improves style

02-spatial-data.Rmd

@@ -35,6 +35,8 @@ install.packages("spData")
 install.packages("spDataLarge", repos = "https://nowosad.r-universe.dev")
 ```
 
+<!-- toDo: explain (maybe also move tmapv4 to main) -->
+
 ```{r}
 #| message: FALSE
 #| results: hide
@@ -834,7 +836,7 @@ sf_use_s2(FALSE)
 india_buffer_without_s2 = st_buffer(india, 1)
 ```
 
-```{r s2example, echo=FALSE, fig.cap="Example of the consequences of turning off the S2 geometry engine. Both representations of a buffer around India were created with the same command but the purple polygon object was created with S2 switched on, resulting in a buffer of 1 m. The larger light green polygon was created with S2 switched off, resulting in a buffer with inaccurate units of degrees longitude/latitude.", fig.asp=0.75}
+```{r s2example, echo=FALSE, fig.cap="Example of the consequences of turning off the S2 geometry engine. Both representations of a buffer around India were created with the same command but the purple polygon object was created with S2 switched on, resulting in a buffer of 1 m. The larger light green polygon was created with S2 switched off, resulting in a buffer with inaccurate units of degrees longitude/latitude.", message=FALSE}
 library(tmap)
 tm1 = tm_shape(india_buffer_with_s2) +
   tm_fill(fill = hcl.colors(4, palette = "purple green")[3]) +

03-attribute-operations.Rmd

@@ -13,6 +13,8 @@ library(dplyr)   # tidyverse package for data frame manipulation
 - It relies on **spData**, which loads datasets used in the code examples of this chapter:
 
 ```{r 03-attribute-operations-2, results='hide'}
+#| message: FALSE
+#| results: hide
 library(spData)  # spatial data package introduced in Chapter 2
 ```
 

05-geometry-operations.Rmd

@@ -53,7 +53,7 @@ The simplified geometry was created by the following command:
 seine_simp = st_simplify(seine, dTolerance = 2000)  # 2000 m
 ```
 
-```{r seine-simp, echo=FALSE, fig.cap="Comparison of the original and simplified geometry of the seine object.", warning=FALSE, fig.scap="Simplification in action.", message=FALSE}
+```{r seine-simp, echo=FALSE, fig.cap="Comparison of the original and simplified geometry of the seine object.", warning=FALSE, fig.scap="Simplification in action.", message=FALSE, fig.asp=0.5}
 library(tmap)
 p_simp1 = tm_shape(seine) + tm_lines() +
   tm_title("Original data")
@@ -78,7 +78,6 @@ Therefore, the first step is to project the data into some adequate projected CR
 
 ```{r 05-geometry-operations-4}
 us_states2163 = st_transform(us_states, "EPSG:2163")
-us_states2163 = us_states2163
 ```
 
 `st_simplify()` works equally well with projected polygons:
@@ -461,7 +460,7 @@ regions2 = us_states |>
 # summarize(africa[buff, "pop"], pop = sum(pop, na.rm = TRUE))
 ```
 
-```{r us-regions, fig.cap="Spatial aggregation on contiguous polygons, illustrated by aggregating the population of US states into regions, with population represented by color. Note the operation automatically dissolves boundaries between states.", echo=FALSE, warning=FALSE, fig.asp=0.2, out.width="100%", fig.scap="Spatial aggregation on contiguous polygons."}
+```{r us-regions, fig.cap="Spatial aggregation on contiguous polygons, illustrated by aggregating the population of US states into regions, with population represented by color. Note the operation automatically dissolves boundaries between states.", echo=FALSE, warning=FALSE, out.width="100%", fig.scap="Spatial aggregation on contiguous polygons."}
 source("code/05-us-regions.R", print.eval = TRUE)
 ```
 

06-raster-vector.Rmd

@@ -70,6 +70,8 @@ Changing the settings of `mask()` yields different results.
 Setting `inverse = TRUE` will mask everything *inside* the bounds of the park (see `?mask` for details) (Figure \@ref(fig:cropmask)(D)), while setting `updatevalue = 0` will set all pixels outside the national park to 0.
 
 ```{r 06-raster-vector-6 }
+#| message: FALSE
+#| results: hide
 srtm_inv_masked = mask(srtm, zion, inverse = TRUE)
 ```
 

07-reproj.Rmd

@@ -336,6 +336,8 @@ london_buff_projected = st_buffer(london_proj, 100000)
 The geometries of the three `london_buff*` objects that *have* a specified CRS created above (`london_buff_s2`, `london_buff_lonlat` and `london_buff_projected`) created in the preceding code chunks are illustrated in Figure \@ref(fig:crs-buf).
 
 ```{r crs-buf-old, include=FALSE, eval=FALSE}
+#| message: FALSE
+#| results: hide
 uk = rnaturalearth::ne_countries(scale = 50) |> 
   st_as_sf() |> 
   filter(grepl(pattern = "United Kingdom|Ire", x = name_long))

09-mapping.Rmd

@@ -278,7 +278,7 @@ This figure shows four ways of coloring regions in New Zealand depending on medi
 ```{r 08-mapping-12, eval=FALSE}
 tm_shape(nz) + tm_polygons(fill = "Median_income")
 tm_shape(nz) + tm_polygons(fill = "Median_income",
-                           fill.scale = tm_scale(breaks = c(0, 30000, 40000, 50000)))
+                        fill.scale = tm_scale(breaks = c(0, 30000, 40000, 50000)))
 tm_shape(nz) + tm_polygons(fill = "Median_income",
                            fill.scale = tm_scale(n = 10))
 tm_shape(nz) + tm_polygons(fill = "Median_income",
@@ -381,8 +381,10 @@ These follow a gradient, for example from light to dark colors (light colors oft
 Sequential palettes can be single (`greens` goes from light to dark blue, for example) or multi-color/hue (`yl_gn_bu` is gradient from light yellow to blue via green, for example), as demonstrated in the code chunk below --- output not shown, run the code yourself to see the results!
 
 ```{r 08-mapping-13, eval=FALSE}
-tm_shape(nz) + tm_polygons("Median_income", fill.scale = tm_scale(values = "greens"))
-tm_shape(nz) + tm_polygons("Median_income", fill.scale = tm_scale(values = "yl_gn_bu"))
+tm_shape(nz) + 
+  tm_polygons("Median_income", fill.scale = tm_scale(values = "greens"))
+tm_shape(nz) + 
+  tm_polygons("Median_income", fill.scale = tm_scale(values = "yl_gn_bu"))
 ```
 
 The third group, diverging palettes, typically range between three distinct colors (purple-white-green in Figure \@ref(fig:colpal)) and are usually created by joining two single-color sequential palettes with the darker colors at each end.
@@ -392,7 +394,8 @@ The reference point's value can be adjusted in **tmap** using the `midpoint` arg
 ```{r 08-mapping-13b, eval=FALSE}
 tm_shape(nz) + 
   tm_polygons("Median_income",
-              fill.scale = tm_scale_continuous(values = "pu_gn_div", midpoint = 28000))
+              fill.scale = tm_scale_continuous(values = "pu_gn_div", 
+                                               midpoint = 28000))
 ```
 
 ```{r colpal, echo=FALSE, message=FALSE, fig.cap="Examples of categorical, sequential and diverging palettes.", out.width="75%"}
@@ -449,9 +452,10 @@ Other than that, we can also customize the location of the legend using the `pos
 
 ```{r}
 map_nza2 = tm_shape(nz) +
-  tm_polygons(fill = "Land_area", fill.legend = tm_legend(title = legend_title,
-                                                          orientation = "landscape",
-                                                          position = tm_pos_out("center", "bottom")))
+  tm_polygons(fill = "Land_area",
+              fill.legend = tm_legend(title = legend_title,
+                                      orientation = "landscape",
+                                      position = tm_pos_out("center", "bottom")))
 ```
 
 The legend position (and also the position of several other map elements in **tmap**) can be customized using one of a few functions.
@@ -1166,7 +1170,7 @@ nz_carto = cartogram_cont(nz, "Median_income", itermax = 5)
 tm_shape(nz_carto) + tm_polygons("Median_income")
 ```
 
-```{r cartomap1, echo=FALSE, message=FALSE, fig.cap="Comparison of standard map (left) and continuous area cartogram (right).", fig.scap="Comparison of standard map and continuous area cartogram."}
+```{r cartomap1, echo=FALSE, message=FALSE, fig.cap="Comparison of standard map (left) and continuous area cartogram (right).", fig.scap="Comparison of standard map and continuous area cartogram.", warning=FALSE}
 carto_map1 = tm_shape(nz) + 
   tm_polygons("Median_income",
               fill.scale = tm_scale(values = "Greens"),

10-gis.Rmd

@@ -176,12 +176,15 @@ We use again the incongruent polygons\index{spatial congruence} we have already
 Both polygon datasets are available in the **spData** package, and for both we would like to use a geographic CRS\index{CRS!geographic} (see also Chapter \@ref(reproj-geo-data)).
 
 ```{r 09-gis-4}
+
 data("incongruent", "aggregating_zones", package = "spData")
 incongr_wgs = st_transform(incongruent, "EPSG:4326")
 aggzone_wgs = st_transform(aggregating_zones, "EPSG:4326")
 ```
 
 ```{r uniondata, echo=FALSE, fig.cap="Illustration of two areal units: incongruent (black lines) and aggregating zones (red borders)."}
+#| message: FALSE
+#| results: hide
 library(tmap)
 tm_shape(incongr_wgs) +
   tm_polygons(col = "grey5") +
@@ -922,11 +925,9 @@ library(rstac)
 # and search for images intersecting our AOI
 s = stac("https://earth-search.aws.element84.com/v0")
 items = s |>
-  stac_search(
-    collections = "sentinel-s2-l2a-cogs",
-    bbox = c(7.1, 51.8, 7.2, 52.8),
-    datetime = "2020-01-01/2020-12-31"
-  ) |>
+  stac_search(collections = "sentinel-s2-l2a-cogs",
+              bbox = c(7.1, 51.8, 7.2, 52.8),
+              datetime = "2020-01-01/2020-12-31") |>
   post_request() |>
   items_fetch()
 ```
@@ -993,36 +994,26 @@ p = processes() # load available processes
 collections = list_collections() # load available collections
 formats = list_file_formats() # load available output formats
 # Load Sentinel-2 collection
-s2 = p$load_collection(
-  id = "SENTINEL2_L2A",
-  spatial_extent = list(
-    west = 7.5, east = 8.5,
-    north = 51.1, south = 50.1
-  ),
-  temporal_extent = list("2021-01-01", "2021-01-31"),
-  bands = list("B04", "B08")
-)
+s2 = p$load_collection(id = "SENTINEL2_L2A",
+                       spatial_extent = list(west = 7.5, east = 8.5,
+                                             north = 51.1, south = 50.1),
+                       temporal_extent = list("2021-01-01", "2021-01-31"),
+                       bands = list("B04", "B08"))
 # Compute NDVI vegetation index
-compute_ndvi = p$reduce_dimension(
-  data = s2, dimension = "bands",
-  reducer = function(data, context) {
-    (data[2] - data[1]) / (data[2] + data[1])
-  }
-)
+compute_ndvi = p$reduce_dimension(data = s2, dimension = "bands",
+                                  reducer = function(data, context) {
+                                      (data[2] - data[1]) / (data[2] + data[1])
+                                  })
 # Compute maximum over time
-reduce_max = p$reduce_dimension(
-  data = compute_ndvi, dimension = "t",
-  reducer = function(x, y) {
-    max(x)
-  }
-)
+reduce_max = p$reduce_dimension(data = compute_ndvi, dimension = "t",
+                                reducer = function(x, y) {
+                                    max(x)
+                                })
 # Export as GeoTIFF
 result = p$save_result(reduce_max, formats$output$GTiff)
 # Login, see https://docs.openeo.cloud/getting-started/r/#authentication
-login(
-  login_type = "oidc", provider = "egi",
-  config = list(client_id = "...", secret = "...")
-)
+login(login_type = "oidc", provider = "egi", 
+      config = list(client_id = "...", secret = "..."))
 # Execute processes
 compute_result(graph = result, output_file = tempfile(fileext = ".tif"))
 ```

16-synthesis.Rmd

@@ -22,6 +22,7 @@ A feature of R\index{R}, and open source software in general, is that there are
 The code chunk below illustrates this by using three functions, covered in Chapters \@ref(attr) and \@ref(geometry-operations), to combine the 16 regions of New Zealand into a single geometry:
 
 ```{r 16-synnthesis-1}
+#| message: FALSE
 library(spData)
 nz_u1 = sf::st_union(nz)
 nz_u2 = aggregate(nz["Population"], list(rep(1, nrow(nz))), sum)