Explore and plot by vector layer attributes
Last updated on 2024-09-10 | Edit this page
Overview
Questions
- How can I examine the attributes of a vector layer?
Objectives
After completing this episode, participants should be able to…
Query attributes of a vector object.
Subset vector objects using specific attribute values.
Plot a vector feature, colored by unique attribute values.
Query Vector Feature Metadata
Let’s have a look at the content of the loaded data, starting with
lines_Delft. In essence, an "sf" object is a
data.frame with a “sticky” geometry column and some extra metadata, like
the CRS, extent and geometry type we examined earlier.
R
lines_Delft
OUTPUT
Simple feature collection with 11244 features and 2 fields
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 81759.58 ymin: 441223.1 xmax: 89081.41 ymax: 449845.8
Projected CRS: Amersfoort / RD New
First 10 features:
osm_id highway geometry
1 4239535 cycleway LINESTRING (86399.68 448599...
2 4239536 cycleway LINESTRING (85493.66 448740...
3 4239537 cycleway LINESTRING (85493.66 448740...
4 4239620 footway LINESTRING (86299.01 448536...
5 4239621 footway LINESTRING (86307.35 448738...
6 4239674 footway LINESTRING (86299.01 448536...
7 4310407 service LINESTRING (84049.47 447778...
8 4310808 steps LINESTRING (84588.83 447828...
9 4348553 footway LINESTRING (84527.26 447861...
10 4348575 footway LINESTRING (84500.15 447255...This means that we can examine and manipulate them as data frames.
For instance, we can look at the number of variables (columns in a data
frame) with ncol().
R
ncol(lines_Delft)
OUTPUT
[1] 3In the case of point_Delft those columns are
"osm_id", "highway" and
"geometry". We can check the names of the columns with the
base R function names().
R
names(lines_Delft)
OUTPUT
[1] "osm_id" "highway" "geometry"Callout
Note that in R the geometry is just another column and counts towards
the number returned by ncol(). This is different from GIS
software with graphical user interfaces, where the geometry is displayed
in a viewport not as a column in the attribute table.
We can also preview the content of the object by looking at the first
6 rows with the head() function, which in the case of an
sf object is similar to examining the object directly.
R
head (lines_Delft)
OUTPUT
Simple feature collection with 6 features and 2 fields
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 85107.1 ymin: 448400.3 xmax: 86399.68 ymax: 449076.2
Projected CRS: Amersfoort / RD New
osm_id highway geometry
1 4239535 cycleway LINESTRING (86399.68 448599...
2 4239536 cycleway LINESTRING (85493.66 448740...
3 4239537 cycleway LINESTRING (85493.66 448740...
4 4239620 footway LINESTRING (86299.01 448536...
5 4239621 footway LINESTRING (86307.35 448738...
6 4239674 footway LINESTRING (86299.01 448536...Explore values within one attribute
Using the $ operator, we can examine the content of a
single field of our lines feature.
We can see the contents of the highway field of our
lines feature:
R
head(lines_Delft$highway, 10)
OUTPUT
[1] "cycleway" "cycleway" "cycleway" "footway" "footway" "footway"
[7] "service" "steps" "footway" "footway" To see only unique values within the highway field, we
can use the unique() function. This function extracts all
possible values of a character variable.
R
unique(lines_Delft$highway)
OUTPUT
[1] "cycleway" "footway" "service" "steps"
[5] "residential" "unclassified" "construction" "secondary"
[9] "busway" "living_street" "motorway_link" "tertiary"
[13] "track" "motorway" "path" "pedestrian"
[17] "primary" "bridleway" "trunk" "tertiary_link"
[21] "services" "secondary_link" "trunk_link" "primary_link"
[25] "platform" "proposed" NA Callout
R is also able to handle categorical variables called factors. With
factors, we can use the levels() function to show unique
values. To examine unique values of the highway variable
this way, we have to first transform it into a factor with the
factor() function:
R
levels(factor(lines_Delft$highway))
OUTPUT
[1] "bridleway" "busway" "construction" "cycleway"
[5] "footway" "living_street" "motorway" "motorway_link"
[9] "path" "pedestrian" "platform" "primary"
[13] "primary_link" "proposed" "residential" "secondary"
[17] "secondary_link" "service" "services" "steps"
[21] "tertiary" "tertiary_link" "track" "trunk"
[25] "trunk_link" "unclassified" Note that this way the values are shown by default in alphabetical
order and NAs are not displayed, whereas using
unique() returns unique values in the order of their
occurrence in the data frame and it also shows NA
values.
Challenge: Attributes for different spatial classes
Explore the attributes associated with the point_Delft
spatial object.
- How many attributes does it have?
- What types of leisure points do the points represent? Give three examples.
- Which of the following is NOT an attribute of the
point_Delftdata object?
- location B) leisure C) osm_id
- To find the number of attributes, we use the
ncol()function:
R
ncol(point_Delft)
OUTPUT
[1] 3- The types of leisure point are in the column named
leisure.
Using the head() function which displays 6 rows by
default, we only see two values and NAs.
R
head(point_Delft)
OUTPUT
Simple feature collection with 6 features and 2 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 83839.59 ymin: 443827.4 xmax: 84967.67 ymax: 447475.5
Projected CRS: Amersfoort / RD New
osm_id leisure geometry
1 472312297 picnic_table POINT (84144.72 443827.4)
2 480470725 marina POINT (84967.67 446120.1)
3 484697679 <NA> POINT (83912.28 447431.8)
4 484697682 <NA> POINT (83895.43 447420.4)
5 484697691 <NA> POINT (83839.59 447455)
6 484697814 <NA> POINT (83892.53 447475.5)We can increase the number of rows with the n argument (e.g.,
head(n = 10) to show 10 rows) until we see at least three
distinct values in the leisure column. Note that printing an
sf object will also display the first 10 rows.
R
head(point_Delft, 10)
OUTPUT
Simple feature collection with 10 features and 2 fields
Geometry type: POINT
Dimension: XY
Bounding box: xmin: 82485.72 ymin: 443827.4 xmax: 85385.25 ymax: 448341.3
Projected CRS: Amersfoort / RD New
osm_id leisure geometry
1 472312297 picnic_table POINT (84144.72 443827.4)
2 480470725 marina POINT (84967.67 446120.1)
3 484697679 <NA> POINT (83912.28 447431.8)
4 484697682 <NA> POINT (83895.43 447420.4)
5 484697691 <NA> POINT (83839.59 447455)
6 484697814 <NA> POINT (83892.53 447475.5)
7 549139430 marina POINT (84479.99 446823.5)
8 603300994 sports_centre POINT (82485.72 445237.5)
9 883518959 sports_centre POINT (85385.25 448341.3)
10 1148515039 playground POINT (84661.3 446818)R
# you might be lucky to see three distinct values
We have our answer (sports_centre is the third value),
but in general this is not a good approach as the first rows might still
have many NAs and three distinct values might still not be
present in the first n rows of the data frame. To remove
NAs, we can use the function na.omit() on the
leisure column to remove NAs completely. Note that we use
the $ operator to examine the content of a single
variable.
R
head(na.omit(point_Delft$leisure)) # this is better
OUTPUT
[1] "picnic_table" "marina" "marina" "sports_centre"
[5] "sports_centre" "playground" To show only unique values, we can use the levels()
function on a factor to only see the first occurrence of each distinct
value. Note NAs are dropped in this case and that we get
the first three of the unique alphabetically ordered values.
R
head(levels(factor(point_Delft$leisure)), n = 3)
OUTPUT
[1] "dance" "dog_park" "escape_game"R
# this is even better
- To see a list of all attribute names, we can use the
names()function.
R
names(point_Delft)
OUTPUT
[1] "osm_id" "leisure" "geometry"Subset features
We can use the filter() function to select a subset of
features from a spatial object, just like with data frames. Let’s select
only cycleways from our street data.
R
cycleway_Delft <- lines_Delft %>%
filter(highway == "cycleway")
Our subsetting operation reduces the number of features from 11244 to 1397.
R
nrow(lines_Delft)
OUTPUT
[1] 11244R
nrow(cycleway_Delft)
OUTPUT
[1] 1397This can be useful, for instance, to calculate the total length of cycleways.
R
cycleway_Delft <- cycleway_Delft %>%
mutate(length = st_length(.))
cycleway_Delft %>%
summarise(total_length = sum(length))
OUTPUT
Simple feature collection with 1 feature and 1 field
Geometry type: MULTILINESTRING
Dimension: XY
Bounding box: xmin: 81759.58 ymin: 441227.3 xmax: 87326.76 ymax: 449834.5
Projected CRS: Amersfoort / RD New
total_length geometry
1 115550.1 [m] MULTILINESTRING ((86399.68 ...Now we can plot only the cycleways.
R
ggplot(data = cycleway_Delft) +
geom_sf() +
labs(
title = "Slow mobility network in Delft",
subtitle = "Cycleways"
) +
coord_sf(datum = st_crs(28992))
Challenge
Challenge: Now with motorways (and pedestrian streets)
- Create a new object that only contains the motorways in Delft.
- How many features does the new object have?
- What is the total length of motorways?
- Plot the motorways.
- Extra: follow the same steps with pedestrian streets.
- To create the new object, we first need to see which value of the
highwaycolumn holds motorways. There is a value calledmotorway.
R
unique(lines_Delft$highway)
OUTPUT
[1] "cycleway" "footway" "service" "steps"
[5] "residential" "unclassified" "construction" "secondary"
[9] "busway" "living_street" "motorway_link" "tertiary"
[13] "track" "motorway" "path" "pedestrian"
[17] "primary" "bridleway" "trunk" "tertiary_link"
[21] "services" "secondary_link" "trunk_link" "primary_link"
[25] "platform" "proposed" NA We extract only the features with the value
motorway.
R
motorway_Delft <- lines_Delft %>%
filter(highway == "motorway")
motorway_Delft
OUTPUT
Simple feature collection with 48 features and 2 fields
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 84501.66 ymin: 442458.2 xmax: 87401.87 ymax: 449205.9
Projected CRS: Amersfoort / RD New
First 10 features:
osm_id highway geometry
1 7531946 motorway LINESTRING (87395.68 442480...
2 7531976 motorway LINESTRING (87401.87 442467...
3 46212227 motorway LINESTRING (86103.56 446928...
4 120945066 motorway LINESTRING (85724.87 447473...
5 120945068 motorway LINESTRING (85710.31 447466...
6 126548650 motorway LINESTRING (86984.12 443630...
7 126548651 motorway LINESTRING (86714.75 444772...
8 126548653 motorway LINESTRING (86700.23 444769...
9 126548654 motorway LINESTRING (86716.35 444766...
10 126548655 motorway LINESTRING (84961.78 448566...- There are 48 features with the value
motorway.
R
motorway_Delft_length <- motorway_Delft %>%
mutate(length = st_length(.)) %>%
select(everything(), geometry) %>%
summarise(total_length = sum(length))
- The total length of motorways is 14877.4361477941.
R
nrow(motorway_Delft)
OUTPUT
[1] 48- Plot the motorways.
R
ggplot(data = motorway_Delft) +
geom_sf(linewidth = 1.5) +
labs(
title = "Fast mobility network",
subtitle = "Motorways"
) +
coord_sf(datum = st_crs(28992))
- Follow the same steps with pedestrian streets.
R
pedestrian_Delft <- lines_Delft %>%
filter(highway == "pedestrian")
pedestrian_Delft %>%
mutate(length = st_length(.)) %>%
select(everything(), geometry) %>%
summarise(total_length = sum(length))
OUTPUT
Simple feature collection with 1 feature and 1 field
Geometry type: MULTILINESTRING
Dimension: XY
Bounding box: xmin: 82388.15 ymin: 444400.2 xmax: 85875.95 ymax: 447987.8
Projected CRS: Amersfoort / RD New
total_length geometry
1 12778.84 [m] MULTILINESTRING ((85538.03 ...R
nrow(pedestrian_Delft)
OUTPUT
[1] 234R
ggplot() +
geom_sf(data = pedestrian_Delft) +
labs(
title = "Slow mobility network",
subtitle = "Pedestrian"
) +
coord_sf(datum = st_crs(28992))
Customize plots
Let’s say that we want to color different road types with different colors and that we want to determine those colors.
R
unique(lines_Delft$highway)
OUTPUT
[1] "cycleway" "footway" "service" "steps"
[5] "residential" "unclassified" "construction" "secondary"
[9] "busway" "living_street" "motorway_link" "tertiary"
[13] "track" "motorway" "path" "pedestrian"
[17] "primary" "bridleway" "trunk" "tertiary_link"
[21] "services" "secondary_link" "trunk_link" "primary_link"
[25] "platform" "proposed" NA If we look at all the unique values of the highway field of our
street network we see more than 20 values. Let’s focus on a subset of
four values to illustrate the use of distinct colors. We use a piped
expression in which we only filter the rows of our data frame that have
one of the four given values "motorway",
"primary", "secondary", and
"cycleway". Note that we do this with the %in
operator which is a more compact equivalent of a series of conditions
joined by the | (or) operator. We also make sure that the
highway column is a factor column.
R
road_types <- c("motorway", "primary", "secondary", "cycleway")
lines_Delft_selection <- lines_Delft %>%
filter(highway %in% road_types) %>%
mutate(highway = factor(highway, levels = road_types))
Next we define the four colors we want to use, one for each type of
road in our vector object. Note that in R you can use named colors like
"blue", "green", "navy", and
"purple". If you are using RStudio, you will see the named
colors previewed in line. A full list of named colors can be listed with
the colors() function.
R
road_colors <- c("blue", "green", "navy", "purple")
We can use the defined color palette in ggplot.
R
ggplot(data = lines_Delft_selection) +
geom_sf(aes(color = highway)) +
scale_color_manual(values = road_colors) +
labs(
color = "Road Type",
title = "Road network of Delft",
subtitle = "Roads & Cycleways"
) +
coord_sf(datum = st_crs(28992))
Adjust line width
Earlier we adjusted the line width universally. We can also adjust
line widths for every factor level. Note that in this case the
size argument, like the color argument, are
within the aes() mapping function. This means that the
values of that visual property will be mapped from a variable of the
object that is being plotted.
R
line_widths <- c(1, 0.75, 0.5, 0.25)
R
ggplot(data = lines_Delft_selection) +
geom_sf(aes(color = highway, linewidth = highway)) +
scale_color_manual(values = road_colors) +
labs(
color = "Road Type",
linewidth = "Road Type",
title = "Mobility network of Delft",
subtitle = "Roads & Cycleways"
) +
scale_linewidth_manual(values = line_widths) +
coord_sf(datum = st_crs(28992))
Challenge: Plot line width by attribute
In the example above, we set the line widths to be 1, 0.75, 0.5, and 0.25. In our case line thicknesses are consistent with the hierarchy of the selected road types, but in some cases we might want to show a different hierarchy.
Let’s create another plot where we show the different line types with the following thicknesses:
- motorways linewidth = 0.25
- primary linewidth = 0.75
- secondary linewidth = 0.5
- cycleway linewidth = 1
R
levels(factor(lines_Delft_selection$highway))
OUTPUT
[1] "motorway" "primary" "secondary" "cycleway" R
line_width <- c(0.25, 0.75, 0.5, 1)
R
ggplot(data = lines_Delft_selection) +
geom_sf(aes(linewidth = highway)) +
scale_linewidth_manual(values = line_width) +
labs(
title = "Mobility network of Delft",
subtitle = "Roads & Cycleways - Line width varies"
) +
coord_sf(datum = st_crs(28992))
Add plot legend
Let’s add a legend to our plot. We will use the
road_colors object that we created above to color the
legend. We can customize the appearance of our legend by manually
setting different parameters.
R
p1 <- ggplot(data = lines_Delft_selection) +
geom_sf(aes(color = highway), linewidth = 1.5) +
scale_color_manual(values = road_colors) +
labs(color = "Road Type") +
labs(
title = "Mobility network of Delft",
subtitle = "Roads & Cycleways - Default Legend"
) +
coord_sf(datum = st_crs(28992))
# show plot
p1
R
p2 <- p1 +
theme(
legend.text = element_text(size = 20),
legend.box.background = element_rect(linewidth = 1)
)
# show plot
p2
Challenge: Plot lines by attributes
Create a plot that emphasizes only roads where bicycles are allowed. To emphasize this, make the lines where bicycles are not allowed THINNER than the roads where bicycles are allowed. Be sure to add a title and legend to your map. You might consider a color palette that has all bike-friendly roads displayed in a bright color. All other lines can be black.
R
class(lines_Delft_selection$highway)
OUTPUT
[1] "factor"R
levels(factor(lines_Delft$highway))
OUTPUT
[1] "bridleway" "busway" "construction" "cycleway"
[5] "footway" "living_street" "motorway" "motorway_link"
[9] "path" "pedestrian" "platform" "primary"
[13] "primary_link" "proposed" "residential" "secondary"
[17] "secondary_link" "service" "services" "steps"
[21] "tertiary" "tertiary_link" "track" "trunk"
[25] "trunk_link" "unclassified" R
# First, create a data frame with only roads where bicycles
# are allowed
lines_Delft_bicycle <- lines_Delft %>%
filter(highway == "cycleway")
# Next, visualise it using ggplot
ggplot(data = lines_Delft) +
geom_sf() +
geom_sf(
data = lines_Delft_bicycle,
aes(color = highway),
linewidth = 1
) +
scale_color_manual(values = "magenta") +
labs(
title = "Mobility network in Delft",
subtitle = "Roads dedicated to Bikes"
) +
coord_sf(datum = st_crs(28992))
Challenge: Plot polygon by attribute
Create a map of the municipal boundaries in the Netherlands using the
data located in your data folder: nl-gemeenten.shp. Apply a
line color to each state using its region value. Add a legend.
R
municipal_boundaries_NL <- st_read("data/nl-gemeenten.shp")
OUTPUT
Reading layer `nl-gemeenten' from data source
`/home/runner/work/r-geospatial-urban/r-geospatial-urban/site/built/data/nl-gemeenten.shp'
using driver `ESRI Shapefile'
Simple feature collection with 344 features and 6 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 10425.16 ymin: 306846.2 xmax: 278026.1 ymax: 621876.3
Projected CRS: Amersfoort / RD NewR
str(municipal_boundaries_NL)
OUTPUT
Classes 'sf' and 'data.frame': 344 obs. of 7 variables:
$ identifica: chr "GM0014" "GM0034" "GM0037" "GM0047" ...
$ naam : chr "Groningen" "Almere" "Stadskanaal" "Veendam" ...
$ code : chr "0014" "0034" "0037" "0047" ...
$ ligtInProv: chr "20" "24" "20" "20" ...
$ ligtInPr_1: chr "Groningen" "Flevoland" "Groningen" "Groningen" ...
$ fuuid : chr "gemeentegebied.ee21436e-5a2d-4a8f-b2bf-113bddd028fc" "gemeentegebied.6e4378d7-0905-4dff-b351-57c1940c9c90" "gemeentegebied.515fbfe4-614e-463d-8b8c-91d35ca93b3b" "gemeentegebied.a3e71341-218c-44bf-ba12-01e2251ea2f6" ...
$ geometry :sfc_MULTIPOLYGON of length 344; first list element: List of 1
..$ :List of 1
.. ..$ : num [1:4749, 1:2] 238742 238741 238740 238738 238735 ...
..- attr(*, "class")= chr [1:3] "XY" "MULTIPOLYGON" "sfg"
- attr(*, "sf_column")= chr "geometry"
- attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA
..- attr(*, "names")= chr [1:6] "identifica" "naam" "code" "ligtInProv" ...R
levels(factor(municipal_boundaries_NL$ligtInPr_1))
OUTPUT
[1] "Drenthe" "Flevoland" "Fryslân" "Gelderland"
[5] "Groningen" "Limburg" "Noord-Brabant" "Noord-Holland"
[9] "Overijssel" "Utrecht" "Zeeland" "Zuid-Holland" R
ggplot(data = municipal_boundaries_NL) +
geom_sf(aes(color = ligtInPr_1), linewidth = 1) +
labs(title = "Contiguous NL Municipal Boundaries") +
coord_sf(datum = st_crs(28992))
Key Points
Spatial objects in
sfare 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
ggplot2package.