Open and Plot Shapefiles
Last updated on 2023-01-03 | Edit this page
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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 shapefiles 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 stored in shapefile format in 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 Shapefiles
We will use the sf package to work with vector data in
R. Notice that the rgdal package automatically loads when
sf is loaded. We will also use the raster
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 shapefiles that we will import 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 located at the NEON Harvard Forest field site.
The first shapefile 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.
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/r-raster-vector-geospatial/r-raster-vector-geospatial/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 18NShapefile Metadata & Attributes
When we import the HarClip_UTMZ18 shapefile layer 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 Shapefile Attributes episode provides more information on both metadata and attributes and using attributes to subset and plot data.
Spatial Metadata
Key metadata for all shapefiles include:
- 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 shapefile covers) of the shapefile. Note that the spatial extent for a shapefile represents the combined extent for all spatial objects in the shapefile.
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:
R
st_geometry_type(aoi_boundary_HARV)
OUTPUT
[1] POLYGON
18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLEOur 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 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 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 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: National Ecological Observatory Network (NEON).
Lastly, we can view all of the metadata and attributes for this shapefile 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 Shapefile
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 Shapefiles
Using the steps above, import the HARV_roads and HARVtower_UTM18N
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?
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/r-raster-vector-geospatial/r-raster-vector-geospatial/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 18NR
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/r-raster-vector-geospatial/r-raster-vector-geospatial/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 18NThen 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.