Fix multi- typos

08-read-write-plot.Rmd

@@ -271,15 +271,15 @@ It is fast and flexible, but it may be worth looking at other packages such as *
 ### Raster data {#raster-data-read}
 
 \index{raster!data input}
-Similar to vector data, raster data comes in many file formats with some supporting multi-layerfiles.
+Similar to vector data, raster data comes in many file formats with some supporting multi-layer files.
 **terra**'s `rast()` command reads in a single layer when a file with just one layer is provided.
 
 ```{r 07-read-write-plot-24, message=FALSE}
 raster_filepath = system.file("raster/srtm.tif", package = "spDataLarge")
 single_layer = rast(raster_filepath)
 ```
 
-It also works in case you want to read a multi-layerfile.
+It also works in case you want to read a multi-layer file.
 
 ```{r 07-read-write-plot-25}
 multilayer_filepath = system.file("raster/landsat.tif", package = "spDataLarge")
@@ -519,7 +519,7 @@ usa_sf = ne_countries(country = "United States of America", returnclass = "sf")
 Country borders can be also accessed with other packages, such as **geodata**, **giscoR**, or **rgeoboundaries**.
 
 A second example downloads a series of rasters containing global monthly precipitation sums with spatial resolution of 10 minutes (~18.5 km at the equator) using the **geodata** package [@R-geodata].
-The result is a multi-layerobject of class `SpatRaster`.
+The result is a multi-layer object of class `SpatRaster`.
 
 ```{r 07-read-write-plot-5, eval=FALSE}
 library(geodata)

15-eco.Rmd

@@ -181,7 +181,7 @@ ep = qgisprocess::qgis_run_algorithm(
 ```
 
 This returns a list named `ep` containing the paths to the computed output rasters.
-Let's read in catchment area as well as catchment slope into a multi-layer`SpatRaster` object (see Section \@ref(raster-classes)).
+Let's read in catchment area as well as catchment slope into a multi-layer `SpatRaster` object (see Section \@ref(raster-classes)).
 Additionally, we will add two more raster objects to it, namely `dem` and `ndvi`.
 
 ```{r 15-eco-7, eval=FALSE}
@@ -191,7 +191,7 @@ ep = ep[c("AREA", "SLOPE")] |>
   rast()
 names(ep) = c("carea", "cslope") # assign better names 
 origin(ep) = origin(dem) # make sure rasters have the same origin
-ep = c(dem, ndvi, ep) # add dem and ndvi to the multi-layerSpatRaster object
+ep = c(dem, ndvi, ep) # add dem and ndvi to the multi-layer SpatRaster object
 ```
 
 Additionally, the catchment area\index{catchment area} values are highly skewed to the right (`hist(ep$carea)`).
@@ -534,7 +534,7 @@ autotuner_rf$predict(task)
 ```
 
 The `predict` method will apply the model to all observations used in the modeling.
-Given a multi-layer`SpatRaster` containing rasters named as the predictors used in the modeling, `terra::predict()` will also make spatial distribution maps, i.e., predict to new data.
+Given a multi-layer `SpatRaster` containing rasters named as the predictors used in the modeling, `terra::predict()` will also make spatial distribution maps, i.e., predict to new data.
 
 ```{r 15-eco-28, cache=TRUE, cache.lazy=FALSE, eval=FALSE}
 pred = terra::predict(ep, model = autotuner_rf, fun = predict)

_15-ex.Rmd

@@ -90,7 +90,7 @@ ep = ep[c("AREA", "SLOPE")] |>
 names(ep) = c("carea", "cslope")
 # make sure all rasters share the same origin
 origin(ep) = origin(dem)
-# add dem and ndvi to the multi-layerSpatRaster object
+# add dem and ndvi to the multi-layer SpatRaster object
 ep = c(dem, ndvi, ep) 
 ep$carea = log10(ep$carea)
 

code/chapters/15-eco.R

@@ -118,7 +118,7 @@ knitr::include_graphics("images/15_sa_mongon_sampling.png")
 ##   terra::rast()
 ## names(ep) = c("carea", "cslope") # assign proper names
 ## terra::origin(ep) = terra::origin(dem) # make sure rasters have the same origin
-## ep = c(dem, ndvi, ep) # add dem and ndvi to the multi-layerSpatRaster object
+## ep = c(dem, ndvi, ep) # add dem and ndvi to the multi-layer SpatRaster object
 
 
 ## ----15-eco-8, eval=FALSE---------------------------------------------------------------------------

code/chapters/_15-ex.R

@@ -84,7 +84,7 @@ ep = ep[c("AREA", "SLOPE")] |>
 names(ep) = c("carea", "cslope")
 # make sure all rasters share the same origin
 origin(ep) = origin(dem)
-# add dem and ndvi to the multi-layerSpatRaster object
+# add dem and ndvi to the multi-layer SpatRaster object
 ep = c(dem, ndvi, ep) 
 ep$carea = log10(ep$carea)