Dataset structure overview

AirPassengers	BJsales	BJsales.lead (BJsales)	BOD	CO2	ChickWeight	DNase	EuStockMarkets	Formaldehyde	HairEyeColor	Harman23.cor	Harman74.cor	Indometh	InsectSprays	JohnsonJohnson	LakeHuron	LifeCycleSavings	Loblolly	Nile	Orange	OrchardSprays	PlantGrowth	Puromycin	Seatbelts	Theoph	Titanic	ToothGrowth	UCBAdmissions	UKDriverDeaths	UKgas	USAccDeaths	USArrests	USJudgeRatings	USPersonalExpenditure	UScitiesD	VADeaths	WWWusage	WorldPhones	ability.cov	airmiles	airquality	anscombe	attenu	attitude	austres	beaver1 (beavers)	beaver2 (beavers)	cars	chickwts	co2	crimtab	discoveries	esoph	euro	euro.cross (euro)	eurodist	faithful	fdeaths (UKLungDeaths)	freeny	freeny.x (freeny)	freeny.y (freeny)	infert	iris	iris3	islands	ldeaths (UKLungDeaths)	lh	longley	lynx	mdeaths (UKLungDeaths)	morley	mtcars	nhtemp	nottem	npk	occupationalStatus	precip	presidents	pressure	quakes	randu	rivers	rock	sleep	stack.loss (stackloss)	stack.x (stackloss)	stackloss	state.abb (state)	state.area (state)	state.center (state)	state.division (state)	state.name (state)	state.region (state)	state.x77 (state)	sunspot.month	sunspot.year	sunspots	swiss	treering	trees	uspop	volcano	warpbreaks	women
Time-Series [1:144] from 1949 to 1961: 112 118 132 129 121 135 148 148 136 119 …	Time-Series [1:150] from 1 to 150: 200 200 199 199 199 …	Time-Series [1:150] from 1 to 150: 10.01 10.07 10.32 9.75 10.33 …	c(“‘data.frame’: 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"")	c(“Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and ‘data.frame’: 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’ language uptake ~ conc | Plant“,” .. ..- attr(, ".Environment")=<environment: R_EmptyEnv> “,” - attr(, "outer")=Class ‘formula’ language ~Treatment Type“,” .. ..- attr(, ".Environment")=<environment: R_EmptyEnv> “,” - 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)"")	c(“Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and ‘data.frame’: 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’ language weight ~ Time | Chick“,” .. ..- attr(, ".Environment")=<environment: R_EmptyEnv> “,” - attr(, "outer")=Class ‘formula’ language ~Diet“,” .. ..- attr(, ".Environment")=<environment: R_EmptyEnv> “,” - attr(, "labels")=List of 2“,” ..$ x: chr "Time"“,” ..$ y: chr "Body weight"“,” - attr(, "units")=List of 2“,” ..$ x: chr "(days)"“,” ..$ y: chr "(gm)"")	c(“Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and ‘data.frame’: 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’ language density ~ conc | Run“,” .. ..- attr(, ".Environment")=<environment: R_EmptyEnv> “,” - attr(, "labels")=List of 2“,” ..$ x: chr "DNase concentration"“,” ..$ y: chr "Optical density"“,” - attr(, "units")=List of 1“,” ..$ x: chr "(ng/ml)"")	c(" Time-Series [1:1860, 1:4] from 1991 to 1999: 1629 1614 1607 1621 1618 …“,” - attr(*, "dimnames")=List of 2“,” ..$ : NULL“,” ..$ : chr [1:4] "DAX" "SMI" "CAC" "FTSE"")	c(“‘data.frame’: 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")	c(" ‘table’ num [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"")	c(“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")	c(“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")	c(“Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and ‘data.frame’: 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’ language conc ~ time | Subject“,” .. ..- attr(, ".Environment")=<environment: R_EmptyEnv> “,” - 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)"")	c(“‘data.frame’: 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 …")	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 …	Time-Series [1:98] from 1875 to 1972: 580 582 581 581 580 …	c(“‘data.frame’: 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 …")	c(“Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and ‘data.frame’: 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’ language height ~ age | Seed“,” .. ..- attr(, ".Environment")=<environment: R_EmptyEnv> “,” - 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)"")	Time-Series [1:100] from 1871 to 1970: 1120 1160 963 1210 1160 1160 813 1230 1370 1140 …	c(“Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and ‘data.frame’: 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’ language circumference ~ age | Tree“,” .. ..- attr(, ".Environment")=<environment: R_EmptyEnv> “,” - 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)"")	c(“‘data.frame’: 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 …")	c(“‘data.frame’: 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 …")	c(“‘data.frame’: 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"")	c(" Time-Series [1:192, 1:8] from 1969 to 1985: 107 97 102 87 119 106 110 106 107 134 …“,” - attr(*, "dimnames")=List of 2“,” ..$ : NULL“,” ..$ : chr [1:8] "DriversKilled" "drivers" "front" "rear" …")	c(“Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and ‘data.frame’: 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’ language conc ~ Time | Subject“,” .. ..- attr(, ".Environment")=<environment: R_EmptyEnv> “,” - 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)"")	c(" ‘table’ num [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"")	c(“‘data.frame’: 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 …")	c(" ‘table’ num [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" …")	Time-Series [1:192] from 1969 to 1985: 1687 1508 1507 1385 1632 …	Time-Series [1:108] from 1960 to 1987: 160.1 129.7 84.8 120.1 160.1 …	Time-Series [1:72] from 1973 to 1979: 9007 8106 8928 9137 10017 …	c(“‘data.frame’: 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 …")	c(“‘data.frame’: 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 …")	c(" 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" …")	c(" ‘dist’ int [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")	c(" 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"")	Time-Series [1:100] from 1 to 100: 88 84 85 85 84 85 83 85 88 89 …	c(" 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" …")	c(“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")	Time-Series [1:24] from 1937 to 1960: 412 480 683 1052 1385 …	c(“‘data.frame’: 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 …")	c(“‘data.frame’: 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 …")	c(“‘data.frame’: 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 …")	c(“‘data.frame’: 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 …")	Time-Series [1:89] from 1971 to 1993: 13067 13130 13198 13254 13304 …	c(“‘data.frame’: 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 …")	c(“‘data.frame’: 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 …")	c(“‘data.frame’: 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 …")	c(“‘data.frame’: 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 …")	Time-Series [1:468] from 1959 to 1998: 315 316 316 318 318 …	c(" ‘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" …")	Time-Series [1:100] from 1860 to 1959: 5 3 0 2 0 3 2 3 6 1 …	c(“‘data.frame’: 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 …")	c(" Named num [1:11] 13.76 40.34 1.96 166.39 5.95 …“,” - attr(*, "names")= chr [1:11] "ATS" "BEF" "DEM" "ESP" …")	c(" 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" …")	c(" ‘dist’ num [1:210] 3313 2963 3175 3339 2762 …“,” - attr(, "Size")= num 21“,” - attr(, "Labels")= chr [1:21] "Athens" "Barcelona" "Brussels" "Calais" …")	c(“‘data.frame’: 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 …")	Time-Series [1:72] from 1974 to 1980: 901 689 827 677 522 406 441 393 387 582 …	c(“‘data.frame’: 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 …")	c(" 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"")	Time-Series [1:39] from 1962 to 1972: 8.79 8.79 8.81 8.81 8.91 …	c(“‘data.frame’: 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 …" )	c(“‘data.frame’: 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 …")	c(" 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"")	c(" Named num [1:48] 11506 5500 16988 2968 16 …“,” - attr(*, "names")= chr [1:48] "Africa" "Antarctica" "Asia" "Australia" …")	Time-Series [1:72] from 1974 to 1980: 3035 2552 2704 2554 2014 …	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 …	c(“‘data.frame’: 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 …")	Time-Series [1:114] from 1821 to 1934: 269 321 585 871 1475 …	Time-Series [1:72] from 1974 to 1980: 2134 1863 1877 1877 1492 …	c(“‘data.frame’: 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 …")	c(“‘data.frame’: 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 …")	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 …	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 …	c(“‘data.frame’: 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 …")	c(" ‘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" …")	c(" 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" …")	Time-Series [1:120] from 1945 to 1975: NA 87 82 75 63 50 43 32 35 60 …	c(“‘data.frame’: 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 …")	c(“‘data.frame’: 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 …")	c(“‘data.frame’: 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 …")	num [1:141] 735 320 325 392 524 …	c(“‘data.frame’: 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 …")	c(“‘data.frame’: 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 …")	num [1:21] 42 37 37 28 18 18 19 20 15 14 …	c(" 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."")	c(“‘data.frame’: 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 …")	chr [1:50] “AL” “AK” “AZ” “AR” “CA” “CO” “CT” “DE” “FL” “GA” “HI” “ID” …	num [1:50] 51609 589757 113909 53104 158693 …	c(“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 …")	Factor w/ 9 levels “New England”,..: 4 9 8 5 9 8 1 3 3 3 …	chr [1:50] “Alabama” “Alaska” “Arizona” “Arkansas” “California” “Colorado” …	Factor w/ 4 levels “Northeast”,“South”,..: 2 4 4 2 4 4 1 2 2 2 …	c(" 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" …")	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 …	Time-Series [1:289] from 1700 to 1988: 5 11 16 23 36 58 29 20 10 8 …	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 …	c(“‘data.frame’: 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 …")	Time-Series [1:7980] from -6000 to 1979: 1.34 1.08 1.54 1.32 1.41 …	c(“‘data.frame’: 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 …")	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 …	num [1:87, 1:61] 100 101 102 103 104 105 105 106 107 108 …	c(“‘data.frame’: 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 …")	c(“‘data.frame’: 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 …")