Explore and Plot by Vector Layer Attributes
Last updated on 2023-01-03 | Edit this page
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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.
R
point_HARV
OUTPUT
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.
R
ncol(lines_HARV)
OUTPUT
[1] 16We can view the individual name of each attribute using the
names() function in R:
R
names(lines_HARV)
OUTPUT
[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:
R
head(lines_HARV)
OUTPUT
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
1 14 48 woods road Locust Opening Rd <NA> 5
2 40 91 footpath <NA> <NA> 6
3 41 106 footpath <NA> <NA> 6
4 211 279 stone wall <NA> <NA> 1
5 212 280 stone wall <NA> <NA> 1
6 213 281 stone wall <NA> <NA> 1
MAPLABEL SHAPE_LENG LABEL BIKEHORSE RESVEHICLE RECMAP
1 Locust Opening Rd 1297.35706 Locust Opening Rd Y R1 Y
2 <NA> 146.29984 <NA> Y R1 Y
3 <NA> 676.71804 <NA> Y R2 Y
4 <NA> 231.78957 <NA> <NA> <NA> <NA>
5 <NA> 45.50864 <NA> <NA> <NA> <NA>
6 <NA> 198.39043 <NA> <NA> <NA> <NA>
Shape_Le_1 ResVehic_1 BicyclesHo
1 1297.10617 R1 - All Research Vehicles Allowed Bicycles and Horses Allowed
2 146.29983 R1 - All Research Vehicles Allowed Bicycles and Horses Allowed
3 676.71807 R2 - 4WD/High Clearance Vehicles Only Bicycles and Horses Allowed
4 231.78962 <NA> <NA>
5 45.50859 <NA> <NA>
6 198.39041 <NA> <NA>
geometry
1 MULTILINESTRING ((730819.2 ...
2 MULTILINESTRING ((732040.2 ...
3 MULTILINESTRING ((732057 47...
4 MULTILINESTRING ((731903.6 ...
5 MULTILINESTRING ((732039.1 ...
6 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_HARVdata object?Which of the following is NOT an attribute of the
point_HARVdata object?
- Latitude B) County C) Country
- To find the number of attributes, we use the
ncol()function:
R
ncol(point_HARV)
OUTPUT
[1] 15R
ncol(aoi_boundary_HARV)
OUTPUT
[1] 2- Ownership information is in a column named
Ownership:
R
point_HARV$Ownership
OUTPUT
[1] "Harvard University, LTER"- To see a list of all of the attributes, we can use the
names()function:
R
names(point_HARV)
OUTPUT
[1] "Un_ID" "Domain" "DomainName" "SiteName" "Type"
[6] "Sub_Type" "Lat" "Long" "Zone" "Easting"
[11] "Northing" "Ownership" "County" "annotation" "geometry" “Country” is not an attribute of this object.
Explore Values within One Attribute
We can explore individual values stored within a particular
attribute. Comparing attributes to a spreadsheet or a data frame, this
is similar to exploring values in a column. We did this with the
gapminder dataframe in an
earlier lesson. For spatial objects, we can use the same syntax:
objectName$attributeName.
We can see the contents of the TYPE field of our lines
feature:
R
lines_HARV$TYPE
OUTPUT
[1] "woods road" "footpath" "footpath" "stone wall" "stone wall"
[6] "stone wall" "stone wall" "stone wall" "stone wall" "boardwalk"
[11] "woods road" "woods road" "woods road"To see only unique values within the TYPE field, we can
use the levels() function for extracting the possible
values of a categorical variable. The special term for categorical
variables within R is factor. We worked with factors a little bit in an
earlier lesson.
R
levels(lines_HARV$TYPE)
OUTPUT
NULLSubset 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.
R
footpath_HARV <- lines_HARV %>%
filter(TYPE == "footpath")
nrow(footpath_HARV)
OUTPUT
[1] 2Our 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:
R
ggplot() +
geom_sf(data = footpath_HARV) +
ggtitle("NEON Harvard Forest Field Site", subtitle = "Footpaths") +
coord_sf()
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.
R
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()
Now, we see that there are in fact two features in our plot!
First we will save an object with only the boardwalk lines:
R
boardwalk_HARV <- lines_HARV %>%
filter(TYPE == "boardwalk")
Let’s check how many features there are in this subset:
R
nrow(boardwalk_HARV)
OUTPUT
[1] 1Now let’s plot that data:
R
ggplot() +
geom_sf(data = boardwalk_HARV, size = 1.5) +
ggtitle("NEON Harvard Forest Field Site", subtitle = "Boardwalks") +
coord_sf()
First we will save an object with only the stone wall lines and check the number of features:
R
stoneWall_HARV <- lines_HARV %>%
filter(TYPE == "stone wall")
nrow(stoneWall_HARV)
OUTPUT
[1] 6Now we can plot the data:
R
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:
R
levels(lines_HARV$TYPE)
OUTPUT
NULLThen we can create a palette of four colors, one for each feature in our vector object.
R
road_colors <- c("blue", "green", "navy", "purple")
We can tell ggplot to use these colors when we plot the
data.
R
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()
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.
R
line_widths <- c(1, 2, 3, 4)
We can use those line widths when we plot the data.
R
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()
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
First we need to look at the levels of our factor to see what order the road types are in:
R
levels(lines_HARV$TYPE)
OUTPUT
NULLWe then can create our line_width vector setting each of
the levels to the desired thickness.
R
line_width <- c(1, 3, 2, 6)
Now we can create our plot.
R
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 usetop,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.
R
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()
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
R
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()
WARNING
Warning: The `size` argument of `element_rect()` is deprecated as of ggplot2 3.4.0.
ℹ Please use the `linewidth` argument instead.
R
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()
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$BicyclesHo attribute.
Be sure to add a title and legend to your map. You might consider a color palette that has all bike/horse-friendly roads displayed in a bright color. All other lines can be black.
First we need to make sure that the BicyclesHo attribute
is a factor and check how many levels it has.
R
class(lines_HARV$BicyclesHo)
OUTPUT
[1] "character"R
levels(lines_HARV$BicyclesHo)
OUTPUT
NULLNext, we will create a new object lines_removeNA that
removes missing values.
R
lines_removeNA <- lines_HARV[!is.na(lines_HARV$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.
R
# 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()
First we read in the data and check how many levels there are in the
region column:
R
state_boundary_US <-
st_read("data/NEON-DS-Site-Layout-Files/US-Boundary-Layers/US-State-Boundaries-Census-2014.shp")
OUTPUT
Reading layer `US-State-Boundaries-Census-2014' from data source
`/home/runner/work/r-raster-vector-geospatial/r-raster-vector-geospatial/site/built/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 84R
levels(state_boundary_US$region)
OUTPUT
NULLNext we set a color vector with that many items:
R
colors <- c("purple", "springgreen", "yellow", "brown", "navy")
Now we can create our plot:
R
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()
