Merge pull request #1180 from geocompx/copilot/trigger-github-workflow-re-run

Add scheduled workflow to detect upstream failures; set weekly cadence

Author: Robinlovelace

.github/workflows/dev-sf.yaml

@@ -15,13 +15,11 @@ jobs:
 
     steps:
         - name: Checkout Project
-          uses: actions/checkout@v1
+          uses: actions/checkout@v4
 
         - name: Build book
           run: |
             Rscript -e 'install.packages("geocompkg", repos = c("https://geocompr.r-universe.dev", "https://cloud.r-project.org"), dependencies = TRUE, force = TRUE)'
-            Rscript -e 'remotes::install_github("r-spatial/sf")'
-            Rscript -e 'remotes::install_github("r-spatial/stars")'
-            Rscript -e 'remotes::install_github("rspatial/terra")'
-            #Rscript -e 'remotes::install_github("geocompx/geocompkg", dependencies = TRUE, force = TRUE)'
+            Rscript -e 'if (!requireNamespace("pak", quietly = TRUE)) install.packages("pak", repos = "https://cloud.r-project.org")'
+            Rscript -e 'pak::pak(c("r-spatial/sf", "r-spatial/stars", "rspatial/terra"))'
             Rscript -e 'bookdown::render_book("index.Rmd")'

.github/workflows/scheduled.yaml

@@ -0,0 +1,18 @@
+name: Scheduled
+on:
+  schedule:
+    - cron: '0 6 * * 1'  # every Monday at 06:00 UTC
+  workflow_dispatch:
+jobs:
+  bookdown:
+    name: Render-Book
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+    container: ghcr.io/geocompx/suggests:latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    steps:
+      - uses: actions/checkout@v2
+      - name: Render Book
+        run: Rscript -e 'bookdown::render_book("index.Rmd")'

08-read-write-plot.Rmd

@@ -292,6 +292,7 @@ multilayer_rast = rast(multilayer_filepath)
 All of the previous examples read spatial information from files stored on your hard drive. 
 However, GDAL also allows reading data directly from online resources, such as HTTP/HTTPS/FTP web resources.
 The only thing we need to do is to add a `/vsicurl/` prefix before the path to the file.
+This tells GDAL to use its virtual file system for network resources, which uses **HTTP Range requests** to fetch only the specific byte ranges needed for an operation rather than downloading the entire file.
 Let's try it by connecting to the global monthly snow probability at 500-m resolution for the period 2000-2012.
 Snow probability for December is stored as a Cloud Optimized GeoTIFF (COG) file (see Section \@ref(file-formats)) at [zenodo.org](https://zenodo.org/record/5774954/files/clm_snow.prob_esacci.dec_p.90_500m_s0..0cm_2000..2012_v2.0.tif).
 To read an online file, we just need to provide its URL together with the `/vsicurl/` prefix.
@@ -304,8 +305,8 @@ snow
 ```
 
 \index{COG} 
-Due to the fact that the input data is COG, we are actually not reading this file to our RAM, but rather creating a connection to it without obtaining any values.
-Its values will be read if we apply any value-based operation (e.g., `crop()` or `extract()`).
+When combined with the `/vsicurl/` prefix, COG files enable highly efficient remote data access.
+Instead of reading the whole file into RAM, GDAL creates a connection that **selectively fetches** only the byte ranges needed for subsequent operations (e.g., `crop()` or `extract()`), making it possible to work with multi-gigabyte files over the internet almost as if they were local.
 This allows us also to just read a tiny portion of the data without downloading the entire file.
 For example, we can get the snow probability for December in Reykjavik (70%) by specifying its coordinates and applying the `extract()` function: