Add scheduled workflow to detect upstream failures; set weekly cadence

.github/workflows/dev-sf.yaml

@@ -15,13 +15,13 @@ 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 'if (!requireNamespace("pak", quietly = TRUE)) install.packages("pak", repos = "https://cloud.r-project.org"); pak::pak("r-spatial/sf")'
+            Rscript -e 'if (!requireNamespace("pak", quietly = TRUE)) install.packages("pak", repos = "https://cloud.r-project.org"); pak::pak("r-spatial/stars")'
+            Rscript -e 'if (!requireNamespace("pak", quietly = TRUE)) install.packages("pak", repos = "https://cloud.r-project.org"); pak::pak("rspatial/terra")'
             #Rscript -e 'remotes::install_github("geocompx/geocompkg", dependencies = TRUE, force = TRUE)'
             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

@@ -292,13 +292,12 @@ 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.
-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).
+Let's try it by connecting to a remote GeoTIFF hosted by [OSGeo](https://download.osgeo.org/geotiff/samples/usgs/o41078a5.tif).
+This sample file is available online as a GeoTIFF (see Section \@ref(file-formats)).
 To read an online file, we just need to provide its URL together with the `/vsicurl/` prefix.
 
 ```{r, linewidth=80}
-myurl = paste0("/vsicurl/https://zenodo.org/record/5774954/files/",
-               "clm_snow.prob_esacci.dec_p.90_500m_s0..0cm_2000..2012_v2.0.tif")
+myurl = "/vsicurl/https://download.osgeo.org/geotiff/samples/usgs/o41078a5.tif"
 snow = rast(myurl)
 snow
 ```