{sf}
operationsBecause {densityarea}
can return {sf}
polygons, this can allow you to use its functionality (see this
cheat sheet for some examples).
As a first example, we’ll estimate how much different data clusters
overlap in the s01
dataset.
We’ll focus on the vowels iy
, ey
,
o
and oh
which correspond to the following
lexical classes:
vowel label | lexical class |
---|---|
iy |
Fleece |
ey |
Face |
o |
Lot |
oh |
Thought |
Within this subset of vowel categories, we’ll get the 80% probability
density estimate as sf::st_polygon()
s.
vowel_subset |>
group_by(plt_vclass) |>
reframe(
density_polygons(lF2,
lF1,
probs = 0.8,
as_sf = TRUE)
) |>
st_sf()->
vowel_polygons
vowel_polygons
#> Simple feature collection with 4 features and 3 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -8.050893 ymin: -7.037138 xmax: -6.996794 ymax: -5.820745
#> CRS: NA
#> # A tibble: 4 × 4
#> plt_vclass level_id prob geometry
#> <chr> <int> <dbl> <POLYGON>
#> 1 ey 1 0.8 ((-7.896047 -6.222708, -7.897749 -6.224405, -7.9032…
#> 2 iy 1 0.8 ((-7.833113 -5.836786, -7.843041 -5.828972, -7.8529…
#> 3 o 1 0.8 ((-7.351372 -6.23354, -7.365699 -6.233222, -7.38002…
#> 4 oh 1 0.8 ((-7.240256 -6.419138, -7.249486 -6.417758, -7.2587…
We can plot these directly by using the sf::geom_sf()
geom for ggplot2.
ggplot(vowel_polygons) +
geom_sf(
aes(fill = plt_vclass),
alpha = 0.6
) +
scale_fill_brewer(palette = "Dark2")
{sf}
operationsAll of the {sf}
operations for geometries are available
to use on vowel_polygons
. For example, we can get the area
of each polygon, with sf::st_area()
and use it in
plotting.
Or, we can get the polygon centroids and plot them.
To use the density polygons like “cookie cutters” on each other, we
need to use st_intersections()
.
vowel_polygons |>
st_intersection() ->
vowel_intersections
vowel_intersections
#> Simple feature collection with 6 features and 6 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -8.050893 ymin: -7.037138 xmax: -6.996794 ymax: -5.820745
#> CRS: NA
#> # A tibble: 6 × 7
#> plt_vclass level_id prob area n.overlaps origins geometry
#> <chr> <int> <dbl> <dbl> <int> <list> <POLYGON>
#> 1 ey 1 0.8 0.0865 1 <int> ((-7.63623 -6.326645, -7.…
#> 2 ey 1 0.8 0.0865 2 <int> ((-7.896883 -6.458929, -7…
#> 3 iy 1 0.8 0.202 1 <int> ((-7.902604 -6.461049, -7…
#> 4 o 1 0.8 0.285 1 <int> ((-7.236759 -6.940033, -7…
#> 5 o 1 0.8 0.285 2 <int> ((-7.093493 -6.493589, -7…
#> 6 oh 1 0.8 0.224 1 <int> ((-7.092569 -6.49308, -7.…
This data frame contains a polygon for each unique intersection of
the input polygons, with a new n.overlaps
column.
The labels of the new overlapping regions aren’t very informative,
but we can create some new labels by using the indices in the
origins
column.
vowel_intersections |>
mutate(
groups = map_chr(
origins,
.f = new_label,
labels = vowel_polygons$plt_vclass
)
) |>
relocate(groups, .after = plt_vclass)->
vowel_intersections
vowel_intersections
#> Simple feature collection with 6 features and 7 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -8.050893 ymin: -7.037138 xmax: -6.996794 ymax: -5.820745
#> CRS: NA
#> # A tibble: 6 × 8
#> plt_vclass groups level_id prob area n.overlaps origins
#> <chr> <chr> <int> <dbl> <dbl> <int> <list>
#> 1 ey ey 1 0.8 0.0865 1 <int [1]>
#> 2 ey ey~iy 1 0.8 0.0865 2 <int [2]>
#> 3 iy iy 1 0.8 0.202 1 <int [1]>
#> 4 o o 1 0.8 0.285 1 <int [1]>
#> 5 o o~oh 1 0.8 0.285 2 <int [2]>
#> 6 oh oh 1 0.8 0.224 1 <int [1]>
#> # ℹ 1 more variable: geometry <POLYGON>
We can also calculate the areas of these new polygons, and compare
them to the original areas (which have been preserved in
areas
.
There are also a number of spatial filters and merges that can be used interestingly if the original data points are also converted to sf objects.
s01 |>
sfheaders::sf_point(
x = "lF2",
y = "lF1",
keep = TRUE
) ->
s01_sf
s01_sf
#> Simple feature collection with 4245 features and 10 fields
#> Geometry type: POINT
#> Dimension: XY
#> Bounding box: xmin: -8.095446 ymin: -7.335764 xmax: -6.541463 ymax: -5.439817
#> CRS: NA
#> First 10 features:
#> name age sex word vowel plt_vclass ipa_vclass F1 F2 dur
#> 1 s01 y f OKAY EY eyF ejF 763.5 2088.1 0.20
#> 2 s01 y f UM AH uh ʌ 699.5 1881.3 0.19
#> 3 s01 y f I'M AY ay aj 888.8 1934.1 0.07
#> 4 s01 y f LIVED IH i ɪ 555.5 1530.5 0.05
#> 5 s01 y f IN IH i ɪ 612.2 2323.4 0.06
#> 6 s01 y f COLUMBUS AH @ ə 612.4 1903.7 0.07
#> 7 s01 y f MY AY ay aj 578.4 1959.3 0.09
#> 8 s01 y f ENTIRE IH i ɪ 529.9 2332.1 0.08
#> 9 s01 y f ENTIRE ER *hr ə˞ 538.4 1682.8 0.18
#> 10 s01 y f LIFE AY ay0 aj0 744.6 1702.1 0.15
#> geometry
#> 1 POINT (-7.64401 -6.637913)
#> 2 POINT (-7.539718 -6.550366)
#> 3 POINT (-7.567397 -6.789872)
#> 4 POINT (-7.33335 -6.319869)
#> 5 POINT (-7.750787 -6.417059)
#> 6 POINT (-7.551555 -6.417386)
#> 7 POINT (-7.580343 -6.360266)
#> 8 POINT (-7.754524 -6.272688)
#> 9 POINT (-7.428214 -6.288602)
#> 10 POINT (-7.439618 -6.612847)
Next, we can get the density polygon for a single vowel,.
s01 |>
filter(plt_vclass == "iy") |>
reframe(
density_polygons(lF2, lF1, probs = 0.8, as_sf =T)
) |>
st_sf()->
iy_sf
Let’s get all of the points in s01_sf
that are “covered
by” the iy_sf
polygon.