Open and Plot Shapefiles in R
Overview
Teaching: 20 min
Exercises: 10 minQuestions
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.
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:
aoi_boundary_HARV <- st_read(
"data/vector/HarClip_UTMZ18.shp")
Reading layer `HarClip_UTMZ18' from data source `/home/travis/build/UW-Madison-DataScience/r-raster-vector-geospatial/_episodes_rmd/data/vector/HarClip_UTMZ18.shp' using driver `ESRI Shapefile'
Simple feature collection with 1 feature and 1 field
geometry type: POLYGON
dimension: XY
bbox: xmin: 732128 ymin: 4713209 xmax: 732251.1 ymax: 4713359
CRS: 32618
Shapefile 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:
st_geometry_type(aoi_boundary_HARV)
[1] POLYGON
18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLE
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 CRS this file data is in:
Remembering from an earlier lesson that CRS information uses a mathematical model to connect data to the Earth’s surface.
When we read in the shapefile into an R object, the metadata included the CRS – in this case is 32618 We can get more information about the CRS with the st_crs() function, and the proj4string method to get information on the CRS in the proj4string.
st_crs(aoi_boundary_HARV)$proj4string
[1] "+proj=utm +zone=18 +datum=WGS84 +units=m +no_defs "
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:
st_bbox(aoi_boundary_HARV)
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:
aoi_boundary_HARV
Simple feature collection with 1 feature and 1 field
geometry type: POLYGON
dimension: XY
bbox: xmin: 732128 ymin: 4713209 xmax: 732251.1 ymax: 4713359
CRS: 32618
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.
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_UTM18Npoint_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:
lines_HARV <- st_read("data/vector/HARV_roads.shp")
Reading layer `HARV_roads' from data source `/home/travis/build/UW-Madison-DataScience/r-raster-vector-geospatial/_episodes_rmd/data/vector/HARV_roads.shp' using driver `ESRI Shapefile' Simple feature collection with 13 features and 15 fields geometry type: MULTILINESTRING dimension: XY bbox: xmin: 730741.2 ymin: 4711942 xmax: 733295.5 ymax: 4714260 CRS: 32618
point_HARV <- st_read("data/vector/HARVtower_UTM18N.shp")
Reading layer `HARVtower_UTM18N' from data source `/home/travis/build/UW-Madison-DataScience/r-raster-vector-geospatial/_episodes_rmd/data/vector/HARVtower_UTM18N.shp' using driver `ESRI Shapefile' Simple feature collection with 1 feature and 14 fields geometry type: POINT dimension: XY bbox: xmin: 732183.2 ymin: 4713265 xmax: 732183.2 ymax: 4713265 CRS: 32618
Then we check the geometry type of each object:
st_geometry_type(lines_HARV)
[1] MULTILINESTRING MULTILINESTRING MULTILINESTRING MULTILINESTRING [5] MULTILINESTRING MULTILINESTRING MULTILINESTRING MULTILINESTRING [9] MULTILINESTRING MULTILINESTRING MULTILINESTRING MULTILINESTRING [13] MULTILINESTRING 18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLE
st_geometry_type(point_HARV)
[1] POINT 18 Levels: GEOMETRY POINT LINESTRING POLYGON MULTIPOINT ... TRIANGLE
We see that the lines_HARV has a number of multilinestring geometry items, and the point_HARV item has a point item.
We also check the CRS and extent of each object:
st_crs(lines_HARV)$proj4string
[1] "+proj=utm +zone=18 +datum=WGS84 +units=m +no_defs "
st_bbox(lines_HARV)
xmin ymin xmax ymax 730741.2 4711942.0 733295.5 4714260.0
st_crs(point_HARV)$proj4string
[1] "+proj=utm +zone=18 +datum=WGS84 +units=m +no_defs "
st_bbox(point_HARV)
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) andpoint_HARV
contains only one point.
Key Points
Shapefile metadata 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 thegeom_sf()
function. No need to convert to a dataframe.