10  Data Sets

Chapter 10 lists several, though not all, data objects from package spatstat.data (v3.1.9), along with selected inspections of these objects.

The function calls in Chapter 10 are exclusively those provided by the spatstat.* family of packages.

search path & loadedNamespaces on author’s computer
search()
# [1] ".GlobalEnv"        "package:stats"     "package:graphics"  "package:grDevices" "package:utils"     "package:datasets"  "package:methods"   "Autoloads"         "package:base"
loadedNamespaces() |> sort.int()
#  [1] "base"        "cli"         "compiler"    "datasets"    "digest"      "evaluate"    "fastmap"     "graphics"    "grDevices"   "htmltools"   "htmlwidgets" "jsonlite"    "knitr"       "methods"    
# [15] "otel"        "rlang"       "rmarkdown"   "rstudioapi"  "stats"       "tools"       "utils"       "xfun"        "yaml"

10.1 anemones

The point-pattern (ppp.object, Chapter 36) anemones from package spatstat.data (v3.1.9) has (Listing 10.2, Figure 10.1)

Listing 10.1: Figure: anemones
Code
par(mar = c(0,0,0,0))
spatstat.data::anemones |>
  spatstat.geom::plot.ppp(main = NULL)
Figure 10.1: anemones
Listing 10.2: Data: anemones
spatstat.data::anemones |>
  spatstat.geom::print.ppp()
# Marked planar point pattern: 231 points
# marks are numeric, of storage type  'integer'
# window: rectangle = [0, 280] x [0, 180] units
Listing 10.3: Review: number of points in anemones
spatstat.data::anemones |>
  spatstat.geom::npoints.ppp()
# [1] 231
Listing 10.4: Review: window of anemones
spatstat.data::anemones |>
  spatstat.geom::Window.ppp()
# window: rectangle = [0, 280] x [0, 180] units
Listing 10.5: Review: storage mode of the marks of anemones
spatstat.data::anemones |>
  spatstat.geom::marks.ppp() |>
  typeof()
# [1] "integer"
Listing 10.6: Review: mark-format of anemones
spatstat.data::anemones |>
  spatstat.geom::markformat.ppp()
# [1] "vector"

10.2 ants

The point-pattern (ppp.object, Chapter 36) ants from package spatstat.data (v3.1.9) has (Listing 10.8, Figure 10.2)

  • 97 points;
  • polygonal window;
  • one multi-type mark with two levels, 'Cataglyphis' and 'Messor' (Listing 10.9);
  • 'vector' mark-format.
Listing 10.7: Figure: ants
Code
par(mar = c(0,0,0,0))
spatstat.data::ants |>
  spatstat.geom::plot.ppp(main = NULL)
Figure 10.2: ants
Listing 10.8: Data: ants
spatstat.data::ants |>
  spatstat.geom::print.ppp()
# Marked planar point pattern: 97 points
# Multitype, with levels = Cataglyphis, Messor 
# window: polygonal boundary
# enclosing rectangle: [-25, 803] x [-49, 717] units (one unit = 0.5 feet)
Listing 10.9: Review: marks of ants
spatstat.data::ants |>
  spatstat.geom::marks.ppp() |>
  table()
# 
# Cataglyphis      Messor 
#          29          68

10.3 austates

The tessellation (Chapter 41) austates from package spatstat.data (v3.1.9) has (Listing 10.11, Figure 10.3)

Listing 10.10: Figure: austates
Code
par(mar = c(0,0,1,0))
spatstat.data::austates |>
  spatstat.geom::plot.tess(main = '')
Figure 10.3: austates
Listing 10.11: Data: austates
spatstat.data::austates |>
  spatstat.geom::print.tess()
# Tessellation
# Tiles are irregular polygons
# 7 tiles (irregular windows)
# window: polygonal boundary
# enclosing rectangle: [113.19392, 153.6692] x [-43.59316, -10.93156] degrees
Listing 10.12: Review: tiles in austates
spatstat.data::austates |>
  spatstat.geom::tiles()
# List of spatial objects
# 
# WA:
# window: polygonal boundary
# enclosing rectangle: [113.19392, 129.01141] x [-35.11407, -13.76426] degrees
# 
# NT:
# window: polygonal boundary
# enclosing rectangle: [129.01141, 138.0038] x [-25.988593, -11.045627] degrees
# 
# ✂️ --- output truncated --- ✂️

10.4 betacells

The point-pattern (ppp.object, Chapter 36) betacells from package spatstat.data (v3.1.9) has (Listing 10.14, Figure 10.4)

Listing 10.13: Figure: betacells
Code
par(mar = c(0,0,0,0))
spatstat.data::betacells |>
  spatstat.geom::plot.ppp(main = '')
Figure 10.4: betacells
Listing 10.14: Data: betacells
spatstat.data::betacells |>
  spatstat.geom::print.ppp()
# Marked planar point pattern: 135 points
# Mark variables: type, area 
# window: rectangle = [28.08, 778.08] x [16.2, 1007.02] microns
Listing 10.15: Review: mark-format of betacells
spatstat.data::betacells |>
  spatstat.geom::markformat.ppp()
# [1] "dataframe"
Listing 10.16: Review: marks of betacells
spatstat.data::betacells |>
  spatstat.geom::marks.ppp()
#     type  area
# 1     on 275.9
# 2    off 241.2
# 3     on 256.0
# ✂️ --- output truncated --- ✂️

10.5 bronzefilter

The point-pattern (ppp.object, Chapter 36) bronzefilter from package spatstat.data (v3.1.9) has (Listing 10.18, Figure 10.5)

  • 678 points;
  • rectangle window;
  • one numeric mark.
Listing 10.17: Figure: bronzefilter
Code
par(mar = c(0,1,0,0))
spatstat.data::bronzefilter |>
  spatstat.geom::plot.ppp(main = NULL)
Figure 10.5: bronzefilter
Listing 10.18: Data: bronzefilter
spatstat.data::bronzefilter |>
  spatstat.geom::print.ppp()
# Marked planar point pattern: 678 points
# marks are numeric, of storage type  'double'
# window: rectangle = [0, 18] x [0, 7] mm

10.6 btb.extra

The point-pattern-list (ppplist, Chapter 37) btb.extra from package spatstat.data (v3.1.9) (Listing 10.20, Figure 10.6)

Listing 10.19: Figure: btb.extra
Code
par(mar = c(0,1,1,1))
spatstat.data::btb.extra |> 
  spatstat.geom::plot.solist() |>
  suppressWarnings() |> suppressMessages()
Figure 10.6: btb.extra
Listing 10.20: Data: btb.extra
spatstat.data::btb.extra
# List of point patterns
# 
# full:
# Marked planar point pattern: 919 points
# Mark variables: year, spoligotype 
# window: polygonal boundary
# enclosing rectangle: [133.5147, 246.0193] x [10.88514, 118.7298] km
# 
# standard:
# Marked planar point pattern: 873 points
# Mark variables: year, spoligotype 
# window: polygonal boundary
# enclosing rectangle: [133.5147, 246.0193] x [10.88514, 118.7298] km
Listing 10.21: Review: inheritance of btb.extra
spatstat.data::btb.extra |> 
  class()
# [1] "ppplist" "solist"  "anylist" "listof"  "list"
Listing 10.22: Review: class of members of btb.extra
spatstat.data::btb.extra |> 
  sapply(FUN = class)
#     full standard 
#    "ppp"    "ppp"

10.7 cetaceans

The hyper data frame (hyperframe, Chapter 26) cetaceans from package spatstat.data (v3.1.9) has (Listing 10.23)

  • 9 rows and 4 (hyper)columns (Listing 10.24)
  • four point-pattern (ppp, Chapter 36) hypercolumns: $whales, $dolphins, $fish and $plankton.
Listing 10.23: Data: cetaceans
spatstat.data::cetaceans |>
  spatstat.geom::print.hyperframe()
# Hyperframe:
#   whales dolphins  fish plankton
# 1  (ppp)    (ppp) (ppp)    (ppp)
# 2  (ppp)    (ppp) (ppp)    (ppp)
# 3  (ppp)    (ppp) (ppp)    (ppp)
# ✂️ --- output truncated --- ✂️
Listing 10.24: Review: dimensions of cetaceans
spatstat.data::cetaceans |>
  spatstat.geom::dim.hyperframe()
# [1] 9 4

10.8 demohyper

The hyper data frame (hyperframe, Chapter 26) demohyper from package spatstat.data (v3.1.9) has (Listing 10.25)

  • 3 rows and 3 (hyper)columns (Listing 10.26)
  • a point-pattern (ppp, Chapter 36) hypercolumn $Points
  • a pixel-image (im, Chapter 28) hypercolumn $Image
  • a regular column $Group.
Listing 10.25: Data: demohyper
spatstat.data::demohyper |>
  spatstat.geom::print.hyperframe()
# Hyperframe:
#   Points Image Group
# 1  (ppp)  (im)     a
# 2  (ppp)  (im)     b
# 3  (ppp)  (im)     a
Listing 10.26: Review: dimensions of demohyper
spatstat.data::demohyper |>
  spatstat.geom::dim.hyperframe()
# [1] 3 3

To view the hyper data frame demohyper in a desired format, readers may call the S3 method spatstat.geom::print.hyperframe() explicitly (Listing 10.25). Alternatively, readers may call the S3 generic function print() by simply typing demohyper at the R console prompt and pressing Enter, after putting the package spatstat.geom (v3.6.1.24)

  • either, in the search() path, by either one of the following approaches,
    • using the function library(), e.g., library(spatstat.geom), which is called internally by the function require();
    • using the function attachNamespace(), e.g., attachNamespace('spatstat.geom');
  • or, in the loadedNamespaces(), by either one of the following approaches,
    • using the function loadNamespace(), e.g., loadNamespace('spatstat.geom'), which is called internally by the function requireNamespace();
    • calling or evaluating any function in the package spatstat.geom (v3.6.1.24) explicitly with its namespace, e.g., spatstat.geom::dim.hyperframe; to print the function itself, or Listing 10.26, Listing 10.27, etc.

The rest of Section 10.8 showcases the *.hyperframe() methods of the .Primitive S3 generic functions names() (Listing 10.27) and `$` (Listing 10.28, Listing 10.29).

Listing 10.27 finds the (hyper)column names of the hyper data frame demohyper,

Listing 10.27: Review: (hyper)column names of demohyper
spatstat.data::demohyper |>
  spatstat.geom::names.hyperframe()
# [1] "Points" "Image"  "Group"

Listing 10.28 and Listing 10.29 observe the ppp-hypercolumn demohyper$Points,

Listing 10.28: Review: ppp-hypercolumn demohyper$Points
spatstat.data::demohyper$Points |>
  class()
# [1] "ppplist" "solist"  "anylist" "listof"  "list"
Listing 10.29: Advanced: ppp-hypercolumn demohyper$Points, nerdy!
spatstat.data::demohyper |>
  spatstat.geom::`$.hyperframe`(name = 'Points') |> # nerdy!!
  identical(y = spatstat.data::demohyper$Points) |>
  stopifnot()

Listing 10.30 and Listing 10.31 find the first point-pattern element of the ppp-hypercolumn demohyper$Points,

Listing 10.30: Review: 1st point-pattern in ppp-hypercolumn demohyper$Points
demohyper_p1 = spatstat.data::demohyper$Points[[1L]] 
demohyper_p1 |>
  spatstat.geom::print.ppp()
# Planar point pattern: 104 points
# window: binary image mask
# 128 x 128 pixel array (ny, nx)
# enclosing rectangle: [2.017, 3.93] x [0.645, 3.278] units
Listing 10.31: Advanced: 1st point-pattern in ppp-hypercolumn demohyper$Points, nerdy!
spatstat.data::demohyper$Points |>
  base::`[[`(i = 1L) |> # nerdy!!
  identical(y = demohyper_p1) |>
  stopifnot()

Listing 10.32 finds the first pixel-image element of the im-hypercolumn demohyper$Image,

Listing 10.32: Review: 1st pixel-image in im-hypercolumn demohyper$Image
spatstat.data::demohyper$Image[[1L]] |>
  spatstat.geom::print.im()
# real-valued pixel image
# 53 x 39 pixel array (ny, nx)
# enclosing rectangle: [2.017, 3.93] x [0.645, 3.278] units

10.9 finpines

The point-pattern (ppp.object, Chapter 36) finpines from package spatstat.data (v3.1.9) has (Listing 10.34, Figure 10.7)

  • 126 points;
  • rectangle window;
  • two numeric marks, diameter and height.
Listing 10.33: Figure: finpines
Code
par(mar = c(0,0,0,0))
spatstat.data::finpines |>
  spatstat.geom::plot.ppp(main = '')
Figure 10.7: finpines
Listing 10.34: Data: finpines
spatstat.data::finpines |>
  spatstat.geom::print.ppp()
# Marked planar point pattern: 126 points
# Mark variables: diameter, height 
# window: rectangle = [-5, 5] x [-8, 2] metres

10.10 flu

The hyper data frame (hyperframe, Chapter 26) flu from package spatstat.data (v3.1.9) has (Listing 10.35)

  • 41 rows and 4 (hyper)columns (Listing 10.36)
  • a point-pattern (ppp, Chapter 36) hypercolumn $pattern
  • regular columns $virustype, $stain, $frameid
Listing 10.35: Data: flu
spatstat.data::flu |>
  spatstat.geom::print.hyperframe()
# Hyperframe:
#                pattern virustype stain frameid
# wt M2-M1 13      (ppp)        wt M2-M1      13
# wt M2-M1 22      (ppp)        wt M2-M1      22
# wt M2-M1 27      (ppp)        wt M2-M1      27
# ✂️ --- output truncated --- ✂️
Listing 10.36: Review: dimensions of flu
spatstat.data::flu |>
  spatstat.geom::dim.hyperframe()
# [1] 41  4

10.11 gorillas

The point-pattern (ppp.object, Chapter 36) gorillas from package spatstat.data (v3.1.9) has (Listing 10.38, Figure 10.8)

  • 647 points;
  • polygonal window;
  • two multi-type marks, group (with two levels 'major' and 'minor') and season (with two levels 'dry' and 'rainy').
Listing 10.37: Figure: gorillas
Code
par(mar = c(0,0,1,0))
spatstat.data::gorillas |>
  spatstat.geom::plot.ppp(which.marks = c('group', 'season'))
Figure 10.8: gorillas
Listing 10.38: Data: gorillas
spatstat.data::gorillas |>
  spatstat.geom::print.ppp()
# Marked planar point pattern: 647 points
# Mark variables: group, season, date 
# window: polygonal boundary
# enclosing rectangle: [580457.9, 585934] x [674172.8, 678739.2] metres

10.12 gorillas.extra

The pixel-image list (imlist, Chapter 29) gorillas.extra from package spatstat.data (v3.1.9) (Listing 10.40, Figure 10.9)

Listing 10.39: Figure: gorillas.extra
Code
par(mar = c(0,0,0,0))
spatstat.data::gorillas.extra |> 
  plot(main = '') |>
  suppressWarnings() |> suppressMessages()
Figure 10.9: gorillas.extra
Listing 10.40: Data: gorillas.extra
spatstat.data::gorillas.extra
# List of pixel images
# 
# aspect:
# factor-valued pixel image
# factor levels:
# [1] "N"  "NE" "E"  "SE" "S"  "SW" "W"  "NW"
# 149 x 181 pixel array (ny, nx)
# enclosing rectangle: [580440, 586000] x [674160, 678730] metres
# 
# elevation:
# integer-valued pixel image
# 149 x 181 pixel array (ny, nx)
# enclosing rectangle: [580440, 586000] x [674160, 678730] metres
# 
# ✂️ --- output truncated --- ✂️
Listing 10.41: Review: inheritance of gorillas.extra
spatstat.data::gorillas.extra |> 
  class()
# [1] "imlist"  "solist"  "anylist" "listof"  "list"
Listing 10.42: Review: class of members of gorillas.extra
spatstat.data::gorillas.extra |> 
  sapply(FUN = class)
#     aspect  elevation       heat slopeangle  slopetype vegetation  waterdist 
#       "im"       "im"       "im"       "im"       "im"       "im"       "im"

10.13 hyytiala

The point-pattern (ppp.object, Chapter 36) hyytiala from package spatstat.data (v3.1.9) has (Listing 10.44, Figure 10.10)

  • 168 points;
  • rectangle window;
  • one multi-type mark with four levels, 'aspen', 'birch', 'pine' and 'rowan' (Listing 10.45);
  • 'vector' mark-format.
Listing 10.43: Figure: hyytiala
Code
par(mar = c(0,0,0,0))
spatstat.data::hyytiala |>
  spatstat.geom::plot.ppp(main = NULL)
Figure 10.10: hyytiala
Listing 10.44: Data: hyytiala
spatstat.data::hyytiala |>
  spatstat.geom::print.ppp()
# Marked planar point pattern: 168 points
# Multitype, with levels = aspen, birch, pine, rowan 
# window: rectangle = [0, 20] x [0, 20] metres
Listing 10.45: Review: marks of hyytiala
spatstat.data::hyytiala |>
  spatstat.geom::marks.ppp() |>
  table()
# 
# aspen birch  pine rowan 
#     1    17   128    22

10.14 Kovesi

The hyper data frame (hyperframe, Chapter 26) Kovesi from package spatstat.data (v3.1.9) has (Listing 10.46)

Listing 10.46: Data: Kovesi
spatstat.data::Kovesi |>
  spatstat.geom::print.hyperframe()
# Hyperframe:
#    linear diverging rainbow cyclic isoluminant ternary colsig l1  l2 chro   n cycsh      values
# 1   FALSE     FALSE   FALSE   TRUE       FALSE   FALSE      j 15  85    0 256     0 (character)
# 2   FALSE     FALSE   FALSE   TRUE       FALSE   FALSE      j 15  85    0 256    25 (character)
# 3   FALSE     FALSE   FALSE   TRUE       FALSE   FALSE  mrybm 35  75   68 256     0 (character)
# 4   FALSE     FALSE   FALSE   TRUE       FALSE   FALSE  mrybm 35  75   68 256    25 (character)
# 5   FALSE     FALSE   FALSE   TRUE       FALSE   FALSE  mygbm 30  95   78 256     0 (character)
# ✂️ --- output truncated --- ✂️
Listing 10.47: Review: dimensions of Kovesi
spatstat.data::Kovesi |>
  spatstat.geom::dim.hyperframe()
# [1] 41 13
Listing 10.48: Review: class of Kovesi$values
spatstat.data::Kovesi$values |>
  class()
# [1] "anylist" "listof"  "list"
Listing 10.49: Review: length of Kovesi$values
spatstat.data::Kovesi$values |>
  length()
# [1] 41
Listing 10.50: Review: lengths of Kovesi$values
spatstat.data::Kovesi$values |>
  lengths() |>
  unique.default()
# [1] 256

10.15 longleaf

The point-pattern (ppp.object, Chapter 36) longleaf from package spatstat.data (v3.1.9) has (Listing 10.52, Figure 10.11)

  • 584 points;
  • rectangle window;
  • one numeric mark.
Listing 10.51: Figure: longleaf
Code
par(mar = c(0,0,0,0))
spatstat.data::longleaf |>
  spatstat.geom::plot.ppp(main = NULL)
Figure 10.11: longleaf
Listing 10.52: Data: longleaf
spatstat.data::longleaf |>
  spatstat.geom::print.ppp()
# Marked planar point pattern: 584 points
# marks are numeric, of storage type  'double'
# window: rectangle = [0, 200] x [0, 200] metres

10.16 meningitis

The spatial-object list (solist, Chapter 39) meningitis from package spatstat.data (v3.1.9) contains (Listing 10.54, Figure 10.12)

Listing 10.53: Figure: meningitis
Code
par(mar = c(0,0,0,0))
spatstat.data::meningitis |>
  spatstat.geom::plot.solist(main = '')
Figure 10.12: meningitis
Listing 10.54: Data: meningitis
spatstat.data::meningitis
# List of spatial objects
# 
# cases:
# Marked planar point pattern: 636 points
# Multitype, with levels = B, C 
# window: polygonal boundary
# enclosing rectangle: [4031.295, 4672.253] x [2684.102, 3549.931] km
# 
# kreise:
# Tessellation
# Tiles are irregular polygons
# 413 tiles (irregular windows)
# Tessellation has a data frame of marks:
#   $marks:     double
# window: polygonal boundary
# enclosing rectangle: [4031.295, 4672.253] x [2684.102, 3549.931] km

10.17 nbfires

The point-pattern (ppp.object, Chapter 36) nbfires from package spatstat.data (v3.1.9) has (Listing 10.56, Figure 10.13)

  • 7108 points;
  • polygonal window;
  • multi-type marks, e.g., $fire.type, $cause and $ign.src;
  • numeric marks, e.g., $fnl.size.
Listing 10.55: Figure: nbfires
Code
par(mar = c(0,0,1,0))
spatstat.data::nbfires |>
  spatstat.geom::plot.ppp(which.marks = c('fire.type', 'cause', 'ign.src', 'fnl.size'))
# Warning: Only 10 out of 16 symbols are shown in the symbol map
Figure 10.13: nbfires
Listing 10.56: Data: nbfires
spatstat.data::nbfires |>
  spatstat.geom::print.ppp()
# Warning: some mark values are NA in the point pattern x
# Marked planar point pattern: 7108 points
# Mark variables: year, fire.type, dis.date, dis.julian, out.date, out.julian, cause, ign.src, fnl.size 
# window: polygonal boundary
# enclosing rectangle: [0, 1000] x [0, 958.9142] units (one unit = 0.403716 km)

10.18 osteo

The hyper data frame (hyperframe, Chapter 26) osteo from package spatstat.data (v3.1.9) has (Listing 10.57)

  • 40 rows and 5 (hyper)columns (Listing 10.58)
  • the serial number of sampling volume $brick nested in the bone sample $id
  • a three-dimensional point-pattern (pp3, Chapter 35) hypercolumn $pts
Listing 10.57: Data: osteo
spatstat.data::osteo |> 
  spatstat.geom::print.hyperframe()
# Hyperframe:
#         id shortid brick   pts depth
# 1   c77za4       4     1 (pp3)    45
# 2   c77za4       4     2 (pp3)    60
# 3   c77za4       4     3 (pp3)    55
# 4   c77za4       4     4 (pp3)    60
# 5   c77za4       4     5 (pp3)    85
# ✂️ --- output truncated --- ✂️
Listing 10.58: Review: dimensions of osteo
spatstat.data::osteo |>
  spatstat.geom::dim.hyperframe()
# [1] 40  5

10.19 spruces

The point-pattern (ppp.object, Chapter 36) spruces from package spatstat.data (v3.1.9) has (Listing 10.60, Figure 10.14)

  • 134 points;
  • rectangle window;
  • one numeric mark.
Listing 10.59: Figure: spruces
Code
par(mar = c(0,0,0,0))
spatstat.data::spruces |>
  spatstat.geom::plot.ppp(main = NULL)
Figure 10.14: spruces
Listing 10.60: Data: spruces
spatstat.data::spruces |>
  spatstat.geom::print.ppp()
# Marked planar point pattern: 134 points
# marks are numeric, of storage type  'double'
# window: rectangle = [0, 56] x [0, 38] metres

10.20 swedishpines

The point-pattern (ppp.object, Chapter 36) swedishpines from package spatstat.data (v3.1.9) has (Listing 10.62, Figure 10.15)

  • the \(x\)- and \(y\)-coordinates of 71 points;
  • rectangle window;
  • no marks, i.e., 'none' mark-format.
Listing 10.61: Figure: swedishpines
Code
par(mar = c(0,0,0,0))
spatstat.data::swedishpines |>
  spatstat.geom::plot.ppp(main = NULL)
Figure 10.15: swedishpines
Listing 10.62: Data: swedishpines
spatstat.data::swedishpines |>
  spatstat.geom::print.ppp()
# Planar point pattern: 71 points
# window: rectangle = [0, 96] x [0, 100] units (one unit = 0.1 metres)

10.21 vesicles

The point-pattern (ppp.object, Chapter 36) vesicles from package spatstat.data (v3.1.9) has (Listing 10.64, Figure 10.16)

  • the \(x\)- and \(y\)-coordinates of 37 points;
  • polygonal window;
  • no marks, i.e., 'none' mark-format.
Listing 10.63: Figure: vesicles
Code
par(mar = c(0,0,0,0))
spatstat.data::vesicles |>
  spatstat.geom::plot.ppp(main = NULL)
Figure 10.16: vesicles
Listing 10.64: Data: vesicles
spatstat.data::vesicles |>
  spatstat.geom::print.ppp()
# Planar point pattern: 37 points
# window: polygonal boundary
# enclosing rectangle: [22.6796, 586.2292] x [11.9756, 1030.7] nm

10.22 vesicles.extra

The spatial-object list (solist, Chapter 39) vesicles.extra from package spatstat.data (v3.1.9) has (Listing 10.65, Listing 10.66)

  • a line-segment-pattern (psp, Chapter 38) $activezone
  • three windows: $mitochondria, $presynapse and $mask
Listing 10.65: Data: vesicles.extra
spatstat.data::vesicles.extra
# List of spatial objects
# 
# activezone:
# planar line segment pattern: 9 line segments
# window: rectangle = [0, 625] x [0, 1050] nm
# 
# mitochondria:
# window: polygonal boundary
# enclosing rectangle: [90.41389, 315.29187] x [532.1753, 781.4376] nm
# 
# presynapse:
# window: polygonal boundary
# enclosing rectangle: [22.6796, 586.2292] x [11.9756, 1030.7] nm
# 
# mask:
# window: binary image mask
# 420 x 250 pixel array (ny, nx)
# enclosing rectangle: [0, 250] x [0, 420] units
Listing 10.66: Review: class of members of vesicles.extra
spatstat.data::vesicles.extra |>
  lapply(FUN = class)
# $activezone
# [1] "psp"  "list"
# 
# $mitochondria
# [1] "owin"
# 
# $presynapse
# [1] "owin"
# 
# $mask
# [1] "owin"

10.23 waterstriders

The point-pattern-list (ppplist, Chapter 37) waterstriders from package spatstat.data (v3.1.9) (Listing 10.68, Figure 10.17)

Listing 10.67: Figure: waterstriders
Code
par(mar = c(0,0,0,0))
spatstat.data::waterstriders |> 
  spatstat.geom::plot.solist(main = '')
Figure 10.17: waterstriders
Listing 10.68: Data: waterstriders
spatstat.data::waterstriders
# List of point patterns
# 
# Component 1:
# Planar point pattern: 38 points
# window: rectangle = [0, 48.1] x [0, 48.1] cm
# 
# Component 2:
# Planar point pattern: 36 points
# window: rectangle = [0, 48.8] x [0, 48.8] cm
# 
# Component 3:
# Planar point pattern: 36 points
# window: rectangle = [0, 46.4] x [0, 46.4] cm
Listing 10.69: Review: class of members of waterstriders
spatstat.data::waterstriders |> 
  sapply(FUN = class)
# [1] "ppp" "ppp" "ppp"