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Overview

About

In this tutorial, we will open and plot point, line and polygon vector data stored in shapefile format in R.

R Skill Level: Introduction - you are just getting started with spatial data in R, but have some of the other R basics down.

Goals / Objectives

After completing this activity, you will:

  • Know the difference between point, line, and polygon vector elements.
  • Understand the differences between opening point, line and polygon shapefiles in R.
  • Understand the components of a spatial object in R.

Things You’ll Need To Complete This Tutorial

You will need the most current version of R and, preferably, RStudio loaded on your computer to complete this tutorial.

Install R Packages

  • raster: install.packages("raster")
  • rgdal: install.packages("rgdal")
  • sp: install.packages("sp")

More on Packages in R - Adapted from Software Carpentry.

Download Data

Download NEON Teaching Data Subset: Site Layout Shapefiles

These vector data provide information on the site characterization and infrastructure at the National Ecological Observatory Network’s Harvard Forest field site. The Harvard Forest shapefiles are from the Harvard Forest GIS & Map archives. US Country and State Boundary layers are from the US Census Bureau.


Set Working Directory: This lesson assumes that you have set your working directory to the location of the downloaded and unzipped data subsets. An overview of setting the working directory in R can be found here.

R Script & Challenge Code: NEON data lessons often contain challenges that reinforce learned skills. If available, the code for challenge solutions is found in the downloadable R script of the entire lesson, available in the footer of each lesson page.

About Vector Data

Vector data are composed of discrete geometric locations (x,y values) known as vertices that define the “shape” of the spatial object. The organization of the vertices, determines the type of vector that we are working with: point, line or polygon.

There are 3 types of vector objects: points, lines or polygons. Each object type has a different structure. Image Source: National Ecological Observatory Network (NEON)
  • Points: Each individual point is defined by a single x, y coordinate. There can be many points in a vector point file. Examples of point data include: sampling locations, the location of individual trees or the location of plots.
  • Lines: Lines are composed of many (at least 2) vertices, or points, that are connected. For instance, a road or a stream may be represented by a line. This line is composed of a series of segments, each “bend” in the road or stream represents a vertex that has defined x, y location.
  • Polygons: A polygon consists of 3 or more vertices that are connected and “closed”. Thus the outlines of plot boundaries, lakes, oceans, and states or countries are often represented by polygons. Occasionally, a polygon can have a hole in the middle of it (like a doughnut), this is something to be aware of but not an issue we will deal with in this tutorial.

Data Tip: Sometimes, boundary layers such as states and countries, are stored as lines rather than polygons. However, these boundaries, when represented as a line, will not create a closed object with a defined “area” that can be “filled”.

Shapefiles: Points, Lines, and Polygons

Geospatial data in vector format are often stored in a shapefile format. Because the structure of points, lines, and polygons are different, each individual shapefile can only contain one vector type (all points, all lines or all polygons). You will not find a mixture of point, line and polygon objects in a single shapefile.

Objects stored in a shapefile often have a set of associated attributes that describe the data. For example, a line shapefile that contains the locations of streams, might contain the associated stream name, stream “order” and other information about each stream line object.

  • More about shapefiles can found on Wikipedia.

Import Shapefiles

We will use the rgdal package to work with vector data in R. Notice that the sp package automatically loads when rgdal is loaded. We will also load the raster package so we can explore raster and vector spatial metadata using similar commands.

# load required libraries
# for vector work; sp package will load with rgdal.
library(rgdal)  
# for metadata/attributes- vectors or rasters
library(raster) 

# set working directory to the directory location on your computer where
# you downloaded and unzipped the data files for the tutorial
# setwd("pathToDirHere")

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 R function readOGR().

readOGR() requires two components:

  1. The directory where our shapefile lives: NEON-DS-Site-Layout-Files/HARV
  2. The name of the shapefile (without the extension): HarClip_UTMZ18

Let’s import our AOI.

# Import a polygon shapefile: readOGR("path","fileName")
# no extension needed as readOGR only imports shapefiles
aoiBoundary_HARV <- readOGR("NEON-DS-Site-Layout-Files/HARV",
                            "HarClip_UTMZ18")

## OGR data source with driver: ESRI Shapefile 
## Source: "NEON-DS-Site-Layout-Files/HARV", layer: "HarClip_UTMZ18"
## with 1 features
## It has 1 fields

Data Tip: The acronym, OGR, refers to the OpenGIS Simple Features Reference Implementation. Learn more about OGR.

Shapefile Metadata & Attributes

When we import the HarClip_UTMZ18 shapefile layer into R (as our aoiBoundary_HARV object), the readOGR() 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 Shapefile Metadata & Attributes in R tutorial provides more information on both metadata and attributes and using attributes to subset and plot data.

Spatial Metadata

Key metadata for all shapefiles include:

  1. Object Type: the class of the imported object.
  2. Coordinate Reference System (CRS): the projection of the data.
  3. Extent: the spatial extent (geographic area that the shapefile covers) of the shapefile. Note that the spatial extent for a shapefile represents the extent for ALL spatial objects in the shapefile.

We can view shapefile metadata using the class, crs and extent methods:

# view just the class for the shapefile
class(aoiBoundary_HARV)

## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"

# view just the crs for the shapefile
crs(aoiBoundary_HARV)

## CRS arguments:
##  +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs +ellps=WGS84
## +towgs84=0,0,0

# view just the extent for the shapefile
extent(aoiBoundary_HARV)

## class       : Extent 
## xmin        : 732128 
## xmax        : 732251.1 
## ymin        : 4713209 
## ymax        : 4713359

# view all metadata at same time
aoiBoundary_HARV

## class       : SpatialPolygonsDataFrame 
## features    : 1 
## extent      : 732128, 732251.1, 4713209, 4713359  (xmin, xmax, ymin, ymax)
## coord. ref. : +proj=utm +zone=18 +datum=WGS84 +units=m +no_defs +ellps=WGS84 +towgs84=0,0,0 
## variables   : 1
## names       : id 
## min values  :  1 
## max values  :  1

Our aoiBoundary_HARV object is a polygon of class SpatialPolygonsDataFrame, in the CRS UTM zone 18N. The CRS is critical to interpreting the object extent values as it specifies units.

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)

Spatial Data Attributes

Each object in a shapefile has one or more attributes associated with it. Shapefile attributes are similar to fields or columns in a spreadsheet. Each row in the spreadsheet has a set of columns associated with it that describe the row element. In the case of a shapefile, each row represents a spatial object - for example, a road, represented as a line in a line shapefile, will have one “row” of attributes associated with it. These attributes can include different types of information that describe objects stored within a shapefile. Thus, our road, may have a name, length, number of lanes, speed limit, type of road and other attributes stored with it.

Each spatial feature in an R spatial object has the same set of associated attributes that describe or characterize the feature. Attribute data are stored in a separate *.dbf file. Attribute data can be compared to a spreadsheet. Each row in a spreadsheet represents one feature in the spatial object. Image Source: National Ecological Observatory Network (NEON)

We view the attributes of a SpatialPolygonsDataFrame using objectName@data (e.g., aoiBoundary_HARV@data).

# alternate way to view attributes 
aoiBoundary_HARV@data

##   id
## 0  1

In this case, our polygon object only has one attribute: id.

Metadata & Attribute Summary

We can view a metadata & attribute summary of each shapefile by entering the name of the R object in the console. Note that the metadata output includes the class, the number of features, the extent, and the coordinate reference system (crs) of the R object. The last two lines of summary show a preview of the R object attributes.

# view a summary of metadata & attributes associated with the spatial object
summary(aoiBoundary_HARV)

## Object of class SpatialPolygonsDataFrame
## Coordinates:
##       min       max
## x  732128  732251.1
## y 4713209 4713359.2
## Is projected: TRUE 
## proj4string :
## [+proj=utm +zone=18 +datum=WGS84 +units=m +no_defs +ellps=WGS84
## +towgs84=0,0,0]
## Data attributes:
##        id   
##  Min.   :1  
##  1st Qu.:1  
##  Median :1  
##  Mean   :1  
##  3rd Qu.:1  
##  Max.   :1

Plot a Shapefile

Next, let’s visualize the data in our R spatialpolygonsdataframe object using plot().

# create a plot of the shapefile
# 'lwd' sets the line width
# 'col' sets internal color
# 'border' sets line color
plot(aoiBoundary_HARV, col="cyan1", border="black", lwd=3,
     main="AOI Boundary Plot")

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:

  1. What type of R spatial object is created when you import each layer?
  2. What is the CRS and extentfor each object?
  3. Do the files contain, points, lines or polygons?
  4. How many spatial objects are in each file?

Plot Multiple Shapefiles

The plot() function can be used for basic plotting of spatial objects. We use the add = TRUE argument to overlay shapefiles on top of each other, as we would when creating a map in a typical GIS application like QGIS.

We can use main="" to give our plot a title. If we want the title to span two lines, we use \n where the line should break.

# Plot multiple shapefiles
plot(aoiBoundary_HARV, col = "lightgreen", 
     main="NEON Harvard Forest\nField Site")
plot(lines_HARV, add = TRUE)

# use the pch element to adjust the symbology of the points
plot(point_HARV, add  = TRUE, pch = 19, col = "purple")

Challenge: Plot Raster & Vector Data Together

You can plot vector data layered on top of raster data using the add=TRUE plot attribute. Create a plot that uses the NEON AOP Canopy Height Model NEON_RemoteSensing/HARV/CHM/HARV_chmCrop.tif as a base layer. On top of the CHM, please add:

  • The study site AOI.
  • Roads.
  • The tower location.

Be sure to give your plot a meaningful title.

For assistance consider using the Shapefile Metadata & Attributes in R, the Plot Raster Data in R tutorials.


Additional Resources: Plot Parameter Options

For more on parameter options in the base R plot() function, check out these resources:


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