Set-up -> setup and set up, for #1115

02-spatial-data.Rmd

@@ -28,7 +28,7 @@ Mac and Linux operating systems (OSs) have additional systems requirements, whic
 See also OS-specific instructions such as that provided by the website [rtask.thinkr.fr](https://rtask.thinkr.fr/installation-of-r-4-2-on-ubuntu-22-04-lts-and-tips-for-spatial-packages/), which covers installing R on the open source OS Ubuntu.
 ```
 
-After you have got a good set-up, it's time to run some code!
+After you have got a good setup, it's time to run some code!
 Unless you already have these packages installed, the first thing to do is to install foundational R packages used in this chapter, with the following commands:^[
 **spDataLarge** is not on CRAN\index{CRAN}, meaning it must be installed via *r-universe* or with the following command: `remotes::install_github("Nowosad/spDataLarge")`.
 ]

12-spatial-cv.Rmd

@@ -390,7 +390,7 @@ identical(fit$coefficients, learner$model$coefficients)
 ```
 -->
 
-The set-up steps for modeling with **mlr3**\index{mlr3 (package)} may seem tedious. 
+The setup steps for modeling with **mlr3**\index{mlr3 (package)} may seem tedious. 
 But remember, this single interface provides access to the 130+ learners shown by `mlr3extralearners::list_mlr3learners()`; it would be far more tedious to learn the interface for each learner!
 Further advantages are simple parallelization of resampling techniques and the ability to tune machine learning hyperparameters\index{hyperparameter} (see Section \@ref(svm)).
 Most importantly, (spatial) resampling in **mlr3spatiotempcv** [@schratz_mlr3spatiotempcv_2021] is straightforward, requiring only two more steps: specifying a resampling method and running it.
@@ -559,7 +559,7 @@ at_ksvm = mlr3tuning::auto_tuner(
 )
 ```
 
-The tuning is now set-up to fit 250 models to determine optimal hyperparameters for one fold.
+The tuning is now set up to fit 250 models to determine optimal hyperparameters for one fold.
 Repeating this for each fold, we end up with 1,250 (250 \* 5) models for each repetition.
 Repeated 100 times means fitting a total of 125,000 models to identify optimal hyperparameters (Figure \@ref(fig:partitioning)).
 These are used in the performance estimation, which requires the fitting of another 500 models (5 folds \* 100 repetitions; see Figure \@ref(fig:partitioning)). 

13-transport.Rmd

@@ -473,7 +473,7 @@ When working with segment or leg-level data, route-level statistics can be retur
 Routing engines in R enable route networks stored as R objects *in memory* to be used as the basis of route calculation.
 Options include [**sfnetworks**](https://luukvdmeer.github.io/sfnetworks/)\index{sfnetworks (package)}, [**dodgr**](https://urbananalyst.github.io/dodgr/) and [**cppRouting**](https://github.com/vlarmet/cppRouting) packages, each of which provide their own class system to represent route networks, the topic of the next section.
 
-While fast and flexible, native R routing options are generally harder to set-up than dedicated routing engines for realistic route calculation.
+While fast and flexible, native R routing options are generally harder to set up than dedicated routing engines for realistic route calculation.
 Routing is a hard problem and many hundreds of hours have been put into open source routing engines that can be downloaded and hosted locally.
 On the other hand, R-based routing engines may be well suited to model experiments and the statistical analysis of the impacts of changes on the network.
 Changing route network characteristics (or weights associated with different route segment types), re-calculating routes, and analyzing results under many scenarios in a single language has benefits for research applications.

code/11-centroid-alg.R

@@ -12,7 +12,7 @@ if (!exists("poly_mat")) {
   )
 }
 
-# Step 1: create sub-triangles, set-up ------------------------------------
+# Step 1: create sub-triangles, set up ------------------------------------
 
 Origin = poly_mat[1, ] # create a point representing the origin
 i = 2:(nrow(poly_mat) - 2)

code/benchmark.R

@@ -1,4 +1,4 @@
-# Aim: benchmark and record how long it takes to build the book on different set-ups
+# Aim: benchmark and record how long it takes to build the book on different setups
 
 remotes::install_cran("benchmarkme")
 sys_details = benchmarkme::get_sys_details()

code/old-to-future-remove/10-earthquakes.R

@@ -1,6 +1,6 @@
 # Aim: create up-to-date map of Earthquakes in previous week
 
-# set-up ------------------------------------------------------------------
+# setup ------------------------------------------------------------------
 
 library(sf)
 library(spData)