README
character
12
This S dump contains 22 data sets from the
book Visualizing Data published by
Hobart Press (books@hobart.com).
The dump was created by data.dump()
and can be read back into S by data.restore().
The name of each S data set is the name of
the data set used in the book. To find the
description of the data set in the book look
under the entry - data, name - in the index.
For example, one data set is barley.
To find the description of barley, look
in the index under the entry - data, barley.
Playfair
structure
3
.Data
list
2
population
numeric
22
60
63
75
80
80
80
90
120
130
140
145
160
180
200
210
220
250
255
380
690
900
1100
diameter
numeric
22
9.1440000000000001
9.652000000000001
10.16
10.667999999999999
10.16
11.43
12.192
14.224
14.478
14.731999999999999
14.731999999999999
15.24
15.748000000000001
16.256
16.256
17.271999999999998
18.795999999999999
19.304000000000002
23.876000000000001
31.496000000000002
35.052
39.624000000000002
row.names
character
22
Edinburgh
Stockholm
Florence
Genoa
Turin
Warsaw
Copenhagen
Lisbon
Palermo
Madrid
Berlin
Rome
Petersburgh
Venice
Dublin
Amsterdam
Moscow
Vienna
Naples
Paris
Constantinople
London
class
character
1
data.frame
animal
structure
3
.Data
list
3
name
structure
3
.Data
integer
64
36
37
40
8
41
57
19
30
61
27
38
22
50
14
16
52
21
59
5
2
31
39
56
23
45
43
60
48
20
13
6
42
64
53
51
49
54
26
35
29
55
24
28
34
11
18
46
47
44
12
15
32
25
58
3
7
4
33
1
63
62
17
9
10
.Label
character
64
African Elephant
African Giant Pouched Rat
Allosaurus
Anatosaurus
Arctic Ground Squirrel
Artic Fox
Asian Elephant
Big Brown Bat
Blue Whale
Brachiosaurus
Brazilian Tapir
Camptosaurus
Cat
Chinchilla
Cow
Desert Hedgehog
Diplodocus
Donkey
Eastern American Mole
Echidna
European Hedgehog
Galago
Genet
Giant Armadillo
Giraffe
Goat
Golden Hamster
Gray Seal
Gray Wolf
Ground Squirrel
Guinea Pig
Horse
Iguanodon
Jaguar
Kangaroo
Lesser Short-tailed Shrew
Little Brown Bat
Mole Rate
Mountain Beaver
Mouse
Musk Shrew
Nine-banded Armadillo
North American Opossum
Okapi
Phalanger
Pig
Protoceratops
Rabbit
Raccoon
Rat
Red Fox
Rock Hyrax (a)
Rock Hyrax (b)
Roe Deer
Sheep
Slow Loris
Star Nosed Mole
Stegosaurus
Tenrec
Tree Hyrax
Tree Shrew
Triceratops
Tyrannosaurus
Water Opossum
class
character
1
factor
body.weight
numeric
64
5
10
23
23
48
60
75
101
104
120
122
200
280
425
550
750
785
900
920
1000
1040
1350
1400
1410
1620
1700
2000
2500
3000
3300
3385
3500
3500
3600
4235
4288
14830
27660
35000
36330
55500
60000
85000
100000
160000
187100
192000
200000
250000
400000
465000
521000
529000
2000000
2300000
2547000
3400000
5000000
6654000
7700000
9400000
11700000
58059000
87000000
brain.weight
numeric
64
0.14000000000000001
0.25
0.29999999999999999
0.40000000000000002
0.33000000000000002
1
1.2
4
2.5
1
3
5
1.8999999999999999
6.4000000000000004
2.3999999999999999
12.300000000000001
3.5
2.6000000000000001
5.7000000000000002
6.5999999999999996
5.5
8.0999999999999996
12.5
17.5
11.4
6.2999999999999998
12.300000000000001
12.1
25
25.600000000000001
44.5
10.800000000000001
3.8999999999999999
21
50.399999999999999
39.200000000000003
98.200000000000003
115
56
119.5
175
81
325
157
169
419
180
30
490
46
423
655
680
56
335
4603
300
250
5712
404
140
100
6800
309
row.names
character
64
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
class
character
1
data.frame
barley
structure
3
.Data
list
4
yield
numeric
120
27
48.866669999999999
27.433340000000001
39.933329999999998
32.966670000000001
28.966670000000001
43.066659999999999
55.200000000000003
28.766670000000001
38.133330000000001
29.133330000000001
29.66667
35.133330000000001
47.333329999999997
25.766670000000001
40.466670000000001
29.66667
25.699999999999999
39.899999999999999
50.233330000000002
26.133330000000001
41.333329999999997
23.033329999999999
26.300000000000001
36.566659999999999
63.833300000000001
43.766669999999998
46.933329999999998
29.766670000000001
33.933329999999998
43.266669999999998
58.100000000000001
28.699999999999999
45.666670000000003
32.166670000000003
33.600000000000001
36.600000000000001
65.7667
30.366669999999999
48.566659999999999
24.933340000000001
28.100000000000001
32.766669999999998
48.566659999999999
29.866669999999999
41.600000000000001
34.700000000000003
32
24.66667
46.766669999999998
22.600000000000001
44.100000000000001
19.699999999999999
33.066659999999999
39.299999999999997
58.799999999999997
29.466670000000001
49.866669999999999
34.466670000000001
31.600000000000001
26.899999999999999
33.466670000000001
34.366660000000003
32.966670000000001
22.133330000000001
22.566669999999998
36.799999999999997
37.733330000000002
35.133330000000001
26.16667
14.43333
25.866669999999999
27.433340000000001
38.5
35.033329999999999
20.633330000000001
16.633330000000001
22.233329999999999
26.800000000000001
37.399999999999999
38.833329999999997
32.066659999999999
32.233330000000002
22.466670000000001
29.066669999999998
49.2333
46.633330000000001
41.833329999999997
20.633330000000001
30.600000000000001
26.433340000000001
42.200000000000003
43.533340000000003
34.333329999999997
19.466670000000001
22.699999999999999
25.566669999999998
44.700000000000003
47
30.533329999999999
19.899999999999999
22.5
28.066669999999998
36.033329999999999
43.200000000000003
25.233329999999999
26.766670000000001
31.366669999999999
30
41.266669999999998
44.233330000000002
32.133330000000001
15.23333
27.366669999999999
38
58.166670000000003
47.166670000000003
35.899999999999999
20.66667
29.33333
variety
structure
3
.Data
integer
120
3
3
3
3
3
3
7
7
7
7
7
7
1
1
1
1
1
1
5
5
5
5
5
5
10
10
10
10
10
10
8
8
8
8
8
8
2
2
2
2
2
2
6
6
6
6
6
6
4
4
4
4
4
4
9
9
9
9
9
9
3
3
3
3
3
3
7
7
7
7
7
7
1
1
1
1
1
1
5
5
5
5
5
5
10
10
10
10
10
10
8
8
8
8
8
8
2
2
2
2
2
2
6
6
6
6
6
6
4
4
4
4
4
4
9
9
9
9
9
9
.Label
character
10
Svansota
No. 462
Manchuria
No. 475
Velvet
Peatland
Glabron
No. 457
Wisconsin No. 38
Trebi
class
character
2
ordered
factor
year
structure
3
.Data
integer
120
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.Label
character
2
1932
1931
class
character
2
ordered
factor
site
structure
3
.Data
integer
120
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
3
6
4
5
1
2
.Label
character
6
Grand Rapids
Duluth
University Farm
Morris
Crookston
Waseca
class
character
2
ordered
factor
row.names
character
120
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
class
character
1
data.frame
bin.packing
structure
3
.Data
list
2
empty.space
numeric
275
1.5771269604424727
1.242905970725666
1.3892459682715281
0.63631701286314279
0.44334998737769443
1.5228420823430029
1.0353560455891073
4.5827678535744401
1.3641070442986873
0.84452098154064703
1.3439380004846906
1.9079580165867267
1.0057079791090084
1.0875469461203788
1.0075709818167302
1.1792910027093397
4.4688637400928481
1.1694929550074666
2.4868290414650325
0.44876799803911532
4.1142191923767104
1.2977850534325515
1.2885470232835798
1.1557819891669459
1.2915450334585945
0.83193498797223564
0.4018910079583865
1.0583230248207849
1.6362520260147813
0.70976502179270351
1.3732329638511493
1.1513919771767527
1.4652309591120101
0.55284501361657989
1.3321080166882286
0.92283201649782942
1.2443710645576149
1.3399829899235403
0.89293801643315429
1.5888580157429617
0.56909398992653881
0.55757497176450466
0.93340999450209283
0.71047699381479135
3.0105467986915553
0.56710602736760662
1.2048669981029188
0.59488701752162565
1.3964569333622188
0.84944802411460629
6.7839408209576142
1.0293910503582888
0.80302101459671693
0.94090097934887218
1.2516690593870883
1.0267230260312654
1.1554289968633087
1.49417200786587
0.61118799462301132
0.89066797242382645
1.1670440448220842
0.86472099798886159
0.76116599136844987
1.2171269562595373
1.1517959846148174
0.69378501457288366
0.95631301130188351
1.1806540492854347
0.97764897423278529
1.2886110412258209
0.8572760170144037
1.3879450281413921
1.4433150234838394
0.89489298903970649
1.5550099265664237
0.76370399435428693
6.4154639549049453
1.57099399722077
1.5267080042533423
0.9522969722857646
1.7636959933338492
1.2516319821123421
0.75221902281070585
0.65995001024610211
1.1576830216557743
1.7331429957465772
0.74075001352434033
1.1794960433893811
1.5746929488312809
1.0782059477961665
1.0093599558126263
1.25177096291026
1.200206035937496
6.756915542505074
1.2850650411795055
1.5675679482445046
0.86519903366892614
0.94155198434967335
0.8830599740324786
1.6171840604879282
1.5418330351048608
1.6147329913232724
0.83562999835853247
1.3315160184764177
1.0043619869064977
1.7923899637331382
1.0623550410535461
1.8674689350026457
1.2953959852957204
1.2516510635851845
1.490779033812184
0.85720401487858888
0.93888300232827615
1.7033058918560773
0.83833700639364006
1.6604151012012909
1.465119940823105
1.483548018060538
1.5467109575680484
1.2854160285067344
0.89554101552722709
0.8030999954857827
1.7989959389015693
1.89845803930901
1.0918409882255795
1.0692160120961405
1.866615959938956
1.5568449436629659
1.2241660363842601
1.4283089698763449
1.3002300429156555
1.9670669829101761
1.3335180081731588
1.2384049845197647
1.4545580445304171
1.4805300470490821
1.0464620574751049
1.3436909668475745
1.5757509538059957
1.9497750393841793
1.9714499802747629
1.4235179525919817
1.298208943903064
1.5412450004557741
1.3978409845129112
2.0961389594372117
1.2835479784322492
1.9253318926917091
1.8639370016414489
1.3165500256375251
2.1987739906052219
1.777329023802926
2.28563910970832
2.334373956779662
1.5815089628848829
1.6943900580528117
1.4312640121441753
1.6972819122214371
2.306060159735309
1.8545240379074468
2.2298802089216081
1.8965329809073432
2.0202961355960776
2.0311318741293927
2.0344838840321726
2.0626399454939786
2.1970310625481893
1.7689950829705492
1.9012818932970827
1.4451199664745285
2.2286670417372472
2.5695528226761226
1.6412680168446689
1.7590030724391124
1.6737108815218811
2.3125391378019007
1.6704309733494613
2.1716409891667201
2.5120620224766395
2.0352608619919637
2.3743409580496579
2.40960004510073
1.9698610418659035
2.0566139997342412
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2.0291909906662555
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1.887240992091916
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1.7887550986621739
1.9947010560858771
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2.4385150465585879
2.3921398494212989
2.46031599444545
2.4622258776205976
2.4857599240393746
2.0973931516550182
1.9970649219950329
3.0389600998045156
2.8542698238327331
2.9161631496622871
2.4045538624996459
2.6733040341941248
2.1496078939941241
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2.8623519334943719
2.2540308888226721
2.1547878755965648
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2.0469720068357113
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2.1601709627811569
2.6176090398188063
2.4628318343045295
2.5244468487474512
2.2368071063808084
2.8529979479007173
3.534629968296565
3.0503699670671933
3.2855060390109427
3.455059021277064
2.6742858673751702
3.0920538194528948
2.6897509402392039
2.9117092514237606
3.4505449485504025
3.0462270514762477
2.8453799959790658
3.2173138984442651
3.0642172147269862
2.9417078524235545
2.8157589545806734
3.0005800876754138
3.6745431854390707
2.8490341086456707
3.8709281111038938
3.6341661011454303
2.9119820970327854
3.2975660851725013
3.1530620030866863
2.677820931107052
3.5184577629232958
4.4707998221034861
4.0357256745316521
3.3955289922667276
4.0412577337863826
4.2052976791094272
3.6619431062957641
3.2683710801391337
3.6933021605658953
4.143940981440223
4.2182829342190722
3.8234282105423669
3.2975372027888894
3.8894389531683258
3.7445631880977279
3.5961520578382813
4.0579349945164154
4.0102976408824196
3.4674039170589555
3.5284369046110786
4.2428380215827781
3.7045668719037219
4.100688117741746
3.8038558568745033
3.6826406903600075
number.runs
numeric
275
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
125
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
250
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
500
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
2000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
4000
8000
8000
8000
8000
8000
8000
8000
8000
8000
8000
8000
8000
8000
8000
8000
8000
8000
8000
8000
8000
8000
8000
8000
8000
8000
16000
16000
16000
16000
16000
16000
16000
16000
16000
16000
16000
16000
16000
16000
16000
16000
16000
16000
16000
16000
16000
16000
16000
16000
16000
32000
32000
32000
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23
24
25
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31
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36
37
38
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41
42
43
44
45
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50
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63
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81
82
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89
90
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100
101
102
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107
108
109
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118
119
120
121
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126
127
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139
140
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145
146
147
148
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152
153
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165
166
167
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255
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16
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190
118
149
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322
44
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269
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314
276
267
272
175
264
175
48
260
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285
187
220
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294
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81
82
213
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253
254
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24
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192
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259
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183
189
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230
259
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112
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224
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238
201
238
14
139
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191
131
223
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11.5
row.names
character
111
1
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4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
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98
99
100
101
102
103
104
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823
1
2
3
4
5
6
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9
10
11
12
13
14
15
16
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727
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729
730
731
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785
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795
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802
803
804
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807
808
809
810
811
812
813
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815
816
817
818
819
820
821
822
823
class
character
1
data.frame
food.web
structure
3
.Data
list
2
mean.length
single
113
3.130000114440918
2.7100000381469727
2.2999999523162842
2.7400000095367432
2
3.8199999332427979
2.7899999618530273
2.440000057220459
2.8599998950958252
2
2
2.25
2.5
2.4000000953674316
2.3299999237060547
2.1400001049041748
3.559999942779541
2
3
3.2599999904632568
4.6100001335144043
3.690000057220459
2.4000000953674316
2.7000000476837158
2.1600000858306885
2.9300000667572021
2.8900001049041748
1.9600000381469727
3.1400001049041748
5.0199999809265137
3.9000000953674316
3.8599998950958252
1.9299999475479126
2.559999942779541
2.7200000286102295
2.0699999332427979
2.75
2.130000114440918
1.7999999523162842
1.8799999952316284
5.9200000762939453
4.9499998092651367
3.130000114440918
3.630000114440918
2.1400001049041748
4.429999828338623
4.2199997901916504
3.5299999713897705
2.559999942779541
2.440000057220459
3.2799999713897705
2.0799999237060547
1.9500000476837158
2.5799999237060547
2.4600000381469727
2.2200000286102295
3.2899999618530273
4.2800002098083496
2.369999885559082
2.3599998950958252
2
3
3.1600000858306885
1.6699999570846558
1.8500000238418579
2.9300000667572021
3.940000057220459
2.630000114440918
3.619999885559082
2.4900000095367432
5.1500000953674316
3.9500000476837158
2.380000114440918
2.380000114440918
2.75
2.6700000762939453
3.630000114440918
3.1500000953674316
3.4100000858306885
3.3499999046325684
2.7300000190734863
3.7100000381469727
2.4500000476837158
3.6099998950958252
3.6099998950958252
4.0900001525878906
2.9100000858306885
1.9500000476837158
2.8900001049041748
1.8400000333786011
3
2
2.119999885559082
3.3499999046325684
2.5
1.9199999570846558
2
3.5399999618530273
2.5099999904632568
3.3399999141693115
1.6699999570846558
3.9700000286102295
5.5900001525878906
3.1600000858306885
3.6700000762939453
2.4100000858306885
2.5
2.2699999809265137
2.880000114440918
2.130000114440918
2.440000057220459
1.8300000429153442
2.1099998950958252
dimension
structure
3
.Data
integer
113
2
2
1
2
2
2
2
2
2
1
1
1
1
2
2
2
3
2
3
3
3
2
1
3
3
2
3
2
3
3
3
3
2
1
1
2
2
2
2
3
3
3
3
2
1
3
2
2
2
1
2
1
1
2
1
1
2
2
3
3
1
1
1
1
1
1
2
3
2
2
3
3
3
1
3
2
2
2
2
2
2
2
2
2
2
3
2
1
3
1
3
1
1
1
1
1
1
1
1
1
2
3
3
1
1
1
1
1
1
1
1
2
2
.Label
character
3
Two
Mixed
Three
class
character
2
ordered
factor
row.names
character
113
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
class
character
1
data.frame
fusion.time
structure
3
.Data
list
2
time
numeric
78
47.200009999999999
21.999980000000001
20.399989999999999
19.700009999999999
17.399999999999999
14.699999999999999
13.399990000000001
13
12.300000000000001
12.200010000000001
10.300000000000001
9.6999999999999993
9.6999999999999993
9.5
9.0999999999999996
8.9000000000000004
8.9000000000000004
8.4000000000000004
8.09999
7.9000000000000004
7.7999999999999998
6.9000000000000004
6.2999999999999998
6.0999999999999996
5.5999999999999996
4.7000000000000002
4.7000000000000002
4.2999999999999998
4.2000000000000002
3.8999999999999999
3.3999999999999999
3.1000000000000001
3.1000000000000001
2.7000000000000002
2.3999999999999999
2.2999999999999998
2.2999999999999998
2.1000000000000001
2.1000000000000001
2
1.8999999999999999
1.7
1.7
19.700009999999999
16.19998
15.9
15.40002
9.6999999999999993
8.9000000000000004
8.5999999999999996
8.5999999999999996
7.4000000000000004
6.2999999999999998
6.0999999999999996
6
6
5.9000000000000004
4.9000000000000004
4.5999999999999996
3.7999999999999998
3.6000000000000001
3.5
3.2999999999999998
3.2999999999999998
2.8999999999999999
2.7999999999999998
2.7000000000000002
2.3999999999999999
2.2999999999999998
2
1.8
1.7
1.7
1.6000000000000001
1.3999999999999999
1.2
1.1000000000000001
1
nv.vv
structure
3
.Data
integer
78
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
.Label
character
2
NV
VV
class
character
1
factor
row.names
character
78
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
class
character
1
data.frame
ganglion
structure
3
.Data
list
2
area
numeric
14
11.051
13.865
22.513999999999999
32.810000000000002
33.771000000000001
31.094000000000001
53.607999999999997
62.600000000000001
81.063999999999993
66.512
106.873
100.55800000000001
113.325
140.91800000000001
cp.ratio
numeric
14
2.5597584798437221
2.8168341294679626
2.957259209444818
3.4223177332059853
3.4966101694915253
3.7512701817771252
4.5491021946353358
4.6822066822066821
6.85273492286115
6.9825723248518647
10.90625
11.255759007678677
11.104895104895105
18.299625468164795
row.names
character
14
1
2
3
4
5
6
7
8
9
10
11
12
13
14
class
character
1
data.frame
hamster
structure
3
.Data
list
6
lung
numeric
73
0.96950000000000003
1.141003
0.872
0.7399
1.139302
1.1256029999999999
0.36720000000000003
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0.49719999999999998
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0.6532
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0.56130000000000002
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0.622
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1.050003
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0.87390000000000001
0.82599999999999996
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1.177802
1.011903
0.98199999999999998
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0.61899999999999999
0.78490000000000004
0.50919999999999999
0.56869999999999998
1.4364030000000001
1.072503
1.1626030000000001
0.96499999999999997
1.023903
0.3216
1.1653020000000001
0.85319999999999996
0.63539999999999996
0.64890000000000003
0.96199999999999997
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0.98240000000000005
0.95920000000000005
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1.1194029999999999
1.074803
0.93640000000000001
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1.094503
1.140603
0.7722
1.026003
1.4602029999999999
0.8891
1.652102
0.81000000000000005
1.1918029999999999
0.72370000000000001
0.83850000000000002
1.066703
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1.0052030000000001
heart
numeric
73
1.3520019999999999
1.499403
1.2685029999999999
1.270203
1.4962029999999999
1.191403
0.6986
0.41930000000000001
0.41339999999999999
0.52980000000000005
0.60199999999999998
0.42299999999999999
0.51290000000000002
0.57150000000000001
0.51870000000000005
0.54790000000000005
0.50009999999999999
0.94230000000000003
0.79500000000000004
0.50509999999999999
0.59989999999999999
1.034402
0.74460000000000004
0.90229999999999999
0.76280000000000003
0.89810000000000001
1.5094030000000001
0.67230000000000001
0.96040000000000003
0.94830099999999995
1.3692029999999999
1.2080029999999999
1.286003
1.0690029999999999
1.000202
1.116703
0.86980000000000002
0.87909999999999999
0.99319999999999997
0.94840000000000002
1.2746029999999999
1.5450029999999999
1.0789029999999999
0.49869999999999998
1.6300030000000001
1.1439029999999999
0.89400000000000002
1.239403
0.80389999999999995
1.038503
1.1543030000000001
1.490302
1.0950029999999999
1.276303
1.4415020000000001
1.1714020000000001
1.2496020000000001
1.1425019999999999
1.524003
1.3694029999999999
1.791703
1.181203
1.536402
1.8390029999999999
1.6295029999999999
1.086903
1.1765030000000001
1.772203
0.93940000000000001
1.3015030000000001
1.035703
1.046203
1.0150030000000001
liver
numeric
73
8.1854960000000005
8.5445069999999994
6.8230089999999999
9.0675089999999994
9.6219929999999998
9.7432090000000002
2.7266029999999999
1.744202
2.6617030000000002
3.262105
3.6231059999999999
2.0776029999999999
2.8608030000000002
2.8093029999999999
3.602503
2.863604
2.9257010000000001
2.6974040000000001
2.746604
3.1271040000000001
3.4968020000000002
3.1108030000000002
2.6780029999999999
8.3070909999999998
3.7972049999999999
4.8976980000000001
7.4927970000000004
2.6366049999999999
12.80401
5.3908969999999998
8.1258909999999993
7.1872040000000004
7.9245020000000004
4.402304
8.0259049999999998
5.9198950000000004
3.264904
3.6023010000000002
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6.9735990000000001
11.9148
6.5425040000000001
8.1096109999999992
1.6264019999999999
7.0897030000000001
4.0120009999999997
2.6877010000000001
8.7761089999999999
3.4576020000000001
8.3850189999999998
7.531301
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15.4923
10.998810000000001
5.9829030000000003
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5.3614030000000001
12.1655
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12.18871
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8.2657070000000008
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8.8597000000000001
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5.7391949999999996
spleen
numeric
73
0.22550020000000001
0.27049980000000001
0.049999990000000001
0.1129998
0.1198
0.1392999
0.047800009999999997
0.0448
0.056799959999999997
0.075100029999999998
0.070100010000000004
0.038300019999999997
0.074499960000000004
0.055900020000000002
0.060400000000000002
0.068700040000000004
0.078000070000000005
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0.065699939999999998
0.075500010000000006
0.043600050000000001
0.066799940000000002
0.04500005
0.099699969999999999
0.068499920000000006
0.082099969999999994
0.1739001
0.053600019999999998
0.1192998
0.082799940000000002
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0.078399979999999994
0.1193999
0.075200020000000006
0.1056
0.095200010000000002
0.087599990000000003
0.1795998
0.1187
0.1042999
0.1072
0.10620010000000001
0.1424
0.029300030000000001
0.1443999
0.07689994
0.046300000000000001
0.1144998
0.071300080000000002
0.19899990000000001
0.059500009999999999
0.15670000000000001
0.2959
0.16170000000000001
0.1308
0.1283
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0.1043998
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0.089600009999999994
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0.062800060000000005
0.13300010000000001
0.073899930000000003
0.12139999999999999
0.16599990000000001
0.042099989999999997
0.066399990000000006
0.1018999
0.056799959999999997
0.095000080000000001
kidney
numeric
73
1.3480030000000001
1.322103
0.89700100000000005
1.278003
1.415003
1.3405020000000001
0.81989999999999996
0.82240000000000002
1.0697030000000001
0.96009999999999995
1.163503
0.81989999999999996
0.98250000000000004
1.007403
1.0751029999999999
0.95960000000000001
0.95889999999999997
1.0797030000000001
1.010103
1.0798030000000001
1.197603
1.2131019999999999
1.0426029999999999
1.368603
1.0629029999999999
1.011903
1.3171029999999999
1.098303
1.5933029999999999
1.187203
1.3538019999999999
1.4338029999999999
1.6396029999999999
1.2131019999999999
1.5638030000000001
1.258203
1.348203
1.177203
1.316503
1.469303
1.4999020000000001
1.267703
1.4012020000000001
0.82520000000000004
1.288403
1.2271030000000001
0.99390100000000003
1.426202
1.1646030000000001
1.3135030000000001
1.386703
1.3642030000000001
1.4378029999999999
1.339402
1.1359030000000001
1.237903
1.179503
1.1625030000000001
1.393303
1.3234030000000001
1.4783029999999999
1.169303
1.3031029999999999
1.2291030000000001
1.7082029999999999
1.1400030000000001
1.2493030000000001
1.738103
1.030402
1.369103
1.3696029999999999
1.1443019999999999
0.89680000000000004
testes
numeric
73
0.34150019999999998
0.41699999999999998
1.220002
0.8145
1.156803
1.359901
0.77699989999999997
0.93179999999999996
2.0503019999999998
1.518802
1.546403
0.50379980000000002
1.524502
1.7051019999999999
0.94489999999999996
1.5587029999999999
1.6449009999999999
0.90539999999999998
1.486202
1.486202
1.9766030000000001
0.65539990000000004
0.7566001
1.5738030000000001
0.32050000000000001
1.2826029999999999
0.97919999999999996
0.8226
1.7674030000000001
0.63870009999999999
0.49259999999999998
0.83160000000000001
0.76280009999999998
0.58509990000000001
0.89960010000000001
0.66020000000000001
0.43480000000000002
0.74770009999999998
0.98109999999999997
0.5630001
1.4698020000000001
0.47689999999999999
1.068703
0.31679990000000002
0.56319989999999998
0.64539999999999997
0.39929999999999999
0.77170000000000005
1.4088020000000001
1.303803
0.38120009999999999
0.30820029999999998
1.4476039999999999
1.3480030000000001
2.1885029999999999
2.2372030000000001
0.96619999999999995
2.2818019999999999
2.4244029999999999
1.754702
1.486003
1.251603
1.1469009999999999
1.7150030000000001
0.97609999999999997
1.1141019999999999
1.9837009999999999
1.7519020000000001
0.4890002
0.75460000000000005
0.58689990000000003
0.48499999999999999
0.43869999999999998
row.names
character
73
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
class
character
1
data.frame
livestock
structure
3
.Data
list
3
livestock.type
structure
3
.Data
integer
130
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
.Label
character
5
Horses
Sheep
Cattle
Pigs
Poultry
class
character
2
ordered
factor
country
structure
3
.Data
integer
130
1
8
11
13
14
9
24
4
23
16
3
10
12
18
15
2
20
7
21
19
5
6
25
22
26
17
1
8
11
13
14
9
24
4
23
16
3
10
12
18
15
2
20
7
21
19
5
6
25
22
26
17
1
8
11
13
14
9
24
4
23
16
3
10
12
18
15
2
20
7
21
19
5
6
25
22
26
17
1
8
11
13
14
9
24
4
23
16
3
10
12
18
15
2
20
7
21
19
5
6
25
22
26
17
1
8
11
13
14
9
24
4
23
16
3
10
12
18
15
2
20
7
21
19
5
6
25
22
26
17
.Label
character
26
Albania
Norway
Greece
Finland
Sweden
Switzerland
Portugal
Austria
Denmark
Hungary
Belgium
Ireland
Bulgaria
Czechoslovakia
Netherlands
East Germany
Yugoslavia
Italy
Spain
Poland
Romania
United Kingdom
France
West Germany
Turkey
Russia et al.
class
character
2
ordered
factor
count
numeric
130
478000
2536000
3246000
1796000
5131000
2873000
15098000
1719000
23599000
5690000
831000
1922000
6908000
8734000
5241000
971000
11912000
1173000
6303000
4495000
1902000
1954000
16983000
13155000
42200000
5474000
125000
3920000
5108000
3808000
7126000
9317000
22478000
1475000
11711000
12107000
1107000
9035000
1031000
8928000
10254000
687000
19471000
2448000
12464000
11263000
2677000
2071000
11000
7975000
33100000
7867000
484000
11371000
29448000
41096000
49212000
16296000
83033000
7763000
234131000
51356000
29846000
45397000
9903000
137999000
88174000
5571000
67244000
4143000
109244000
42824000
11393000
6188000
59660000
130018000
385000000
65690000
43000
35000
31000
120000
44000
41000
369000
20000
278000
81000
420000
120000
68000
273000
59000
25000
1734000
23000
566000
242000
57000
45000
784000
140000
1556000
573000
914000
326000
123000
11271000
990000
58000
1172000
104000
14346000
2220000
12669000
3183000
3323000
10497000
1210000
1999000
3899000
2811000
17748000
16543000
437000
336000
70093000
32888000
21000000
7384000
row.names
character
130
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
class
character
1
data.frame
melanoma
structure
3
.Data
list
2
year
numeric
37
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
incidence
numeric
37
0.90000000000000002
0.80000000000000004
0.80000000000000004
1.3
1.3999999999999999
1.2
1.7
1.8
1.6000000000000001
1.5
1.5
2
2.5
2.7000000000000002
2.8999999999999999
2.5
3.1000000000000001
2.3999999999999999
2.2000000000000002
2.8999999999999999
2.5
2.6000000000000001
3.2000000000000002
3.7999999999999998
4.2000000000000002
3.8999999999999999
3.7000000000000002
3.2999999999999998
3.7000000000000002
3.8999999999999999
4.0999999999999996
3.7999999999999998
4.7000000000000002
4.4000000000000004
4.7999999999999998
4.7999999999999998
4.7999999999999998
row.names
character
37
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
class
character
1
data.frame
ozone
structure
3
.Data
list
2
yonkers
numeric
132
47
37
45
52
51
22
27
25
55
72
132
106
42
45
80
107
21
50
31
37
19
33
22
45
36
24
88
111
117
31
37
93
106
64
83
97
79
36
51
75
104
107
68
19
67
20
35
30
31
81
119
76
108
85
96
48
60
54
71
50
37
47
71
46
41
49
59
25
45
40
13
46
62
80
39
74
66
82
47
28
44
55
34
60
70
41
96
54
100
44
44
75
86
70
53
36
117
43
27
77
75
87
47
114
66
18
25
14
27
9
16
67
74
74
75
74
42
38
23
50
34
58
35
24
27
17
21
14
32
51
15
21
stamford
numeric
132
66
52
49
64
68
26
86
52
75
87
188
103
82
71
103
240
31
40
47
51
31
47
14
71
61
47
196
131
173
37
47
215
230
69
98
125
94
72
72
125
143
192
122
32
114
32
23
71
38
136
169
152
201
134
206
92
101
119
124
83
60
124
142
124
64
75
103
46
68
87
27
73
59
119
64
111
80
68
24
24
82
100
55
91
87
64
170
86
202
71
85
122
155
80
71
28
212
80
24
80
169
174
141
202
113
38
38
28
52
14
38
94
89
99
150
146
113
66
38
80
80
99
71
42
52
33
38
24
61
108
38
28
row.names
character
132
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
class
character
1
data.frame
polarization
structure
3
.Data
list
2
concentration
numeric
355
9
9
10
12
12
13.5
14
15
15
15
15
15
15.5
15.5
15.5
16
16
16
16
16
17
19
19
19
19
19
19
19
19
19
19
19
19
20
20.5
20.5
21
21
21
22
22
22.5
23
24
24
24
24
24
24.5
25
25
25.5
25.5
25.5
25.5
26.5
26.5
26.5
26.5
26.5
27
27
27.5
27.5
27.5
27.5
29
29
29
29.5
29.5
29.5
30
30
30
30
30.5
30.5
30.5
30.5
30.5
30.5
30.5
30.5
30.5
30.5
31
31.5
31.5
31.5
31.5
31.5
31.5
31.5
31.5
31.5
31.5
31.5
31.5
31.5
31.5
31.5
33
33
33
34
34
34
34.5
34.5
35
35
35.5
35.5
35.5
35.5
35.5
36
36
36
37
37
37
37
37
37.5
37.5
38.5
38.5
39
39
39
39
39
39.5
39.5
39.5
39.5
39.5
40
40
40
40.5
40.5
40.5
40.5
41
41.5
41.5
41.5
41.5
41.5
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
42
43
43.5
44.5
45
45.5
45.5
45.5
45.5
45.5
45.5
45.5
45.5
45.5
45.5
46
46
46
46.5
46.5
47
47
47
48
48
48.5
48.5
48.5
48.5
48.5
49
49
50.5
50.5
50.5
50.5
50.5
50.5
50.5
51
51
51
51
51
51.5
52
52
52.5
52.5
52.5
54
54
54
55.5
56.5
56.5
56.5
56.5
56.5
56.5
56.5
56.5
57.5
59.5
60
60
61
61
61
62
62
62
62
62
62
62
62
62
62
62.5
62.5
63
63
63
63
63
63.5
64
64.5
64.5
64.5
65
65
66
66
66
66
66.5
66.5
66.5
66.5
66.5
66.5
68.5
68.5
68.5
69
69
69
69.5
69.5
70
70
70
70
70.5
70.5
70.5
71.5
71.5
71.5
72
72.5
73
73.5
73.5
74
75
76
77
77.5
77.5
78
78
78
78
78
78
79.5
80
80
80
80
80.5
80.5
80.5
81
81
81.5
81.5
81.5
82
82
82
82.5
85
85
85.5
85.5
86.5
86.5
89.5
89.5
90
90
90
90
90.5
92
92
92
92
96
98
98
100.5
106
107
108.5
112
116
118.5
118.5
121.5
122.5
babinet
numeric
355
25.899999999999999
24.600000000000001
20.699999999999999
22.5
25.300000000000001
23.899999999999999
25.5
20.600000000000001
20.899999999999999
24.199999999999999
24.300000000000001
24.600000000000001
24.100000000000001
24.199999999999999
25.699999999999999
23.399999999999999
24.100000000000001
24.199999999999999
24.600000000000001
25.5
22.5
22.199999999999999
23.699999999999999
23.899999999999999
23.899999999999999
24
24
24.899999999999999
24.899999999999999
25.100000000000001
25.300000000000001
25.399999999999999
25.600000000000001
25
22.600000000000001
23.199999999999999
21.300000000000001
21.5
24.199999999999999
23.300000000000001
24.699999999999999
24.199999999999999
24
24.300000000000001
24.800000000000001
25.100000000000001
25.300000000000001
25.800000000000001
25.699999999999999
23.100000000000001
23.699999999999999
24.199999999999999
24.800000000000001
25.100000000000001
25.300000000000001
23.300000000000001
23.600000000000001
23.800000000000001
23.899999999999999
25.100000000000001
18
18
22.399999999999999
22.699999999999999
24.100000000000001
24.300000000000001
23.199999999999999
24.699999999999999
24.899999999999999
22.699999999999999
23.5
23.699999999999999
24
24.199999999999999
24.399999999999999
24.5
18
18
20
21.199999999999999
21.800000000000001
24
24
24.699999999999999
24.699999999999999
24.899999999999999
24.399999999999999
21.199999999999999
21.399999999999999
21.800000000000001
22.199999999999999
22.199999999999999
22.300000000000001
22.300000000000001
22.300000000000001
22.399999999999999
22.5
22.699999999999999
23.399999999999999
23.600000000000001
23.899999999999999
24.199999999999999
23.699999999999999
23.800000000000001
23.899999999999999
23.600000000000001
23.800000000000001
23.899999999999999
22.199999999999999
22.5
20.800000000000001
21
21.100000000000001
21.899999999999999
22.399999999999999
23.100000000000001
23.600000000000001
22.100000000000001
22.600000000000001
23.199999999999999
20.800000000000001
21.800000000000001
21.899999999999999
22.300000000000001
23.300000000000001
22.5
23.100000000000001
21.100000000000001
21.300000000000001
20.600000000000001
20.800000000000001
21.5
21.5
21.800000000000001
22.600000000000001
22.600000000000001
22.699999999999999
22.899999999999999
23.100000000000001
20.100000000000001
21
25
20.5
22.5
22.600000000000001
23.300000000000001
21.399999999999999
20.800000000000001
21.100000000000001
22.5
22.600000000000001
22.699999999999999
19.5
19.699999999999999
19.800000000000001
20
20.199999999999999
20.300000000000001
20.399999999999999
20.600000000000001
21.5
21.699999999999999
22
22.100000000000001
22.199999999999999
22.300000000000001
22.899999999999999
23.199999999999999
23.5
24.399999999999999
23.800000000000001
21.399999999999999
19.699999999999999
23.800000000000001
22.600000000000001
20.5
20.699999999999999
20.800000000000001
20.899999999999999
21
21.300000000000001
21.399999999999999
21.600000000000001
21.699999999999999
22.300000000000001
22.5
23.800000000000001
24.199999999999999
20.699999999999999
22.699999999999999
20
20.100000000000001
20.199999999999999
21
22.399999999999999
19.699999999999999
20.5
20.600000000000001
21.600000000000001
21.800000000000001
20.199999999999999
20.300000000000001
19.399999999999999
19.899999999999999
20
20.199999999999999
20.300000000000001
24.199999999999999
24.199999999999999
21.600000000000001
22.300000000000001
22.5
24.699999999999999
24.699999999999999
17.5
19.899999999999999
20.899999999999999
24.100000000000001
24.100000000000001
24.100000000000001
20.300000000000001
22.199999999999999
22.300000000000001
17.899999999999999
17.699999999999999
17.899999999999999
19.100000000000001
19.300000000000001
19.699999999999999
19.800000000000001
19.899999999999999
21.199999999999999
22.300000000000001
19.899999999999999
18.199999999999999
20
18.100000000000001
22.699999999999999
23.300000000000001
18.199999999999999
18.699999999999999
18.899999999999999
19
19.100000000000001
19.300000000000001
19.600000000000001
19.699999999999999
23.899999999999999
23.899999999999999
20.699999999999999
20.899999999999999
23.600000000000001
23.600000000000001
24.199999999999999
24.199999999999999
24.199999999999999
19.699999999999999
18
23.899999999999999
23.899999999999999
23.899999999999999
19.899999999999999
21
18.800000000000001
19
19.199999999999999
20.800000000000001
18
18.100000000000001
18.300000000000001
20.399999999999999
20.399999999999999
20.399999999999999
18.5
18.600000000000001
20.5
18.100000000000001
18.300000000000001
18.899999999999999
20.699999999999999
20.699999999999999
14.5
14.5
18.5
19.800000000000001
18.199999999999999
18.300000000000001
18.899999999999999
17.300000000000001
18.699999999999999
21.600000000000001
18.5
16.399999999999999
19
19.300000000000001
19.699999999999999
17.300000000000001
18.300000000000001
18.800000000000001
17.399999999999999
21
21
17.5
17.600000000000001
17.699999999999999
17.800000000000001
18
18.5
17.699999999999999
19.5
21.899999999999999
21.899999999999999
21.899999999999999
16
16.800000000000001
17.300000000000001
20
18
15.800000000000001
22.699999999999999
22.699999999999999
16.800000000000001
17.399999999999999
22.399999999999999
18.199999999999999
17.899999999999999
18.399999999999999
17.300000000000001
17.699999999999999
18
18.199999999999999
17.399999999999999
17.5
15
22.899999999999999
22.899999999999999
22.899999999999999
17.100000000000001
15
15.800000000000001
17.399999999999999
17.5
16.100000000000001
16.199999999999999
16
15
15.699999999999999
15.5
15.9
17.899999999999999
16.100000000000001
15.199999999999999
15.1
16.399999999999999
15.6
row.names
character
355
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
class
character
1
data.frame
rubber
structure
3
.Data
list
5
hardness
integer
30
45
55
61
66
71
71
81
86
53
60
64
68
79
81
56
68
75
83
88
59
71
80
82
89
51
59
65
74
81
86
tensile.strength
integer
30
162
233
232
231
231
237
224
219
203
189
210
210
196
180
200
173
188
161
119
161
151
165
151
128
161
146
148
144
134
127
abrasion.loss
integer
30
372
206
175
154
136
112
55
45
221
166
164
113
82
32
228
196
128
97
64
249
219
186
155
114
341
340
283
267
215
148
ts.low
numeric
30
-18
0
0
0
0
0
0
0
0
0
0
0
0
0
0
-7
0
-19
-61
-19
-29
-15
-29
-52
-19
-34
-32
-36
-46
-53
ts.high
numeric
30
0
53
52
51
51
57
44
39
23
9
30
30
16
0
20
0
8
0
0
0
0
0
0
0
0
0
0
0
0
0
row.names
character
30
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
class
character
1
data.frame
run.time
structure
3
.Data
list
4
time
numeric
36
1.25
1.3899999999999999
1.78
3.2400000000000002
2.48
3.8900000000000001
0.80000000000000004
0.89000000000000001
1.23
2.0099999999999998
2.1000000000000001
2.8199999999999998
0.56999999999999995
0.68000000000000005
0.88
0.88
1.7
1.6200000000000001
0.96999999999999997
0.95999999999999996
1.6699999999999999
3.8900000000000001
1.3600000000000001
4.1699999999999999
0.53000000000000003
0.56000000000000005
1.01
2.2000000000000002
0.93000000000000005
2.3100000000000001
0.17000000000000001
0.20999999999999999
0.27000000000000002
0.28000000000000003
0.52000000000000002
0.45000000000000001
algorithm
structure
4
.Data
integer
36
3
3
3
3
3
3
2
2
2
2
2
2
1
1
1
1
1
1
3
3
3
3
3
3
2
2
2
2
2
2
1
1
1
1
1
1
.Label
character
3
new
ucb
7th
.Names
character
36
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
class
character
2
ordered
factor
machine
structure
4
.Data
integer
36
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.Label
character
2
mips
vax
.Names
character
36
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
class
character
2
ordered
factor
input
structure
4
.Data
integer
36
1
2
3
5
4
6
1
2
3
5
4
6
1
2
3
5
4
6
1
2
3
5
4
6
1
2
3
5
4
6
1
2
3
5
4
6
.Label
character
6
integer
float
double
pointer
record
string
.Names
character
36
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
class
character
2
ordered
factor
row.names
character
36
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
class
character
1
data.frame
singer
structure
3
.Data
list
2
height
integer
235
64
62
66
65
60
61
65
66
65
63
67
65
62
65
68
65
63
65
62
65
66
62
65
63
65
66
65
62
65
66
65
61
65
66
65
62
63
67
60
67
66
62
65
62
61
62
66
60
65
65
61
64
68
64
63
62
64
62
64
65
60
65
70
63
67
66
65
62
68
67
67
63
67
66
63
72
62
61
66
64
60
61
66
66
66
62
70
65
64
63
65
69
61
66
65
61
63
64
67
66
68
70
65
65
65
64
66
64
70
63
70
64
63
67
65
63
66
66
64
64
70
70
66
66
66
69
67
65
69
72
71
66
76
74
71
66
68
67
70
65
72
70
68
64
73
66
68
67
64
68
73
69
71
69
76
71
69
71
66
69
71
71
71
69
70
69
68
70
68
69
72
70
72
69
73
71
72
68
68
71
66
68
71
73
73
70
68
70
75
68
71
70
74
70
75
75
69
72
71
70
71
68
70
75
72
66
72
70
69
72
75
67
75
74
72
72
74
72
72
74
70
66
68
75
68
70
72
67
70
70
69
72
71
74
75
voice.part
structure
3
.Data
integer
235
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
8
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.Label
character
8
Bass 2
Bass 1
Tenor 2
Tenor 1
Alto 2
Alto 1
Soprano 2
Soprano 1
class
character
2
ordered
factor
row.names
character
235
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
class
character
1
data.frame
slope
structure
3
.Data
list
4
error
numeric
44
18.90625
16.33333
19.4375
17.44791
17.036460000000002
14.59896
11.63021
9.1354199999999999
7.5833300000000001
6.0416660000000002
5.4583300000000001
16.057279999999999
16.973960000000002
13.69792
12.05729
11.95833
11.97396
9.8177099999999999
9.1822909999999993
7.65625
5.3072900000000001
5.9166660000000002
8.2447909999999993
9.2135409999999993
9.4791699999999999
9.0260409999999993
8.1354199999999999
5.96875
6.3229160000000002
6.40625
6.078125
4.8020800000000001
3.1041669999999999
5.78125
6.3489599999999999
7.9947900000000001
7.9739599999999999
6.5729199999999999
6.4791660000000002
6.9010400000000001
7.1145800000000001
6.6354199999999999
5.7708300000000001
3.3697900000000001
percent
numeric
44
50
55
60
65
70
75
80
85
90
95
100
50
55
60
65
70
75
80
85
90
95
100
50
55
60
65
70
75
80
85
90
95
100
50
55
60
65
70
75
80
85
90
95
100
distance
numeric
44
37.405230000000003
37.152299999999997
36.899790000000003
36.647799999999997
36.396270000000001
36.145299999999999
35.894930000000002
35.645159999999997
35.396079999999998
35.147669999999998
34.899999999999999
30.075050000000001
29.579699999999999
29.087689999999998
28.599270000000001
28.114730000000002
27.634229999999999
27.158049999999999
26.686340000000001
26.21932
25.757149999999999
25.299990000000001
17.435690000000001
16.53969
15.66512
14.81283
13.9834
13.17728
12.39471
11.6358
10.900499999999999
10.18866
9.5
0.264824
1.5744860000000001
2.7964500000000001
3.9352200000000002
4.9957539999999998
5.9832299999999998
6.9028200000000002
7.7595999999999998
8.5583899999999993
9.3037600000000005
10
resolution
numeric
44
5.010459
4.5045900000000003
3.9995970000000001
3.495549
2.9925299999999999
2.4906069999999998
1.9898560000000001
1.4903500000000001
0.99215900000000001
0.49535259999999998
0
9.5500889999999998
8.5593789999999998
7.5753690000000002
6.5985659999999999
5.6294490000000001
4.6684789999999996
3.7160890000000002
2.7726839999999999
1.8386400000000001
0.91430299999999998
0
15.87139
14.079359999999999
12.33024
10.62565
8.9668100000000006
7.3545590000000001
5.7894300000000003
4.2716029999999998
2.8010060000000001
1.377318
0
19.47035
16.851019999999998
14.40709
12.12956
10.00848
8.0335300000000007
6.1943400000000004
4.4807899999999998
2.883213
1.3924639999999999
0
row.names
character
44
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
class
character
1
data.frame
soil
structure
3
.Data
list
5
northing
numeric
8641
0.017999999999999999
0.0178
0.017600000000000001
0.017399999999999999
0.017100000000000001
0.016899999999999998
0.0167
0.016500000000000001
0.016300000000000002
0.0161
0.015900000000000001
0.015599999999999999
0.0154
0.0152
0.014999999999999999
0.014800000000000001
0.0146
0.0144
0.014199999999999999
0.013900000000000001
0.013699999999999999
0.0135
0.013300000000000001
0.013099999999999999
0.0129
0.012699999999999999
0.0124
0.012199999999999999
0.012
0.011800000000000001
0.011599999999999999
0.0114
0.0112
0.010999999999999999
0.010800000000000001
0.010500000000000001
0.0103
0.0101
0.0099000000000000008
0.0096999999999999986
0.0094999999999999998
0.009300000000000001
0.0089999999999999993
0.0088000000000000005
0.0086
0.0084000000000000012
0.008199999999999999
0.0080000000000000002
0.0077999999999999996
0.0076
0.0073000000000000001
0.0070999999999999995
0.0069000000000000008
0.0067000000000000002
0.0064999999999999997
0.0063
0.0060999999999999995
0.0057999999999999996
0.0055999999999999999
0.0054000000000000003
0.0051999999999999998
0.0050000000000000001
0.50800000000000001
0.49910000000000004
0.49030000000000001
0.48139999999999999
0.47249999999999998
0.46360000000000001
0.45480000000000004
0.44589999999999996
0.437
0.42810000000000004
0.41930000000000001
0.41039999999999999
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numeric
8641
0.016
0.0252
0.034500000000000003
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1.1536
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1.1089
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1.091
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1.0284
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0.80510000000000004
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0.86250000000000004
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0.9103
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0.9294
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0.9486
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1.006
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1.0347
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1.0825
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1.1112
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1.159
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1.1877
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1.2259
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1.2642
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1.4842
1.4937
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1.5129000000000001
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1.532
resistivity
numeric
8641
34.149999999999999
34.810000000000002
35.649999999999999
36.439999999999998
37.100000000000001
37.530000000000001
37.579999999999998
37.409999999999997
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39.32
39.93
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36.450000000000003
35.170000000000002
34.57
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36.189999999999998
37.310000000000002
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41.090000000000003
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42.840000000000003
41.350000000000001
37.979999999999997
34.380000000000003
32.170000000000002
31.379999999999999
31.02
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42.020000000000003
51.789999999999999
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44.57
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34.539999999999999
35.310000000000002
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41.350000000000001
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48.43
49.899999999999999
49.82
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43.799999999999997
43.039999999999999
45.840000000000003
50.869999999999997
56.25
61.210000000000001
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72.420000000000002
78.569999999999993
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70.030000000000001
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39.119999999999997
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37.990000000000002
36.640000000000001
34.869999999999997
33.18
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29.18
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32.020000000000003
33.960000000000001
57.240000000000002
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41.380000000000003
40.920000000000002
44.18
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44.840000000000003
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31.260000000000002
28.489999999999998
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31.34
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34.460000000000001
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33.25
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28.02
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30.82
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29.66
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28.890000000000001
28.75
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30.329999999999998
30.640000000000001
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30.149999999999999
30.84
32.979999999999997
36.43
40.630000000000003
45.32
51.060000000000002
58.810000000000002
67.980000000000004
75.230000000000004
76.5
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62.780000000000001
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50.210000000000001
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29.84
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31.27
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31.98
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34.619999999999997
33.600000000000001
31.02
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26.43
25.879999999999999
25.960000000000001
26.010000000000002
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25.68
25.68
25.84
26.030000000000001
26.219999999999999
26.489999999999998
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27.52
28.059999999999999
28.370000000000001
28.329999999999998
27.93
27.25
26.449999999999999
25.699999999999999
25.219999999999999
25.289999999999999
25.98
26.949999999999999
27.719999999999999
28.109999999999999
28.300000000000001
28.43
28.510000000000002
28.489999999999998
28.440000000000001
28.43
28.489999999999998
28.539999999999999
28.420000000000002
28.059999999999999
27.469999999999999
26.780000000000001
26.16
25.75
25.600000000000001
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32.07
30.960000000000001
29.219999999999999
27.640000000000001
26.789999999999999
26.600000000000001
26.719999999999999
27
27.5
28.170000000000002
28.68
28.57
27.68
26.5
25.850000000000001
26.190000000000001
27.43
29.219999999999999
31.260000000000002
33.229999999999997
34.840000000000003
36.100000000000001
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38.259999999999998
37.460000000000001
35.759999999999998
33.530000000000001
31.059999999999999
28.559999999999999
26.390000000000001
25.02
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35.420000000000002
35.590000000000003
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35.520000000000003
34.340000000000003
32.460000000000001
30.68
29.670000000000002
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31.199999999999999
29.879999999999999
28.23
27.09
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28.48
29.030000000000001
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30.07
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29.859999999999999
29.109999999999999
28.57
28.52
28.800000000000001
28.91
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27.530000000000001
26.68
26.219999999999999
25.949999999999999
25.52
24.920000000000002
24.420000000000002
24.149999999999999
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25.93
28.030000000000001
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31.329999999999998
27.170000000000002
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29.77
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30.399999999999999
29.98
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30.91
30.91
30.68
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32.189999999999998
33.950000000000003
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36.82
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41.619999999999997
44.420000000000002
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50.119999999999997
54.25
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51.420000000000002
53.280000000000001
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46.469999999999999
39.899999999999999
35.799999999999997
34.369999999999997
32.509999999999998
28.280000000000001
23.52
21.420000000000002
22.5
24.48
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82.909999999999997
81.730000000000004
80.5
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75.760000000000005
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84.879999999999995
97.450000000000003
111.47
120.77
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88.170000000000002
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81.25
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76.340000000000003
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106.17
113.64
124
138.43000000000001
152.55000000000001
61.810000000000002
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104.44
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60
61.640000000000001
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62
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56.420000000000002
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48.32
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42.75
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48.82
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