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Type 'q()' to quit R. > #### Simple integrity tests of the system datasets > > options(useFancyQuotes=FALSE) > 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 Time-Series [1:150] from 1 to 150: 10.01 10.07 10.32 9.75 10.33 ... ** 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 ... - attr(*, "formula")=Class 'formula' length 3 uptake ~ conc | Plant .. ..- attr(*, ".Environment")= - attr(*, "outer")=Class 'formula' length 2 ~Treatment * Type .. ..- attr(*, ".Environment")= - 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 ... - attr(*, "formula")=Class 'formula' length 3 weight ~ Time | Chick .. ..- attr(*, ".Environment")= - attr(*, "outer")=Class 'formula' length 2 ~Diet .. ..- attr(*, ".Environment")= - 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 ... - attr(*, "formula")=Class 'formula' length 3 density ~ conc | Run .. ..- attr(*, ".Environment")= - 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 mts [1:1860, 1:4] 1629 1614 1607 1621 1618 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:4] "DAX" "SMI" "CAC" "FTSE" - attr(*, "tsp")= num [1:3] 1991 1999 260 - attr(*, "class")= chr [1:3] "mts" "ts" "matrix" ** 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 table [1:4, 1:4, 1:2] 32 53 10 3 11 50 10 30 10 25 ... - 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 ... - attr(*, "formula")=Class 'formula' length 3 conc ~ time | Subject .. ..- attr(*, ".Environment")= - attr(*, "labels")=List of 2 ..$ x: chr "Time since drug administration" ..$ y: chr "Indomethacin concentration" - 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: $ sr : num 11.43 12.07 13.17 5.75 12.88 ... $ 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 ... $ dpi : num 2330 1508 2108 189 728 ... $ 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: $ height: num 4.51 10.89 28.72 41.74 52.7 ... $ 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 ... - attr(*, "formula")=Class 'formula' length 3 height ~ age | Seed .. ..- attr(*, ".Environment")= - 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 ... $ age : num 118 484 664 1004 1231 ... $ circumference: num 30 58 87 115 120 142 145 33 69 111 ... - attr(*, "formula")=Class 'formula' length 3 circumference ~ age | Tree .. ..- attr(*, ".Environment")= - 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 mts [1:192, 1:8] 107 97 102 87 119 106 110 106 107 134 ... - attr(*, "dimnames")=List of 2 ..$ : NULL ..$ : chr [1:8] "DriversKilled" "drivers" "front" "rear" ... - attr(*, "tsp")= num [1:3] 1969 1985 12 - attr(*, "class")= chr [1:2] "mts" "ts" ** 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 ... $ Time : num 0 0.25 0.57 1.12 2.02 ... $ conc : num 0.74 2.84 6.57 10.5 9.66 8.58 8.36 7.47 6.89 5.94 ... - attr(*, "formula")=Class 'formula' length 3 conc ~ Time | Subject .. ..- attr(*, ".Environment")= - 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 Time-Series [1:108] from 1960 to 1987: 160.1 129.7 84.8 120.1 160.1 ... ** structure of dataset USAccDeaths Time-Series [1:72] from 1973 to 1979: 9007 8106 8928 9137 10017 ... ** 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" ... ** 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 ** 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 $ cov : num [1:6, 1:6] 24.64 5.99 33.52 6.02 20.75 ... ..- 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 Time-Series [1:24] from 1937 to 1960: 412 480 683 1052 1385 ... ** 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 ... $ y3: num 7.46 6.77 12.74 7.11 7.81 ... $ 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 Named num [1:11] 13.76 40.34 1.96 166.39 5.95 ... - attr(*, "names")= chr [1:11] "ATS" "BEF" "DEM" "ESP" ... ** structure of dataset euro.cross num [1:11, 1:11] 1 0.3411 7.0355 0.0827 2.3143 ... - 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: $ eruptions: num 3.6 1.8 3.33 2.28 4.53 ... $ 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 ... $ 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 ... $ income.level : num 5.82 5.83 5.83 5.84 5.85 ... $ market.potential : num 13 13 13 13 13 ... ** structure of dataset freeny.x num [1:39, 1:4] 8.8 8.79 8.79 8.81 8.81 ... - 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 Named num [1:48] 11506 5500 16988 2968 16 ... - 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: $ GNP.deflator: num 83 88.5 88.2 89.5 96.2 ... $ 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 Time-Series [1:114] from 1821 to 1934: 269 321 585 871 1475 ... ** 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 ... $ qsec: num 16.5 17 18.6 19.4 17 ... $ 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 ... ** 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 ... ** 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" ... ** 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 ... $ pressure : num 0.0002 0.0012 0.006 0.03 0.09 0.27 0.75 1.85 4.2 8.8 ... ** structure of dataset quakes 'data.frame': 1000 obs. of 5 variables: $ lat : num -20.4 -20.6 -26 -18 -20.4 ... $ 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: $ x: num 0.000031 0.044495 0.82244 0.322291 0.393595 ... $ 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 'data.frame': 20 obs. of 3 variables: $ 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 ... $ ID : Factor w/ 10 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ... ** 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 chr [1:50] "AL" "AK" "AZ" "AR" "CA" "CO" "CT" "DE" "FL" ... ** structure of dataset state.area num [1:50] 51609 589757 113909 53104 158693 ... ** structure of dataset state.center List of 2 $ x: num [1:50] -86.8 -127.2 -111.6 -92.3 -119.8 ... $ 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 chr [1:50] "Alabama" "Alaska" "Arizona" "Arkansas" "California" ... ** 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 num [1:50, 1:8] 3615 365 2212 2110 21198 ... - 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 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 ... ** 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 ... $ Catholic : num 9.96 84.84 93.4 33.77 5.16 ... $ 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 ... >