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R version 3.2.4 RC (2016-03-02 r70281) -- "Very Secure Dishes"
Copyright (C) 2016 The R Foundation for Statistical Computing
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Platform: x86_64-pc-linux-gnu (64-bit)
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> #### Simple integrity tests of the system datasets
> 
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> options(useFancyQuotes=FALSE)
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> env <- as.environment("package:datasets")
> d <- ls(env) # don't want .names
> for(f in d) {
+     cat("\n** structure of dataset ", f, "\n", sep="")
+     str(get(f, envir=env, inherits=FALSE))
+ }

** structure of dataset AirPassengers
 Time-Series [1:144] from 1949 to 1961: 112 118 132 129 121 135 148 148 136 119 ...

** structure of dataset BJsales
 Time-Series [1:150] from 1 to 150: 200 200 199 199 199 ...

** structure of dataset BJsales.lead
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 Time-Series [1:150] from 1 to 150: 10.01 10.07 10.32 9.75 10.33 ...
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** structure of dataset BOD
'data.frame':	6 obs. of  2 variables:
 $ Time  : num  1 2 3 4 5 7
 $ demand: num  8.3 10.3 19 16 15.6 19.8
 - attr(*, "reference")= chr "A1.4, p. 270"

** structure of dataset CO2
Classes 'nfnGroupedData', 'nfGroupedData', 'groupedData' and 'data.frame':	84 obs. of  5 variables:
 $ Plant    : Ord.factor w/ 12 levels "Qn1"<"Qn2"<"Qn3"<..: 1 1 1 1 1 1 1 2 2 2 ...
 $ Type     : Factor w/ 2 levels "Quebec","Mississippi": 1 1 1 1 1 1 1 1 1 1 ...
 $ Treatment: Factor w/ 2 levels "nonchilled","chilled": 1 1 1 1 1 1 1 1 1 1 ...
 $ conc     : num  95 175 250 350 500 675 1000 95 175 250 ...
 $ uptake   : num  16 30.4 34.8 37.2 35.3 39.2 39.7 13.6 27.3 37.1 ...
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 - attr(*, "formula")=Class 'formula' length 3 uptake ~ conc | Plant
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  .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv> 
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 - attr(*, "outer")=Class 'formula' length 2 ~Treatment * Type
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  .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv> 
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 - attr(*, "labels")=List of 2
  ..$ x: chr "Ambient carbon dioxide concentration"
  ..$ y: chr "CO2 uptake rate"
 - attr(*, "units")=List of 2
  ..$ x: chr "(uL/L)"
  ..$ y: chr "(umol/m^2 s)"

** structure of dataset ChickWeight
Classes 'nfnGroupedData', 'nfGroupedData', 'groupedData' and 'data.frame':	578 obs. of  4 variables:
 $ weight: num  42 51 59 64 76 93 106 125 149 171 ...
 $ Time  : num  0 2 4 6 8 10 12 14 16 18 ...
 $ Chick : Ord.factor w/ 50 levels "18"<"16"<"15"<..: 15 15 15 15 15 15 15 15 15 15 ...
 $ Diet  : Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 1 1 1 1 1 ...
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 - attr(*, "formula")=Class 'formula' length 3 weight ~ Time | Chick
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  .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv> 
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 - attr(*, "outer")=Class 'formula' length 2 ~Diet
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  .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv> 
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 - attr(*, "labels")=List of 2
  ..$ x: chr "Time"
  ..$ y: chr "Body weight"
 - attr(*, "units")=List of 2
  ..$ x: chr "(days)"
  ..$ y: chr "(gm)"

** structure of dataset DNase
Classes 'nfnGroupedData', 'nfGroupedData', 'groupedData' and 'data.frame':	176 obs. of  3 variables:
 $ Run    : Ord.factor w/ 11 levels "10"<"11"<"9"<..: 4 4 4 4 4 4 4 4 4 4 ...
 $ conc   : num  0.0488 0.0488 0.1953 0.1953 0.3906 ...
 $ density: num  0.017 0.018 0.121 0.124 0.206 0.215 0.377 0.374 0.614 0.609 ...
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 - attr(*, "formula")=Class 'formula' length 3 density ~ conc | Run
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  .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv> 
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 - attr(*, "labels")=List of 2
  ..$ x: chr "DNase concentration"
  ..$ y: chr "Optical density"
 - attr(*, "units")=List of 1
  ..$ x: chr "(ng/ml)"

** structure of dataset EuStockMarkets
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 mts [1:1860, 1:4] 1629 1614 1607 1621 1618 ...
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 - attr(*, "dimnames")=List of 2
  ..$ : NULL
  ..$ : chr [1:4] "DAX" "SMI" "CAC" "FTSE"
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 - attr(*, "tsp")= num [1:3] 1991 1999 260
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 - attr(*, "class")= chr [1:3] "mts" "ts" "matrix"
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** structure of dataset Formaldehyde
'data.frame':	6 obs. of  2 variables:
 $ carb  : num  0.1 0.3 0.5 0.6 0.7 0.9
 $ optden: num  0.086 0.269 0.446 0.538 0.626 0.782

** structure of dataset HairEyeColor
105
 table [1:4, 1:4, 1:2] 32 53 10 3 11 50 10 30 10 25 ...
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
 - attr(*, "dimnames")=List of 3
  ..$ Hair: chr [1:4] "Black" "Brown" "Red" "Blond"
  ..$ Eye : chr [1:4] "Brown" "Blue" "Hazel" "Green"
  ..$ Sex : chr [1:2] "Male" "Female"

** structure of dataset Harman23.cor
List of 3
 $ cov   : num [1:8, 1:8] 1 0.846 0.805 0.859 0.473 0.398 0.301 0.382 0.846 1 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:8] "height" "arm.span" "forearm" "lower.leg" ...
  .. ..$ : chr [1:8] "height" "arm.span" "forearm" "lower.leg" ...
 $ center: num [1:8] 0 0 0 0 0 0 0 0
 $ n.obs : num 305

** structure of dataset Harman74.cor
List of 3
 $ cov   : num [1:24, 1:24] 1 0.318 0.403 0.468 0.321 0.335 0.304 0.332 0.326 0.116 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:24] "VisualPerception" "Cubes" "PaperFormBoard" "Flags" ...
  .. ..$ : chr [1:24] "VisualPerception" "Cubes" "PaperFormBoard" "Flags" ...
 $ center: num [1:24] 0 0 0 0 0 0 0 0 0 0 ...
 $ n.obs : num 145

** structure of dataset Indometh
Classes 'nfnGroupedData', 'nfGroupedData', 'groupedData' and 'data.frame':	66 obs. of  3 variables:
 $ Subject: Ord.factor w/ 6 levels "1"<"4"<"2"<"5"<..: 1 1 1 1 1 1 1 1 1 1 ...
 $ time   : num  0.25 0.5 0.75 1 1.25 2 3 4 5 6 ...
 $ conc   : num  1.5 0.94 0.78 0.48 0.37 0.19 0.12 0.11 0.08 0.07 ...
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 - attr(*, "formula")=Class 'formula' length 3 conc ~ time | Subject
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  .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv> 
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 - attr(*, "labels")=List of 2
  ..$ x: chr "Time since drug administration"
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  ..$ y: chr "Indomethacin concentration"
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 - attr(*, "units")=List of 2
  ..$ x: chr "(hr)"
  ..$ y: chr "(mcg/ml)"

** structure of dataset InsectSprays
'data.frame':	72 obs. of  2 variables:
 $ count: num  10 7 20 14 14 12 10 23 17 20 ...
 $ spray: Factor w/ 6 levels "A","B","C","D",..: 1 1 1 1 1 1 1 1 1 1 ...

** structure of dataset JohnsonJohnson
 Time-Series [1:84] from 1960 to 1981: 0.71 0.63 0.85 0.44 0.61 0.69 0.92 0.55 0.72 0.77 ...

** structure of dataset LakeHuron
 Time-Series [1:98] from 1875 to 1972: 580 582 581 581 580 ...

** structure of dataset LifeCycleSavings
'data.frame':	50 obs. of  5 variables:
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 $ sr   : num  11.43 12.07 13.17 5.75 12.88 ...
157 158
 $ pop15: num  29.4 23.3 23.8 41.9 42.2 ...
 $ pop75: num  2.87 4.41 4.43 1.67 0.83 2.85 1.34 0.67 1.06 1.14 ...
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 $ dpi  : num  2330 1508 2108 189 728 ...
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 $ ddpi : num  2.87 3.93 3.82 0.22 4.56 2.43 2.67 6.51 3.08 2.8 ...

** structure of dataset Loblolly
Classes 'nfnGroupedData', 'nfGroupedData', 'groupedData' and 'data.frame':	84 obs. of  3 variables:
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 $ height: num  4.51 10.89 28.72 41.74 52.7 ...
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 $ age   : num  3 5 10 15 20 25 3 5 10 15 ...
 $ Seed  : Ord.factor w/ 14 levels "329"<"327"<"325"<..: 10 10 10 10 10 10 13 13 13 13 ...
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 - attr(*, "formula")=Class 'formula' length 3 height ~ age | Seed
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  .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv> 
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 - attr(*, "labels")=List of 2
  ..$ x: chr "Age of tree"
  ..$ y: chr "Height of tree"
 - attr(*, "units")=List of 2
  ..$ x: chr "(yr)"
  ..$ y: chr "(ft)"

** structure of dataset Nile
 Time-Series [1:100] from 1871 to 1970: 1120 1160 963 1210 1160 1160 813 1230 1370 1140 ...

** structure of dataset Orange
Classes 'nfnGroupedData', 'nfGroupedData', 'groupedData' and 'data.frame':	35 obs. of  3 variables:
 $ Tree         : Ord.factor w/ 5 levels "3"<"1"<"5"<"2"<..: 2 2 2 2 2 2 2 4 4 4 ...
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 $ age          : num  118 484 664 1004 1231 ...
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 $ circumference: num  30 58 87 115 120 142 145 33 69 111 ...
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 - attr(*, "formula")=Class 'formula' length 3 circumference ~ age | Tree
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  .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv> 
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 - attr(*, "labels")=List of 2
  ..$ x: chr "Time since December 31, 1968"
  ..$ y: chr "Trunk circumference"
 - attr(*, "units")=List of 2
  ..$ x: chr "(days)"
  ..$ y: chr "(mm)"

** structure of dataset OrchardSprays
'data.frame':	64 obs. of  4 variables:
 $ decrease : num  57 95 8 69 92 90 15 2 84 6 ...
 $ rowpos   : num  1 2 3 4 5 6 7 8 1 2 ...
 $ colpos   : num  1 1 1 1 1 1 1 1 2 2 ...
 $ treatment: Factor w/ 8 levels "A","B","C","D",..: 4 5 2 8 7 6 3 1 3 2 ...

** structure of dataset PlantGrowth
'data.frame':	30 obs. of  2 variables:
 $ weight: num  4.17 5.58 5.18 6.11 4.5 4.61 5.17 4.53 5.33 5.14 ...
 $ group : Factor w/ 3 levels "ctrl","trt1",..: 1 1 1 1 1 1 1 1 1 1 ...

** structure of dataset Puromycin
'data.frame':	23 obs. of  3 variables:
 $ conc : num  0.02 0.02 0.06 0.06 0.11 0.11 0.22 0.22 0.56 0.56 ...
 $ rate : num  76 47 97 107 123 139 159 152 191 201 ...
 $ state: Factor w/ 2 levels "treated","untreated": 1 1 1 1 1 1 1 1 1 1 ...
 - attr(*, "reference")= chr "A1.3, p. 269"

** structure of dataset Seatbelts
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 mts [1:192, 1:8] 107 97 102 87 119 106 110 106 107 134 ...
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 - attr(*, "dimnames")=List of 2
  ..$ : NULL
  ..$ : chr [1:8] "DriversKilled" "drivers" "front" "rear" ...
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 - attr(*, "tsp")= num [1:3] 1969 1985 12
 - attr(*, "class")= chr [1:2] "mts" "ts"
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** structure of dataset Theoph
Classes 'nfnGroupedData', 'nfGroupedData', 'groupedData' and 'data.frame':	132 obs. of  5 variables:
 $ Subject: Ord.factor w/ 12 levels "6"<"7"<"8"<"11"<..: 11 11 11 11 11 11 11 11 11 11 ...
 $ Wt     : num  79.6 79.6 79.6 79.6 79.6 79.6 79.6 79.6 79.6 79.6 ...
 $ Dose   : num  4.02 4.02 4.02 4.02 4.02 4.02 4.02 4.02 4.02 4.02 ...
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 $ Time   : num  0 0.25 0.57 1.12 2.02 ...
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 $ conc   : num  0.74 2.84 6.57 10.5 9.66 8.58 8.36 7.47 6.89 5.94 ...
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 - attr(*, "formula")=Class 'formula' length 3 conc ~ Time | Subject
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  .. ..- attr(*, ".Environment")=<environment: R_EmptyEnv> 
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 - attr(*, "labels")=List of 2
  ..$ x: chr "Time since drug administration"
  ..$ y: chr "Theophylline concentration in serum"
 - attr(*, "units")=List of 2
  ..$ x: chr "(hr)"
  ..$ y: chr "(mg/l)"

** structure of dataset Titanic
 table [1:4, 1:2, 1:2, 1:2] 0 0 35 0 0 0 17 0 118 154 ...
 - attr(*, "dimnames")=List of 4
  ..$ Class   : chr [1:4] "1st" "2nd" "3rd" "Crew"
  ..$ Sex     : chr [1:2] "Male" "Female"
  ..$ Age     : chr [1:2] "Child" "Adult"
  ..$ Survived: chr [1:2] "No" "Yes"

** structure of dataset ToothGrowth
'data.frame':	60 obs. of  3 variables:
 $ len : num  4.2 11.5 7.3 5.8 6.4 10 11.2 11.2 5.2 7 ...
 $ supp: Factor w/ 2 levels "OJ","VC": 2 2 2 2 2 2 2 2 2 2 ...
 $ dose: num  0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...

** structure of dataset UCBAdmissions
 table [1:2, 1:2, 1:6] 512 313 89 19 353 207 17 8 120 205 ...
 - attr(*, "dimnames")=List of 3
  ..$ Admit : chr [1:2] "Admitted" "Rejected"
  ..$ Gender: chr [1:2] "Male" "Female"
  ..$ Dept  : chr [1:6] "A" "B" "C" "D" ...

** structure of dataset UKDriverDeaths
 Time-Series [1:192] from 1969 to 1985: 1687 1508 1507 1385 1632 ...

** structure of dataset UKgas
261
 Time-Series [1:108] from 1960 to 1987: 160.1 129.7 84.8 120.1 160.1 ...
262 263

** structure of dataset USAccDeaths
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 Time-Series [1:72] from 1973 to 1979: 9007 8106 8928 9137 10017 ...
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

** structure of dataset USArrests
'data.frame':	50 obs. of  4 variables:
 $ Murder  : num  13.2 10 8.1 8.8 9 7.9 3.3 5.9 15.4 17.4 ...
 $ Assault : int  236 263 294 190 276 204 110 238 335 211 ...
 $ UrbanPop: int  58 48 80 50 91 78 77 72 80 60 ...
 $ Rape    : num  21.2 44.5 31 19.5 40.6 38.7 11.1 15.8 31.9 25.8 ...

** structure of dataset USJudgeRatings
'data.frame':	43 obs. of  12 variables:
 $ CONT: num  5.7 6.8 7.2 6.8 7.3 6.2 10.6 7 7.3 8.2 ...
 $ INTG: num  7.9 8.9 8.1 8.8 6.4 8.8 9 5.9 8.9 7.9 ...
 $ DMNR: num  7.7 8.8 7.8 8.5 4.3 8.7 8.9 4.9 8.9 6.7 ...
 $ DILG: num  7.3 8.5 7.8 8.8 6.5 8.5 8.7 5.1 8.7 8.1 ...
 $ CFMG: num  7.1 7.8 7.5 8.3 6 7.9 8.5 5.4 8.6 7.9 ...
 $ DECI: num  7.4 8.1 7.6 8.5 6.2 8 8.5 5.9 8.5 8 ...
 $ PREP: num  7.1 8 7.5 8.7 5.7 8.1 8.5 4.8 8.4 7.9 ...
 $ FAMI: num  7.1 8 7.5 8.7 5.7 8 8.5 5.1 8.4 8.1 ...
 $ ORAL: num  7.1 7.8 7.3 8.4 5.1 8 8.6 4.7 8.4 7.7 ...
 $ WRIT: num  7 7.9 7.4 8.5 5.3 8 8.4 4.9 8.5 7.8 ...
 $ PHYS: num  8.3 8.5 7.9 8.8 5.5 8.6 9.1 6.8 8.8 8.5 ...
 $ RTEN: num  7.8 8.7 7.8 8.7 4.8 8.6 9 5 8.8 7.9 ...

** structure of dataset USPersonalExpenditure
 num [1:5, 1:5] 22.2 10.5 3.53 1.04 0.341 44.5 15.5 5.76 1.98 0.974 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:5] "Food and Tobacco" "Household Operation" "Medical and Health" "Personal Care" ...
  ..$ : chr [1:5] "1940" "1945" "1950" "1955" ...

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** structure of dataset UScitiesD
Class 'dist'  atomic [1:45] 587 1212 701 1936 604 748 2139 2182 543 920 ...
  ..- attr(*, "Labels")= chr [1:10] "Atlanta" "Chicago" "Denver" "Houston" ...
  ..- attr(*, "Size")= int 10
  ..- attr(*, "call")= language as.dist.default(m = t(cities.mat))
  ..- attr(*, "Diag")= logi FALSE
  ..- attr(*, "Upper")= logi FALSE

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** structure of dataset VADeaths
 num [1:5, 1:4] 11.7 18.1 26.9 41 66 8.7 11.7 20.3 30.9 54.3 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:5] "50-54" "55-59" "60-64" "65-69" ...
  ..$ : chr [1:4] "Rural Male" "Rural Female" "Urban Male" "Urban Female"

** structure of dataset WWWusage
 Time-Series [1:100] from 1 to 100: 88 84 85 85 84 85 83 85 88 89 ...

** structure of dataset WorldPhones
 num [1:7, 1:7] 45939 60423 64721 68484 71799 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:7] "1951" "1956" "1957" "1958" ...
  ..$ : chr [1:7] "N.Amer" "Europe" "Asia" "S.Amer" ...

** structure of dataset ability.cov
List of 3
319
 $ cov   : num [1:6, 1:6] 24.64 5.99 33.52 6.02 20.75 ...
320 321 322 323 324 325 326
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:6] "general" "picture" "blocks" "maze" ...
  .. ..$ : chr [1:6] "general" "picture" "blocks" "maze" ...
 $ center: num [1:6] 0 0 0 0 0 0
 $ n.obs : num 112

** structure of dataset airmiles
327
 Time-Series [1:24] from 1937 to 1960: 412 480 683 1052 1385 ...
328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345

** structure of dataset airquality
'data.frame':	153 obs. of  6 variables:
 $ Ozone  : int  41 36 12 18 NA 28 23 19 8 NA ...
 $ Solar.R: int  190 118 149 313 NA NA 299 99 19 194 ...
 $ Wind   : num  7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
 $ Temp   : int  67 72 74 62 56 66 65 59 61 69 ...
 $ Month  : int  5 5 5 5 5 5 5 5 5 5 ...
 $ Day    : int  1 2 3 4 5 6 7 8 9 10 ...

** structure of dataset anscombe
'data.frame':	11 obs. of  8 variables:
 $ x1: num  10 8 13 9 11 14 6 4 12 7 ...
 $ x2: num  10 8 13 9 11 14 6 4 12 7 ...
 $ x3: num  10 8 13 9 11 14 6 4 12 7 ...
 $ x4: num  8 8 8 8 8 8 8 19 8 8 ...
 $ y1: num  8.04 6.95 7.58 8.81 8.33 ...
 $ y2: num  9.14 8.14 8.74 8.77 9.26 8.1 6.13 3.1 9.13 7.26 ...
346
 $ y3: num  7.46 6.77 12.74 7.11 7.81 ...
347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414
 $ y4: num  6.58 5.76 7.71 8.84 8.47 7.04 5.25 12.5 5.56 7.91 ...

** structure of dataset attenu
'data.frame':	182 obs. of  5 variables:
 $ event  : num  1 2 2 2 2 2 2 2 2 2 ...
 $ mag    : num  7 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 ...
 $ station: Factor w/ 117 levels "1008","1011",..: 24 13 15 68 39 74 22 1 8 55 ...
 $ dist   : num  12 148 42 85 107 109 156 224 293 359 ...
 $ accel  : num  0.359 0.014 0.196 0.135 0.062 0.054 0.014 0.018 0.01 0.004 ...

** structure of dataset attitude
'data.frame':	30 obs. of  7 variables:
 $ rating    : num  43 63 71 61 81 43 58 71 72 67 ...
 $ complaints: num  51 64 70 63 78 55 67 75 82 61 ...
 $ privileges: num  30 51 68 45 56 49 42 50 72 45 ...
 $ learning  : num  39 54 69 47 66 44 56 55 67 47 ...
 $ raises    : num  61 63 76 54 71 54 66 70 71 62 ...
 $ critical  : num  92 73 86 84 83 49 68 66 83 80 ...
 $ advance   : num  45 47 48 35 47 34 35 41 31 41 ...

** structure of dataset austres
 Time-Series [1:89] from 1971 to 1993: 13067 13130 13198 13254 13304 ...

** structure of dataset beaver1
'data.frame':	114 obs. of  4 variables:
 $ day  : num  346 346 346 346 346 346 346 346 346 346 ...
 $ time : num  840 850 900 910 920 930 940 950 1000 1010 ...
 $ temp : num  36.3 36.3 36.4 36.4 36.5 ...
 $ activ: num  0 0 0 0 0 0 0 0 0 0 ...

** structure of dataset beaver2
'data.frame':	100 obs. of  4 variables:
 $ day  : num  307 307 307 307 307 307 307 307 307 307 ...
 $ time : num  930 940 950 1000 1010 1020 1030 1040 1050 1100 ...
 $ temp : num  36.6 36.7 36.9 37.1 37.2 ...
 $ activ: num  0 0 0 0 0 0 0 0 0 0 ...

** structure of dataset cars
'data.frame':	50 obs. of  2 variables:
 $ speed: num  4 4 7 7 8 9 10 10 10 11 ...
 $ dist : num  2 10 4 22 16 10 18 26 34 17 ...

** structure of dataset chickwts
'data.frame':	71 obs. of  2 variables:
 $ weight: num  179 160 136 227 217 168 108 124 143 140 ...
 $ feed  : Factor w/ 6 levels "casein","horsebean",..: 2 2 2 2 2 2 2 2 2 2 ...

** structure of dataset co2
 Time-Series [1:468] from 1959 to 1998: 315 316 316 318 318 ...

** structure of dataset crimtab
 'table' int [1:42, 1:22] 0 0 0 0 0 0 1 0 0 0 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:42] "9.4" "9.5" "9.6" "9.7" ...
  ..$ : chr [1:22] "142.24" "144.78" "147.32" "149.86" ...

** structure of dataset discoveries
 Time-Series [1:100] from 1860 to 1959: 5 3 0 2 0 3 2 3 6 1 ...

** structure of dataset esoph
'data.frame':	88 obs. of  5 variables:
 $ agegp    : Ord.factor w/ 6 levels "25-34"<"35-44"<..: 1 1 1 1 1 1 1 1 1 1 ...
 $ alcgp    : Ord.factor w/ 4 levels "0-39g/day"<"40-79"<..: 1 1 1 1 2 2 2 2 3 3 ...
 $ tobgp    : Ord.factor w/ 4 levels "0-9g/day"<"10-19"<..: 1 2 3 4 1 2 3 4 1 2 ...
 $ ncases   : num  0 0 0 0 0 0 0 0 0 0 ...
 $ ncontrols: num  40 10 6 5 27 7 4 7 2 1 ...

** structure of dataset euro
415
 Named num [1:11] 13.76 40.34 1.96 166.39 5.95 ...
416 417 418
 - attr(*, "names")= chr [1:11] "ATS" "BEF" "DEM" "ESP" ...

** structure of dataset euro.cross
419
 num [1:11, 1:11] 1 0.3411 7.0355 0.0827 2.3143 ...
420 421 422 423 424 425 426 427 428 429 430
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:11] "ATS" "BEF" "DEM" "ESP" ...
  ..$ : chr [1:11] "ATS" "BEF" "DEM" "ESP" ...

** structure of dataset eurodist
Class 'dist'  atomic [1:210] 3313 2963 3175 3339 2762 ...
  ..- attr(*, "Size")= num 21
  ..- attr(*, "Labels")= chr [1:21] "Athens" "Barcelona" "Brussels" "Calais" ...

** structure of dataset faithful
'data.frame':	272 obs. of  2 variables:
431
 $ eruptions: num  3.6 1.8 3.33 2.28 4.53 ...
432 433 434 435 436 437 438 439
 $ waiting  : num  79 54 74 62 85 55 88 85 51 85 ...

** structure of dataset fdeaths
 Time-Series [1:72] from 1974 to 1980: 901 689 827 677 522 406 441 393 387 582 ...

** structure of dataset freeny
'data.frame':	39 obs. of  5 variables:
 $ y                    : Time-Series  from 1962 to 1972: 8.79 8.79 8.81 8.81 8.91 ...
440 441
 $ lag.quarterly.revenue: num  8.8 8.79 8.79 8.81 8.81 ...
 $ price.index          : num  4.71 4.7 4.69 4.69 4.64 ...
442
 $ income.level         : num  5.82 5.83 5.83 5.84 5.85 ...
443
 $ market.potential     : num  13 13 13 13 13 ...
444 445

** structure of dataset freeny.x
446
 num [1:39, 1:4] 8.8 8.79 8.79 8.81 8.81 ...
447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480
 - attr(*, "dimnames")=List of 2
  ..$ : NULL
  ..$ : chr [1:4] "lag quarterly revenue" "price index" "income level" "market potential"

** structure of dataset freeny.y
 Time-Series [1:39] from 1962 to 1972: 8.79 8.79 8.81 8.81 8.91 ...

** structure of dataset infert
'data.frame':	248 obs. of  8 variables:
 $ education     : Factor w/ 3 levels "0-5yrs","6-11yrs",..: 1 1 1 1 2 2 2 2 2 2 ...
 $ age           : num  26 42 39 34 35 36 23 32 21 28 ...
 $ parity        : num  6 1 6 4 3 4 1 2 1 2 ...
 $ induced       : num  1 1 2 2 1 2 0 0 0 0 ...
 $ case          : num  1 1 1 1 1 1 1 1 1 1 ...
 $ spontaneous   : num  2 0 0 0 1 1 0 0 1 0 ...
 $ stratum       : int  1 2 3 4 5 6 7 8 9 10 ...
 $ pooled.stratum: num  3 1 4 2 32 36 6 22 5 19 ...

** structure of dataset iris
'data.frame':	150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

** structure of dataset iris3
 num [1:50, 1:4, 1:3] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 - attr(*, "dimnames")=List of 3
  ..$ : NULL
  ..$ : chr [1:4] "Sepal L." "Sepal W." "Petal L." "Petal W."
  ..$ : chr [1:3] "Setosa" "Versicolor" "Virginica"

** structure of dataset islands
481
 Named num [1:48] 11506 5500 16988 2968 16 ...
482 483 484 485 486 487 488 489 490 491
 - attr(*, "names")= chr [1:48] "Africa" "Antarctica" "Asia" "Australia" ...

** structure of dataset ldeaths
 Time-Series [1:72] from 1974 to 1980: 3035 2552 2704 2554 2014 ...

** structure of dataset lh
 Time-Series [1:48] from 1 to 48: 2.4 2.4 2.4 2.2 2.1 1.5 2.3 2.3 2.5 2 ...

** structure of dataset longley
'data.frame':	16 obs. of  7 variables:
492
 $ GNP.deflator: num  83 88.5 88.2 89.5 96.2 ...
493 494 495 496 497 498 499 500
 $ GNP         : num  234 259 258 285 329 ...
 $ Unemployed  : num  236 232 368 335 210 ...
 $ Armed.Forces: num  159 146 162 165 310 ...
 $ Population  : num  108 109 110 111 112 ...
 $ Year        : int  1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 ...
 $ Employed    : num  60.3 61.1 60.2 61.2 63.2 ...

** structure of dataset lynx
501
 Time-Series [1:114] from 1821 to 1934: 269 321 585 871 1475 ...
502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519

** structure of dataset mdeaths
 Time-Series [1:72] from 1974 to 1980: 2134 1863 1877 1877 1492 ...

** structure of dataset morley
'data.frame':	100 obs. of  3 variables:
 $ Expt : int  1 1 1 1 1 1 1 1 1 1 ...
 $ Run  : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Speed: int  850 740 900 1070 930 850 950 980 980 880 ...

** structure of dataset mtcars
'data.frame':	32 obs. of  11 variables:
 $ mpg : num  21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
 $ cyl : num  6 6 4 6 8 6 8 4 4 6 ...
 $ disp: num  160 160 108 258 360 ...
 $ hp  : num  110 110 93 110 175 105 245 62 95 123 ...
 $ drat: num  3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
 $ wt  : num  2.62 2.88 2.32 3.21 3.44 ...
520
 $ qsec: num  16.5 17 18.6 19.4 17 ...
521 522 523 524 525 526 527 528 529 530 531
 $ vs  : num  0 0 1 1 0 1 0 1 1 1 ...
 $ am  : num  1 1 1 0 0 0 0 0 0 0 ...
 $ gear: num  4 4 4 3 3 3 3 4 4 4 ...
 $ carb: num  4 4 1 1 2 1 4 2 2 4 ...

** structure of dataset nhtemp
 Time-Series [1:60] from 1912 to 1971: 49.9 52.3 49.4 51.1 49.4 47.9 49.8 50.9 49.3 51.9 ...

** structure of dataset nottem
 Time-Series [1:240] from 1920 to 1940: 40.6 40.8 44.4 46.7 54.1 58.5 57.7 56.4 54.3 50.5 ...

532 533 534 535 536 537 538 539
** structure of dataset npk
'data.frame':	24 obs. of  5 variables:
 $ block: Factor w/ 6 levels "1","2","3","4",..: 1 1 1 1 2 2 2 2 3 3 ...
 $ N    : Factor w/ 2 levels "0","1": 1 2 1 2 2 2 1 1 1 2 ...
 $ P    : Factor w/ 2 levels "0","1": 2 2 1 1 1 2 1 2 2 2 ...
 $ K    : Factor w/ 2 levels "0","1": 2 1 1 2 1 2 2 1 1 2 ...
 $ yield: num  49.5 62.8 46.8 57 59.8 58.5 55.5 56 62.8 55.8 ...

540 541 542 543 544 545
** structure of dataset occupationalStatus
 'table' int [1:8, 1:8] 50 16 12 11 2 12 0 0 19 40 ...
 - attr(*, "dimnames")=List of 2
  ..$ origin     : chr [1:8] "1" "2" "3" "4" ...
  ..$ destination: chr [1:8] "1" "2" "3" "4" ...

546 547 548 549 550 551 552 553 554 555
** structure of dataset precip
 Named num [1:70] 67 54.7 7 48.5 14 17.2 20.7 13 43.4 40.2 ...
 - attr(*, "names")= chr [1:70] "Mobile" "Juneau" "Phoenix" "Little Rock" ...

** structure of dataset presidents
 Time-Series [1:120] from 1945 to 1975: NA 87 82 75 63 50 43 32 35 60 ...

** structure of dataset pressure
'data.frame':	19 obs. of  2 variables:
 $ temperature: num  0 20 40 60 80 100 120 140 160 180 ...
556
 $ pressure   : num  0.0002 0.0012 0.006 0.03 0.09 0.27 0.75 1.85 4.2 8.8 ...
557 558 559

** structure of dataset quakes
'data.frame':	1000 obs. of  5 variables:
560
 $ lat     : num  -20.4 -20.6 -26 -18 -20.4 ...
561 562 563 564 565 566 567
 $ long    : num  182 181 184 182 182 ...
 $ depth   : int  562 650 42 626 649 195 82 194 211 622 ...
 $ mag     : num  4.8 4.2 5.4 4.1 4 4 4.8 4.4 4.7 4.3 ...
 $ stations: int  41 15 43 19 11 12 43 15 35 19 ...

** structure of dataset randu
'data.frame':	400 obs. of  3 variables:
568
 $ x: num  0.000031 0.044495 0.82244 0.322291 0.393595 ...
569 570 571 572 573 574 575 576 577 578 579 580 581 582
 $ y: num  0.000183 0.155732 0.873416 0.648545 0.826873 ...
 $ z: num  0.000824 0.533939 0.838542 0.990648 0.418881 ...

** structure of dataset rivers
 num [1:141] 735 320 325 392 524 ...

** structure of dataset rock
'data.frame':	48 obs. of  4 variables:
 $ area : int  4990 7002 7558 7352 7943 7979 9333 8209 8393 6425 ...
 $ peri : num  2792 3893 3931 3869 3949 ...
 $ shape: num  0.0903 0.1486 0.1833 0.1171 0.1224 ...
 $ perm : num  6.3 6.3 6.3 6.3 17.1 17.1 17.1 17.1 119 119 ...

** structure of dataset sleep
583
'data.frame':	20 obs. of  3 variables:
584 585
 $ extra: num  0.7 -1.6 -0.2 -1.2 -0.1 3.4 3.7 0.8 0 2 ...
 $ group: Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
586
 $ ID   : Factor w/ 10 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604

** structure of dataset stack.loss
 num [1:21] 42 37 37 28 18 18 19 20 15 14 ...

** structure of dataset stack.x
 num [1:21, 1:3] 80 80 75 62 62 62 62 62 58 58 ...
 - attr(*, "dimnames")=List of 2
  ..$ : NULL
  ..$ : chr [1:3] "Air.Flow" "Water.Temp" "Acid.Conc."

** structure of dataset stackloss
'data.frame':	21 obs. of  4 variables:
 $ Air.Flow  : num  80 80 75 62 62 62 62 62 58 58 ...
 $ Water.Temp: num  27 27 25 24 22 23 24 24 23 18 ...
 $ Acid.Conc.: num  89 88 90 87 87 87 93 93 87 80 ...
 $ stack.loss: num  42 37 37 28 18 18 19 20 15 14 ...

** structure of dataset state.abb
605
 chr [1:50] "AL" "AK" "AZ" "AR" "CA" "CO" "CT" "DE" "FL" ...
606 607

** structure of dataset state.area
608
 num [1:50] 51609 589757 113909 53104 158693 ...
609 610 611

** structure of dataset state.center
List of 2
612
 $ x: num [1:50] -86.8 -127.2 -111.6 -92.3 -119.8 ...
613 614 615 616 617 618
 $ y: num [1:50] 32.6 49.2 34.2 34.7 36.5 ...

** structure of dataset state.division
 Factor w/ 9 levels "New England",..: 4 9 8 5 9 8 1 3 3 3 ...

** structure of dataset state.name
619
 chr [1:50] "Alabama" "Alaska" "Arizona" "Arkansas" "California" ...
620 621 622 623 624

** structure of dataset state.region
 Factor w/ 4 levels "Northeast","South",..: 2 4 4 2 4 4 1 2 2 2 ...

** structure of dataset state.x77
625
 num [1:50, 1:8] 3615 365 2212 2110 21198 ...
626 627 628 629 630
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:50] "Alabama" "Alaska" "Arizona" "Arkansas" ...
  ..$ : chr [1:8] "Population" "Income" "Illiteracy" "Life Exp" ...

** structure of dataset sunspot.month
631
 Time-Series [1:3177] from 1749 to 2014: 58 62.6 70 55.7 85 83.5 94.8 66.3 75.9 75.5 ...
632 633 634 635 636 637 638 639 640 641 642 643 644

** structure of dataset sunspot.year
 Time-Series [1:289] from 1700 to 1988: 5 11 16 23 36 58 29 20 10 8 ...

** structure of dataset sunspots
 Time-Series [1:2820] from 1749 to 1984: 58 62.6 70 55.7 85 83.5 94.8 66.3 75.9 75.5 ...

** structure of dataset swiss
'data.frame':	47 obs. of  6 variables:
 $ Fertility       : num  80.2 83.1 92.5 85.8 76.9 76.1 83.8 92.4 82.4 82.9 ...
 $ Agriculture     : num  17 45.1 39.7 36.5 43.5 35.3 70.2 67.8 53.3 45.2 ...
 $ Examination     : int  15 6 5 12 17 9 16 14 12 16 ...
 $ Education       : int  12 9 5 7 15 7 7 8 7 13 ...
645
 $ Catholic        : num  9.96 84.84 93.4 33.77 5.16 ...
646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673
 $ Infant.Mortality: num  22.2 22.2 20.2 20.3 20.6 26.6 23.6 24.9 21 24.4 ...

** structure of dataset treering
 Time-Series [1:7980] from -6000 to 1979: 1.34 1.08 1.54 1.32 1.41 ...

** structure of dataset trees
'data.frame':	31 obs. of  3 variables:
 $ Girth : num  8.3 8.6 8.8 10.5 10.7 10.8 11 11 11.1 11.2 ...
 $ Height: num  70 65 63 72 81 83 66 75 80 75 ...
 $ Volume: num  10.3 10.3 10.2 16.4 18.8 19.7 15.6 18.2 22.6 19.9 ...

** structure of dataset uspop
 Time-Series [1:19] from 1790 to 1970: 3.93 5.31 7.24 9.64 12.9 17.1 23.2 31.4 39.8 50.2 ...

** structure of dataset volcano
 num [1:87, 1:61] 100 101 102 103 104 105 105 106 107 108 ...

** structure of dataset warpbreaks
'data.frame':	54 obs. of  3 variables:
 $ breaks : num  26 30 54 25 70 52 51 26 67 18 ...
 $ wool   : Factor w/ 2 levels "A","B": 1 1 1 1 1 1 1 1 1 1 ...
 $ tension: Factor w/ 3 levels "L","M","H": 1 1 1 1 1 1 1 1 1 2 ...

** structure of dataset women
'data.frame':	15 obs. of  2 variables:
 $ height: num  58 59 60 61 62 63 64 65 66 67 ...
 $ weight: num  115 117 120 123 126 129 132 135 139 142 ...
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