9  Data Sets

Note

Chapter 9 demonstrates several, though not all, data objects from package datasets (R version 4.5.3 (2026-03-11)) and package spatstat.data (v3.1.9, GPL (>= 2)).

The function calls in Chapter 9 are exclusively those provided in package base and stats (R version 4.5.3 (2026-03-11)), and in the spatstat.* family of packages.

9.1 anemones

The point-pattern (ppp.object, Chapter 24) anemones from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.2, Figure 9.1)

Listing 9.1: Figure: anemones
Code 
par(mar = c(0,0,0,0))
spatstat.data::anemones |>
  spatstat.geom::plot.ppp(main = NULL)
Figure 9.1: anemones
Listing 9.2: 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 9.3: Review: number of points in anemones 
spatstat.data::anemones |>
  spatstat.geom::npoints.ppp()
[1] 231
Listing 9.4: Review: window of anemones 
spatstat.data::anemones |>
  spatstat.geom::Window.ppp()
window: rectangle = [0, 280] x [0, 180] units
Listing 9.5: Review: storage mode of the marks of anemones 
spatstat.data::anemones |>
  spatstat.geom::marks.ppp() |>
  typeof()
[1] "integer"
Listing 9.6: Review: mark-format of anemones 
spatstat.data::anemones |>
  spatstat.geom::markformat.ppp()
[1] "vector"

9.2 ants

The point-pattern (ppp.object, Chapter 24) ants from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.8, Figure 9.2)

  • 97 points;
  • polygonal window;
  • one multi-type mark with two levels, 'Cataglyphis' and 'Messor' (Listing 9.9);
  • 'vector' mark-format.
Listing 9.7: Figure: ants
Code 
par(mar = c(0,0,0,0))
spatstat.data::ants |>
  spatstat.geom::plot.ppp(main = NULL)
Figure 9.2: ants
Listing 9.8: 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 9.9: Review: marks of ants 
spatstat.data::ants |>
  spatstat.geom::marks.ppp() |>
  table()

Cataglyphis      Messor 
         29          68

9.3 austates

The tessellation (Chapter 29) austates from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.11, Figure 9.3)

Listing 9.10: Figure: austates
Code 
par(mar = c(0,0,1,0))
spatstat.data::austates |>
  spatstat.geom::plot.tess(main = '')
Figure 9.3: austates
Listing 9.11: 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 9.12: 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

SA:
window: polygonal boundary
enclosing rectangle: [129.01141, 141.0076] x [-37.96578, -25.98859] degrees

QLD:
window: polygonal boundary
enclosing rectangle: [138.0038, 153.47909] x [-29.163498, -10.931559] degrees

NSW:
window: polygonal boundary
enclosing rectangle: [141.0076, 153.6692] x [-37.45247, -28.07985] degrees

VIC:
window: polygonal boundary
enclosing rectangle: [140.95057, 149.79087] x [-39.04943, -33.91635] degrees

TAS:
window: polygonal boundary
enclosing rectangle: [144.63878, 148.34601] x [-43.59316, -40.58935] degrees

9.4 betacells

The point-pattern (ppp.object, Chapter 24) betacells from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.14, Figure 9.4)

Listing 9.13: Figure: betacells
Code 
par(mar = c(0,0,0,0))
spatstat.data::betacells |>
  spatstat.geom::plot.ppp(main = '')
Figure 9.4: betacells
Listing 9.14: 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 9.15: Review: mark-format of betacells 
spatstat.data::betacells |>
  spatstat.geom::markformat.ppp()
[1] "dataframe"
Listing 9.16: 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
4     on 442.9
5    off 209.4
6    off 260.4
7     on 348.8
8     on 315.2
9    off 275.3
10   off 317.8
11    on 310.0
12   off 279.4
13    on 375.3
14   off 307.3
15    on 378.2
16    on 286.9
17   off 303.0
18   off 202.4
19   off 277.3
20   off 278.8
21    on 244.1
22    on 341.5
23   off 322.5
24   off 248.4
25   off 319.9
26    on 315.5
27   off 353.1
28    on 514.4
29    on 404.2
30    on 360.4
31    on 252.8
32   off 276.1
33   off 274.4
34   off 251.7
35   off 298.9
36    on 370.0
37    on 207.9
38   off 257.2
39    on 325.4
40   off 310.0
41    on 305.0
42    on 317.0
43    on 373.5
44    on 435.1
45    on 366.8
46   off 245.5
47    on 276.1
48   off 268.9
49   off 252.2
50   off 227.4
51   off 319.0
52    on 320.5
53    on 327.2
54    on 384.6
55    on 285.8
56    on 321.3
57   off 245.5
58   off 245.2
59   off 256.0
60    on 303.8
61   off 225.4
62   off 294.2
63   off 244.4
64   off 257.2
65   off 199.5
66    on 263.3
67    on 345.5
68    on 279.9
69    on 427.8
70   off 239.4
71   off 249.3
72    on 228.3
73   off 320.5
74    on 340.9
75   off 257.8
76    on 363.3
77   off 274.4
78   off 246.7
79    on 348.2
80    on 350.5
81    on 287.2
82   off 258.6
83   off 168.3
84   off 260.7
85   off 263.9
86    on 286.1
87   off 189.0
88   off 220.4
89    on 345.3
90    on 345.5
91   off 308.8
92   off 257.5
93   off 258.4
94    on 412.3
95   off 235.0
96    on 273.8
97    on 312.9
98   off 302.1
99    on 391.3
100  off 266.2
101   on 362.2
102  off 243.2
103   on 360.1
104   on 224.8
105  off 209.4
106  off 239.4
107  off 242.3
108   on 377.3
109   on 255.7
110  off 173.5
111  off 198.3
112  off 223.4
113   on 439.7
114  off 219.3
115   on 281.7
116  off 214.3
117   on 291.9
118  off 231.5
119   on 323.1
120  off 262.4
121  off 342.0
122  off 195.4
123   on 274.4
124  off 278.2
125   on 293.6
126  off 254.6
127  off 286.1
128   on 233.6
129   on 337.4
130   on 345.5
131  off 360.1
132  off 285.2
133   on 305.9
134   on 229.8
135   on 251.7

9.5 bronzefilter

The point-pattern (ppp.object, Chapter 24) bronzefilter from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.18, Figure 9.5)

  • 678 points;
  • rectangle window;
  • one numeric mark.
Listing 9.17: Figure: bronzefilter
Code 
par(mar = c(0,1,0,0))
spatstat.data::bronzefilter |>
  spatstat.geom::plot.ppp(main = NULL)
Figure 9.5: bronzefilter
Listing 9.18: 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

9.6 btb.extra

The point-pattern-list (ppplist, Chapter 25) btb.extra from package spatstat.data (v3.1.9, GPL (>= 2)) (Listing 9.20, Figure 9.6)

Listing 9.19: Figure: btb.extra
Code 
par(mar = c(0,1,1,1))
spatstat.data::btb.extra |> 
  spatstat.geom::plot.solist()
Figure 9.6: btb.extra
Listing 9.20: 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 9.21: Review: inheritance of btb.extra 
spatstat.data::btb.extra |> 
  class()
[1] "ppplist" "solist"  "anylist" "listof"  "list"
Listing 9.22: Review: class of members of btb.extra 
spatstat.data::btb.extra |> 
  sapply(FUN = class)
    full standard 
   "ppp"    "ppp"

9.7 cars

The data frame (data.frame, Chapter 12) cars from package datasets (R version 4.5.3 (2026-03-11)) has (Listing 9.23)

  • 50 rows and 2 columns (Listing 9.24)
  • two numeric columns: $speed and $dist.
Listing 9.23: Data: cars 
datasets::cars |>
  print.data.frame()
   speed dist
1      4    2
2      4   10
3      7    4
4      7   22
5      8   16
6      9   10
7     10   18
8     10   26
9     10   34
10    11   17
11    11   28
12    12   14
13    12   20
14    12   24
15    12   28
16    13   26
17    13   34
18    13   34
19    13   46
20    14   26
21    14   36
22    14   60
23    14   80
24    15   20
25    15   26
26    15   54
27    16   32
28    16   40
29    17   32
30    17   40
31    17   50
32    18   42
33    18   56
34    18   76
35    18   84
36    19   36
37    19   46
38    19   68
39    20   32
40    20   48
41    20   52
42    20   56
43    20   64
44    22   66
45    23   54
46    24   70
47    24   92
48    24   93
49    24  120
50    25   85
Listing 9.24: Review: dimensions of cars 
datasets::cars |>
  dim.data.frame()
[1] 50  2

9.8 cetaceans

The hyper data frame (hyperframe, Chapter 16) cetaceans from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.25)

  • 9 rows and 4 (hyper)columns (Listing 9.26)
  • four point-pattern (ppp, Chapter 24) hypercolumns: $whales, $dolphins, $fish and $plankton.
Listing 9.25: 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)
Listing 9.26: Review: dimensions of cetaceans 
spatstat.data::cetaceans |>
  spatstat.geom::dim.hyperframe()
[1] 9 4

9.9 demohyper

The hyper data frame (hyperframe, Chapter 16) demohyper from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.27)

  • 3 rows and 3 (hyper)columns (Listing 9.28)
  • a point-pattern (ppp, Chapter 24) hypercolumn $Points
  • a pixel-image (im, Chapter 18) hypercolumn $Image
  • a regular column $Group.
Listing 9.27: 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 9.28: 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 9.27). 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 (v3.7.3, GPL (>= 2))

The rest of Section 9.9 showcases the *.hyperframe() methods of the .Primitive S3 generic functions names() (Listing 9.29) and `$` (Listing 9.30, Listing 9.31).

Listing 9.29 finds the (hyper)column names of the hyper data frame demohyper,

Listing 9.29: Review: (hyper)column names of demohyper 
spatstat.data::demohyper |>
  spatstat.geom::names.hyperframe()
[1] "Points" "Image"  "Group"

Listing 9.30 and Listing 9.31 observe the ppp-hypercolumn $Points,

Listing 9.30: Review: ppp-hypercolumn $Points 
spatstat.data::demohyper$Points |>
  class()
[1] "ppplist" "solist"  "anylist" "listof"  "list"
Listing 9.31: Advanced: ppp-hypercolumn $Points, nerdy! 
spatstat.data::demohyper |>
  spatstat.geom::`$.hyperframe`(name = 'Points') |> # nerdy!!
  identical(y = spatstat.data::demohyper$Points) |>
  stopifnot()

Listing 9.32 and Listing 9.33 find the first point-pattern element of the ppp-hypercolumn $Points,

Listing 9.32: Review: 1st point-pattern in ppp-hypercolumn $Points 
demohyper_p1 = spatstat.data::demohyper$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 9.33: Advanced: 1st point-pattern in ppp-hypercolumn $Points, nerdy! 
spatstat.data::demohyper$Points |>
  base::`[[`(i = 1L) |> # nerdy!!
  identical(y = demohyper_p1) |>
  stopifnot()

Listing 9.34 finds the first pixel-image element of the im-hypercolumn $Image,

Listing 9.34: Review: 1st pixel-image in im-hypercolumn $Image 
spatstat.data::demohyper$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

9.10 faithful

The data frame (data.frame, Chapter 12) faithful from package datasets (R version 4.5.3 (2026-03-11)) has (Listing 9.35)

  • 272 rows and 2 columns (Listing 9.36)
  • two numeric columns: $eruptions and $waiting.
Listing 9.35: Data: faithful 
datasets::faithful |>
  print.data.frame()
    eruptions waiting
1       3.600      79
2       1.800      54
3       3.333      74
4       2.283      62
5       4.533      85
6       2.883      55
7       4.700      88
8       3.600      85
9       1.950      51
10      4.350      85
11      1.833      54
12      3.917      84
13      4.200      78
14      1.750      47
15      4.700      83
16      2.167      52
17      1.750      62
18      4.800      84
19      1.600      52
20      4.250      79
21      1.800      51
22      1.750      47
23      3.450      78
24      3.067      69
25      4.533      74
26      3.600      83
27      1.967      55
28      4.083      76
29      3.850      78
30      4.433      79
31      4.300      73
32      4.467      77
33      3.367      66
34      4.033      80
35      3.833      74
36      2.017      52
37      1.867      48
38      4.833      80
39      1.833      59
40      4.783      90
41      4.350      80
42      1.883      58
43      4.567      84
44      1.750      58
45      4.533      73
46      3.317      83
47      3.833      64
48      2.100      53
49      4.633      82
50      2.000      59
51      4.800      75
52      4.716      90
53      1.833      54
54      4.833      80
55      1.733      54
56      4.883      83
57      3.717      71
58      1.667      64
59      4.567      77
60      4.317      81
61      2.233      59
62      4.500      84
63      1.750      48
64      4.800      82
65      1.817      60
66      4.400      92
67      4.167      78
68      4.700      78
69      2.067      65
70      4.700      73
71      4.033      82
72      1.967      56
73      4.500      79
74      4.000      71
75      1.983      62
76      5.067      76
77      2.017      60
78      4.567      78
79      3.883      76
80      3.600      83
81      4.133      75
82      4.333      82
83      4.100      70
84      2.633      65
85      4.067      73
86      4.933      88
87      3.950      76
88      4.517      80
89      2.167      48
90      4.000      86
91      2.200      60
92      4.333      90
93      1.867      50
94      4.817      78
95      1.833      63
96      4.300      72
97      4.667      84
98      3.750      75
99      1.867      51
100     4.900      82
101     2.483      62
102     4.367      88
103     2.100      49
104     4.500      83
105     4.050      81
106     1.867      47
107     4.700      84
108     1.783      52
109     4.850      86
110     3.683      81
111     4.733      75
112     2.300      59
113     4.900      89
114     4.417      79
115     1.700      59
116     4.633      81
117     2.317      50
118     4.600      85
119     1.817      59
120     4.417      87
121     2.617      53
122     4.067      69
123     4.250      77
124     1.967      56
125     4.600      88
126     3.767      81
127     1.917      45
128     4.500      82
129     2.267      55
130     4.650      90
131     1.867      45
132     4.167      83
133     2.800      56
134     4.333      89
135     1.833      46
136     4.383      82
137     1.883      51
138     4.933      86
139     2.033      53
140     3.733      79
141     4.233      81
142     2.233      60
143     4.533      82
144     4.817      77
145     4.333      76
146     1.983      59
147     4.633      80
148     2.017      49
149     5.100      96
150     1.800      53
151     5.033      77
152     4.000      77
153     2.400      65
154     4.600      81
155     3.567      71
156     4.000      70
157     4.500      81
158     4.083      93
159     1.800      53
160     3.967      89
161     2.200      45
162     4.150      86
163     2.000      58
164     3.833      78
165     3.500      66
166     4.583      76
167     2.367      63
168     5.000      88
169     1.933      52
170     4.617      93
171     1.917      49
172     2.083      57
173     4.583      77
174     3.333      68
175     4.167      81
176     4.333      81
177     4.500      73
178     2.417      50
179     4.000      85
180     4.167      74
181     1.883      55
182     4.583      77
183     4.250      83
184     3.767      83
185     2.033      51
186     4.433      78
187     4.083      84
188     1.833      46
189     4.417      83
190     2.183      55
191     4.800      81
192     1.833      57
193     4.800      76
194     4.100      84
195     3.966      77
196     4.233      81
197     3.500      87
198     4.366      77
199     2.250      51
200     4.667      78
201     2.100      60
202     4.350      82
203     4.133      91
204     1.867      53
205     4.600      78
206     1.783      46
207     4.367      77
208     3.850      84
209     1.933      49
210     4.500      83
211     2.383      71
212     4.700      80
213     1.867      49
214     3.833      75
215     3.417      64
216     4.233      76
217     2.400      53
218     4.800      94
219     2.000      55
220     4.150      76
221     1.867      50
222     4.267      82
223     1.750      54
224     4.483      75
225     4.000      78
226     4.117      79
227     4.083      78
228     4.267      78
229     3.917      70
230     4.550      79
231     4.083      70
232     2.417      54
233     4.183      86
234     2.217      50
235     4.450      90
236     1.883      54
237     1.850      54
238     4.283      77
239     3.950      79
240     2.333      64
241     4.150      75
242     2.350      47
243     4.933      86
244     2.900      63
245     4.583      85
246     3.833      82
247     2.083      57
248     4.367      82
249     2.133      67
250     4.350      74
251     2.200      54
252     4.450      83
253     3.567      73
254     4.500      73
255     4.150      88
256     3.817      80
257     3.917      71
258     4.450      83
259     2.000      56
260     4.283      79
261     4.767      78
262     4.533      84
263     1.850      58
264     4.250      83
265     1.983      43
266     2.250      60
267     4.750      75
268     4.117      81
269     2.150      46
270     4.417      90
271     1.817      46
272     4.467      74
Listing 9.36: Review: dimensions of faithful 
datasets::faithful |>
  dim.data.frame()
[1] 272   2

9.11 finpines

The point-pattern (ppp.object, Chapter 24) finpines from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.38, Figure 9.7)

  • 126 points;
  • rectangle window;
  • two numeric marks, diameter and height.
Listing 9.37: Figure: finpines
Code 
par(mar = c(0,0,0,0))
spatstat.data::finpines |>
  spatstat.geom::plot.ppp(main = '')
Figure 9.7: finpines
Listing 9.38: 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

9.12 flu

The hyper data frame (hyperframe, Chapter 16) flu from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.39)

  • 41 rows and 4 (hyper)columns (Listing 9.40)
  • a point-pattern (ppp, Chapter 24) hypercolumn $pattern
  • regular columns $virustype, $stain, $frameid
Listing 9.39: 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
Listing 9.40: Review: dimensions of flu 
spatstat.data::flu |>
  spatstat.geom::dim.hyperframe()
[1] 41  4

9.13 gorillas

The point-pattern (ppp.object, Chapter 24) gorillas from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.42, Figure 9.8)

  • 647 points;
  • polygonal window;
  • two multi-type marks, group (with two levels 'major' and 'minor') and season (with two levels 'dry' and 'rainy').
Listing 9.41: Figure: gorillas
Code 
par(mar = c(0,0,1,0))
spatstat.data::gorillas |>
  spatstat.geom::plot.ppp(which.marks = c('group', 'season'))
Figure 9.8: gorillas
Listing 9.42: 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

9.14 gorillas.extra

The pixel-image list (imlist, Chapter 19) gorillas.extra from package spatstat.data (v3.1.9, GPL (>= 2)) (Listing 9.44, Figure 9.9)

Listing 9.43: Figure: gorillas.extra
Code 
par(mar = c(0,0,0,0))
spatstat.data::gorillas.extra |> 
  plot(main = '')
Figure 9.9: gorillas.extra
Listing 9.44: 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

heat:
factor-valued pixel image
factor levels:
[1] "Warmest"  "Moderate" "Coolest" 
149 x 181 pixel array (ny, nx)
enclosing rectangle: [580440, 586000] x [674160, 678730] metres

slopeangle:
real-valued pixel image
149 x 181 pixel array (ny, nx)
enclosing rectangle: [580440, 586000] x [674160, 678730] metres

slopetype:
factor-valued pixel image
factor levels:
[1] "Valley"   "Toe"      "Flat"     "Midslope" "Upper"    "Ridge"   
149 x 181 pixel array (ny, nx)
enclosing rectangle: [580440, 586000] x [674160, 678730] metres

vegetation:
factor-valued pixel image
factor levels:
[1] "Disturbed"  "Colonising" "Grassland"  "Primary"    "Secondary" 
[6] "Transition"
149 x 181 pixel array (ny, nx)
enclosing rectangle: [580440, 586000] x [674160, 678730] metres

waterdist:
real-valued pixel image
149 x 181 pixel array (ny, nx)
enclosing rectangle: [580440, 586000] x [674160, 678730] metres
Listing 9.45: Review: inheritance of gorillas.extra 
spatstat.data::gorillas.extra |> 
  class()
[1] "imlist"  "solist"  "anylist" "listof"  "list"
Listing 9.46: 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"

9.15 hyytiala

The point-pattern (ppp.object, Chapter 24) hyytiala from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.48, Figure 9.10)

  • 168 points;
  • rectangle window;
  • one multi-type mark with four levels, 'aspen', 'birch', 'pine' and 'rowan' (Listing 9.49);
  • 'vector' mark-format.
Listing 9.47: Figure: hyytiala
Code 
par(mar = c(0,0,0,0))
spatstat.data::hyytiala |>
  spatstat.geom::plot.ppp(main = NULL)
Figure 9.10: hyytiala
Listing 9.48: 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 9.49: Review: marks of hyytiala 
spatstat.data::hyytiala |>
  spatstat.geom::marks.ppp() |>
  table()

aspen birch  pine rowan 
    1    17   128    22

9.16 Kovesi

The hyper data frame (hyperframe, Chapter 16) Kovesi from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.50)

Listing 9.50: Data: Kovesi 
spatstat.data::Kovesi |>
  spatstat.geom::print.hyperframe()
Hyperframe:
   linear diverging rainbow cyclic isoluminant ternary colsig l1  l2 chro   n
1   FALSE     FALSE   FALSE   TRUE       FALSE   FALSE      j 15  85    0 256
2   FALSE     FALSE   FALSE   TRUE       FALSE   FALSE      j 15  85    0 256
3   FALSE     FALSE   FALSE   TRUE       FALSE   FALSE  mrybm 35  75   68 256
4   FALSE     FALSE   FALSE   TRUE       FALSE   FALSE  mrybm 35  75   68 256
5   FALSE     FALSE   FALSE   TRUE       FALSE   FALSE  mygbm 30  95   78 256
6   FALSE     FALSE   FALSE   TRUE       FALSE   FALSE  mygbm 30  95   78 256
7   FALSE     FALSE   FALSE   TRUE       FALSE   FALSE  wrwbw 40  90   42 256
8   FALSE     FALSE   FALSE   TRUE       FALSE   FALSE  wrwbw 40  90   42 256
9   FALSE      TRUE   FALSE  FALSE       FALSE   FALSE    bkr 55  10   35 256
10  FALSE      TRUE   FALSE  FALSE       FALSE   FALSE    bky 60  10   30 256
11  FALSE      TRUE   FALSE  FALSE       FALSE   FALSE    bwr 40  95   42 256
12  FALSE      TRUE   FALSE  FALSE       FALSE   FALSE    bwr 55  98   37 256
13  FALSE      TRUE   FALSE  FALSE       FALSE   FALSE    cwm 80 100   22 256
14  FALSE      TRUE   FALSE  FALSE       FALSE   FALSE    gkr 60  10   40 256
15  FALSE      TRUE   FALSE  FALSE       FALSE   FALSE    gwr 55  95   38 256
16  FALSE      TRUE   FALSE  FALSE       FALSE   FALSE    gwv 55  95   39 256
17  FALSE      TRUE   FALSE  FALSE        TRUE   FALSE    cjm 75  75   24 256
18  FALSE      TRUE   FALSE  FALSE        TRUE   FALSE    cjo 70  70   25 256
19   TRUE      TRUE   FALSE  FALSE       FALSE   FALSE    bjr 30  55   53 256
20   TRUE      TRUE   FALSE  FALSE       FALSE   FALSE    bjy 30  90   45 256
21  FALSE      TRUE    TRUE  FALSE       FALSE   FALSE  bgymr 45  85   67 256
22  FALSE     FALSE   FALSE  FALSE        TRUE   FALSE    cgo 70  70   39 256
23  FALSE     FALSE   FALSE  FALSE        TRUE   FALSE    cgo 80  80   38 256
24  FALSE     FALSE   FALSE  FALSE        TRUE   FALSE     cm 70  70   39 256
25   TRUE     FALSE   FALSE  FALSE       FALSE   FALSE      b  5  95   73 256
26   TRUE     FALSE   FALSE  FALSE       FALSE   FALSE      b 95  50   20 256
27   TRUE     FALSE   FALSE  FALSE       FALSE   FALSE    bgy 10  95   74 256
28   TRUE     FALSE   FALSE  FALSE       FALSE   FALSE    bmw  5  95   89 256
29   TRUE     FALSE   FALSE  FALSE       FALSE   FALSE    bmy 10  95   78 256
30   TRUE     FALSE   FALSE  FALSE       FALSE   FALSE      g  5  95   69 256
31   TRUE     FALSE   FALSE  FALSE       FALSE   FALSE    gow 60  85   27 256
32   TRUE     FALSE   FALSE  FALSE       FALSE   FALSE    gow 65  90   35 256
33   TRUE     FALSE   FALSE  FALSE       FALSE   FALSE      j  0 100    0 256
34   TRUE     FALSE   FALSE  FALSE       FALSE   FALSE      j 10  95    0 256
35   TRUE     FALSE   FALSE  FALSE       FALSE   FALSE    kry  5  98   75 256
36   TRUE     FALSE   FALSE  FALSE       FALSE   FALSE   kryw  5 100   67 256
37   TRUE     FALSE   FALSE  FALSE       FALSE    TRUE      b  0  44   57 256
38   TRUE     FALSE   FALSE  FALSE       FALSE    TRUE      g  0  46   42 256
39   TRUE     FALSE   FALSE  FALSE       FALSE    TRUE      r  0  50   52 256
40  FALSE     FALSE    TRUE  FALSE       FALSE   FALSE   bgyr 35  85   73 256
41  FALSE     FALSE    TRUE  FALSE       FALSE   FALSE  bgyrm 35  85   71 256
   cycsh      values
1      0 (character)
2     25 (character)
3      0 (character)
4     25 (character)
5      0 (character)
6     25 (character)
7      0 (character)
8     25 (character)
9      0 (character)
10     0 (character)
11     0 (character)
12     0 (character)
13     0 (character)
14     0 (character)
15     0 (character)
16     0 (character)
17     0 (character)
18     0 (character)
19     0 (character)
20     0 (character)
21     0 (character)
22     0 (character)
23     0 (character)
24     0 (character)
25     0 (character)
26     0 (character)
27     0 (character)
28     0 (character)
29     0 (character)
30     0 (character)
31     0 (character)
32     0 (character)
33     0 (character)
34     0 (character)
35     0 (character)
36     0 (character)
37     0 (character)
38     0 (character)
39     0 (character)
40     0 (character)
41     0 (character)
Listing 9.51: Review: dimensions of Kovesi 
spatstat.data::Kovesi |>
  spatstat.geom::dim.hyperframe()
[1] 41 13
Listing 9.52: Review: class of hypercolumn $values 
spatstat.data::Kovesi$values |>
  class()
[1] "anylist" "listof"  "list"
Listing 9.53: Review: length of hypercolumn $values 
spatstat.data::Kovesi$values |>
  length()
[1] 41
Listing 9.54: Review: lengths of hypercolumn $values 
spatstat.data::Kovesi$values |>
  lengths() |>
  unique.default()
[1] 256

9.17 longleaf

The point-pattern (ppp.object, Chapter 24) longleaf from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.56, Figure 9.11)

  • 584 points;
  • rectangle window;
  • one numeric mark.
Listing 9.55: Figure: longleaf
Code 
par(mar = c(0,0,0,0))
spatstat.data::longleaf |>
  spatstat.geom::plot.ppp(main = NULL)
Figure 9.11: longleaf
Listing 9.56: 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

9.18 meningitis

The spatial-object list (solist, Chapter 27) meningitis from package spatstat.data (v3.1.9, GPL (>= 2)) contains (Listing 9.58, Figure 9.12)

Listing 9.57: Figure: meningitis
Code 
par(mar = c(0,0,0,0))
spatstat.data::meningitis |>
  spatstat.geom::plot.solist(main = '')
Figure 9.12: meningitis
Listing 9.58: 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

9.19 nbfires

The point-pattern (ppp.object, Chapter 24) nbfires from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.60, Figure 9.13)

  • 7108 points;
  • polygonal window;
  • multi-type marks, e.g., $fire.type, $cause and $ign.src;
  • numeric marks, e.g., $fnl.size.
Listing 9.59: 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 9.13: nbfires
Listing 9.60: 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)

9.20 osteo

The hyper data frame (hyperframe, Chapter 16) osteo from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.61)

  • 40 rows and 5 (hyper)columns (Listing 9.62)
  • the serial number of sampling volume $brick nested in the bone sample $id
  • a three-dimensional point-pattern (pp3, Chapter 23) hypercolumn $pts
Listing 9.61: 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
6   c77za4       4     6 (pp3)    90
7   c77za4       4     7 (pp3)    95
8   c77za4       4     8 (pp3)    65
9   c77za4       4     9 (pp3)   100
10  c77za4       4    10 (pp3)   100
11  c77za5       5     1 (pp3)    45
21  c77za5       5     2 (pp3)    30
31  c77za5       5     3 (pp3)    40
41  c77za5       5     4 (pp3)    45
51  c77za5       5     5 (pp3)    40
61  c77za5       5     6 (pp3)    50
71  c77za5       5     7 (pp3)    40
81  c77za5       5     8 (pp3)    60
91  c77za5       5     9 (pp3)    65
101 c77za5       5    10 (pp3)    60
12  c77za8       8     1 (pp3)    40
22  c77za8       8     2 (pp3)    55
32  c77za8       8     3 (pp3)    60
42  c77za8       8     4 (pp3)    50
52  c77za8       8     5 (pp3)    45
62  c77za8       8     6 (pp3)    30
72  c77za8       8     7 (pp3)    50
82  c77za8       8     8 (pp3)    45
92  c77za8       8     9 (pp3)    70
102 c77za8       8    10 (pp3)   110
13  c77za9       9     1 (pp3)    60
23  c77za9       9     2 (pp3)    65
33  c77za9       9     3 (pp3)    55
43  c77za9       9     4 (pp3)    70
53  c77za9       9     5 (pp3)    55
63  c77za9       9     6 (pp3)   100
73  c77za9       9     7 (pp3)    80
83  c77za9       9     8 (pp3)    75
93  c77za9       9     9 (pp3)    85
103 c77za9       9    10 (pp3)    60
Listing 9.62: Review: dimensions of osteo 
spatstat.data::osteo |>
  spatstat.geom::dim.hyperframe()
[1] 40  5

9.21 spruces

The point-pattern (ppp.object, Chapter 24) spruces from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.64, Figure 9.14)

  • 134 points;
  • rectangle window;
  • one numeric mark.
Listing 9.63: Figure: spruces
Code 
par(mar = c(0,0,0,0))
spatstat.data::spruces |>
  spatstat.geom::plot.ppp(main = NULL)
Figure 9.14: spruces
Listing 9.64: 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

9.22 swedishpines

The point-pattern (ppp.object, Chapter 24) swedishpines from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.66, Figure 9.15)

  • the \(x\)- and \(y\)-coordinates of 71 points;
  • rectangle window;
  • no marks, i.e., 'none' mark-format.
Listing 9.65: Figure: swedishpines
Code 
par(mar = c(0,0,0,0))
spatstat.data::swedishpines |>
  spatstat.geom::plot.ppp(main = NULL)
Figure 9.15: swedishpines
Listing 9.66: 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)

9.23 VADeaths

The matrix VADeaths from package datasets (R version 4.5.3 (2026-03-11)) has (Listing 9.67)

Listing 9.67: Data: VADeaths 
datasets::VADeaths |>
  print.default()
      Rural Male Rural Female Urban Male Urban Female
50-54       11.7          8.7       15.4          8.4
55-59       18.1         11.7       24.3         13.6
60-64       26.9         20.3       37.0         19.3
65-69       41.0         30.9       54.6         35.1
70-74       66.0         54.3       71.1         50.0
Listing 9.68: Review: dimensions of VADeaths 
datasets::VADeaths |>
  dim()
[1] 5 4

9.24 vesicles

The point-pattern (ppp.object, Chapter 24) vesicles from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.70, Figure 9.16)

  • the \(x\)- and \(y\)-coordinates of 37 points;
  • polygonal window;
  • no marks, i.e., 'none' mark-format.
Listing 9.69: Figure: vesicles
Code 
par(mar = c(0,0,0,0))
spatstat.data::vesicles |>
  spatstat.geom::plot.ppp(main = NULL)
Figure 9.16: vesicles
Listing 9.70: 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

9.25 vesicles.extra

The spatial-object list (solist, Chapter 27) vesicles.extra from package spatstat.data (v3.1.9, GPL (>= 2)) has (Listing 9.71, Listing 9.72)

  • a line-segment-pattern (psp, Chapter 26) $activezone
  • three windows: $mitochondria, $presynapse and $mask
Listing 9.71: 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 9.72: 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"

9.26 waterstriders

The point-pattern-list (ppplist, Chapter 25) waterstriders from package spatstat.data (v3.1.9, GPL (>= 2)) (Listing 9.74, Figure 9.17)

Listing 9.73: Figure: waterstriders
Code 
par(mar = c(0,0,0,0))
spatstat.data::waterstriders |> 
  spatstat.geom::plot.solist(main = '')
Figure 9.17: waterstriders
Listing 9.74: 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 9.75: Review: class of members of waterstriders 
spatstat.data::waterstriders |> 
  sapply(FUN = class)
[1] "ppp" "ppp" "ppp"