Correcting 3 typos and a code error

04-spatial-operations.Rmd

@@ -29,7 +29,7 @@ Spatial joining (Section \@ref(spatial-joining)) and aggregation (Section \@ref(
 
 Spatial operations differ from non-spatial operations in a number of ways, however:
 Spatial joins, for example, can be done in a number of ways --- including matching entities that intersect with or are within a certain distance of the target dataset --- while the attribution joins discussed in Section \@ref(vector-attribute-joining) in the previous chapter can only be done in one way (except when using fuzzy joins, as described in the documentation of the [**fuzzyjoin**](https://cran.r-project.org/package=fuzzyjoin) package).
-The *type* of spatial relationship between objects must be considered when undertaking spatial operations, as described in Section \@ref(topological-relations), on topological relations between vector features.
+The *type* of spatial relationship between objects must be considered when undertaking spatial operations, as described in Section \@ref(topological-relations), on topological relations between vector features
 \index{spatial operations}.
 Another unique aspect of spatial objects is distance: all spatial objects are related through space, and distance calculations can be used to explore the strength of this relationship, as described in the context of vector data in Section \@ref(distance-relations).
 
@@ -52,7 +52,7 @@ Section \@ref(spatial-ras) presents spatial operations on raster datasets using
 Spatial subsetting is the process of taking a spatial object and returning a new object containing only features that *relate* in space to another object.
 Analogous to *attribute subsetting* (covered in Section \@ref(vector-attribute-subsetting)), subsets of `sf` data frames can be created with square bracket (`[`) operator using the syntax `x[y, , op = st_intersects]`, where `x` is an `sf` object from which a subset of rows will be returned, `y` is the 'subsetting object' and `, op = st_intersects` is an optional argument that specifies the topological relation (also known as the binary predicate) used to do the subsetting.
 The default topological relation used when an `op` argument is not provided is `st_intersects()`: the command `x[y, ]` is identical to `x[y, , op = st_intersects]` shown above but not `x[y, , op = st_disjoint]` (the meaning of these and other topological relations is described in the next section).
-The `filter()` function from the **tidyverse**\index{tidyverse (package)} can also be used but this approach more verbose, as we will see in the examples below.
+The `filter()` function from the **tidyverse**\index{tidyverse (package)} can also be used but this approach is more verbose, as we will see in the examples below.
 \index{vector!subsetting}
 \index{spatial!subsetting}
 
@@ -248,7 +248,7 @@ st_within(point_sf, polygon_sfc)
 st_touches(point_sf, polygon_sfc)
 ```
 
-Note that although the first point is *touches* the polygon, it is not within it; the third point is within the polygon but does not touch any part of its border.
+Note that although the first point *touches* the polygon, it is not within it; the third point is within the polygon but does not touch any part of its border.
 The opposite of `st_intersects()` is `st_disjoint()`, which returns only objects that do not spatially relate in any way to the selecting object (note `[, 1]` converts the result into a vector):
 
 ```{r 04-spatial-operations-11}

08-read-write-plot.Rmd

@@ -194,7 +194,7 @@ The example below searches for John Snow blue plaque coordinates located on a bu
 ```{r, eval=FALSE}
 library(tidygeocoder)
 geo_df = data.frame(address = "54 Frith St, London W1D 4SJ, UK")
-geo_df = geocode(df, address, method = "osm")
+geo_df = geocode(geo_df, address, method = "osm")
 geo_df
 ```