Merge pull request #412 from bechandcock/rename-shapefile-from-main

Issue # 312 to update the terms “shapefile”

Author: jsta

episodes/06-vector-open-shapefile-in-r.Rmd

@@ -1,5 +1,5 @@
 ---
-title: Open and Plot Shapefiles
+title: Open and Plot Vector Layers
 teaching: 20
 exercises: 10
 source: Rmd
@@ -12,7 +12,7 @@ source("setup.R")
 ::::::::::::::::::::::::::::::::::::::: objectives
 
 - Know the difference between point, line, and polygon vector elements.
-- Load point, line, and polygon shapefiles into R.
+- Load point, line, and polygon vector layers into R.
 - Access the attributes of a spatial object in R.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
@@ -34,44 +34,44 @@ library(sf)
 
 ## Things You'll Need To Complete This Episode
 
-See the [lesson homepage](.) for detailed information about the software, data, 
-and other prerequisites you will need to work through the examples in this 
+See the [lesson homepage](.) for detailed information about the software, data,
+and other prerequisites you will need to work through the examples in this
 episode.
 
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
-Starting with this episode, we will be moving from working with raster data to 
-working with vector data. In this episode, we will open and plot point, line 
-and polygon vector data stored in shapefile format in R. These data refer to 
-the 
-[NEON Harvard Forest field site](https://www.neonscience.org/field-sites/field-sites-map/HARV), 
-which we have been working with in previous episodes. In later episodes, we 
-will learn how to work with raster and vector data together and combine them 
+Starting with this episode, we will be moving from working with raster data to
+working with vector data. In this episode, we will open and plot point, line
+and polygon vector data loaded from ESRI's `shapefile` format into R. These data refer to
+the
+[NEON Harvard Forest field site](https://www.neonscience.org/field-sites/field-sites-map/HARV),
+which we have been working with in previous episodes. In later episodes, we
+will learn how to work with raster and vector data together and combine them
 into a single plot.
 
-## Import Shapefiles
+## Import Vector Data
 
-We will use the `sf` package to work with vector data in R. We will also use 
-the `terra` package, which has been loaded in previous episodes, so we can 
-explore raster and vector spatial metadata using similar commands. Make sure 
+We will use the `sf` package to work with vector data in R. We will also use
+the `terra` package, which has been loaded in previous episodes, so we can
+explore raster and vector spatial metadata using similar commands. Make sure
 you have the `sf` library loaded.
 
 ```{r load-sf, results="hide", eval=FALSE, message=FALSE}
 library(sf)
 ```
 
-The shapefiles that we will import are:
+The vector layers that we will import from ESRI's `shapefile` format are:
 
-- A polygon shapefile representing our field site boundary,
-- A line shapefile representing roads, and
-- A point shapefile representing the location of the [Fisher flux tower](https://www.neonscience.org/data-collection/flux-tower-measurements)
+- A polygon vector layer representing our field site boundary,
+- A line vector layer representing roads, and
+- A point vector layer representing the location of the [Fisher flux tower](https://www.neonscience.org/data-collection/flux-tower-measurements)
   located at the [NEON Harvard Forest field site](https://www.neonscience.org/field-sites/field-sites-map/HARV).
 
-The first shapefile that we will open contains the boundary of our study area
+The first vector layer that we will open contains the boundary of our study area
 (or our Area Of Interest or AOI, hence the name `aoiBoundary`). To import
-shapefiles we use the `sf` function `st_read()`. `st_read()` requires the file 
-path to the shapefile.
+a vector layer from an ESRI `shapefile` we use the `sf` function `st_read()`. `st_read()`
+requires the file path to the ESRI `shapefile`.
 
 Let's import our AOI:
 
@@ -80,20 +80,20 @@ aoi_boundary_HARV <- st_read(
   "data/NEON-DS-Site-Layout-Files/HARV/HarClip_UTMZ18.shp")
 ```
 
-## Shapefile Metadata \& Attributes
+## Vector Layer Metadata \& Attributes
 
-When we import the `HarClip_UTMZ18` shapefile layer into R (as our
+When we import the `HarClip_UTMZ18` vector layer from an ESRI `shapefile` into R (as our
 `aoi_boundary_HARV` object), the `st_read()` function automatically stores
 information about the data. We are particularly interested in the geospatial
-metadata, describing the format, CRS, extent, and other components of the 
-vector data, and the attributes which describe properties associated with each 
+metadata, describing the format, CRS, extent, and other components of the
+vector data, and the attributes which describe properties associated with each
 individual vector object.
 
 :::::::::::::::::::::::::::::::::::::::::  callout
 
 ## Data Tip
 
-The [Explore and Plot by Shapefile Attributes](07-vector-shapefile-attributes-in-r/)
+The [Explore and Plot by Vector Layer Attributes](07-vector-shapefile-attributes-in-r/)
 episode provides more information on both metadata and attributes
 and using attributes to subset and plot data.
 
@@ -102,46 +102,46 @@ and using attributes to subset and plot data.
 
 ## Spatial Metadata
 
-Key metadata for all shapefiles include:
+Key metadata for all vector layers includes:
 
 1. **Object Type:** the class of the imported object.
 2. **Coordinate Reference System (CRS):** the projection of the data.
-3. **Extent:** the spatial extent (i.e. geographic area that the shapefile 
-   covers) of the shapefile. Note that the spatial extent for a shapefile 
-   represents the combined extent for all spatial objects in the shapefile.
+3. **Extent:** the spatial extent (i.e. geographic area that the vector layer
+   covers) of the data. Note that the spatial extent for a vector layer
+   represents the combined extent for all individual objects in the vector layer.
 
-We can view shapefile metadata using the `st_geometry_type()`, `st_crs()` and 
-`st_bbox()` functions. First, let's view the geometry type for our AOI 
-shapefile:
+We can view metadata of a vector layer using the `st_geometry_type()`, `st_crs()` and
+`st_bbox()` functions. First, let's view the geometry type for our AOI
+vector layer:
 
 ```{r}
 st_geometry_type(aoi_boundary_HARV)
 ```
 
-Our `aoi_boundary_HARV` is a polygon object. The 18 levels shown below our 
-output list the possible categories of the geometry type. Now let's check what 
+Our `aoi_boundary_HARV` is a polygon spatial object. The 18 levels shown below our
+output list the possible categories of the geometry type. Now let's check what
 CRS this file data is in:
 
 ```{r}
 st_crs(aoi_boundary_HARV)
 ```
 
-Our data in the CRS **UTM zone 18N**. The CRS is critical to interpreting the 
-object's extent values as it specifies units. To find the extent of our AOI, we 
+Our data in the CRS **UTM zone 18N**. The CRS is critical to interpreting the
+spatial object's extent values as it specifies units. To find the extent of our AOI, we
 can use the `st_bbox()` function:
 
 ```{r}
 st_bbox(aoi_boundary_HARV)
 ```
 
-The spatial extent of a shapefile or R spatial object represents the geographic 
-"edge" or location that is the furthest north, south east and west. Thus is 
-represents the overall geographic coverage of the spatial object. Image Source: 
+The spatial extent of a vector layer or R spatial object represents the geographic
+"edge" or location that is the furthest north, south east and west. Thus it
+represents the overall geographic coverage of the spatial object. Image Source:
 National Ecological Observatory Network (NEON).
 
 ![](fig/dc-spatial-vector/spatial_extent.png){alt='Extent image'}
 
-Lastly, we can view all of the metadata and attributes for this shapefile 
+Lastly, we can view all of the metadata and attributes for this R spatial
 object by printing it to the screen:
 
 ```{r}
@@ -150,33 +150,33 @@ aoi_boundary_HARV
 
 ## Spatial Data Attributes
 
-We introduced the idea of spatial data attributes in 
-[an earlier lesson](https://datacarpentry.org/organization-geospatial/02-intro-vector-data). 
-Now we will explore how to use spatial data attributes stored in our data to 
+We introduced the idea of spatial data attributes in
+[an earlier lesson](https://datacarpentry.org/organization-geospatial/02-intro-vector-data).
+Now we will explore how to use spatial data attributes stored in our data to
 plot different features.
 
-## Plot a Shapefile
+## Plot a vector layer
 
-Next, let's visualize the data in our `sf` object using the `ggplot` package. 
-Unlike with raster data, we do not need to convert vector data to a dataframe 
+Next, let's visualize the data in our `sf` object using the `ggplot` package.
+Unlike with raster data, we do not need to convert vector data to a dataframe
 before plotting with `ggplot`.
 
-We're going to customize our boundary plot by setting the size, color, and fill 
-for our plot. When plotting `sf` objects with `ggplot2`, you need to use the 
+We're going to customize our boundary plot by setting the size, color, and fill
+for our plot. When plotting `sf` objects with `ggplot2`, you need to use the
 `coord_sf()` coordinate system.
 
 ```{r plot-shapefile}
-ggplot() + 
-  geom_sf(data = aoi_boundary_HARV, size = 3, color = "black", fill = "cyan1") + 
-  ggtitle("AOI Boundary Plot") + 
+ggplot() +
+  geom_sf(data = aoi_boundary_HARV, size = 3, color = "black", fill = "cyan1") +
+  ggtitle("AOI Boundary Plot") +
   coord_sf()
 ```
 
 :::::::::::::::::::::::::::::::::::::::  challenge
 
-## Challenge: Import Line and Point Shapefiles
+## Challenge: Import Line and Point Vector Layers
 
-Using the steps above, import the HARV\_roads and HARVtower\_UTM18N layers into
+Using the steps above, import the HARV\_roads and HARVtower\_UTM18N vector layers into
 R. Call the HARV\_roads object `lines_HARV` and the HARVtower\_UTM18N
 `point_HARV`.
 
@@ -217,8 +217,8 @@ st_crs(point_HARV)
 st_bbox(point_HARV)
 ```
 
-To see the number of objects in each file, we can look at the output from when 
-we read these objects into R. `lines_HARV` contains 13 features (all lines) and 
+To see the number of objects in each file, we can look at the output from when
+we read these objects into R. `lines_HARV` contains 13 features (all lines) and
 `point_HARV` contains only one point.
 
 
@@ -231,9 +231,9 @@ we read these objects into R. `lines_HARV` contains 13 features (all lines) and
 
 :::::::::::::::::::::::::::::::::::::::: keypoints
 
-- Shapefile metadata include geometry type, CRS, and extent.
+- Metadata for vector layers include geometry type, CRS, and extent.
 - Load spatial objects into R with the `st_read()` function.
-- Spatial objects can be plotted directly with `ggplot` using the `geom_sf()` 
+- Spatial objects can be plotted directly with `ggplot` using the `geom_sf()`
   function. No need to convert to a dataframe.
 
 ::::::::::::::::::::::::::::::::::::::::::::::::::