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

08-read-write-plot.Rmd

@@ -91,9 +91,9 @@ Aside from GeoPackage, there are other geospatial data exchange formats worth ch
 \index{COG}
 The GeoTIFF format seems to be the most prominent raster data format.
 It allows spatial information, such as CRS, to be embedded within a TIFF file. 
-Similar to ESRI Shapefile, this format was firstly developed in the 1990s, but as an open format.
+Similar to ESRI Shapefile, this format was first developed in the 1990s, but as an open format.
 Additionally, GeoTIFF is still being expanded and improved.
-One of the most significant recent addition to the GeoTIFF format is its variant called [COG](https://www.cogeo.org/) (*Cloud Optimized GeoTIFF*).
+One of the most significant recent additions to the GeoTIFF format is its variant called [COG](https://www.cogeo.org/) (*Cloud Optimized GeoTIFF*).
 Raster objects saved as COGs can be hosted on HTTP servers, so other people can read only parts of the file without downloading the whole file (see Sections \@ref(raster-data-read) and \@ref(raster-data-write)).
 
 There are many geographic file formats beyond those shown in Table \@ref(tab:formats) and new data formats capable of representing geographic are being developed.
@@ -111,7 +111,7 @@ However, this approach has downsides: it is challenging for storing geometries m
 Executing commands such as `sf::read_sf()` (the main function we use for loading vector data) or `terra::rast()` (the main function used for loading raster data) silently sets off a chain of events that reads data from files.
 Many R packages provide example datasets (e.g. the dataset `spData::world` that we used in earlier chapters) and functions to get geographic datasets from a range of data sources.
 All of them load the data into R or, more precisely, assign objects to your workspace.
-This means that when objects are imported into R they are stored in RAM^[There are some exceptions to this rule, e.g., **terra**'s `SpatRaster` objects (which are C++ pointers to the actual data) and objects that represent database connections that can be imported into memory with functions such as `dplyr::collect()`, as outlined in Section \@ref(postgis)], can be listed with the `ls()` (and should be viewable in 'Environment' panels in your development environement) and can be accessed from the [`.GlobalEnv`](http://adv-r.had.co.nz/Environments.html) of the R session.
+This means that when objects are imported into R they are stored in RAM^[There are some exceptions to this rule, e.g., **terra**'s `SpatRaster` objects (which are C++ pointers to the actual data) and objects that represent database connections that can be imported into memory with functions such as `dplyr::collect()`, as outlined in Section \@ref(postgis)], can be listed with `ls()` (and should be viewable in 'Environment' panels in your development environement) and can be accessed from the [`.GlobalEnv`](http://adv-r.had.co.nz/Environments.html) of the R session.
 
 ### Vector data {#iovec}
 
@@ -132,8 +132,8 @@ knitr::kable(head(sf_drivers, n = 6),
   kableExtra::column_spec(2, width = "7em")
 ```
 
-The following commands show the first three drivers reported the computer's GDAL installation (results can vary depending on the GDAL version installed) and a summary of the their features.
-Note that the majority of drivers can write data while only a dozen or so formats can efficiently represent raster data in addition to vector data (see `?st_drivers()` for details):
+The following commands show the first three drivers reported the computer's GDAL installation (results can vary depending on the GDAL version installed) and a summary of their features.
+Note that the majority of drivers can write data, while only a dozen or so formats can efficiently represent raster data in addition to vector data (see `?st_drivers()` for details):
 
 ```{r 07-read-write-plot-17, eval=FALSE}
 sf_drivers = st_drivers()