature of <code>sp</code>, <code>rgeos</code> and <code>rgdal</code> in a single, more ergonomic package. This package offers simple objects (following the <a href="https://en.wikipedia.org/wiki/Simple_Features"><em>simple feature</em></a> standard) which are easier to manipulate. Particular attention has been paid to the compatibility of the package with the <em>pipe</em> syntax and the operators of the <code>tidyverse</code>.</p>
<p><code>sf</code> directly uses the GDAL, GEOS and PROJ libraries.</p>
<div class="quarto-figure quarto-figure-center">
<figure class="figure">
<p><img src="img/sf_deps.png" class="img-fluid figure-img" width="600"></p>
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<p><a href="https://r-spatial.org/r/2020/03/17/wkt.html">From r-spatial.org</a></p>
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<p>Website of package <code>sf</code> :</p>
<p><a href="https://r-spatial.github.io/sf/">Simple Features for R</a></p>
</div>
</div>
</div>
<section id="format-of-spatial-objects-sf" class="level3" data-number="1.2.1">
<h3 data-number="1.2.1" class="anchored" data-anchor-id="format-of-spatial-objects-sf"><span class="header-section-number">1.2.1</span> Format of spatial objects <code>sf</code></h3>
<div class="quarto-figure quarto-figure-center">
<figure class="figure">
<p><img src="img/sf.png" class="img-fluid figure-img" width="600"></p>
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<p>Objects<code>sf</code> are objects in <code>data.frame</code> which one of the columns contains geometries. This column is the class of sfc (<em>simple feature column</em>) and each individual of the column is a sfg <em>(simple feature geometry)</em>. This format is very practical insofa as the data and the geometries are intrinsically linked in the same object.</p>
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<p>Thumbnail describing the simple feature format:</p>
<p><a href="https://r-spatial.github.io/sf/articles/sf1.html">Simple Features for R</a></p>
</div>
</div>
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<p>A benchmark of vector processing libraries is available <a href="https://github.com/kadyb/vector-benchmark">here</a>.</p>
</div>
</div>
</section>
</section>
<section id="package-mapsf" class="level2" data-number="1.3">
<h2 data-number="1.3" class="anchored" data-anchor-id="package-mapsf"><span class="header-section-number">1.3</span> Package <code>mapsf</code></h2>
<p>The free R software spatial ecosystem is rich, dynamic and mature and several packages allow to import, process and represent spatial data. The package <a href="https://CRAN.R-project.org/package=maps"><code>mapsf</code></a> <span class="citation" data-cites="mapsf">(<a href="references.html#ref-mapsf" role="doc-biblioref">Giraud 2022</a>)</span> relies on this ecosystem to integrate the creation of quality thematic maps into processing chains with R.</p>
<p>Other packages can be used to make thematic maps. The package <code>ggplot2</code> <span class="citation" data-cites="ggplot2">(<a href="references.html#ref-ggplot2" role="doc-biblioref">Wickham 2016</a>)</span>, in association with the package <code>ggspatial</code> <span class="citation" data-cites="ggspatial">(<a href="references.html#ref-ggspatial" role="doc-biblioref">Dunnington 2021</a>)</span>, allows for example to display spatial objects and to make simple thematic maps. The package <code>tmap</code> <span class="citation" data-cites="tmap">(<a href="references.html#ref-tmap" role="doc-biblioref">Tennekes 2018</a>)</span> is dedicated to the creation of thematic maps, it uses a syntax close to that of <code>ggplot2</code> (sequence of instructions combined with the ‘+’ sign). Documentation and tutorials for using these two packages are readily available on the web.</p>
<p>Here, we will mainly use the package <code>mapsf</code> whose functionalities are quite complete and the handling rather simple. In addition, the package is relatively light.</p>
<p><img src="img/logo_mapsf.png" align="right" width="120"></p>
<p><code>mapsf</code> allows you to create most of the types of map usually used in statistical cartography (choropleth maps, typologies, proportional or graduated symbols, etc.). For each type of map, several parameters are used to customize the cartographic representation. These parameters are the same as those found in the usual GIS or cartography software (for example, the choice of discretizations and color palettes, the modification of the size of the symbols or the customization of the legends). Associated with the data representation functions, other functions are dedicated to cartographic dressing (themes or graphic charters, legends, scales, orientation arrows, title, credits, annotations, etc.), the creation of boxes or the exporting maps.<br>
<code>mapsf</code> is the successor of <a href="http://riatelab.github.io/cartography/docs/"><code>cartography</code></a> <span class="citation" data-cites="cartography">(<a href="references.html#ref-cartography" role="doc-biblioref">Giraud and Lambert 2016</a>)</span>, it offers the same main functionalities while being lighter and more ergonomic.</p>
<p>To use this package several sources can be consulted:</p>
<ul>
<li><p>The package documentation accessible <a href="http://riatelab.github.io/mapsf/">on the internet</a> or directly in R (<code>?mapsf</code>),</p></li>
<li><p>A <a href="https://raw.githubusercontent.com/riatelab/mapsf/master/vignettes/web_only/img/mapsf_cheatsheet.pdf"><em>cheat sheet</em></a>,</p></li>
</ul>
<div class="quarto-figure quarto-figure-center">
<figure class="figure">
<p><img src="img/mapsf_cheatsheet.png" class="img-fluid figure-img" width="600"></p>
</figure>
</div>
<ul>
<li><p>The <a href="https://riatelab.github.io/mapsf/articles/">vignettes</a> associated with the package show sample scripts,</p></li>
<li><p>The <a href="https://rgeomatic.hypotheses.org/">R Geomatics</a> blog which provides resources and examples related to the package and more generally to the R spatial ecosystem.</p></li>
</ul>
</section>
<section id="the-package-terra" class="level2" data-number="1.4">
<h2 data-number="1.4" class="anchored" data-anchor-id="the-package-terra"><span class="header-section-number">1.4</span> The package <code>terra</code></h2>
<p><img src="img/logo_terra.png" align="right" width="150"> The package <code>terra</code> was release in early 2020 by Robert J. Hijmans (also author of <code>raster</code>). Its objective is to propose methods of treatment and analysis of raster data. This package is very similar to the package <code>raster</code>; but it has more features, it’s easier to use, and it’s faster.</p>
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<p>Website of package <code>terra</code> :</p>
<p><a href="https://rspatial.org/terra/">Spatial Data Science with R and “terra”</a></p>
</div>
</div>
</div>
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<p>A benchmark of raster processing libraries is available <a href="https://github.com/kadyb/raster-benchmark">here</a>.</p>
</div>
</div>
<div id="refs" class="references csl-bib-body hanging-indent" role="doc-bibliography" style="display: none">
<div id="ref-rgdal" class="csl-entry" role="doc-biblioentry">
Bivand, Roger, Tim Keitt, and Barry Rowlingson. 2022. <span>“Rgdal: Bindings for the ’Geospatial’ Data Abstraction Library.”</span> <a href="https://CRAN.R-project.org/package=rgdal">https://CRAN.R-project.org/package=rgdal</a>.
</div>
<div id="ref-rgeos" class="csl-entry" role="doc-biblioentry">
Bivand, Roger, and Colin Rundel. 2021. <span>“Rgeos: Interface to Geometry Engine - Open Source (’GEOS’).”</span> <a href="https://CRAN.R-project.org/package=rgeos">https://CRAN.R-project.org/package=rgeos</a>.
</div>
<div id="ref-ggspatial" class="csl-entry" role="doc-biblioentry">
Dunnington, Dewey. 2021. <span>“Ggspatial: Spatial Data Framework for Ggplot2.”</span> <a href="https://CRAN.R-project.org/package=ggspatial">https://CRAN.R-project.org/package=ggspatial</a>.
</div>
<div id="ref-GDAL" class="csl-entry" role="doc-biblioentry">
GDAL/OGR contributors. n.d. <em><span>GDAL/OGR</span> Geospatial Data Abstraction Software Library</em>. Open Source Geospatial Foundation. <a href="https://gdal.org">https://gdal.org</a>.
</div>
<div id="ref-mapsf" class="csl-entry" role="doc-biblioentry">
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