Explore and Plot by Vector Layer Attributes

Overview

Teaching: 10 min
Exercises: 10 min

Questions

How can I compute on the attributes of a spatial object?

Objectives

Query attributes of a spatial object.

Subset spatial objects using specific attribute values.

Plot a vector feature, colored by unique attribute values.

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.

This episode continues our discussion of vector layer attributes and covers how to work with vector layer attributes in R. It covers how to identify and query layer attributes, as well as how to subset features by specific attribute values. Finally, we will learn how to plot a feature according to a set of attribute values.

Load the Data

We will continue using the sf, raster and ggplot2 packages in this episode. Make sure that you have these packages loaded. We will continue to work with the three shapefiles (vector layers) that we loaded in the Open and Plot Shapefiles in R episode.

Query Vector Feature Metadata

As we discussed in the Open and Plot Shapefiles in R episode, we can view metadata associated with an R object using:

st_geometry_type() - The type of vector data stored in the object.
nrow() - The number of features in the object
st_bbox() - The spatial extent (geographic area covered by) of the object.
st_crs() - The CRS (spatial projection) of the data.

We started to explore our point_HARV object in the previous episode. To see a summary of all of the metadata associated with our point_HARV object, we can view the object with View(point_HARV) or print a summary of the object itself to the console.

point_HARV

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
  Un_ID Domain DomainName       SiteName Type       Sub_Type     Lat      Long
1     A      1  Northeast Harvard Forest Core Advanced Tower 42.5369 -72.17266
  Zone  Easting Northing                Ownership    County annotation
1   18 732183.2  4713265 Harvard University, LTER Worcester         C1
                  geometry
1 POINT (732183.2 4713265)

We can use the ncol function to count the number of attributes associated with a spatial object too. Note that the geometry is just another column and counts towards the total.

ncol(lines_HARV)

[1] 16

We can view the individual name of each attribute using the names() function in R:

names(lines_HARV)

 [1] "OBJECTID_1" "OBJECTID"   "TYPE"       "NOTES"      "MISCNOTES" 
 [6] "RULEID"     "MAPLABEL"   "SHAPE_LENG" "LABEL"      "BIKEHORSE" 
[11] "RESVEHICLE" "RECMAP"     "Shape_Le_1" "ResVehic_1" "BicyclesHo"
[16] "geometry"  

We could also view just the first 6 rows of attribute values using the head() function to get a preview of the data:

head(lines_HARV)

Simple feature collection with 6 features and 15 fields
Geometry type: MULTILINESTRING
Dimension:     XY
Bounding box:  xmin: 730741.2 ymin: 4712685 xmax: 732232.3 ymax: 4713726
Projected CRS: WGS 84 / UTM zone 18N
  OBJECTID_1 OBJECTID       TYPE             NOTES MISCNOTES RULEID
       14       48 woods road Locust Opening Rd      <NA>      5
       40       91   footpath              <NA>      <NA>      6
       41      106   footpath              <NA>      <NA>      6
      211      279 stone wall              <NA>      <NA>      1
      212      280 stone wall              <NA>      <NA>      1
      213      281 stone wall              <NA>      <NA>      1
           MAPLABEL SHAPE_LENG             LABEL BIKEHORSE RESVEHICLE RECMAP
Locust Opening Rd 1297.35706 Locust Opening Rd         Y         R1      Y
            <NA>  146.29984              <NA>         Y         R1      Y
            <NA>  676.71804              <NA>         Y         R2      Y
            <NA>  231.78957              <NA>      <NA>       <NA>   <NA>
            <NA>   45.50864              <NA>      <NA>       <NA>   <NA>
            <NA>  198.39043              <NA>      <NA>       <NA>   <NA>
  Shape_Le_1                            ResVehic_1                  BicyclesHo
1297.10617    R1 - All Research Vehicles Allowed Bicycles and Horses Allowed
146.29983    R1 - All Research Vehicles Allowed Bicycles and Horses Allowed
676.71807 R2 - 4WD/High Clearance Vehicles Only Bicycles and Horses Allowed
231.78962                                  <NA>                        <NA>
 45.50859                                  <NA>                        <NA>
198.39041                                  <NA>                        <NA>
                        geometry
MULTILINESTRING ((730819.2 ...
MULTILINESTRING ((732040.2 ...
MULTILINESTRING ((732057 47...
MULTILINESTRING ((731903.6 ...
MULTILINESTRING ((732039.1 ...
MULTILINESTRING ((732056.2 ...

Challenge: Attributes for Different Spatial Classes

Explore the attributes associated with the point_HARV and aoi_boundary_HARV spatial objects.

How many attributes does each have?

Who owns the site in the point_HARV data object?

Which of the following is NOT an attribute of the point_HARV data object?

A) Latitude B) County C) Country
Answers

1) To find the number of attributes, we use the ncol() function:
ncol(point_HARV)
[1] 15
ncol(aoi_boundary_HARV)
[1] 2
2) Ownership information is in a column named Ownership:
point_HARV$Ownership
[1] "Harvard University, LTER"
3) To see a list of all of the attributes, we can use the names() function:
names(point_HARV)
 [1] "Un_ID"      "Domain"     "DomainName" "SiteName"   "Type"      
 [6] "Sub_Type"   "Lat"        "Long"       "Zone"       "Easting"   
[11] "Northing"   "Ownership"  "County"     "annotation" "geometry"  
“Country” is not an attribute of this object.

Explore Values within One Attribute

We can explore individual values stored within a particular attribute. Comparing attributes to a spreadsheet or a data frame, this is similar to exploring values in a column. We did this with the gapminder dataframe in an earlier lesson. For spatial objects, we can use the same syntax: objectName$attributeName.

We can see the contents of the TYPE field of our lines feature:

lines_HARV$TYPE

 [1] "woods road" "footpath"   "footpath"   "stone wall" "stone wall"
 [6] "stone wall" "stone wall" "stone wall" "stone wall" "boardwalk" 
[11] "woods road" "woods road" "woods road"

To see only unique values within the TYPE field, we can use the levels() function for extracting the possible values of a categorical variable. The special term for categorical variables within R is factor. We worked with factors a little bit in an earlier lesson.

levels(lines_HARV$TYPE)

NULL

Subset Features

We can use the filter() function from dplyr that we worked with in an earlier lesson to select a subset of features from a spatial object in R, just like with data frames.

For example, we might be interested only in features that are of TYPE “footpath”. Once we subset out this data, we can use it as input to other code so that code only operates on the footpath lines.

footpath_HARV <- lines_HARV %>% 
  filter(TYPE == "footpath")
nrow(footpath_HARV)

[1] 2

Our subsetting operation reduces the features count to 2. This means that only two feature lines in our spatial object have the attribute TYPE == footpath. We can plot only the footpath lines:

ggplot() + 
  geom_sf(data = footpath_HARV) +
  ggtitle("NEON Harvard Forest Field Site", subtitle = "Footpaths") + 
  coord_sf()

Map of the footpaths in the study area.

There are two features in our footpaths subset. Why does the plot look like there is only one feature? Let’s adjust the colors used in our plot. If we have 2 features in our vector object, we can plot each using a unique color by assigning a column name to the color aesthetic (color =). We use the syntax aes(color = ) to do this. We can also alter the default line thickness by using the size = parameter, as the default value of 0.5 can be hard to see. Note that size is placed outside of the aes() function, as we are not connecting line thickness to a data variable.

ggplot() + 
  geom_sf(data = footpath_HARV, aes(color = factor(OBJECTID)), size = 1.5) +
  labs(color = 'Footpath ID') +
  ggtitle("NEON Harvard Forest Field Site", subtitle = "Footpaths") + 
  coord_sf()

Map of the footpaths in the study area where each feature is colored differently.

Now, we see that there are in fact two features in our plot!

Challenge: Subset Spatial Line Objects Part 1

Subset out all boardwalk from the lines layer and plot it.
Answers

First we will save an object with only the boardwalk lines:
boardwalk_HARV <- lines_HARV %>% 
  filter(TYPE == "boardwalk")
Let’s check how many features there are in this subset:
nrow(boardwalk_HARV)
[1] 1
Now let’s plot that data:
ggplot() + 
  geom_sf(data = boardwalk_HARV, size = 1.5) +
  ggtitle("NEON Harvard Forest Field Site", subtitle = "Boardwalks") + 
  coord_sf()

Challenge: Subset Spatial Line Objects Part 2

Subset out all stone wall features from the lines layer and plot it. For each plot, color each feature using a unique color.
Answer

First we will save an object with only the stone wall lines and check the number of features:
stoneWall_HARV <- lines_HARV %>% 
  filter(TYPE == "stone wall")
nrow(stoneWall_HARV)
[1] 6
Now we can plot the data:
ggplot() +
  geom_sf(data = stoneWall_HARV, aes(color = factor(OBJECTID)), size = 1.5) +
  labs(color = 'Wall ID') +
  ggtitle("NEON Harvard Forest Field Site", subtitle = "Stonewalls") + 
  coord_sf()

Customize Plots

In the examples above, ggplot() automatically selected colors for each line based on a default color order. If we don’t like those default colors, we can create a vector of colors - one for each feature. To create this vector we can use the following syntax:

c("color_one", "color_two", "color_three")[object$factor]

Note in the above example we have

a vector of colors - one for each factor value (unique attribute value)
the attribute itself ([object$factor]) of class factor.

First we will check how many unique levels our factor has:

levels(lines_HARV$TYPE)

NULL

Then we can create a palette of four colors, one for each feature in our vector object.

road_colors <- c("blue", "green", "navy", "purple")

We can tell ggplot to use these colors when we plot the data.

ggplot() +
  geom_sf(data = lines_HARV, aes(color = TYPE)) + 
  scale_color_manual(values = road_colors) +
  labs(color = 'Road Type') +
  ggtitle("NEON Harvard Forest Field Site", subtitle = "Roads & Trails") + 
  coord_sf()

Roads and trails in the area.

Adjust Line Width

We adjusted line width universally earlier. If we want a unique line width for each factor level or attribute category in our spatial object, we can use the same syntax that we used for colors, above.

We already know that we have four different TYPE levels in the lines_HARV object, so we will set four different line widths.

line_widths <- c(1, 2, 3, 4)

We can use those line widths when we plot the data.

ggplot() +
  geom_sf(data = lines_HARV, aes(color = TYPE, size = TYPE)) + 
  scale_color_manual(values = road_colors) +
  labs(color = 'Road Type') +
  scale_size_manual(values = line_widths) +
  ggtitle("NEON Harvard Forest Field Site", subtitle = "Roads & Trails - Line width varies") + 
  coord_sf()

Roads and trails in the area demonstrating how to use different line thickness and colors.

Note that we could also use aes(size = TYPE) to tie the line thickness to the TYPE variable, so long as we had been careful to set factor levels appropriately. ggplot prints a warning when you do this, because it is not considered a good practice to plot non-spatial data this way.

Challenge: Plot Line Width by Attribute

In the example above, we set the line widths to be 1, 2, 3, and 4. Because R orders factor levels alphabetically by default, this gave us a plot where woods roads (the last factor level) were the thickest and boardwalks were the thinnest.

Let’s create another plot where we show the different line types with the following thicknesses:

woods road size = 6

boardwalks size = 1

footpath size = 3

stone wall size = 2
Answers

First we need to look at the levels of our factor to see what order the road types are in:
levels(lines_HARV$TYPE)
NULL
We then can create our line_width vector setting each of the levels to the desired thickness.
line_width <- c(1, 3, 2, 6)
Now we can create our plot.
ggplot() +
  geom_sf(data = lines_HARV, aes(size = TYPE)) +
  scale_size_manual(values = line_width) +
  ggtitle("NEON Harvard Forest Field Site", subtitle = "Roads & Trails - Line width varies") + 
  coord_sf()

Add Plot Legend

We can add a legend to our plot too. When we add a legend, we use the following elements to specify labels and colors:

bottomright: We specify the location of our legend by using a default keyword. We could also use top, topright, etc.
levels(objectName$attributeName): Label the legend elements using the categories of levels in an attribute (e.g., levels(lines_HARV$TYPE) means use the levels boardwalk, footpath, etc).
fill =: apply unique colors to the boxes in our legend. palette() is the default set of colors that R applies to all plots.

Let’s add a legend to our plot. We will use the road_colors object that we created above to color the legend. We can customize the appearance of our legend by manually setting different parameters.

ggplot() + 
  geom_sf(data = lines_HARV, aes(color = TYPE), size = 1.5) +
  scale_color_manual(values = road_colors) +
  labs(color = 'Road Type') + 
  ggtitle("NEON Harvard Forest Field Site", 
          subtitle = "Roads & Trails - Default Legend") + 
  coord_sf()

Roads and trails in the study area using thicker lines than the previous figure.

We can change the appearance of our legend by manually setting different parameters.

legend.text: change the font size
legend.box.background: add an outline box

ggplot() + 
  geom_sf(data = lines_HARV, aes(color = TYPE), size = 1.5) +
  scale_color_manual(values = road_colors) + 
  labs(color = 'Road Type') +
  theme(legend.text = element_text(size = 20), 
        legend.box.background = element_rect(size = 1)) + 
  ggtitle("NEON Harvard Forest Field Site", 
          subtitle = "Roads & Trails - Modified Legend") +
  coord_sf()

Map of the paths in the study area with large-font and border around the legend.

new_colors <- c("springgreen", "blue", "magenta", "orange")

ggplot() + 
  geom_sf(data = lines_HARV, aes(color = TYPE), size = 1.5) + 
  scale_color_manual(values = new_colors) +
  labs(color = 'Road Type') +
  theme(legend.text = element_text(size = 20), 
        legend.box.background = element_rect(size = 1)) + 
  ggtitle("NEON Harvard Forest Field Site", 
          subtitle = "Roads & Trails - Pretty Colors") +
  coord_sf()

Map of the paths in the study area using a different color palette.

Data Tip

You can modify the default R color palette using the palette method. For example palette(rainbow(6)) or palette(terrain.colors(6)). You can reset the palette colors using palette("default")!

Challenge: Plot Lines by Attribute

Create a plot that emphasizes only roads where bicycles and horses are allowed. To emphasize this, make the lines where bicycles are not allowed THINNER than the roads where bicycles are allowed. NOTE: this attribute information is located in the lines_HARV$BicyclesHo attribute.

Be sure to add a title and legend to your map. You might consider a color palette that has all bike/horse-friendly roads displayed in a bright color. All other lines can be black.
Answers

First we need to make sure that the BicyclesHo attribute is a factor and check how many levels it has.
class(lines_HARV$BicyclesHo)
[1] "character"
levels(lines_HARV$BicyclesHo)
NULL
Next, we will create a new object lines_removeNA that removes missing values.
lines_removeNA <- lines_HARV[!is.na(lines_HARV$BicyclesHo),] 
In our plot, we will set colors so that only the allowed roads are magenta, and we will set line width so that the first factor level is thicker than the others.
# First, create a data frame with only those roads where bicycles and horses are allowed
lines_showHarv <- lines_removeNA %>% filter(BicyclesHo == "Bicycles and Horses Allowed")

# Next, visualise using ggplot
ggplot() + 
  geom_sf(data = lines_HARV) + 
  geom_sf(data = lines_showHarv, aes(color = BicyclesHo), size = 2) + 
  scale_color_manual(values = "magenta") +
  ggtitle("NEON Harvard Forest Field Site", subtitle = "Roads Where Bikes and Horses Are Allowed") + 
  coord_sf()

Challenge: Plot Polygon by Attribute

Create a map of the state boundaries in the United States using the data located in your downloaded data folder: NEON-DS-Site-Layout-Files/US-Boundary-Layers\US-State-Boundaries-Census-2014. Apply a fill color to each state using its region value. Add a legend.
Answers

First we read in the data and check how many levels there are in the region column:
state_boundary_US <- 
st_read("data/NEON-DS-Site-Layout-Files/US-Boundary-Layers/US-State-Boundaries-Census-2014.shp")
Reading layer `US-State-Boundaries-Census-2014' from data source 
  `/home/runner/work/r-raster-vector-geospatial/r-raster-vector-geospatial/_episodes_rmd/data/NEON-DS-Site-Layout-Files/US-Boundary-Layers/US-State-Boundaries-Census-2014.shp' 
  using driver `ESRI Shapefile'
Simple feature collection with 58 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension:     XYZ
Bounding box:  xmin: -124.7258 ymin: 24.49813 xmax: -66.9499 ymax: 49.38436
z_range:       zmin: 0 zmax: 0
Geodetic CRS:  WGS 84
levels(state_boundary_US$region)
NULL
Next we set a color vector with that many items:
colors <- c("purple", "springgreen", "yellow", "brown", "navy")
Now we can create our plot:
ggplot() +
  geom_sf(data = state_boundary_US, aes(color = region), size = 1) +
  scale_color_manual(values = colors) +
  ggtitle("Contiguous U.S. State Boundaries") + 
  coord_sf()

Key Points

Spatial objects in sf are similar to standard data frames and can be manipulated using the same functions.

Almost any feature of a plot can be customized using the various functions and options in the ggplot2 package.

previous episode

Introduction to Geospatial Raster and Vector Data with R

next episode

Explore and Plot by Vector Layer Attributes

Overview

Things You’ll Need To Complete This Episode

Load the Data

Query Vector Feature Metadata

Challenge: Attributes for Different Spatial Classes

Answers

Explore Values within One Attribute

Subset Features

Challenge: Subset Spatial Line Objects Part 1

Answers

Challenge: Subset Spatial Line Objects Part 2

Answer

Customize Plots

Adjust Line Width

Challenge: Plot Line Width by Attribute

Answers

Add Plot Legend

Data Tip

Challenge: Plot Lines by Attribute

Answers

Challenge: Plot Polygon by Attribute

Answers

Key Points

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