8 Data Sets

The authors use the data sets provided in package spatstat.data (v3.1.9) to demonstrate the use of functions.

Chapter 8 lists several, though not all, data objects from package spatstat.data, along with selected inspections of these objects. The function calls shown in Chapter 8 are exclusively those provided by the spatstat.* family of packages.

8.1 `anemones`

The ppp.object (Chapter 23) anemones from package spatstat.data (v3.1.9) has

231 points;
rectangle window;
one integer-mark;
'vector'-markformat.

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
spatstat.data::anemones |>
  spatstat.geom::markformat.ppp()
# [1] "vector"
spatstat.data::anemones |>
  spatstat.geom::marks.ppp() |>
  summary.default()
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#   2.000   3.000   4.000   4.294   5.000   8.000
spatstat.data::anemones |>
  spatstat.geom::marks.ppp() |>
  typeof()
# [1] "integer"

8.2 `ants`

The ppp.object (Chapter 23) ants from package spatstat.data (v3.1.9) has

97 points;
polygonal window;
one multitype-mark with two levels, 'Cataglyphis' and 'Messor';
'vector'-markformat.

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)
spatstat.data::ants |>
  spatstat.geom::markformat.ppp()
# [1] "vector"
spatstat.data::ants |>
  spatstat.geom::marks.ppp() |>
  table()
# 
# Cataglyphis      Messor 
#          29          68

8.3 `austates`

The tessellation (Chapter 26) object austates from package spatstat.data (v3.1.9) has

7 tiles;
polygonal window;
no marks.

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
spatstat.data::austates |>
  spatstat.geom::marks.tess() |>
  is.null()
# [1] TRUE

8.4 `betacells`

The ppp.object (Chapter 23) betacells from package spatstat.data (v3.1.9) has

135 points;
rectangle window;
one numeric-mark area;
one multitype-mark type with two levels, 'off' and 'on';
'dataframe'-markformat.

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
spatstat.data::betacells |>
  spatstat.geom::markformat.ppp()
# [1] "dataframe"
spatstat.data::betacells |>
  spatstat.geom::marks.ppp() |>
  head(n = 3L)
#   type  area
# 1   on 275.9
# 2  off 241.2
# 3   on 256.0

8.5 `bronzefilter`

The ppp.object (Chapter 23) bronzefilter from package spatstat.data (v3.1.9) has

678 points;
rectangle window;
one numeric-mark
'vector'-markformat.

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
spatstat.data::bronzefilter |> 
  spatstat.geom::markformat.ppp()
# [1] "vector"
spatstat.data::bronzefilter |> 
  spatstat.geom::marks.ppp() |>
  summary.default()
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#   0.013   0.120   0.160   0.167   0.200   0.467

8.6 `btb.extra`

Data set btb.extra from package spatstat.data (v3.1.9) is a length-2 ppplist (Chapter 24).

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
spatstat.data::btb.extra |> 
  class()
# [1] "ppplist" "solist"  "anylist" "listof"  "list"
spatstat.data::btb.extra |> 
  sapply(FUN = class)
#     full standard 
#    "ppp"    "ppp"
if (FALSE) {
  spatstat.data::btb.extra |> 
    plot()
    
  
}

8.7 `cetaceans`

The hyperframe (Chapter 17) cetaceans from package spatstat.data (v3.1.9) has

four point-pattern (ppp (Chapter 23)) hypercolumns whales, dolphins, fish and plankton.

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)
# 4  (ppp)    (ppp) (ppp)    (ppp)
# 5  (ppp)    (ppp) (ppp)    (ppp)
# 6  (ppp)    (ppp) (ppp)    (ppp)
# 7  (ppp)    (ppp) (ppp)    (ppp)
# 8  (ppp)    (ppp) (ppp)    (ppp)
# 9  (ppp)    (ppp) (ppp)    (ppp)

8.8 `demohyper`

The hyperframe (Chapter 17) demohyper from package spatstat.data (v3.1.9) has

a regular column Group,
a point-pattern (ppp, Chapter 23) hypercolumn Points, and
a pixel image (im, Chapter 19) hypercolumn Image.

To view demohyper in a desired format, readers may use either one of the following approaches,

call library(spatstat.geom), then call the S3 generic function base::print() by simply typing the object’s name at the console prompt and pressing Enter;
call library(groupedHyperframe), then call the S3 generic function base::print() by simply typing the object’s name at the console prompt and pressing Enter. This approach works as intended, since package groupedHyperframe (v0.3.0.20251020) Imports the package spatstat.geom (v3.6.0.3);
call the S3 method dispatch spatstat.geom::print.hyperframe() directly, as in Listing 8.1.

Listing 8.1: Data: demohyper

Code

spatstat.data::demohyper |>
  spatstat.geom::print.hyperframe()
# Hyperframe:
#   Points Image Group
# 1  (ppp)  (im)     a
# 2  (ppp)  (im)     b
# 3  (ppp)  (im)     a

To find the (hyper)column names of demohyper, readers may use either one of the following approaches,

call library(spatstat.geom), then call the .Primitive S3 generic function base::names();
call library(groupedHyperframe), then call the .Primitive S3 generic function base::names(). This approach works as intended, since package groupedHyperframe (v0.3.0.20251020) Imports the package spatstat.geom (v3.6.0.3);
call the S3 method dispatch spatstat.geom::names.hyperframe() directly, as in Listing 8.2.

Listing 8.2: Review: (hyper)column names of demohyper

Code

spatstat.data::demohyper |>
  spatstat.geom::names.hyperframe()
# [1] "Points" "Image"  "Group"

To observe the ppp-hypercolumn demohyper$Points and the im-hypercolumn demohyper$Image, readers may use either one of the following approaches,

call library(spatstat.geom), then call the .Primitive S3 generic function base::`$`;
call library(groupedHyperframe), then call the .Primitive S3 generic function base::`$`. This approach works as intended, since package groupedHyperframe (v0.3.0.20251020) Imports the package spatstat.geom (v3.6.0.3), as in Listing 8.3;
call the S3 method dispatch spatstat.geom::`$.hyperframe` directly, as in Listing 8.4.

Listing 8.3: Review: observe ppp-hypercolumn demohyper$Points

Code

spatstat.data::demohyper$Points |>
  class()
# [1] "ppplist" "solist"  "anylist" "listof"  "list"

Listing 8.4: Advanced: observe ppp-hypercolumn demohyper$Points, super nerdy way!

Code

spatstat.data::demohyper |>
  spatstat.geom::`$.hyperframe`(name = 'Points') # only if you know what you are doing!!
# List of point patterns
# 
# 1:
# 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
# 
# 2:
# Planar point pattern: 104 points
# window: binary image mask
# 128 x 128 pixel array (ny, nx)
# enclosing rectangle: [0.645, 3.278] x [2.017, 3.93] units
# 
# 3:
# Planar point pattern: 101 points
# window: binary image mask
# 128 x 129 pixel array (ny, nx)
# enclosing rectangle: [-3.278, -0.645] x [-3.93, -2.017] units

Review: observe the 1st ppp.object in ppp-hypercolumn demohyper$Points

spatstat.data::demohyper$Points[[1L]] |>
  spatstat.geom::print.ppp()
# Planar point pattern: 104 points
# window: binary image mask
# 128 x 128 pixel array (ny, nx)
# enclosing rectangle: [2.017, 3.93] x [0.645, 3.278] units
if (FALSE) {
  spatstat.data::demohyper$Points[[1L]] |>
    spatstat.geom::plot.ppp()
} # figure suppressed to reduce vignette size

Review: observe im-hypercolumn demohyper$Image

spatstat.data::demohyper$Image
# List of pixel images
# 
# 1:
# real-valued pixel image
# 53 x 39 pixel array (ny, nx)
# enclosing rectangle: [2.017, 3.93] x [0.645, 3.278] units
# 
# 2:
# real-valued pixel image
# 39 x 53 pixel array (ny, nx)
# enclosing rectangle: [0.645, 3.278] x [2.017, 3.93] units
# 
# 3:
# real-valued pixel image
# 39 x 53 pixel array (ny, nx)
# enclosing rectangle: [-3.278, -0.645] x [-3.93, -2.017] units
spatstat.data::demohyper$Image |>
  class()
# [1] "imlist"  "solist"  "anylist" "listof"  "list"

Review: observe the 1st im.object in im-hypercolumn demohyper$Image

spatstat.data::demohyper$Image[[1L]] |>
  spatstat.geom::print.im()
# real-valued pixel image
# 53 x 39 pixel array (ny, nx)
# enclosing rectangle: [2.017, 3.93] x [0.645, 3.278] units
if (FALSE) {
  spatstat.data::demohyper$Image[[1L]] |>
    spatstat.geom::plot.im()
} # figure suppressed to reduce vignette size

8.9 `finpines`

The ppp.object (Chapter 23) finpines from package spatstat.data (v3.1.9) has

126 points;
rectangle window;
two numeric-marks, diameter and height;
'dataframe'-markformat.

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
spatstat.data::finpines |>
  spatstat.geom::markformat.ppp()
# [1] "dataframe"
spatstat.data::finpines |>
  spatstat.geom::marks.ppp() |>
  head(n = 3L)
#   diameter height
# 1        1    1.7
# 2        1    1.7
# 3        1    1.6

8.10 `flu`

Data set flu from package spatstat.data (v3.1.9) is a hyperframe (Chapter 17).

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
# wt M2-M1 43      (ppp)        wt M2-M1      43
# wt M2-M1 49      (ppp)        wt M2-M1      49
# wt M2-M1 65      (ppp)        wt M2-M1      65
# wt M2-M1 71      (ppp)        wt M2-M1      71
# wt M2-M1 84      (ppp)        wt M2-M1      84
# wt M2-HA 3       (ppp)        wt M2-HA       3
# wt M2-HA 4       (ppp)        wt M2-HA       4
# wt M2-HA 5       (ppp)        wt M2-HA       5
# wt M2-HA 17      (ppp)        wt M2-HA      17
# wt M2-HA 54      (ppp)        wt M2-HA      54
# wt M2-HA 74      (ppp)        wt M2-HA      74
# wt M2-HA 78      (ppp)        wt M2-HA      78
# wt M2-HA 82      (ppp)        wt M2-HA      82
# wt M2-HA 85      (ppp)        wt M2-HA      85
# wt M2-HA 100     (ppp)        wt M2-HA     100
# wt M2-HA 110     (ppp)        wt M2-HA     110
# mut1 M2-M1 11    (ppp)      mut1 M2-M1      11
# mut1 M2-M1 13    (ppp)      mut1 M2-M1      13
# mut1 M2-M1 15    (ppp)      mut1 M2-M1      15
# mut1 M2-M1 17    (ppp)      mut1 M2-M1      17
# mut1 M2-M1 28    (ppp)      mut1 M2-M1      28
# mut1 M2-M1 29    (ppp)      mut1 M2-M1      29
# mut1 M2-M1 33    (ppp)      mut1 M2-M1      33
# mut1 M2-M1 38    (ppp)      mut1 M2-M1      38
# mut1 M2-M1 41    (ppp)      mut1 M2-M1      41
# mut1 M2-M1 44    (ppp)      mut1 M2-M1      44
# mut1 M2-M1 59    (ppp)      mut1 M2-M1      59
# mut1 M2-HA 8     (ppp)      mut1 M2-HA       8
# mut1 M2-HA 14    (ppp)      mut1 M2-HA      14
# mut1 M2-HA 23    (ppp)      mut1 M2-HA      23
# mut1 M2-HA 42    (ppp)      mut1 M2-HA      42
# mut1 M2-HA 51    (ppp)      mut1 M2-HA      51
# mut1 M2-HA 59    (ppp)      mut1 M2-HA      59
# mut1 M2-HA 73    (ppp)      mut1 M2-HA      73
# mut1 M2-HA 79    (ppp)      mut1 M2-HA      79
# mut1 M2-HA 86    (ppp)      mut1 M2-HA      86
# mut1 M2-HA 104   (ppp)      mut1 M2-HA     104
# mut1 M2-HA 147   (ppp)      mut1 M2-HA     147

The ppp-hypercolumn of the hyper data frame flu, flu$pattern, is a ppplist (Chapter 24).

Data: a ppplist object, as ppp-hypercolumn, flu$pattern

spatstat.data::flu$pattern |>
  head(n = 3L)
# List of point patterns
# 
# wt M2-M1 13:
# Marked planar point pattern: 471 points
# Multitype, with levels = M2, M1 
# window: rectangle = [0, 3331] x [0, 3331] nm
# 
# wt M2-M1 22:
# Marked planar point pattern: 217 points
# Multitype, with levels = M2, M1 
# window: rectangle = [0, 3331] x [0, 3331] nm
# 
# wt M2-M1 27:
# Marked planar point pattern: 214 points
# Multitype, with levels = M2, M1 
# window: rectangle = [0, 3331] x [0, 3331] nm
spatstat.data::flu$pattern |>
  class()
# [1] "ppplist" "solist"  "anylist" "listof"  "list"
spatstat.data::flu$pattern |>
  sapply(FUN = class) |>
  head(n = 3L)
# wt M2-M1 13 wt M2-M1 22 wt M2-M1 27 
#       "ppp"       "ppp"       "ppp"

Many examples in the Appendix use a subset of flu.

Data: fluM, a subset of flu

fluM = spatstat.data::flu |>
  spatstat.geom::subset.hyperframe(subset = (stain == 'M2-M1') & (virustype == 'wt'))
fluM
# 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
# wt M2-M1 43   (ppp)        wt M2-M1      43
# wt M2-M1 49   (ppp)        wt M2-M1      49
# wt M2-M1 65   (ppp)        wt M2-M1      65
# wt M2-M1 71   (ppp)        wt M2-M1      71
# wt M2-M1 84   (ppp)        wt M2-M1      84

8.11 `gorillas`

The ppp.object (Chapter 23) gorillas from package spatstat.data (v3.1.9) has

647 points;
polygonal window;
two multitype-marks, group (with two levels 'major' and 'minor') and season (with two levels 'dry' and 'rainy');
'dataframe'-markformat.

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
spatstat.data::gorillas |>
  spatstat.geom::markformat.ppp()
# [1] "dataframe"
spatstat.data::gorillas |>
  spatstat.geom::marks.ppp() |>
  head(n = 3L)
#   group season       date
# 1 major    dry 2006-01-06
# 2 major    dry 2006-01-10
# 3 major    dry 2006-01-15

8.12 `gorillas.extra`

Data set gorillas.extra from package spatstat.data (v3.1.9) is an imlist (Chapter 20).

Data: gorillas.extra

spatstat.data::gorillas.extra |>
  class()
# [1] "imlist"  "solist"  "anylist" "listof"  "list"
spatstat.data::gorillas.extra |>
  sapply(FUN = class)
#     aspect  elevation       heat slopeangle  slopetype vegetation  waterdist 
#       "im"       "im"       "im"       "im"       "im"       "im"       "im"
if (FALSE) {
  spatstat.data::gorillas.extra$aspect |> 
    spatstat.geom::plot.im()
} # figure suppressed to reduce vignette file size

8.13 `Kovesi`

Data set Kovesi from package spatstat.data (v3.1.9) is a hyperframe (Chapter 17).

Data: Kovesi

spatstat.data::Kovesi |>
  spatstat.geom::dim.hyperframe()
# [1] 41 13
spatstat.data::Kovesi |>
  spatstat.geom::head.hyperframe(n = 3L) |> 
  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)

8.14 `longleaf`

The ppp.object (Chapter 23) longleaf from package spatstat.data (v3.1.9) has

584 points;
rectangle window;
one numeric-mark
'vector'-markformat.

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
spatstat.data::longleaf |> 
  spatstat.geom::markformat.ppp()
# [1] "vector"
spatstat.data::longleaf |> 
  spatstat.geom::marks.ppp() |>
  summary.default()
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#    2.00    9.10   26.15   26.84   42.12   75.90

8.15 `nbfires`

The ppp.object (Chapter 23) nbfires from package spatstat.data (v3.1.9) has

7108 points;
polygonal window;
two multitype-marks fire.type and cause;
one numeric-mark fnl.size;
'dataframe'-markformat.

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)
spatstat.data::nbfires |>
  spatstat.geom::markformat.ppp()
# [1] "dataframe"
spatstat.data::nbfires |>
  spatstat.geom::marks.ppp() |>
  head(n = 3L)
#   year fire.type            dis.date dis.julian            out.date out.julian cause      ign.src fnl.size
# 1 2000    forest 2000-05-04 19:15:00   124.7604 2000-05-05 12:00:00   125.4583 resid  burn.w.perm      1.6
# 2 2000    forest 2000-04-18 14:00:00   108.5417 2000-04-18 19:00:00   108.7500  misc         cigs      7.0
# 3 2000     grass 2000-05-03 16:30:00   123.6458 2000-05-03 18:00:00   123.7083 resid burn.no.perm      1.0

8.16 `osteo`

The hyperframe (Chapter 17) osteo from package spatstat.data (v3.1.9) has

the serial number of sampling volume brick nested in the bone sample id

Data: osteo

spatstat.data::osteo |>
  spatstat.geom::dim.hyperframe()
# [1] 40  5
spatstat.data::osteo |> 
  spatstat.geom::head.hyperframe(n = 6L) |> 
  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
# 6 c77za4       4     6 (pp3)    90

8.17 `spruces`

The ppp.object (Chapter 23) spruces from package spatstat.data (v3.1.9) has

134 points;
rectangle window;
one numeric-mark
'vector'-markformat.

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
spatstat.data::spruces |> 
  spatstat.geom::markformat.ppp()
# [1] "vector"
spatstat.data::spruces |> 
  spatstat.geom::marks.ppp() |>
  summary.default()
#    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#  0.1600  0.2200  0.2450  0.2504  0.2700  0.3700

8.18 `swedishpines`

The ppp.object (Chapter 23) swedishpines from package spatstat.data (v3.1.9) has

the $x$- and $y$-coordinates of 71 points-only;
rectangle window;
no marks, i.e., 'none'-markformat.

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)
spatstat.data::swedishpines |>
  spatstat.geom::markformat.ppp()
# [1] "none"
spatstat.data::swedishpines |>
  spatstat.geom::marks.ppp() |>
  is.null()
# [1] TRUE

8.19 `vesicles`

The ppp.object (Chapter 23) vesicles from package spatstat.data (v3.1.9) has

the $x$- and $y$-coordinates of 37 points-only;
polygonal window;
no marks, i.e., 'none'-markformat.

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
spatstat.data::vesicles |>
  spatstat.geom::markformat.ppp()
# [1] "none"
spatstat.data::vesicles |>
  spatstat.geom::marks.ppp() |>
  is.null()
# [1] TRUE

8.20 `vesicles.extra`

Data set vesicles.extra from package spatstat.data (v3.1.9) is a length-4 spatial object list (solist (Chapter 25)).

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
spatstat.data::vesicles.extra |>
  class()
# [1] "solist"  "anylist" "listof"  "list"
spatstat.data::vesicles.extra |>
  lapply(FUN = class)
# $activezone
# [1] "psp"  "list"
# 
# $mitochondria
# [1] "owin"
# 
# $presynapse
# [1] "owin"
# 
# $mask
# [1] "owin"

8.21 `waterstriders`

Data set waterstriders from package spatstat.data (v3.1.9) is a length-3 ppplist (Chapter 24).

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
spatstat.data::waterstriders |>
  class()
# [1] "ppplist" "solist"  "anylist" "listof"  "list"
spatstat.data::waterstriders |>
  sapply(FUN = class)
# [1] "ppp" "ppp" "ppp"

8.1 anemones

8.2 ants

8.3 austates

8.4 betacells

8.5 bronzefilter

8.6 btb.extra

8.7 cetaceans

8.8 demohyper

8.9 finpines

8.10 flu

8.11 gorillas

8.12 gorillas.extra

8.13 Kovesi

8.14 longleaf

8.15 nbfires

8.16 osteo

8.17 spruces

8.18 swedishpines

8.19 vesicles

8.20 vesicles.extra

8.21 waterstriders

8.1 `anemones`

8.2 `ants`

8.3 `austates`

8.4 `betacells`

8.5 `bronzefilter`

8.6 `btb.extra`

8.7 `cetaceans`

8.8 `demohyper`

8.9 `finpines`

8.10 `flu`

8.11 `gorillas`

8.12 `gorillas.extra`

8.13 `Kovesi`

8.14 `longleaf`

8.15 `nbfires`

8.16 `osteo`

8.17 `spruces`

8.18 `swedishpines`

8.19 `vesicles`

8.20 `vesicles.extra`

8.21 `waterstriders`