Merge remote-tracking branch 'refs/remotes/origin/main'

Author: Nowosad

.github/workflows/main.yaml

@@ -7,15 +7,15 @@ jobs:
   bookdown:
     name: Render-Book
     runs-on: ubuntu-latest
-    container: geocompr/geocompr:suggests
+    container: ghcr.io/geocompx/docker:suggests
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
     steps:
       - uses: actions/checkout@v2
       - name: Render Book
         run: |
-          Rscript -e 'install.packages("remotes")'
-          Rscript -e 'remotes::install_github("geocompx/geocompkg", dependencies = TRUE, force = TRUE)'
+          #Rscript -e 'install.packages("remotes")'
+          #Rscript -e 'remotes::install_github("geocompx/geocompkg", dependencies = TRUE, force = TRUE)'
           #Rscript -e 'install.packages("geocompkg", repos = c("https://geocompr.r-universe.dev", "https://cloud.r-project.org"), dependencies = TRUE, force = TRUE)'
           Rscript -e 'bookdown::render_book("index.Rmd")'
           cp -fvr _redirects _book/

01-introduction.Rmd

@@ -53,7 +53,7 @@ This may sound simple and easy to achieve (which it is if you carefully maintain
 
 We define geocomputation as
 
-> A field of research, software development and practical application that uses geographic data to solve problems, with a focus on reproducibility, flexibility and tool development.
+> Academic research, software development and practical applications that use geographic data to solve problems, with a focus on reproducibility, flexibility and tool development.
 
 Geocomputation\index{geocomputation!definition} is a young term, dating back to the first conference on the subject in 1996.^[
 The first 'GeoComputation' conference took place at the University of Leeds, where one of the authors (Robin) is currently based.

02-spatial-data.Rmd

@@ -37,7 +37,7 @@ Unless you already have these packages installed, the first thing to do is to in
 install.packages("sf")
 install.packages("terra")
 install.packages("spData")
-install.packages("spDataLarge", repos = "https://nowosad.r-universe.dev")
+install.packages("spDataLarge", repos = "https://geocompr.r-universe.dev")
 ```
 
 ```{r, eval=FALSE, echo=FALSE, message=FALSE, results='hide'}

04-spatial-operations.Rmd

@@ -148,7 +148,7 @@ waldo::compare(canterbury_height2, canterbury_height4)
 At this point, there are three identical (in all but row names) versions of `canterbury_height`, one created using the `[` operator, one created via an intermediary selection object, and another using **sf**'s convenience function `st_filter()`.
 <!-- RL: commented out for now as old. Todo: if we ever update that vignette uncomment the next line. -->
 <!-- To explore spatial subsetting in more detail, see the supplementary vignettes on `subsetting` and [`tidyverse-pitfalls`](https://geocompr.github.io/geocompkg/articles/) on the [geocompkg website](https://geocompr.github.io/geocompkg/articles/). -->
-The next section explores different types of spatial relation, also known as binary predicates, that can be used to identify whether or not two features are spatially related or not.
+The next section explores different types of spatial relation, also known as binary predicates, that can be used to identify whether two features are spatially related or not.
 
 ### Topological relations
 

13-transport.Rmd

@@ -540,7 +540,7 @@ This is done using the publicly available OSRM service with the **stplanr** func
 
 ```{r 13-transport-18, message=FALSE}
 routes_short = route(l = desire_lines_short, route_fun = route_osrm,
-                     osrm.profile = "bike")
+                     osrm.profile = "car")
 ```
 
 The output is `routes_short`, an `sf` object representing routes on the transport network\index{network} that are suitable for cycling (according to the OSRM routing engine at least), one for each desire line.

code/03-cont-raster-plot.R

@@ -8,12 +8,12 @@ colfunc2 = c("clay" = "brown", "silt" = "sandybrown", "sand" = "rosybrown")
 p1 = tm_shape(elev) + 
   tm_raster(col.scale = tm_scale_continuous(),
             col.legend = tm_legend(title = "",
-                                   position = tm_pos_auto_in(),
+                                   position = tm_pos_in(),
                                    bg.color = "white")) 
 
 p2 = tm_shape(grain) + 
   tm_raster(col.scale = tm_scale_categorical(values = colfunc2),
             col.legend = tm_legend(title = "",
-                                   position = tm_pos_auto_in(),
+                                   position = tm_pos_in(),
                                    bg.color = "white")) 
 tmap_arrange(p1, p2, nrow = 1)