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Euclid is a package for computation geometry in R. Not just that, it is also the foundation for a whole universe of packages devoted to specific subsets of computational geometry. In order to understand this universe, you must at least have a passing understanding of euclid. Fear not, the purpose of the euclid API is to make computational geometry as R-like as possible so it seems like triangles, segments, lines, etc. are just vectors in line with other basic R vectors like logicals and numerics.

The foundation

At the heart of euclid is a range of new atomic vector types that describe geometric primitives. These primitives are e.g. segments, points, triangles, planes, circles, etc. Some of these exists in variants for both 2D and 3D space, while others are confined to either of these (e.g. a plane is only defined in 3D space). The geometries can be created through their constructors or by coercing one geometry to another. To create a point, you can supply its x and y coordinates:

library(euclid)
p <- point(c(5, 3, 7), 2)
p
#> <2D points [3]>
#> [1] <x:5, y:2> <x:3, y:2> <x:7, y:2>

As you can see, the input arguments are recycled, so that all 3 points here have the same y coordinate. Note that the input arguments are not named and only rely on their positions. This might seem odd in the case of points where using x and y as argument names are obvious, but it becomes clear soon why this approach doesn’t scale.

A vector can, as with points, be created from a set of x and y coordinates, but you could also create it from a vector of points:

v <- vec(p)
v
#> <2D vectors [3]>
#> [1] <x:5, y:2> <x:3, y:2> <x:7, y:2>

As the complexity of the geometry increases so does its number of ways to construct it. A circle in 2D can for instance be constructed from a point and a radius, but it can just as well be constructed from three points in which case it will created the unique circle that goes through all three points (unless they are co-linear):

# Create 3 new points because the ones defined above are co-linear
p <- point(
  c(3, 6, 2),
  c(7, 1, 5)
)
circ <- circle(p[1], p[2], p[3])
circ
#> <2D circles [1]>
#> [1] <x:5.5, y:4.5, r2:12.5>

plot(circ)
plot(p, add = TRUE)

It is advised to consult the documentation for each geometry to see the different ways in which they can be created.

Extracting values

While the geometries in euclid are atomic, they still encode information that can be extracted. For instance, you might be interested in getting the y coordinates of the points in a vector. Such information can be accessed with def(), short for definition:

def(p, 'y')
#> <exact numerics [3]>
#> [1] 7 1 5

The definition names of each geometry can be accessed with definition_names():

definition_names(circ)
#> [1] "x"  "y"  "r2"

You can also use def() to alter the geometries:

def(circ, "r2") <- 1
circ
#> <2D circles [1]>
#> [1] <x:5.5, y:4.5, r2:1>

Another type of information that can be extracted are the points that make up the geometry. For circles, that would be the center, for segments it would be either the start or end, etc. This can be accessed with the vertex() function:

vert(circ)
#> <2D points [1]>
#> [1] <x:5.5, y:4.5>

For most geometries there is only one vertex to access, but some have several (e.g. segments). The number of vertices in a geometry can be found out with the cardinality() function:

tri <- triangle(p[1], p[2], p[3])
cardinality(tri)
#> [1] 3
vert(tri, 3)
#> <2D points [1]>
#> [1] <x:2, y:5>

If you do not specify a vertex index it will extract all the vertices at once:

vert(tri)
#> <2D points [3]>
#> [1] <x:3, y:7> <x:6, y:1> <x:2, y:5>

Like def(), vert() can also be used for altering the geometries:

vert(tri, 1) <- point(0, 0)
tri
#> <2D triangles [1]>
#> [1] [<x:0, y:0>, <x:6, y:1>, <x:2, y:5>]

For geometries consisting of multiple vertices (segments, triangles, tetrahedrons, iso rectangles, and iso cuboids) it is also possible to extract the edges of the geometries as a segments vector:

edge(tri, 1)
#> <2D segments [1]>
#> [1] [<x:0, y:0>, <x:6, y:1>]

Again, not specifying an index extracts them all:

edge(tri)
#> <2D segments [3]>
#> [1] [<x:0, y:0>, <x:6, y:1>] [<x:6, y:1>, <x:2, y:5>] [<x:2, y:5>, <x:0, y:0>]

edge() cannot be used to alter geometries - for that you’d need to alter each vertex individually.

A last choice is to convert the geometry to a matrix. This will put all the definitions in a separate column and create as many rows as the cardinality of the geometry for each element in the vector:

as.matrix(tri)
#>      [,1] [,2]
#> [1,]    0    0
#> [2,]    6    1
#> [3,]    2    5

One thing to note with this last approach is that the output will be given in standard R numerics, whereas def() will output something else… read on!

A note on exactness

You might have paused at the output of def():

def(tri, 'x')
#> <exact numerics [3]>
#> [1] 0 6 2

This doesn’t look like a standard R vector. It isn’t! The output is an exact numeric, which is a new type of numeric vector introduced with euclid. The reason for the inclusion of this is that floating points can cause all types of issues with compounding imprecision. Normally this is not an issue in programming (until it is), but in computational geometry it is an absolute nightmare to deal with. Imagine creating a plane based on three points in 3D space, only to find that one of the points doesn’t lie on that plane… not fun!

# Floating point issues at play
a <- c(12, 10, 2) / 10
a[1] - a[2] == a[3]
#> [1] FALSE

# None of that in euclid
b <- exact_numeric(c(12, 10, 2)) / 10
b[1] - b[2] == b[3]
#> [1] TRUE

As you can see, you can perform arithmetic on exact numerics in the way you are used to, and great care has been put in making them behave just like numeric vectors. There are limits, however, since some math operations cannot be performed while ensuring exactness, e.g. sqrt() (This is also why the radius of circles are given as the squared radius). If this is needed it is necessary to convert to standard numerics, but be aware that any exactness has been lost at that point.

Everything in euclid is based on exact numbers and thus doesn’t exhibit any issues related to floating point numbers, as long as you don’t convert back and forth between euclid and R types. In the case exact results are not possible, euclid will sometimes provide a function prefixed with approx_* that returns standard numerics (e.g. approx_area()). Approx in this sense shouldn’t mean that it is only calculating an approximation, but simply that the answer cannot be given exact, i.e. the result of approx_area() is true down to the precision of the numeric type (2.220446^{-16}).

Working with geometries

While having geometries as atomic vectors is in itself a nice thing, it is what we can do with them that are truly interesting. As said in the introduction the intent is that specialized algorithms are relegated to their own packages. However, euclid comes with a slew of basic algorithms that can get you pretty far.

Predicates

Predicates are at the heart of many geometric algorithms: Do these two geometries intersect?, Does this point lie to the right of this line? Are this line parallel with this plane? euclid implement many of these, both as standard functions and infix operators:

We will not showcase all of these - the documentations all have examples. But just to show how they are used we can see how point inclusion in a triangle can be queried:

p <- point(sample(10, 50, TRUE), sample(10, 50, TRUE))
t <- triangle(point(1, 5), point(10, 10), point(5, 1))
plot(t, col = "grey", border = NA)
euclid_plot(p[p %is_on% t], pch = 16, col = "firebrick")
euclid_plot(p[p %is_inside% t], pch = 16, col = "steelblue")
euclid_plot(p[p %is_outside% t], pch = 16, col = "goldenrod")

Locations

One or a set of geometries can give rise to a new geometry, e.g. a triangle has a centroid, two points gives rise to a bisector, etc. euclid has a set of location functions that returns new geometries based on the input:

  • circumcenter() can calculate the circumcenter of triangles, tetrahedrons, or point sets

  • barycenter() calculates the center of mass off a set of weighted points

  • bisector() splits the space in two based on two points

  • centroid() calculates the geometric mean of points, a triangle, or a tetrahedron

  • equidistant_line() calculates the line that remains at equal distance to 2 or 3 points along its trajectory

  • radical() calculates the radical line or plane of two circles/spheres or the radical point of three circles/spheres

c1 <- circle(point(3, 6), 12)
c2 <- circle(point(-5, 0), 3)
c3 <- circle(point(-3, 7), 1)

plot(c(c1, c2, c3), bg = "grey", fg = NA)
plot(c(radical(c1, c2), radical(c2, c3), radical(c1, c3)), col = "firebrick", add = TRUE)

Intersections

Whether or not two geometries intersect, and how, is often needed. We saw in the predicates section that euclid has functionality for calculation if two geometries intersect. It can, however, also return the actual intersection. In general, the return value of an intersection calculation can be quite heterogeneous, as e.g. two segments can intersect in either a point (if the cross), a new segment (if they are parallel and overlap), or not at all. Because of this intersection() returns a list of geometries instead of a geometry vector. If type stability is of concern, there are versions for that (e.g. intersection_point()), but with these it is not possible to distinguish between lack of intersection and an intersection of the wrong type (both are converted to NA).

t <- triangle(point(0, 0), point(1, 1), point(0, 1))
l <- line(1, -1, c(0, 1, 2))
intersection(t, l)
#> [[1]]
#> <2D segments [1]>
#> [1] [<x:1, y:1>, <x:0, y:0>]
#> 
#> [[2]]
#> <2D points [1]>
#> [1] <x:0, y:1>
#> 
#> [[3]]
#> NULL

intersection_segment(t, l)
#> <2D segments [3]>
#> [1] [<x:1, y:1>, <x:0, y:0>] <NA>                     <NA>

Projections

Geometries may be projected to lines are planes, which means their coordinates are moved to their closest location on a line or plane. There is also a variant where 3D geometries are converted to their 2D counterpart by mapping their location to a plane.

p <- point(sample(100, 8), sample(100, 8))
s <- segment(p[1:4], p[5:8])
l <- line(3, 1, -6)

s_proj <- project(s, l)
s_proj
#> <2D segments [4]>
#> [1] [<x:-16.8, y:56.4>, <x:-16.4, y:55.2>]
#> [2] [<x:-15, y:51>, <x:7.6, y:-16.8>]     
#> [3] [<x:-9.3, y:33.9>, <x:-8, y:30>]      
#> [4] [<x:-3.4, y:16.2>, <x:-13.4, y:46.2>]

plot(l, xlim = c(-50, 100), ylim = c(-50, 100))
plot(s, col = c("firebrick", "goldenrod", "steelblue", "forestgreen"), add = TRUE)
plot(
  s_proj, 
  col = c("firebrick", "goldenrod", "steelblue", "forestgreen"),
  lwd = seq(10, 2, length.out = 4),
  add = TRUE
)

Transformations

Geometries exists in euclidean space (at least those provided by euclid does), and as such can be moved around. Euclid supports all types of affine transformations, with the caveats that circles and spheres remain as circles and spheres even when stretched or sheared (i.e. those transformations only affect their location).

Euclid has support for creating and combining affine transformation matrices through the affine transformation matrix vector type, which thus allows vectorised transformations of your geometries using the transform() generic.

tri <- triangle(point(1, 0), point(3, 1), point(2, 3))

scale <- affine_scale(2)

tri_scaled <- transform(tri, scale)

plot(c(tri, tri_scaled), lty = c(2, 1))


center <- centroid(tri)

move_to_middle <- affine_translate(-vec(center))
# Transformations can be stacked by multiplication in reverse order
scale_center <- inverse(move_to_middle) * scale * move_to_middle

tri_scaled2 <- transform(tri, scale_center)

plot(c(tri, tri_scaled2), lty = c(2, 1))

All the rest

There are more things we haven’t discussed, as some of it are specific to certain geometries, such as querying whether a direction lies between two other directions, or adding vectors together, or sorting point, or interpolating between geometries, or getting bounding boxes, or… you get the idea. Please consult the thorough documentation to dive deeper into the possibilities of euclid