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@@ -177,6 +177,11 @@ <h3 id="using-the-console">Using the Console</h3> | |
| <pre><code class="language-R">root_folder <- tempdir() # Mandatory, variable must be in the R environment. | ||
| dirs <- initProj(root_folder = root_folder, standard_setup = "baseSpatial",folders = c("data/rawdata/provider1/", "docs/quarto/")) | ||
| </code></pre> | ||
| <p>A more complex call will be:</p> | ||
| <pre><code class="language-R"> initProj(root_folder = tempdir(), folders = c("data/newdata/"), | ||
| init_git = TRUE, init_renv = TRUE, code_subfolder = c("src", "src/functions","src/deprec"), | ||
| standard_setup = "baseSpatial",loc_name = "oldplace", appendlibs = c("raster"),openproject=TRUE) | ||
| </code></pre> | ||
| </div> | ||
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| <head> | ||
| <meta charset="utf-8"> | ||
| <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes"> | ||
| <title>Reproducible projects</title> | ||
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| </head> | ||
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| <div class="frontmatter"> | ||
| <div class="title"><h1>Reproducible projects</h1></div> | ||
| <div class="title"><h1>GEOSTAT 2018 Examples</h1></div> | ||
| <div class="author"><h2>Chris Reudenbach</h2></div> | ||
| <div class="date"><h3>2024-05-07</h3></div> | ||
| <div class="date"><h3>2024-05-10</h3></div> | ||
| </div> | ||
| <div class="body"> | ||
| <h1 id="reproducible-project-structure">Reproducible Project Structure</h1> | ||
| <p>Reproducible projects in R emphasize streamlined project setup and efficient workflows. There are a number of very helpful tools in the R universe, such as <a href="https://cran.r-project.org/package=renv"><code>renv</code></a>, <a href="https://cran.r-project.org/package=usethis"><code>usethis</code></a>, or <a href="https://cran.r-project.org/package=here"><code>here</code></a>, that range from setting up a stable R environment to generating custom project structures to getting the necessary paths in an easy way. In addition, there are a number of project structure packages and templates for creating easy-to-use and transparent project structures. Namely, <a href="https://cran.r-project.org/package=tinyProject"><code>tinProjects</code></a>, <a href="https://cran.r-project.org/package=prodigenr"><code>prodigenr</code></a> or <a href="https://cran.r-project.org/package=workflowr"><code>workflowr</code></a> are R packages designed to facilitate reproducible research through automated project structuring and standardization. They all promote organized project directories, emphasize reproducibility by integrating with tools like <code>Git' and </code>renv’, and reduce manual setup efforts to ensure consistent and error-free project initialization. These packages help build a solid foundation for research and ensure that best practices include using separate scripts for data processing, analysis, and reporting, and combining code with narrative in R Markdown documents from the start. This organized setup improves reproducibility by making it easier to maintain, share, and replicate research. For a more comprehensive overview, have a look at the <a href="https://CRAN.R-project.org/view=ReproducibleResearch"> CRAN Task View Reproducible Research</a>.</p> | ||
| <h2 id="why-initproj-then">Why initProj then?</h2> | ||
| <p>In the context of link2GI, which relies heavily on third-party command-line APIs and requires complex and stable folder and file structures, a flexible, lightweight R project setup greatly improves the integration of OS command-line tools into spatial workflows by</p> | ||
| <ol> | ||
| <li>**Streamlining integration: Simplifies the integration of essential command-line tools such as <code>GDAL' or the sophisticated </code>Orfeo Toolbox’ (OTB) and the growing universe of r(-)spatial packages for advanced geospatial processing.</li> | ||
| <li><strong>Improve data exchange</strong>: Organized variable and metadata management ensures accurate and efficient data transfer between different and especially command-line based processes and APIs.</li> | ||
| <li>**Enhanced Cross-Platform Compatibility: Facilitates cross-platform adaptability, which is critical when using multiple spatial analysis tools, even more so when using different shells.</li> | ||
| <li>**Performance Optimization: Switching between generic R and command-line tools takes advantage of the speed and efficiency of command-line tools, which is especially beneficial when handling large spatial datasets.</li> | ||
| </ol> | ||
| <p>For this reason, the link2GI package includes a lean and lightweight but focused approach that integrates <code>git', </code>renv’, and a highly flexible folder and package setup process that is simpler than existing approaches, increasing efficiency, accuracy, and performance in geospatial workflows.</p> | ||
| <h3 id="using-the-rstudio-gui">Using the RStudio GUI</h3> | ||
| <p><img src="https://raw.githubusercontent.com/r-spatial/link2GI/master/figures/usegui.gif" alt="Use the RStudio Template Option" /></p> | ||
| <h3 id="using-the-console">Using the Console</h3> | ||
| <p>The basic setup of a default project, which initializes Git and renv, is done with the following call.</p> | ||
| <pre><code class="language-R">root_folder <- tempdir() # Mandatory, variable must be in the R environment. | ||
| envrmt <- initProj(root_folder = root_folder, standard_setup = "baseSpatial") | ||
|
|
||
| </code></pre> | ||
| <p>It is easy to customize the folder structure. By default you will create</p> | ||
| <pre><code class="language-R">link2GI::setup_default()$baseSpatial$folders | ||
| </code></pre> | ||
| <p><code>[1] "docs" "docs/figures" "tmp" "data/source" "data/results" "data/level0" "data/level1" </code></p> | ||
| <pre><code class="language-R"> | ||
| link2GI::setup_default()$baseSpatial$code_subfolders | ||
|
|
||
| </code></pre> | ||
| <p><code>[1] "src" "src/functions"</code></p> | ||
| <p>Use the <code>folders</code> argument to create a specific structure or subfolder structure of your project.</p> | ||
| <pre><code class="language-R">root_folder <- tempdir() # Mandatory, variable must be in the R environment. | ||
| envrmt <- initProj(root_folder = root_folder, standard_setup = "baseSpatial",folders = c("data/rawdata/provider1/", "docs/quarto/")) | ||
| </code></pre> | ||
| <h3 id="usecases-presented-on-the-geostat-august-2018">Usecases presented on the GEOSTAT August 2018</h3> | ||
| <p>During the GEOSTAT 2018 (see <a href="https://opengeohub.org">https://opengeohub.org</a>) in Prague some more complex use cases have been presented.</p> | ||
| <h4 id="find-slides-and-materials">Find slides and materials</h4> | ||
| <ul> | ||
| <li><a href="https://gisma.github.io/link2gi2018/link2gi2018.html#1">Presentation slides</a></li> | ||
| <li><a href="https://github.com/gisma/link2gi2018">Github Repository</a></li> | ||
| </ul> | ||
| <h4 id="the-examples">The examples</h4> | ||
| <ul> | ||
| <li>Basic usage of <code>SAGA</code> and <code>OTB</code> calls - <a href="https://github.com/gisma/link2gi2018/blob/master/R/usecases/saga-otb/useCaseSAGA-OTB.R">SAGA & OTB basic usecase</a></li> | ||
| <li>Wrapping a <a href="https://neteler.gitlab.io/grass-gis-analysis/02_grass-gis_ecad_analysis/">GRASS GIS example</a> of Markus Neteler as presented on GEOSTAT 2018 - <a href="https://github.com/gisma/link2gi2018/blob/master/R/usecases/grass/useCaseGRASS-Neteler2018.R">Analysing the ECA&D climatic data - reloaded</a></li> | ||
| <li>Performing a <code>GRASS</code> based cost analysis on a huge cost raster - <a href="https://github.com/gisma/link2gi2018/blob/master/R/usecases/cost-analysis/useCaseBeetle.R">Beetle spread over high asia</a></li> | ||
| <li>Deriving a canopy height model using a mixed API approach - <a href="https://github.com/gisma/link2gi2018/blob/master/R/usecases/uav-pc/useCaseCHM.R">Canopy Height Model from UAV derived point clouds</a></li> | ||
| </ul> | ||
| </div> | ||
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| </body> | ||
| </html> | ||
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