Open and Plot Vector Layers

Last updated on 2024-03-12 | Edit this page

Estimated time: 30 minutes

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Overview

Questions

How can I distinguish between and visualize point, line and polygon vector data?

Objectives

Know the difference between point, line, and polygon vector elements.
Load point, line, and polygon vector layers into R.
Access the attributes of a spatial object in R.

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 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 loaded from ESRI’s shapefile format into R. These data refer to the NEON Harvard Forest field site, 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 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 you have the sf library loaded.

R

library(sf)

The vector layers that we will import from ESRI’s shapefile format are:

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 located at the NEON Harvard Forest field site.

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 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:

R

aoi_boundary_HARV <- st_read(
  "data/NEON-DS-Site-Layout-Files/HARV/HarClip_UTMZ18.shp")

OUTPUT

Reading layer `HarClip_UTMZ18' from data source 
  `/home/runner/work/DTRA_workshop_GIS/DTRA_workshop_GIS/site/built/data/NEON-DS-Site-Layout-Files/HARV/HarClip_UTMZ18.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 1 field
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 732128 ymin: 4713209 xmax: 732251.1 ymax: 4713359
Projected CRS: WGS 84 / UTM zone 18N

Vector Layer Metadata & Attributes

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 individual vector object.

Data Tip

The Explore and Plot by Vector Layer Attributes episode provides more information on both metadata and attributes and using attributes to subset and plot data.

Spatial Metadata

Key metadata for all vector layers includes:

Object Type: the class of the imported object.
Coordinate Reference System (CRS): the projection of the data.
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 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)

OUTPUT

[1] POLYGON
18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLE

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)

OUTPUT

Coordinate Reference System:
  User input: WGS 84 / UTM zone 18N 
  wkt:
PROJCRS["WGS 84 / UTM zone 18N",
    BASEGEOGCRS["WGS 84",
        DATUM["World Geodetic System 1984",
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4326]],
    CONVERSION["UTM zone 18N",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",0,
            ANGLEUNIT["Degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",-75,
            ANGLEUNIT["Degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",0.9996,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",500000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",0,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    ID["EPSG",32618]]

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)

OUTPUT

     xmin      ymin      xmax      ymax 
 732128.0 4713208.7  732251.1 4713359.2

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).

Lastly, we can view all of the metadata and attributes for this R spatial object by printing it to the screen:

R

aoi_boundary_HARV

OUTPUT

Simple feature collection with 1 feature and 1 field
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: 732128 ymin: 4713209 xmax: 732251.1 ymax: 4713359
Projected CRS: WGS 84 / UTM zone 18N
  id                       geometry
1  1 POLYGON ((732128 4713359, 7...

Spatial Data Attributes

We introduced the idea of spatial data attributes in an earlier lesson. Now we will explore how to use spatial data attributes stored in our data to plot different features.

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 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 coord_sf() coordinate system.

R

ggplot() +
  geom_sf(data = aoi_boundary_HARV, size = 3, color = "black", fill = "cyan1") +
  ggtitle("AOI Boundary Plot") +
  coord_sf()

Challenge: Import Line and Point Vector Layers

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.

Answer the following questions:

What type of R spatial object is created when you import each layer?
What is the CRS and extent for each object?
Do the files contain points, lines, or polygons?
How many spatial objects are in each file?

Answers

First we import the data:

R

lines_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp")

OUTPUT

Reading layer `HARV_roads' from data source 
  `/home/runner/work/DTRA_workshop_GIS/DTRA_workshop_GIS/site/built/data/NEON-DS-Site-Layout-Files/HARV/HARV_roads.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 13 features and 15 fields
Geometry type: MULTILINESTRING
Dimension:     XY
Bounding box:  xmin: 730741.2 ymin: 4711942 xmax: 733295.5 ymax: 4714260
Projected CRS: WGS 84 / UTM zone 18N

R

point_HARV <- st_read("data/NEON-DS-Site-Layout-Files/HARV/HARVtower_UTM18N.shp")

OUTPUT

Reading layer `HARVtower_UTM18N' from data source 
  `/home/runner/work/DTRA_workshop_GIS/DTRA_workshop_GIS/site/built/data/NEON-DS-Site-Layout-Files/HARV/HARVtower_UTM18N.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 14 fields
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 732183.2 ymin: 4713265 xmax: 732183.2 ymax: 4713265
Projected CRS: WGS 84 / UTM zone 18N

Then we check its class:

R

class(lines_HARV)

OUTPUT

[1] "sf"         "data.frame"

R

class(point_HARV)

OUTPUT

[1] "sf"         "data.frame"

We also check the CRS and extent of each object:

R

st_crs(lines_HARV)

OUTPUT

Coordinate Reference System:
  User input: WGS 84 / UTM zone 18N 
  wkt:
PROJCRS["WGS 84 / UTM zone 18N",
    BASEGEOGCRS["WGS 84",
        DATUM["World Geodetic System 1984",
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4326]],
    CONVERSION["UTM zone 18N",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",0,
            ANGLEUNIT["Degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",-75,
            ANGLEUNIT["Degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",0.9996,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",500000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",0,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    ID["EPSG",32618]]

R

st_bbox(lines_HARV)

OUTPUT

     xmin      ymin      xmax      ymax 
 730741.2 4711942.0  733295.5 4714260.0

R

st_crs(point_HARV)

OUTPUT

Coordinate Reference System:
  User input: WGS 84 / UTM zone 18N 
  wkt:
PROJCRS["WGS 84 / UTM zone 18N",
    BASEGEOGCRS["WGS 84",
        DATUM["World Geodetic System 1984",
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4326]],
    CONVERSION["UTM zone 18N",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",0,
            ANGLEUNIT["Degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",-75,
            ANGLEUNIT["Degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",0.9996,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",500000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",0,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["(E)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["(N)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    ID["EPSG",32618]]

R

st_bbox(point_HARV)

OUTPUT

     xmin      ymin      xmax      ymax 
 732183.2 4713265.0  732183.2 4713265.0

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.

Key Points

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() function. No need to convert to a dataframe.