Overview on All Datasets in an R Package
str_data.RdProvide an overview over all datasets available by
data() in a (list of) given R packages.
Value
invisibly (see invisible) a list with
named components matching the pkgs argument. Each of these
components is a named list with one entry per data(.) argument
name. Each entry is a character vector of the names
of all objects, typically only one.
The side effect is, as with str(), to print
everything (via cat) to the console.
Examples
str_data("cluster")
#>
#> All data sets in R package 'cluster' :
#> -------------------------- =======
#>
#> agriculture : 'data.frame': 12 obs. of 2 variables:
#> $ x: num 16.8 21.3 18.7 5.9 11.4 17.8 10.9 16.6 21 16.4 ...
#> $ y: num 2.7 5.7 3.5 22.2 10.9 6 14 8.5 3.5 4.3 ...
#> --------------
#> animals : 'data.frame': 20 obs. of 6 variables:
#> $ war: int 1 1 2 1 2 2 2 2 2 1 ...
#> $ fly: int 1 2 1 1 1 1 2 2 1 2 ...
#> $ ver: int 1 1 2 1 2 2 2 2 2 1 ...
#> $ end: int 1 1 1 1 2 1 1 2 2 1 ...
#> $ gro: int 2 2 1 1 2 2 2 1 2 1 ...
#> $ hai: int 1 2 2 2 2 2 1 1 1 1 ...
#> --------------
#> chorSub : int [1:61, 1:10] 101 50 5 -40 -13 -49 44 285 4 -48 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : chr [1:61] "190" "191" "192" "193" ...
#> ..$ : chr [1:10] "Al" "Ca" "Fe" "K" ...
#> --------------
#> flower : 'data.frame': 18 obs. of 8 variables:
#> $ V1: Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 2 2 ...
#> $ V2: Factor w/ 2 levels "0","1": 2 1 2 1 2 2 1 1 2 2 ...
#> $ V3: Factor w/ 2 levels "0","1": 2 1 1 2 1 1 1 2 1 1 ...
#> $ V4: Factor w/ 5 levels "1","2","3","4",..: 4 2 3 4 5 4 4 2 3 5 ...
#> $ V5: Ord.factor w/ 3 levels "1"<"2"<"3": 3 1 3 2 2 3 3 2 1 2 ...
#> $ V6: Ord.factor w/ 18 levels "1"<"2"<"3"<"4"<..: 15 3 1 16 2 12 13 7 4 14 ...
#> $ V7: num 25 150 150 125 20 50 40 100 25 100 ...
#> $ V8: num 15 50 50 50 15 40 20 15 15 60 ...
#> --------------
#> plantTraits : 'data.frame': 136 obs. of 31 variables:
#> $ pdias : num 96.84 110.72 0.06 0.08 1.48 ...
#> $ longindex: num 0 0 0.667 0.489 0.476 ...
#> $ durflow : int 2 3 3 2 3 3 3 3 3 3 ...
#> $ height : Ord.factor w/ 8 levels "1"<"2"<"3"<"4"<..: 7 8 2 2 2 5 2 2 3 2 ...
#> $ begflow : Ord.factor w/ 9 levels "1"<"2"<"3"<"4"<..: 5 4 6 7 5 4 6 3 7 4 ...
#> $ mycor : Ord.factor w/ 3 levels "0"<"1"<"2": 3 3 3 2 3 1 3 3 3 3 ...
#> $ vegaer : Ord.factor w/ 3 levels "0"<"1"<"2": 1 1 1 3 3 1 1 1 1 1 ...
#> $ vegsout : Ord.factor w/ 3 levels "0"<"1"<"2": 1 1 2 1 1 1 1 3 1 1 ...
#> $ autopoll : Ord.factor w/ 4 levels "0"<"1"<"2"<"3": 1 1 1 1 2 4 4 2 1 3 ...
#> $ insects : Ord.factor w/ 5 levels "0"<"1"<"2"<"3"<..: 5 5 1 1 4 4 3 4 4 1 ...
#> $ wind : Ord.factor w/ 5 levels "0"<"1"<"2"<"3"<..: 1 1 5 5 1 1 1 1 1 4 ...
#> $ lign : Factor w/ 2 levels "0","1": 2 2 1 1 1 1 1 1 1 1 ...
#> $ piq : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
#> $ ros : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
#> $ semiros : Factor w/ 2 levels "0","1": 1 1 1 1 2 2 1 2 2 1 ...
#> $ leafy : Factor w/ 2 levels "0","1": 2 2 2 2 1 1 2 1 1 2 ...
#> $ suman : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 2 1 1 1 ...
#> $ winan : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 1 1 ...
#> $ monocarp : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 2 1 ...
#> $ polycarp : Factor w/ 2 levels "0","1": 2 2 2 2 2 1 1 2 2 2 ...
#> $ seasaes : Factor w/ 2 levels "0","1": 2 2 1 1 1 1 1 1 2 1 ...
#> $ seashiv : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
#> $ seasver : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 1 ...
#> $ everalw : Factor w/ 2 levels "0","1": 1 1 2 2 2 1 1 1 1 2 ...
#> $ everparti: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 1 1 ...
#> $ elaio : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
#> $ endozoo : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
#> $ epizoo : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 2 ...
#> $ aquat : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
#> $ windgl : Factor w/ 2 levels "0","1": 2 2 1 1 1 1 1 1 1 1 ...
#> $ unsp : Factor w/ 2 levels "0","1": 1 1 2 2 1 2 2 1 1 1 ...
#> --------------
#> pluton : 'data.frame': 45 obs. of 4 variables:
#> $ Pu238: num 0.126 0.133 0.127 0.156 0.503 ...
#> $ Pu239: num 75.8 75.5 75.2 78.9 73.3 ...
#> $ Pu240: num 21.2 21.4 21.7 18.4 20.2 ...
#> $ Pu241: num 2.18 2.24 2.31 1.91 4.13 ...
#> --------------
#> ruspini : 'data.frame': 75 obs. of 2 variables:
#> $ x: int 4 5 10 9 13 13 12 15 18 19 ...
#> $ y: int 53 63 59 77 49 69 88 75 61 65 ...
#> --------------
#> votes.repub : 'data.frame': 50 obs. of 31 variables:
#> $ X1856: num NA NA NA NA 18.8 ...
#> $ X1860: num NA NA NA NA 33 ...
#> $ X1864: num NA NA NA NA 58.6 ...
#> $ X1868: num 51.4 NA NA 53.7 50.2 ...
#> $ X1872: num 53.2 NA NA 52.2 56.4 ...
#> $ X1876: num 40 NA NA 39.9 50.9 ...
#> $ X1880: num 37 NA NA 39.5 48.9 ...
#> $ X1884: num 38.4 NA NA 40.5 52.1 ...
#> $ X1888: num 32.3 NA NA 38.1 50 ...
#> $ X1892: num 3.95 NA NA 32.01 43.76 ...
#> $ X1896: num 28.1 NA NA 25.1 49.1 ...
#> $ X1900: num 34.7 NA NA 35 54.5 ...
#> $ X1904: num 20.6 NA NA 40.2 61.9 ...
#> $ X1908: num 24.4 NA NA 37.3 55.5 ...
#> $ X1912: num 8.26 NA 12.74 19.73 0.58 ...
#> $ X1916: num 22 NA 35.4 28 46.3 ...
#> $ X1920: num 31 NA 55.4 38.7 66.2 ...
#> $ X1924: num 27 NA 41.3 29.3 57.2 ...
#> $ X1928: num 48.5 NA 57.6 39.3 64.7 ...
#> $ X1932: num 14.2 NA 30.5 12.9 37.4 ...
#> $ X1936: num 12.8 NA 26.9 17.9 31.7 ...
#> $ X1940: num 14.3 NA 36 20.9 41.4 ...
#> $ X1944: num 18.2 NA 40.9 29.8 43 ...
#> $ X1948: num 19 NA 43.8 21 47.1 ...
#> $ X1952: num 35 NA 58.4 43.8 56.4 ...
#> $ X1956: num 39.4 NA 61 45.8 55.4 ...
#> $ X1960: num 41.8 50.9 55.5 43.1 50.1 ...
#> $ X1964: num 69.5 34.1 50.4 43.9 40.9 38.7 32.2 39.1 48.9 54.1 ...
#> $ X1968: num 14 45.3 54.8 30.8 47.8 50.5 44.3 45.1 40.5 30.4 ...
#> $ X1972: num 72.4 58.1 64.7 68.9 55 62.6 58.6 59.6 71.9 75 ...
#> $ X1976: num 43.5 62.9 58.6 35 50.9 ...
#> --------------
#> xclara : 'data.frame': 3000 obs. of 2 variables:
#> $ V1: num 2.07 17.94 1.08 11.12 23.71 ...
#> $ V2: num -3.24 15.78 7.32 14.41 2.56 ...
#> --------------
str_data("datasets", max=0, give.attr = FALSE)
#>
#> All data sets in R package 'datasets' :
#> -------------------------- ========
#>
#> AirPassengers : Time-Series [1:144] from 1949 to 1961: 112 118 132 129 121 135 148 148 136 119 ...
#> --------------
#> BJsales :
#> BJsales : Time-Series [1:150] from 1 to 150: 200 200 199 199 199 ...
#> BJsales.lead : Time-Series [1:150] from 1 to 150: 10.01 10.07 10.32 9.75 10.33 ...
#> --------------
#> BOD : 'data.frame': 6 obs. of 2 variables:
#> --------------
#> CO2 : Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and 'data.frame': 84 obs. of 5 variables:
#> --------------
#> ChickWeight : Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and 'data.frame': 578 obs. of 4 variables:
#> --------------
#> DNase : Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and 'data.frame': 176 obs. of 3 variables:
#> --------------
#> EuStockMarkets : Time-Series [1:1860, 1:4] from 1991 to 1999: 1629 1614 1607 1621 1618 ...
#> --------------
#> Formaldehyde : 'data.frame': 6 obs. of 2 variables:
#> --------------
#> HairEyeColor : 'table' num [1:4, 1:4, 1:2] 32 53 10 3 11 50 10 30 10 25 ...
#> --------------
#> Harman23.cor : List of 3
#> --------------
#> Harman74.cor : List of 3
#> --------------
#> Indometh : Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and 'data.frame': 66 obs. of 3 variables:
#> --------------
#> InsectSprays : 'data.frame': 72 obs. of 2 variables:
#> --------------
#> 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 ...
#> --------------
#> LakeHuron : Time-Series [1:98] from 1875 to 1972: 580 582 581 581 580 ...
#> --------------
#> LifeCycleSavings : 'data.frame': 50 obs. of 5 variables:
#> --------------
#> Loblolly : Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and 'data.frame': 84 obs. of 3 variables:
#> --------------
#> Nile : Time-Series [1:100] from 1871 to 1970: 1120 1160 963 1210 1160 1160 813 1230 1370 1140 ...
#> --------------
#> Orange : Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and 'data.frame': 35 obs. of 3 variables:
#> --------------
#> OrchardSprays : 'data.frame': 64 obs. of 4 variables:
#> --------------
#> PlantGrowth : 'data.frame': 30 obs. of 2 variables:
#> --------------
#> Puromycin : 'data.frame': 23 obs. of 3 variables:
#> --------------
#> Seatbelts : Time-Series [1:192, 1:8] from 1969 to 1985: 107 97 102 87 119 106 110 106 107 134 ...
#> --------------
#> Theoph : Classes ‘nfnGroupedData’, ‘nfGroupedData’, ‘groupedData’ and 'data.frame': 132 obs. of 5 variables:
#> --------------
#> Titanic : 'table' num [1:4, 1:2, 1:2, 1:2] 0 0 35 0 0 0 17 0 118 154 ...
#> --------------
#> ToothGrowth : 'data.frame': 60 obs. of 3 variables:
#> --------------
#> UCBAdmissions : 'table' num [1:2, 1:2, 1:6] 512 313 89 19 353 207 17 8 120 205 ...
#> --------------
#> UKDriverDeaths : Time-Series [1:192] from 1969 to 1985: 1687 1508 1507 1385 1632 ...
#> --------------
#> UKgas : Time-Series [1:108] from 1960 to 1987: 160.1 129.7 84.8 120.1 160.1 ...
#> --------------
#> USAccDeaths : Time-Series [1:72] from 1973 to 1979: 9007 8106 8928 9137 10017 ...
#> --------------
#> USArrests : 'data.frame': 50 obs. of 4 variables:
#> --------------
#> USJudgeRatings : 'data.frame': 43 obs. of 12 variables:
#> --------------
#> 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 ...
#> --------------
#> UScitiesD : 'dist' int [1:45] 587 1212 701 1936 604 748 2139 2182 543 920 ...
#> --------------
#> VADeaths : num [1:5, 1:4] 11.7 18.1 26.9 41 66 8.7 11.7 20.3 30.9 54.3 ...
#> --------------
#> WWWusage : Time-Series [1:100] from 1 to 100: 88 84 85 85 84 85 83 85 88 89 ...
#> --------------
#> WorldPhones : num [1:7, 1:7] 45939 60423 64721 68484 71799 ...
#> --------------
#> ability.cov : List of 3
#> --------------
#> airmiles : Time-Series [1:24] from 1937 to 1960: 412 480 683 1052 1385 ...
#> --------------
#> airquality : 'data.frame': 153 obs. of 6 variables:
#> --------------
#> anscombe : 'data.frame': 11 obs. of 8 variables:
#> --------------
#> attenu : 'data.frame': 182 obs. of 5 variables:
#> --------------
#> attitude : 'data.frame': 30 obs. of 7 variables:
#> --------------
#> austres : Time-Series [1:89] from 1971 to 1993: 13067 13130 13198 13254 13304 ...
#> --------------
#> cars : 'data.frame': 50 obs. of 2 variables:
#> --------------
#> chickwts : 'data.frame': 71 obs. of 2 variables:
#> --------------
#> co2 : Time-Series [1:468] from 1959 to 1998: 315 316 316 318 318 ...
#> --------------
#> crimtab : 'table' int [1:42, 1:22] 0 0 0 0 0 0 1 0 0 0 ...
#> --------------
#> discoveries : Time-Series [1:100] from 1860 to 1959: 5 3 0 2 0 3 2 3 6 1 ...
#> --------------
#> esoph : 'data.frame': 88 obs. of 5 variables:
#> --------------
#> euro :
#> euro : Named num [1:11] 13.76 40.34 1.96 166.39 5.95 ...
#> euro.cross : num [1:11, 1:11] 1 0.3411 7.0355 0.0827 2.3143 ...
#> --------------
#> eurodist : 'dist' num [1:210] 3313 2963 3175 3339 2762 ...
#> --------------
#> faithful : 'data.frame': 272 obs. of 2 variables:
#> --------------
#> freeny :
#> freeny : 'data.frame': 39 obs. of 5 variables:
#> freeny.x : num [1:39, 1:4] 8.8 8.79 8.79 8.81 8.81 ...
#> freeny.y : Time-Series [1:39] from 1962 to 1972: 8.79 8.79 8.81 8.81 8.91 ...
#> --------------
#> gait : num [1:20, 1:39, 1:2] 37 36 33 29 23 18 15 12 9 6 ...
#> --------------
#> infert : 'data.frame': 248 obs. of 8 variables:
#> --------------
#> iris : 'data.frame': 150 obs. of 5 variables:
#> --------------
#> 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 ...
#> --------------
#> islands : Named num [1:48] 11506 5500 16988 2968 16 ...
#> --------------
#> 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 ...
#> --------------
#> longley : 'data.frame': 16 obs. of 7 variables:
#> --------------
#> lynx : Time-Series [1:114] from 1821 to 1934: 269 321 585 871 1475 ...
#> --------------
#> morley : 'data.frame': 100 obs. of 3 variables:
#> --------------
#> mtcars : 'data.frame': 32 obs. of 11 variables:
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> npk : 'data.frame': 24 obs. of 5 variables:
#> --------------
#> occupationalStatus : 'table' int [1:8, 1:8] 50 16 12 11 2 12 0 0 19 40 ...
#> --------------
#> penguins :
#> penguins : 'data.frame': 344 obs. of 8 variables:
#> penguins_raw : 'data.frame': 344 obs. of 17 variables:
#> --------------
#> precip : Named num [1:70] 67 54.7 7 48.5 14 17.2 20.7 13 43.4 40.2 ...
#> --------------
#> presidents : Time-Series [1:120] from 1945 to 1975: NA 87 82 75 63 50 43 32 35 60 ...
#> --------------
#> pressure : 'data.frame': 19 obs. of 2 variables:
#> --------------
#> quakes : 'data.frame': 1000 obs. of 5 variables:
#> --------------
#> randu : 'data.frame': 400 obs. of 3 variables:
#> --------------
#> rivers : num [1:141] 735 320 325 392 524 ...
#> --------------
#> rock : 'data.frame': 48 obs. of 4 variables:
#> --------------
#> sleep : 'data.frame': 20 obs. of 3 variables:
#> --------------
#> stackloss :
#> stack.loss : num [1:21] 42 37 37 28 18 18 19 20 15 14 ...
#> stack.x : num [1:21, 1:3] 80 80 75 62 62 62 62 62 58 58 ...
#> stackloss : 'data.frame': 21 obs. of 4 variables:
#> --------------
#> sunspot.month :
#> sunspot.m2014 : 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 ...
#> sunspot.month : Time-Series [1:3310] from 1749 to 2025: 96.7 104.3 116.7 92.8 141.7 ...
#> --------------
#> sunspot.year : Time-Series [1:289] from 1700 to 1988: 5 11 16 23 36 58 29 20 10 8 ...
#> --------------
#> 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 ...
#> --------------
#> swiss : 'data.frame': 47 obs. of 6 variables:
#> --------------
#> treering : Time-Series [1:7980] from -6000 to 1979: 1.34 1.08 1.54 1.32 1.41 ...
#> --------------
#> trees : 'data.frame': 31 obs. of 3 variables:
#> --------------
#> 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 ...
#> --------------
#> volcano : num [1:87, 1:61] 100 101 102 103 104 105 105 106 107 108 ...
#> --------------
#> warpbreaks : 'data.frame': 54 obs. of 3 variables:
#> --------------
#> women : 'data.frame': 15 obs. of 2 variables:
#> --------------
## Filtering (and return value)
dfl <- str_data("datasets", filterFUN=is.data.frame)
#>
#> All data sets in R package 'datasets' filtered by is.data.frame():
#> -------------------------- ========
#>
#> 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"
#> --------------
#> 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' 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)"
#> --------------
#> 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' 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)"
#> --------------
#> 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' 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)"
#> --------------
#> 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
#> --------------
#> 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' 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)"
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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' 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)"
#> --------------
#> 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' 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)"
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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"
#> --------------
#> 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' 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)"
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> penguins :
#> penguins : 'data.frame': 344 obs. of 8 variables:
#> $ species : Factor w/ 3 levels "Adelie","Chinstrap",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ island : Factor w/ 3 levels "Biscoe","Dream",..: 3 3 3 3 3 3 3 3 3 3 ...
#> $ bill_len : num 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
#> $ bill_dep : num 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
#> $ flipper_len: int 181 186 195 NA 193 190 181 195 193 190 ...
#> $ body_mass : int 3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 ...
#> $ sex : Factor w/ 2 levels "female","male": 2 1 1 NA 1 2 1 2 NA NA ...
#> $ year : int 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...
#> penguins_raw : 'data.frame': 344 obs. of 17 variables:
#> $ studyName : chr "PAL0708" "PAL0708" "PAL0708" "PAL0708" ...
#> $ Sample Number : num 1 2 3 4 5 6 7 8 9 10 ...
#> $ Species : chr "Adelie Penguin (Pygoscelis adeliae)" "Adelie Penguin (Pygoscelis adeliae)" "Adelie Penguin (Pygoscelis adeliae)" "Adelie Penguin (Pygoscelis adeliae)" ...
#> $ Region : chr "Anvers" "Anvers" "Anvers" "Anvers" ...
#> $ Island : chr "Torgersen" "Torgersen" "Torgersen" "Torgersen" ...
#> $ Stage : chr "Adult, 1 Egg Stage" "Adult, 1 Egg Stage" "Adult, 1 Egg Stage" "Adult, 1 Egg Stage" ...
#> $ Individual ID : chr "N1A1" "N1A2" "N2A1" "N2A2" ...
#> $ Clutch Completion : chr "Yes" "Yes" "Yes" "Yes" ...
#> $ Date Egg : Date, format: "2007-11-11" "2007-11-11" ...
#> $ Culmen Length (mm) : num 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
#> $ Culmen Depth (mm) : num 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
#> $ Flipper Length (mm): num 181 186 195 NA 193 190 181 195 193 190 ...
#> $ Body Mass (g) : num 3750 3800 3250 NA 3450 ...
#> $ Sex : chr "MALE" "FEMALE" "FEMALE" NA ...
#> $ Delta 15 N (o/oo) : num NA 8.95 8.37 NA 8.77 ...
#> $ Delta 13 C (o/oo) : num NA -24.7 -25.3 NA -25.3 ...
#> $ Comments : chr "Not enough blood for isotopes." NA NA "Adult not sampled." ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
#> 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 ...
#> --------------
str(df.d <- dfl$datasets)
#> List of 43
#> $ BOD : chr "BOD"
#> $ CO2 : chr "CO2"
#> $ ChickWeight : chr "ChickWeight"
#> $ DNase : chr "DNase"
#> $ Formaldehyde : chr "Formaldehyde"
#> $ Indometh : chr "Indometh"
#> $ InsectSprays : chr "InsectSprays"
#> $ LifeCycleSavings: chr "LifeCycleSavings"
#> $ Loblolly : chr "Loblolly"
#> $ Orange : chr "Orange"
#> $ OrchardSprays : chr "OrchardSprays"
#> $ PlantGrowth : chr "PlantGrowth"
#> $ Puromycin : chr "Puromycin"
#> $ Theoph : chr "Theoph"
#> $ ToothGrowth : chr "ToothGrowth"
#> $ USArrests : chr "USArrests"
#> $ USJudgeRatings : chr "USJudgeRatings"
#> $ airquality : chr "airquality"
#> $ anscombe : chr "anscombe"
#> $ attenu : chr "attenu"
#> $ attitude : chr "attitude"
#> $ cars : chr "cars"
#> $ chickwts : chr "chickwts"
#> $ esoph : chr "esoph"
#> $ faithful : chr "faithful"
#> $ freeny : chr "freeny"
#> $ infert : chr "infert"
#> $ iris : chr "iris"
#> $ longley : chr "longley"
#> $ morley : chr "morley"
#> $ mtcars : chr "mtcars"
#> $ npk : chr "npk"
#> $ penguins : chr [1:2] "penguins" "penguins_raw"
#> $ pressure : chr "pressure"
#> $ quakes : chr "quakes"
#> $ randu : chr "randu"
#> $ rock : chr "rock"
#> $ sleep : chr "sleep"
#> $ stackloss : chr "stackloss"
#> $ swiss : chr "swiss"
#> $ trees : chr "trees"
#> $ warpbreaks : chr "warpbreaks"
#> $ women : chr "women"
## dim() of all those data frames:
t(sapply(unlist(df.d), function(.) dim(get(.))))
#> [,1] [,2]
#> BOD 6 2
#> CO2 84 5
#> ChickWeight 578 4
#> DNase 176 3
#> Formaldehyde 6 2
#> Indometh 66 3
#> InsectSprays 72 2
#> LifeCycleSavings 50 5
#> Loblolly 84 3
#> Orange 35 3
#> OrchardSprays 64 4
#> PlantGrowth 30 2
#> Puromycin 23 3
#> Theoph 132 5
#> ToothGrowth 60 3
#> USArrests 50 4
#> USJudgeRatings 43 12
#> airquality 153 6
#> anscombe 11 8
#> attenu 182 5
#> attitude 30 7
#> cars 50 2
#> chickwts 71 2
#> esoph 88 5
#> faithful 272 2
#> freeny 39 5
#> infert 248 8
#> iris 150 5
#> longley 16 7
#> morley 100 3
#> mtcars 32 11
#> npk 24 5
#> penguins1 344 8
#> penguins2 344 17
#> pressure 19 2
#> quakes 1000 5
#> randu 400 3
#> rock 48 4
#> sleep 20 3
#> stackloss 21 4
#> swiss 47 6
#> trees 31 3
#> warpbreaks 54 3
#> women 15 2
### Data sets in all attached packages but "datasets" (and stubs):
s <- search()
(Apkgs <- sub("^package:", '', s[grep("^package:", s)]))
#> [1] "tools" "gmp" "lokern" "MASS" "cluster" "rpart"
#> [7] "sfsmisc" "stats" "graphics" "grDevices" "utils" "datasets"
#> [13] "methods" "base"
str_data(Apkgs[!Apkgs %in% c("datasets", "stats", "base")])
#>
#> All data sets in R package 'tools' :
#> -------------------------- =====
#>
#>
#> All data sets in R package 'gmp' :
#> -------------------------- ===
#>
#> Oakley1 : 'bigz' raw (2 %% 15525180923007089351309181312584817556313340494345143132023511949029662399491021072586694538765916424429100| __truncated__
#> - attr(*, "mod")= 'bigz' raw 15525180923007089351309181312584817556313340494345143132023511949029662399491021072586694538765916424429100076802| __truncated__
#> --------------
#> Oakley2 : 'bigz' raw (2 %% 17976931348623159077083915679378745319786029604875601170644442368419718021615851936894783379586492554150218| __truncated__
#> - attr(*, "mod")= 'bigz' raw 17976931348623159077083915679378745319786029604875601170644442368419718021615851936894783379586492554150218056548| __truncated__
#> --------------
#>
#> All data sets in R package 'lokern' :
#> -------------------------- ======
#>
#> xSim : num [1:75] 1.97 1.9 1.64 1.43 2.4 ...
#> --------------
#>
#> All data sets in R package 'MASS' :
#> -------------------------- ====
#>
#> Aids2 : 'data.frame': 2843 obs. of 7 variables:
#> $ state : Factor w/ 4 levels "NSW","Other",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ sex : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 2 2 2 ...
#> $ diag : int 10905 11029 9551 9577 10015 9971 10746 10042 10464 10439 ...
#> $ death : int 11081 11096 9983 9654 10290 10344 11135 11069 10956 10873 ...
#> $ status : Factor w/ 2 levels "A","D": 2 2 2 2 2 2 2 2 2 2 ...
#> $ T.categ: Factor w/ 8 levels "hs","hsid","id",..: 1 1 1 5 1 1 8 1 1 2 ...
#> $ age : int 35 53 42 44 39 36 36 31 26 27 ...
#> --------------
#> Animals : 'data.frame': 28 obs. of 2 variables:
#> $ body : num 1.35 465 36.33 27.66 1.04 ...
#> $ brain: num 8.1 423 119.5 115 5.5 ...
#> --------------
#> Boston : 'data.frame': 506 obs. of 14 variables:
#> $ crim : num 0.00632 0.02731 0.02729 0.03237 0.06905 ...
#> $ zn : num 18 0 0 0 0 0 12.5 12.5 12.5 12.5 ...
#> $ indus : num 2.31 7.07 7.07 2.18 2.18 2.18 7.87 7.87 7.87 7.87 ...
#> $ chas : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ nox : num 0.538 0.469 0.469 0.458 0.458 0.458 0.524 0.524 0.524 0.524 ...
#> $ rm : num 6.58 6.42 7.18 7 7.15 ...
#> $ age : num 65.2 78.9 61.1 45.8 54.2 58.7 66.6 96.1 100 85.9 ...
#> $ dis : num 4.09 4.97 4.97 6.06 6.06 ...
#> $ rad : int 1 2 2 3 3 3 5 5 5 5 ...
#> $ tax : num 296 242 242 222 222 222 311 311 311 311 ...
#> $ ptratio: num 15.3 17.8 17.8 18.7 18.7 18.7 15.2 15.2 15.2 15.2 ...
#> $ black : num 397 397 393 395 397 ...
#> $ lstat : num 4.98 9.14 4.03 2.94 5.33 ...
#> $ medv : num 24 21.6 34.7 33.4 36.2 28.7 22.9 27.1 16.5 18.9 ...
#> --------------
#> Cars93 : 'data.frame': 93 obs. of 27 variables:
#> $ Manufacturer : Factor w/ 32 levels "Acura","Audi",..: 1 1 2 2 3 4 4 4 4 5 ...
#> $ Model : Factor w/ 93 levels "100","190E","240",..: 49 56 9 1 6 24 54 74 73 35 ...
#> $ Type : Factor w/ 6 levels "Compact","Large",..: 4 3 1 3 3 3 2 2 3 2 ...
#> $ Min.Price : num 12.9 29.2 25.9 30.8 23.7 14.2 19.9 22.6 26.3 33 ...
#> $ Price : num 15.9 33.9 29.1 37.7 30 15.7 20.8 23.7 26.3 34.7 ...
#> $ Max.Price : num 18.8 38.7 32.3 44.6 36.2 17.3 21.7 24.9 26.3 36.3 ...
#> $ MPG.city : int 25 18 20 19 22 22 19 16 19 16 ...
#> $ MPG.highway : int 31 25 26 26 30 31 28 25 27 25 ...
#> $ AirBags : Factor w/ 3 levels "Driver & Passenger",..: 3 1 2 1 2 2 2 2 2 2 ...
#> $ DriveTrain : Factor w/ 3 levels "4WD","Front",..: 2 2 2 2 3 2 2 3 2 2 ...
#> $ Cylinders : Factor w/ 6 levels "3","4","5","6",..: 2 4 4 4 2 2 4 4 4 5 ...
#> $ EngineSize : num 1.8 3.2 2.8 2.8 3.5 2.2 3.8 5.7 3.8 4.9 ...
#> $ Horsepower : int 140 200 172 172 208 110 170 180 170 200 ...
#> $ RPM : int 6300 5500 5500 5500 5700 5200 4800 4000 4800 4100 ...
#> $ Rev.per.mile : int 2890 2335 2280 2535 2545 2565 1570 1320 1690 1510 ...
#> $ Man.trans.avail : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 1 1 1 1 1 ...
#> $ Fuel.tank.capacity: num 13.2 18 16.9 21.1 21.1 16.4 18 23 18.8 18 ...
#> $ Passengers : int 5 5 5 6 4 6 6 6 5 6 ...
#> $ Length : int 177 195 180 193 186 189 200 216 198 206 ...
#> $ Wheelbase : int 102 115 102 106 109 105 111 116 108 114 ...
#> $ Width : int 68 71 67 70 69 69 74 78 73 73 ...
#> $ Turn.circle : int 37 38 37 37 39 41 42 45 41 43 ...
#> $ Rear.seat.room : num 26.5 30 28 31 27 28 30.5 30.5 26.5 35 ...
#> $ Luggage.room : int 11 15 14 17 13 16 17 21 14 18 ...
#> $ Weight : int 2705 3560 3375 3405 3640 2880 3470 4105 3495 3620 ...
#> $ Origin : Factor w/ 2 levels "USA","non-USA": 2 2 2 2 2 1 1 1 1 1 ...
#> $ Make : Factor w/ 93 levels "Acura Integra",..: 1 2 4 3 5 6 7 9 8 10 ...
#> --------------
#> Cushings : 'data.frame': 27 obs. of 3 variables:
#> $ Tetrahydrocortisone: num 3.1 3 1.9 3.8 4.1 1.9 8.3 3.8 3.9 7.8 ...
#> $ Pregnanetriol : num 11.7 1.3 0.1 0.04 1.1 0.4 1 0.2 0.6 1.2 ...
#> $ Type : Factor w/ 4 levels "a","b","c","u": 1 1 1 1 1 1 2 2 2 2 ...
#> --------------
#> DDT : num [1:15] 2.79 2.93 3.22 3.78 3.22 3.38 3.18 3.33 3.34 3.06 ...
#> --------------
#> GAGurine : 'data.frame': 314 obs. of 2 variables:
#> $ Age: num 0 0 0 0 0.01 0.01 0.01 0.01 0.01 0.01 ...
#> $ GAG: num 23 23.8 16.9 18.6 17.9 25.9 16.5 26.3 26.9 17.9 ...
#> --------------
#> Insurance : 'data.frame': 64 obs. of 5 variables:
#> $ District: Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 1 1 1 1 1 ...
#> $ Group : Ord.factor w/ 4 levels "<1l"<"1-1.5l"<..: 1 1 1 1 2 2 2 2 3 3 ...
#> $ Age : Ord.factor w/ 4 levels "<25"<"25-29"<..: 1 2 3 4 1 2 3 4 1 2 ...
#> $ Holders : int 197 264 246 1680 284 536 696 3582 133 286 ...
#> $ Claims : int 38 35 20 156 63 84 89 400 19 52 ...
#> --------------
#> Melanoma : 'data.frame': 205 obs. of 7 variables:
#> $ time : int 10 30 35 99 185 204 210 232 232 279 ...
#> $ status : int 3 3 2 3 1 1 1 3 1 1 ...
#> $ sex : int 1 1 1 0 1 1 1 0 1 0 ...
#> $ age : int 76 56 41 71 52 28 77 60 49 68 ...
#> $ year : int 1972 1968 1977 1968 1965 1971 1972 1974 1968 1971 ...
#> $ thickness: num 6.76 0.65 1.34 2.9 12.08 ...
#> $ ulcer : int 1 0 0 0 1 1 1 1 1 1 ...
#> --------------
#> OME : 'data.frame': 1097 obs. of 7 variables:
#> $ ID : int 1 1 1 1 1 1 1 1 1 1 ...
#> $ Age : int 30 30 30 30 30 30 30 30 30 30 ...
#> $ OME : Factor w/ 3 levels "N/A","high","low": 3 3 3 3 3 3 3 3 3 3 ...
#> $ Loud : int 35 35 40 40 45 45 50 50 55 55 ...
#> $ Noise : Factor w/ 2 levels "coherent","incoherent": 1 2 1 2 1 2 1 2 1 2 ...
#> $ Correct: int 1 4 0 1 2 2 3 4 3 2 ...
#> $ Trials : int 4 5 3 1 4 2 3 4 3 2 ...
#> --------------
#> Pima.te : 'data.frame': 332 obs. of 8 variables:
#> $ npreg: int 6 1 1 3 2 5 0 1 3 9 ...
#> $ glu : int 148 85 89 78 197 166 118 103 126 119 ...
#> $ bp : int 72 66 66 50 70 72 84 30 88 80 ...
#> $ skin : int 35 29 23 32 45 19 47 38 41 35 ...
#> $ bmi : num 33.6 26.6 28.1 31 30.5 25.8 45.8 43.3 39.3 29 ...
#> $ ped : num 0.627 0.351 0.167 0.248 0.158 0.587 0.551 0.183 0.704 0.263 ...
#> $ age : int 50 31 21 26 53 51 31 33 27 29 ...
#> $ type : Factor w/ 2 levels "No","Yes": 2 1 1 2 2 2 2 1 1 2 ...
#> --------------
#> Pima.tr : 'data.frame': 200 obs. of 8 variables:
#> $ npreg: int 5 7 5 0 0 5 3 1 3 2 ...
#> $ glu : int 86 195 77 165 107 97 83 193 142 128 ...
#> $ bp : int 68 70 82 76 60 76 58 50 80 78 ...
#> $ skin : int 28 33 41 43 25 27 31 16 15 37 ...
#> $ bmi : num 30.2 25.1 35.8 47.9 26.4 35.6 34.3 25.9 32.4 43.3 ...
#> $ ped : num 0.364 0.163 0.156 0.259 0.133 ...
#> $ age : int 24 55 35 26 23 52 25 24 63 31 ...
#> $ type : Factor w/ 2 levels "No","Yes": 1 2 1 1 1 2 1 1 1 2 ...
#> --------------
#> Pima.tr2 : 'data.frame': 300 obs. of 8 variables:
#> $ npreg: int 5 7 5 0 0 5 3 1 3 2 ...
#> $ glu : int 86 195 77 165 107 97 83 193 142 128 ...
#> $ bp : int 68 70 82 76 60 76 58 50 80 78 ...
#> $ skin : int 28 33 41 43 25 27 31 16 15 37 ...
#> $ bmi : num 30.2 25.1 35.8 47.9 26.4 35.6 34.3 25.9 32.4 43.3 ...
#> $ ped : num 0.364 0.163 0.156 0.259 0.133 ...
#> $ age : int 24 55 35 26 23 52 25 24 63 31 ...
#> $ type : Factor w/ 2 levels "No","Yes": 1 2 1 1 1 2 1 1 1 2 ...
#> --------------
#> Rabbit : 'data.frame': 60 obs. of 5 variables:
#> $ BPchange : num 0.5 4.5 10 26 37 32 1 1.25 4 12 ...
#> $ Dose : num 6.25 12.5 25 50 100 200 6.25 12.5 25 50 ...
#> $ Run : Factor w/ 10 levels "C1","C2","C3",..: 1 1 1 1 1 1 2 2 2 2 ...
#> $ Treatment: Factor w/ 2 levels "Control","MDL": 1 1 1 1 1 1 1 1 1 1 ...
#> $ Animal : Factor w/ 5 levels "R1","R2","R3",..: 1 1 1 1 1 1 2 2 2 2 ...
#> --------------
#> Rubber : 'data.frame': 30 obs. of 3 variables:
#> $ loss: int 372 206 175 154 136 112 55 45 221 166 ...
#> $ hard: int 45 55 61 66 71 71 81 86 53 60 ...
#> $ tens: int 162 233 232 231 231 237 224 219 203 189 ...
#> --------------
#> SP500 : num [1:2780] -0.259 -0.865 -0.98 0.45 -1.186 ...
#> --------------
#> Sitka : 'data.frame': 395 obs. of 4 variables:
#> $ size : num 4.51 4.98 5.41 5.9 6.15 4.24 4.2 4.68 4.92 4.96 ...
#> $ Time : num 152 174 201 227 258 152 174 201 227 258 ...
#> $ tree : int 1 1 1 1 1 2 2 2 2 2 ...
#> $ treat: Factor w/ 2 levels "control","ozone": 2 2 2 2 2 2 2 2 2 2 ...
#> --------------
#> Sitka89 : 'data.frame': 632 obs. of 4 variables:
#> $ size : num 6.16 6.18 6.48 6.65 6.87 6.95 6.99 7.04 5.2 5.22 ...
#> $ Time : num 469 496 528 556 579 613 639 674 469 496 ...
#> $ tree : int 1 1 1 1 1 1 1 1 2 2 ...
#> $ treat: Factor w/ 2 levels "control","ozone": 2 2 2 2 2 2 2 2 2 2 ...
#> --------------
#> Skye : 'data.frame': 23 obs. of 3 variables:
#> $ A: int 52 52 47 45 40 37 27 27 23 22 ...
#> $ F: int 42 44 48 49 50 54 58 54 59 59 ...
#> $ M: int 6 4 5 6 10 9 15 19 18 19 ...
#> --------------
#> Traffic : 'data.frame': 184 obs. of 4 variables:
#> $ year : int 1961 1961 1961 1961 1961 1961 1961 1961 1961 1961 ...
#> $ day : int 1 2 3 4 5 6 7 8 9 10 ...
#> $ limit: Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
#> $ y : int 9 11 9 20 31 26 18 19 18 13 ...
#> --------------
#> UScereal : 'data.frame': 65 obs. of 11 variables:
#> $ mfr : Factor w/ 6 levels "G","K","N","P",..: 3 2 2 1 2 1 6 4 5 1 ...
#> $ calories : num 212 212 100 147 110 ...
#> $ protein : num 12.12 12.12 8 2.67 2 ...
#> $ fat : num 3.03 3.03 0 2.67 0 ...
#> $ sodium : num 394 788 280 240 125 ...
#> $ fibre : num 30.3 27.3 28 2 1 ...
#> $ carbo : num 15.2 21.2 16 14 11 ...
#> $ sugars : num 18.2 15.2 0 13.3 14 ...
#> $ shelf : int 3 3 3 1 2 3 1 3 2 1 ...
#> $ potassium: num 848.5 969.7 660 93.3 30 ...
#> $ vitamins : Factor w/ 3 levels "100%","enriched",..: 2 2 2 2 2 2 2 2 2 2 ...
#> --------------
#> UScrime : 'data.frame': 47 obs. of 16 variables:
#> $ M : int 151 143 142 136 141 121 127 131 157 140 ...
#> $ So : int 1 0 1 0 0 0 1 1 1 0 ...
#> $ Ed : int 91 113 89 121 121 110 111 109 90 118 ...
#> $ Po1 : int 58 103 45 149 109 118 82 115 65 71 ...
#> $ Po2 : int 56 95 44 141 101 115 79 109 62 68 ...
#> $ LF : int 510 583 533 577 591 547 519 542 553 632 ...
#> $ M.F : int 950 1012 969 994 985 964 982 969 955 1029 ...
#> $ Pop : int 33 13 18 157 18 25 4 50 39 7 ...
#> $ NW : int 301 102 219 80 30 44 139 179 286 15 ...
#> $ U1 : int 108 96 94 102 91 84 97 79 81 100 ...
#> $ U2 : int 41 36 33 39 20 29 38 35 28 24 ...
#> $ GDP : int 394 557 318 673 578 689 620 472 421 526 ...
#> $ Ineq: int 261 194 250 167 174 126 168 206 239 174 ...
#> $ Prob: num 0.0846 0.0296 0.0834 0.0158 0.0414 ...
#> $ Time: num 26.2 25.3 24.3 29.9 21.3 ...
#> $ y : int 791 1635 578 1969 1234 682 963 1555 856 705 ...
#> --------------
#> VA : 'data.frame': 137 obs. of 8 variables:
#> $ stime : num 72 411 228 126 118 10 82 110 314 100 ...
#> $ status : num 1 1 1 1 1 1 1 1 1 0 ...
#> $ treat : Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 1 1 ...
#> $ age : num 69 64 38 63 65 49 69 68 43 70 ...
#> $ Karn : num 60 70 60 60 70 20 40 80 50 70 ...
#> $ diag.time: num 7 5 3 9 11 5 10 29 18 6 ...
#> $ cell : Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 1 1 1 1 1 ...
#> $ prior : Factor w/ 2 levels "0","10": 1 2 1 2 2 1 2 1 1 1 ...
#> --------------
#> abbey : num [1:31] 5.2 6.5 6.9 7 7 7 7.4 8 8 8 ...
#> --------------
#> accdeaths : Time-Series [1:72] from 1973 to 1979: 9007 8106 8928 9137 10017 ...
#> --------------
#> anorexia : 'data.frame': 72 obs. of 3 variables:
#> $ Treat : Factor w/ 3 levels "CBT","Cont","FT": 2 2 2 2 2 2 2 2 2 2 ...
#> $ Prewt : num 80.7 89.4 91.8 74 78.1 88.3 87.3 75.1 80.6 78.4 ...
#> $ Postwt: num 80.2 80.1 86.4 86.3 76.1 78.1 75.1 86.7 73.5 84.6 ...
#> --------------
#> bacteria : 'data.frame': 220 obs. of 6 variables:
#> $ y : Factor w/ 2 levels "n","y": 2 2 2 2 2 2 1 2 2 2 ...
#> $ ap : Factor w/ 2 levels "a","p": 2 2 2 2 1 1 1 1 1 1 ...
#> $ hilo: Factor w/ 2 levels "hi","lo": 1 1 1 1 1 1 1 1 2 2 ...
#> $ week: int 0 2 4 11 0 2 6 11 0 2 ...
#> $ ID : Factor w/ 50 levels "X01","X02","X03",..: 1 1 1 1 2 2 2 2 3 3 ...
#> $ trt : Factor w/ 3 levels "placebo","drug",..: 1 1 1 1 3 3 3 3 2 2 ...
#> --------------
#> beav1 : 'data.frame': 114 obs. of 4 variables:
#> $ day : int 346 346 346 346 346 346 346 346 346 346 ...
#> $ time : int 840 850 900 910 920 930 940 950 1000 1010 ...
#> $ temp : num 36.3 36.3 36.4 36.4 36.5 ...
#> $ activ: int 0 0 0 0 0 0 0 0 0 0 ...
#> --------------
#> beav2 : 'data.frame': 100 obs. of 4 variables:
#> $ day : int 307 307 307 307 307 307 307 307 307 307 ...
#> $ time : int 930 940 950 1000 1010 1020 1030 1040 1050 1100 ...
#> $ temp : num 36.6 36.7 36.9 37.1 37.2 ...
#> $ activ: int 0 0 0 0 0 0 0 0 0 0 ...
#> --------------
#> biopsy : 'data.frame': 699 obs. of 11 variables:
#> $ ID : chr "1000025" "1002945" "1015425" "1016277" ...
#> $ V1 : int 5 5 3 6 4 8 1 2 2 4 ...
#> $ V2 : int 1 4 1 8 1 10 1 1 1 2 ...
#> $ V3 : int 1 4 1 8 1 10 1 2 1 1 ...
#> $ V4 : int 1 5 1 1 3 8 1 1 1 1 ...
#> $ V5 : int 2 7 2 3 2 7 2 2 2 2 ...
#> $ V6 : int 1 10 2 4 1 10 10 1 1 1 ...
#> $ V7 : int 3 3 3 3 3 9 3 3 1 2 ...
#> $ V8 : int 1 2 1 7 1 7 1 1 1 1 ...
#> $ V9 : int 1 1 1 1 1 1 1 1 5 1 ...
#> $ class: Factor w/ 2 levels "benign","malignant": 1 1 1 1 1 2 1 1 1 1 ...
#> --------------
#> birthwt : 'data.frame': 189 obs. of 10 variables:
#> $ low : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ age : int 19 33 20 21 18 21 22 17 29 26 ...
#> $ lwt : int 182 155 105 108 107 124 118 103 123 113 ...
#> $ race : int 2 3 1 1 1 3 1 3 1 1 ...
#> $ smoke: int 0 0 1 1 1 0 0 0 1 1 ...
#> $ ptl : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ ht : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ ui : int 1 0 0 1 1 0 0 0 0 0 ...
#> $ ftv : int 0 3 1 2 0 0 1 1 1 0 ...
#> $ bwt : int 2523 2551 2557 2594 2600 2622 2637 2637 2663 2665 ...
#> --------------
#> cabbages : 'data.frame': 60 obs. of 4 variables:
#> $ Cult : Factor w/ 2 levels "c39","c52": 1 1 1 1 1 1 1 1 1 1 ...
#> $ Date : Factor w/ 3 levels "d16","d20","d21": 1 1 1 1 1 1 1 1 1 1 ...
#> $ HeadWt: num 2.5 2.2 3.1 4.3 2.5 4.3 3.8 4.3 1.7 3.1 ...
#> $ VitC : int 51 55 45 42 53 50 50 52 56 49 ...
#> --------------
#> caith : 'data.frame': 4 obs. of 5 variables:
#> $ fair : int 326 688 343 98
#> $ red : int 38 116 84 48
#> $ medium: int 241 584 909 403
#> $ dark : int 110 188 412 681
#> $ black : int 3 4 26 85
#> --------------
#> cats : 'data.frame': 144 obs. of 3 variables:
#> $ Sex: Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
#> $ Bwt: num 2 2 2 2.1 2.1 2.1 2.1 2.1 2.1 2.1 ...
#> $ Hwt: num 7 7.4 9.5 7.2 7.3 7.6 8.1 8.2 8.3 8.5 ...
#> --------------
#> cement : 'data.frame': 13 obs. of 5 variables:
#> $ x1: int 7 1 11 11 7 11 3 1 2 21 ...
#> $ x2: int 26 29 56 31 52 55 71 31 54 47 ...
#> $ x3: int 6 15 8 8 6 9 17 22 18 4 ...
#> $ x4: int 60 52 20 47 33 22 6 44 22 26 ...
#> $ y : num 78.5 74.3 104.3 87.6 95.9 ...
#> --------------
#> chem : num [1:24] 2.9 3.1 3.4 3.4 3.7 3.7 2.8 2.5 2.4 2.4 ...
#> --------------
#> coop : 'data.frame': 252 obs. of 4 variables:
#> $ Lab : Factor w/ 6 levels "L1","L2","L3",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ Spc : Factor w/ 7 levels "S1","S2","S3",..: 1 1 1 1 1 1 2 2 2 2 ...
#> $ Bat : Factor w/ 3 levels "B1","B2","B3": 1 1 2 2 3 3 1 1 2 2 ...
#> $ Conc: num 0.29 0.33 0.33 0.32 0.34 0.31 0.13 0.14 0.16 0.11 ...
#> --------------
#> cpus : 'data.frame': 209 obs. of 9 variables:
#> $ name : Factor w/ 209 levels "ADVISOR 32/60",..: 1 3 2 4 5 6 8 9 10 7 ...
#> $ syct : int 125 29 29 29 29 26 23 23 23 23 ...
#> $ mmin : int 256 8000 8000 8000 8000 8000 16000 16000 16000 32000 ...
#> $ mmax : int 6000 32000 32000 32000 16000 32000 32000 32000 64000 64000 ...
#> $ cach : int 256 32 32 32 32 64 64 64 64 128 ...
#> $ chmin : int 16 8 8 8 8 8 16 16 16 32 ...
#> $ chmax : int 128 32 32 32 16 32 32 32 32 64 ...
#> $ perf : int 198 269 220 172 132 318 367 489 636 1144 ...
#> $ estperf: int 199 253 253 253 132 290 381 381 749 1238 ...
#> --------------
#> crabs : 'data.frame': 200 obs. of 8 variables:
#> $ sp : Factor w/ 2 levels "B","O": 1 1 1 1 1 1 1 1 1 1 ...
#> $ sex : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 2 2 2 ...
#> $ index: int 1 2 3 4 5 6 7 8 9 10 ...
#> $ FL : num 8.1 8.8 9.2 9.6 9.8 10.8 11.1 11.6 11.8 11.8 ...
#> $ RW : num 6.7 7.7 7.8 7.9 8 9 9.9 9.1 9.6 10.5 ...
#> $ CL : num 16.1 18.1 19 20.1 20.3 23 23.8 24.5 24.2 25.2 ...
#> $ CW : num 19 20.8 22.4 23.1 23 26.5 27.1 28.4 27.8 29.3 ...
#> $ BD : num 7 7.4 7.7 8.2 8.2 9.8 9.8 10.4 9.7 10.3 ...
#> --------------
#> deaths : Time-Series [1:72] from 1974 to 1980: 3035 2552 2704 2554 2014 ...
#> --------------
#> drivers : Time-Series [1:192] from 1969 to 1985: 1687 1508 1507 1385 1632 ...
#> --------------
#> eagles : 'data.frame': 8 obs. of 5 variables:
#> $ y: int 17 29 17 20 1 15 0 1
#> $ n: int 24 29 27 20 12 16 28 4
#> $ P: Factor w/ 2 levels "L","S": 1 1 1 1 2 2 2 2
#> $ A: Factor w/ 2 levels "A","I": 1 1 2 2 1 1 2 2
#> $ V: Factor w/ 2 levels "L","S": 1 2 1 2 1 2 1 2
#> --------------
#> epil : 'data.frame': 236 obs. of 9 variables:
#> $ y : num 5 3 3 3 3 5 3 3 2 4 ...
#> $ trt : Factor w/ 2 levels "placebo","progabide": 1 1 1 1 1 1 1 1 1 1 ...
#> $ base : int 11 11 11 11 11 11 11 11 6 6 ...
#> $ age : int 31 31 31 31 30 30 30 30 25 25 ...
#> $ V4 : int 0 0 0 1 0 0 0 1 0 0 ...
#> $ subject: int 1 1 1 1 2 2 2 2 3 3 ...
#> $ period : int 1 2 3 4 1 2 3 4 1 2 ...
#> $ lbase : num -0.756 -0.756 -0.756 -0.756 -0.756 ...
#> $ lage : num 0.1142 0.1142 0.1142 0.1142 0.0814 ...
#> --------------
#> farms : 'data.frame': 20 obs. of 4 variables:
#> $ Mois : Factor w/ 4 levels "M1","M2","M4",..: 1 1 2 2 1 1 1 4 3 2 ...
#> $ Manag : Factor w/ 4 levels "BF","HF","NM",..: 4 1 4 4 2 2 2 2 2 1 ...
#> $ Use : Factor w/ 3 levels "U1","U2","U3": 2 2 2 2 1 2 3 3 1 1 ...
#> $ Manure: Factor w/ 5 levels "C0","C1","C2",..: 5 3 5 5 3 3 4 4 2 2 ...
#> --------------
#> fgl : 'data.frame': 214 obs. of 10 variables:
#> $ RI : num 3.01 -0.39 -1.82 -0.34 -0.58 ...
#> $ Na : num 13.6 13.9 13.5 13.2 13.3 ...
#> $ Mg : num 4.49 3.6 3.55 3.69 3.62 3.61 3.6 3.61 3.58 3.6 ...
#> $ Al : num 1.1 1.36 1.54 1.29 1.24 1.62 1.14 1.05 1.37 1.36 ...
#> $ Si : num 71.8 72.7 73 72.6 73.1 ...
#> $ K : num 0.06 0.48 0.39 0.57 0.55 0.64 0.58 0.57 0.56 0.57 ...
#> $ Ca : num 8.75 7.83 7.78 8.22 8.07 8.07 8.17 8.24 8.3 8.4 ...
#> $ Ba : num 0 0 0 0 0 0 0 0 0 0 ...
#> $ Fe : num 0 0 0 0 0 0.26 0 0 0 0.11 ...
#> $ type: Factor w/ 6 levels "WinF","WinNF",..: 1 1 1 1 1 1 1 1 1 1 ...
#> --------------
#> forbes : 'data.frame': 17 obs. of 2 variables:
#> $ bp : num 194 194 198 198 199 ...
#> $ pres: num 20.8 20.8 22.4 22.7 23.1 ...
#> --------------
#> galaxies : num [1:82] 9172 9350 9483 9558 9775 ...
#> --------------
#> gehan : 'data.frame': 42 obs. of 4 variables:
#> $ pair : int 1 1 2 2 3 3 4 4 5 5 ...
#> $ time : int 1 10 22 7 3 32 12 23 8 22 ...
#> $ cens : int 1 1 1 1 1 0 1 1 1 1 ...
#> $ treat: Factor w/ 2 levels "6-MP","control": 2 1 2 1 2 1 2 1 2 1 ...
#> --------------
#> genotype : 'data.frame': 61 obs. of 3 variables:
#> $ Litter: Factor w/ 4 levels "A","B","I","J": 1 1 1 1 1 1 1 1 1 1 ...
#> $ Mother: Factor w/ 4 levels "A","B","I","J": 1 1 1 1 1 2 2 2 3 3 ...
#> $ Wt : num 61.5 68.2 64 65 59.7 55 42 60.2 52.5 61.8 ...
#> --------------
#> geyser : 'data.frame': 299 obs. of 2 variables:
#> $ waiting : num 80 71 57 80 75 77 60 86 77 56 ...
#> $ duration: num 4.02 2.15 4 4 4 ...
#> --------------
#> gilgais : 'data.frame': 365 obs. of 9 variables:
#> $ pH00: num 7 6.7 7.8 8.9 7 8.5 7 7.4 7.4 7.2 ...
#> $ pH30: num 9.4 9.2 9.3 8.4 8.7 8.1 9 8.4 8.7 8.9 ...
#> $ pH80: num 7.9 9.2 8 7.8 8.5 8.2 8 8.2 8.1 8.5 ...
#> $ e00 : int 20 12 11 55 20 90 11 10 23 15 ...
#> $ e30 : int 37 27 44 290 150 350 44 50 110 89 ...
#> $ e80 : int 370 80 350 460 270 360 340 270 270 220 ...
#> $ c00 : int 60 45 20 480 180 1350 55 20 250 75 ...
#> $ c30 : int 60 38 155 2885 1500 2350 300 550 1225 790 ...
#> $ c80 : int 505 450 1325 1900 3200 2435 1240 1400 2425 1650 ...
#> --------------
#> hills : 'data.frame': 35 obs. of 3 variables:
#> $ dist : num 2.5 6 6 7.5 8 8 16 6 5 6 ...
#> $ climb: int 650 2500 900 800 3070 2866 7500 800 800 650 ...
#> $ time : num 16.1 48.4 33.6 45.6 62.3 ...
#> --------------
#> housing : 'data.frame': 72 obs. of 5 variables:
#> $ Sat : Ord.factor w/ 3 levels "Low"<"Medium"<..: 1 2 3 1 2 3 1 2 3 1 ...
#> $ Infl: Factor w/ 3 levels "Low","Medium",..: 1 1 1 2 2 2 3 3 3 1 ...
#> $ Type: Factor w/ 4 levels "Tower","Apartment",..: 1 1 1 1 1 1 1 1 1 2 ...
#> $ Cont: Factor w/ 2 levels "Low","High": 1 1 1 1 1 1 1 1 1 1 ...
#> $ Freq: int 21 21 28 34 22 36 10 11 36 61 ...
#> --------------
#> immer : 'data.frame': 30 obs. of 4 variables:
#> $ Loc: Factor w/ 6 levels "C","D","GR","M",..: 5 5 5 5 5 6 6 6 6 6 ...
#> $ Var: Factor w/ 5 levels "M","P","S","T",..: 1 3 5 4 2 1 3 5 4 2 ...
#> $ Y1 : num 81 105.4 119.7 109.7 98.3 ...
#> $ Y2 : num 80.7 82.3 80.4 87.2 84.2 ...
#> --------------
#> leuk : 'data.frame': 33 obs. of 3 variables:
#> $ wbc : int 2300 750 4300 2600 6000 10500 10000 17000 5400 7000 ...
#> $ ag : Factor w/ 2 levels "absent","present": 2 2 2 2 2 2 2 2 2 2 ...
#> $ time: int 65 156 100 134 16 108 121 4 39 143 ...
#> --------------
#> mammals : 'data.frame': 62 obs. of 2 variables:
#> $ body : num 3.38 0.48 1.35 465 36.33 ...
#> $ brain: num 44.5 15.5 8.1 423 119.5 ...
#> --------------
#> mcycle : 'data.frame': 133 obs. of 2 variables:
#> $ times: num 2.4 2.6 3.2 3.6 4 6.2 6.6 6.8 7.8 8.2 ...
#> $ accel: num 0 -1.3 -2.7 0 -2.7 -2.7 -2.7 -1.3 -2.7 -2.7 ...
#> --------------
#> menarche : 'data.frame': 25 obs. of 3 variables:
#> $ Age : num 9.21 10.21 10.58 10.83 11.08 ...
#> $ Total : num 376 200 93 120 90 88 105 111 100 93 ...
#> $ Menarche: num 0 0 0 2 2 5 10 17 16 29 ...
#> --------------
#> michelson : 'data.frame': 100 obs. of 3 variables:
#> $ Speed: int 850 740 900 1070 930 850 950 980 980 880 ...
#> $ Run : Factor w/ 20 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
#> $ Expt : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
#> --------------
#> minn38 : 'data.frame': 168 obs. of 5 variables:
#> $ hs : Factor w/ 3 levels "L","M","U": 1 1 1 1 1 1 1 1 1 1 ...
#> $ phs: Factor w/ 4 levels "C","E","N","O": 1 1 1 1 1 1 1 3 3 3 ...
#> $ fol: Factor w/ 7 levels "F1","F2","F3",..: 1 2 3 4 5 6 7 1 2 3 ...
#> $ sex: Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 2 2 2 ...
#> $ f : int 87 72 52 88 32 14 20 3 6 17 ...
#> --------------
#> motors : 'data.frame': 40 obs. of 3 variables:
#> $ temp: int 150 150 150 150 150 150 150 150 150 150 ...
#> $ time: int 8064 8064 8064 8064 8064 8064 8064 8064 8064 8064 ...
#> $ cens: int 0 0 0 0 0 0 0 0 0 0 ...
#> --------------
#> muscle : 'data.frame': 60 obs. of 3 variables:
#> $ Strip : Factor w/ 21 levels "S01","S02","S03",..: 1 1 1 1 2 2 2 2 3 3 ...
#> $ Conc : num 1 2 3 4 1 2 3 4 0.25 0.5 ...
#> $ Length: num 15.8 20.8 22.6 23.8 20.6 26.8 28.4 27 7.2 15.4 ...
#> --------------
#> newcomb : num [1:66] 28 -44 29 30 24 28 37 32 36 27 ...
#> --------------
#> nlschools : 'data.frame': 2287 obs. of 6 variables:
#> $ lang : int 46 45 33 46 20 30 30 57 36 36 ...
#> $ IQ : num 15 14.5 9.5 11 8 9.5 9.5 13 9.5 11 ...
#> $ class: Factor w/ 133 levels "180","280","1082",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ GS : int 29 29 29 29 29 29 29 29 29 29 ...
#> $ SES : int 23 10 15 23 10 10 23 10 13 15 ...
#> $ COMB : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
#> --------------
#> 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 ...
#> --------------
#> npr1 : 'data.frame': 104 obs. of 4 variables:
#> $ x : num 8 13.1 13.9 13.4 13.4 ...
#> $ y : num 2.38 2.5 3.13 2.63 2.13 2.13 2.5 2.25 3 2.13 ...
#> $ perm: int 327 3369 4770 938 568 667 2561 2538 1078 1078 ...
#> $ por : int 33 34 40 35 32 34 32 34 36 34 ...
#> --------------
#> oats : 'data.frame': 72 obs. of 4 variables:
#> $ B: Factor w/ 6 levels "I","II","III",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ V: Factor w/ 3 levels "Golden.rain",..: 3 3 3 3 1 1 1 1 2 2 ...
#> $ N: Factor w/ 4 levels "0.0cwt","0.2cwt",..: 1 2 3 4 1 2 3 4 1 2 ...
#> $ Y: int 111 130 157 174 117 114 161 141 105 140 ...
#> --------------
#> painters : 'data.frame': 54 obs. of 5 variables:
#> $ Composition: int 10 15 8 12 0 15 8 15 4 17 ...
#> $ Drawing : int 8 16 13 16 15 16 17 16 12 18 ...
#> $ Colour : int 16 4 16 9 8 4 4 7 10 12 ...
#> $ Expression : int 3 14 7 8 0 14 8 6 4 18 ...
#> $ School : Factor w/ 8 levels "A","B","C","D",..: 1 1 1 1 1 1 1 1 1 1 ...
#> --------------
#> petrol : 'data.frame': 32 obs. of 6 variables:
#> $ No : Factor w/ 10 levels "A","B","C","D",..: 1 1 1 1 2 2 2 3 3 3 ...
#> $ SG : num 50.8 50.8 50.8 50.8 40.8 40.8 40.8 40 40 40 ...
#> $ VP : num 8.6 8.6 8.6 8.6 3.5 3.5 3.5 6.1 6.1 6.1 ...
#> $ V10: int 190 190 190 190 210 210 210 217 217 217 ...
#> $ EP : int 205 275 345 407 218 273 347 212 272 340 ...
#> $ Y : num 12.2 22.3 34.7 45.7 8 13.1 26.6 7.4 18.2 30.4 ...
#> --------------
#> phones : List of 2
#> $ year : num [1:24] 50 51 52 53 54 55 56 57 58 59 ...
#> $ calls: num [1:24] 4.4 4.7 4.7 5.9 6.6 7.3 8.1 8.8 10.6 12 ...
#> --------------
#> quine : 'data.frame': 146 obs. of 5 variables:
#> $ Eth : Factor w/ 2 levels "A","N": 1 1 1 1 1 1 1 1 1 1 ...
#> $ Sex : Factor w/ 2 levels "F","M": 2 2 2 2 2 2 2 2 2 2 ...
#> $ Age : Factor w/ 4 levels "F0","F1","F2",..: 1 1 1 1 1 1 1 1 2 2 ...
#> $ Lrn : Factor w/ 2 levels "AL","SL": 2 2 2 1 1 1 1 1 2 2 ...
#> $ Days: int 2 11 14 5 5 13 20 22 6 6 ...
#> --------------
#> road : 'data.frame': 26 obs. of 6 variables:
#> $ deaths : int 968 43 588 640 4743 566 325 118 115 1545 ...
#> $ drivers: int 158 11 91 92 952 109 167 30 35 298 ...
#> $ popden : num 64 0.4 12 34 100 ...
#> $ rural : num 66 5.9 33 73 118 73 5.1 3.4 0 57 ...
#> $ temp : int 62 30 64 51 65 42 37 41 44 67 ...
#> $ fuel : num 119 6.2 65 74 105 78 95 20 23 216 ...
#> --------------
#> rotifer : 'data.frame': 20 obs. of 5 variables:
#> $ density: num 1.02 1.02 1.02 1.03 1.03 ...
#> $ pm.y : int 11 7 10 19 9 21 13 34 10 36 ...
#> $ pm.tot : int 58 86 76 83 56 73 29 44 31 56 ...
#> $ kc.y : int 13 14 30 10 14 35 26 32 22 23 ...
#> $ kc.tot : int 161 248 234 283 129 161 167 286 117 162 ...
#> --------------
#> ships : 'data.frame': 40 obs. of 5 variables:
#> $ type : Factor w/ 5 levels "A","B","C","D",..: 1 1 1 1 1 1 1 1 2 2 ...
#> $ year : int 60 60 65 65 70 70 75 75 60 60 ...
#> $ period : int 60 75 60 75 60 75 60 75 60 75 ...
#> $ service : int 127 63 1095 1095 1512 3353 0 2244 44882 17176 ...
#> $ incidents: int 0 0 3 4 6 18 0 11 39 29 ...
#> --------------
#> shoes : List of 2
#> $ A: num [1:10] 13.2 8.2 10.9 14.3 10.7 6.6 9.5 10.8 8.8 13.3
#> $ B: num [1:10] 14 8.8 11.2 14.2 11.8 6.4 9.8 11.3 9.3 13.6
#> --------------
#> shrimp : num [1:18] 32.2 33 30.8 33.8 32.2 33.3 31.7 35.7 32.4 31.2 ...
#> --------------
#> shuttle : 'data.frame': 256 obs. of 7 variables:
#> $ stability: Factor w/ 2 levels "stab","xstab": 2 2 2 2 2 2 2 2 2 2 ...
#> $ error : Factor w/ 4 levels "LX","MM","SS",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ sign : Factor w/ 2 levels "nn","pp": 2 2 2 2 2 2 1 1 1 1 ...
#> $ wind : Factor w/ 2 levels "head","tail": 1 1 1 2 2 2 1 1 1 2 ...
#> $ magn : Factor w/ 4 levels "Light","Medium",..: 1 2 4 1 2 4 1 2 4 1 ...
#> $ vis : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
#> $ use : Factor w/ 2 levels "auto","noauto": 1 1 1 1 1 1 1 1 1 1 ...
#> --------------
#> snails : 'data.frame': 96 obs. of 6 variables:
#> $ Species : Factor w/ 2 levels "A","B": 1 1 1 1 1 1 1 1 1 1 ...
#> $ Exposure: int 1 1 1 1 1 1 1 1 1 1 ...
#> $ Rel.Hum : num 60 60 60 65.8 65.8 65.8 70.5 70.5 70.5 75.8 ...
#> $ Temp : int 10 15 20 10 15 20 10 15 20 10 ...
#> $ Deaths : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ N : int 20 20 20 20 20 20 20 20 20 20 ...
#> --------------
#> steam : 'data.frame': 14 obs. of 2 variables:
#> $ Temp : int 0 10 20 30 40 50 60 70 80 85 ...
#> $ Press: num 4.14 8.52 16.31 32.18 64.62 ...
#> --------------
#> stormer : 'data.frame': 23 obs. of 3 variables:
#> $ Viscosity: num 14.7 27.5 42 75.7 89.7 ...
#> $ Wt : int 20 20 20 20 20 20 20 50 50 50 ...
#> $ Time : num 35.6 54.3 75.6 121.2 150.8 ...
#> --------------
#> survey : 'data.frame': 237 obs. of 12 variables:
#> $ Sex : Factor w/ 2 levels "Female","Male": 1 2 2 2 2 1 2 1 2 2 ...
#> $ Wr.Hnd: num 18.5 19.5 18 18.8 20 18 17.7 17 20 18.5 ...
#> $ NW.Hnd: num 18 20.5 13.3 18.9 20 17.7 17.7 17.3 19.5 18.5 ...
#> $ W.Hnd : Factor w/ 2 levels "Left","Right": 2 1 2 2 2 2 2 2 2 2 ...
#> $ Fold : Factor w/ 3 levels "L on R","Neither",..: 3 3 1 3 2 1 1 3 3 3 ...
#> $ Pulse : int 92 104 87 NA 35 64 83 74 72 90 ...
#> $ Clap : Factor w/ 3 levels "Left","Neither",..: 1 1 2 2 3 3 3 3 3 3 ...
#> $ Exer : Factor w/ 3 levels "Freq","None",..: 3 2 2 2 3 3 1 1 3 3 ...
#> $ Smoke : Factor w/ 4 levels "Heavy","Never",..: 2 4 3 2 2 2 2 2 2 2 ...
#> $ Height: num 173 178 NA 160 165 ...
#> $ M.I : Factor w/ 2 levels "Imperial","Metric": 2 1 NA 2 2 1 1 2 2 2 ...
#> $ Age : num 18.2 17.6 16.9 20.3 23.7 ...
#> --------------
#> synth.te : 'data.frame': 1000 obs. of 3 variables:
#> $ xs: num -0.971 -0.632 -0.774 -0.606 -0.539 ...
#> $ ys: num 0.429 0.252 0.691 0.176 0.377 ...
#> $ yc: int 0 0 0 0 0 0 0 0 0 0 ...
#> --------------
#> synth.tr : 'data.frame': 250 obs. of 3 variables:
#> $ xs: num 0.051 -0.748 -0.773 0.218 0.373 ...
#> $ ys: num 0.161 0.089 0.263 0.127 0.497 ...
#> $ yc: int 0 0 0 0 0 0 0 0 0 0 ...
#> --------------
#> topo : 'data.frame': 52 obs. of 3 variables:
#> $ x: num 0.3 1.4 2.4 3.6 5.7 1.6 2.9 3.4 3.4 4.8 ...
#> $ y: num 6.1 6.2 6.1 6.2 6.2 5.2 5.1 5.3 5.7 5.6 ...
#> $ z: int 870 793 755 690 800 800 730 728 710 780 ...
#> --------------
#> waders : 'data.frame': 15 obs. of 19 variables:
#> $ S1 : int 12 99 197 0 77 19 1023 87 788 82 ...
#> $ S2 : int 2027 2112 160 17 1948 203 2655 745 2174 350 ...
#> $ S3 : int 0 9 0 0 0 48 0 1447 0 760 ...
#> $ S4 : int 0 87 4 3 19 45 18 125 19 197 ...
#> $ S5 : int 2070 3481 126 50 310 20 320 4330 224 858 ...
#> $ S6 : int 39 470 17 6 1 433 49 789 178 962 ...
#> $ S7 : int 219 2063 1 4 1 0 8 228 1 10 ...
#> $ S8 : int 153 28 32 7 64 0 121 529 423 511 ...
#> $ S9 : int 0 17 0 0 0 11 9 289 0 251 ...
#> $ S10: int 15 145 2 1 22 167 82 904 195 987 ...
#> $ S11: int 51 31 9 2 81 12 48 34 162 191 ...
#> $ S12: int 8336 1515 477 16 2792 1 3411 1710 2161 34 ...
#> $ S13: int 2031 1917 1 0 221 0 14 7869 25 87 ...
#> $ S14: int 14941 17321 548 0 7422 26 9101 2247 1784 417 ...
#> $ S15: int 19 3378 13 3 10 1790 43 4558 3 4496 ...
#> $ S16: int 3566 20164 273 69 4519 2916 3230 40880 1254 15835 ...
#> $ S17: int 0 177 0 1 12 473 587 7166 0 5327 ...
#> $ S18: int 5 1759 0 0 0 658 10 1632 0 1312 ...
#> $ S19: int 0 53 0 0 0 55 5 498 0 1020 ...
#> --------------
#> whiteside : 'data.frame': 56 obs. of 3 variables:
#> $ Insul: Factor w/ 2 levels "Before","After": 1 1 1 1 1 1 1 1 1 1 ...
#> $ Temp : num -0.8 -0.7 0.4 2.5 2.9 3.2 3.6 3.9 4.2 4.3 ...
#> $ Gas : num 7.2 6.9 6.4 6 5.8 5.8 5.6 4.7 5.8 5.2 ...
#> --------------
#> wtloss : 'data.frame': 52 obs. of 2 variables:
#> $ Days : int 0 4 7 7 11 18 24 30 32 43 ...
#> $ Weight: num 184 183 180 180 178 ...
#> --------------
#>
#> All data sets in R package 'cluster' :
#> -------------------------- =======
#>
#> agriculture : 'data.frame': 12 obs. of 2 variables:
#> $ x: num 16.8 21.3 18.7 5.9 11.4 17.8 10.9 16.6 21 16.4 ...
#> $ y: num 2.7 5.7 3.5 22.2 10.9 6 14 8.5 3.5 4.3 ...
#> --------------
#> animals : 'data.frame': 20 obs. of 6 variables:
#> $ war: int 1 1 2 1 2 2 2 2 2 1 ...
#> $ fly: int 1 2 1 1 1 1 2 2 1 2 ...
#> $ ver: int 1 1 2 1 2 2 2 2 2 1 ...
#> $ end: int 1 1 1 1 2 1 1 2 2 1 ...
#> $ gro: int 2 2 1 1 2 2 2 1 2 1 ...
#> $ hai: int 1 2 2 2 2 2 1 1 1 1 ...
#> --------------
#> chorSub : int [1:61, 1:10] 101 50 5 -40 -13 -49 44 285 4 -48 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : chr [1:61] "190" "191" "192" "193" ...
#> ..$ : chr [1:10] "Al" "Ca" "Fe" "K" ...
#> --------------
#> flower : 'data.frame': 18 obs. of 8 variables:
#> $ V1: Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 2 2 ...
#> $ V2: Factor w/ 2 levels "0","1": 2 1 2 1 2 2 1 1 2 2 ...
#> $ V3: Factor w/ 2 levels "0","1": 2 1 1 2 1 1 1 2 1 1 ...
#> $ V4: Factor w/ 5 levels "1","2","3","4",..: 4 2 3 4 5 4 4 2 3 5 ...
#> $ V5: Ord.factor w/ 3 levels "1"<"2"<"3": 3 1 3 2 2 3 3 2 1 2 ...
#> $ V6: Ord.factor w/ 18 levels "1"<"2"<"3"<"4"<..: 15 3 1 16 2 12 13 7 4 14 ...
#> $ V7: num 25 150 150 125 20 50 40 100 25 100 ...
#> $ V8: num 15 50 50 50 15 40 20 15 15 60 ...
#> --------------
#> plantTraits : 'data.frame': 136 obs. of 31 variables:
#> $ pdias : num 96.84 110.72 0.06 0.08 1.48 ...
#> $ longindex: num 0 0 0.667 0.489 0.476 ...
#> $ durflow : int 2 3 3 2 3 3 3 3 3 3 ...
#> $ height : Ord.factor w/ 8 levels "1"<"2"<"3"<"4"<..: 7 8 2 2 2 5 2 2 3 2 ...
#> $ begflow : Ord.factor w/ 9 levels "1"<"2"<"3"<"4"<..: 5 4 6 7 5 4 6 3 7 4 ...
#> $ mycor : Ord.factor w/ 3 levels "0"<"1"<"2": 3 3 3 2 3 1 3 3 3 3 ...
#> $ vegaer : Ord.factor w/ 3 levels "0"<"1"<"2": 1 1 1 3 3 1 1 1 1 1 ...
#> $ vegsout : Ord.factor w/ 3 levels "0"<"1"<"2": 1 1 2 1 1 1 1 3 1 1 ...
#> $ autopoll : Ord.factor w/ 4 levels "0"<"1"<"2"<"3": 1 1 1 1 2 4 4 2 1 3 ...
#> $ insects : Ord.factor w/ 5 levels "0"<"1"<"2"<"3"<..: 5 5 1 1 4 4 3 4 4 1 ...
#> $ wind : Ord.factor w/ 5 levels "0"<"1"<"2"<"3"<..: 1 1 5 5 1 1 1 1 1 4 ...
#> $ lign : Factor w/ 2 levels "0","1": 2 2 1 1 1 1 1 1 1 1 ...
#> $ piq : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
#> $ ros : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
#> $ semiros : Factor w/ 2 levels "0","1": 1 1 1 1 2 2 1 2 2 1 ...
#> $ leafy : Factor w/ 2 levels "0","1": 2 2 2 2 1 1 2 1 1 2 ...
#> $ suman : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 2 1 1 1 ...
#> $ winan : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 1 1 ...
#> $ monocarp : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 2 1 ...
#> $ polycarp : Factor w/ 2 levels "0","1": 2 2 2 2 2 1 1 2 2 2 ...
#> $ seasaes : Factor w/ 2 levels "0","1": 2 2 1 1 1 1 1 1 2 1 ...
#> $ seashiv : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
#> $ seasver : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 1 ...
#> $ everalw : Factor w/ 2 levels "0","1": 1 1 2 2 2 1 1 1 1 2 ...
#> $ everparti: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 1 1 ...
#> $ elaio : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
#> $ endozoo : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
#> $ epizoo : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 2 ...
#> $ aquat : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
#> $ windgl : Factor w/ 2 levels "0","1": 2 2 1 1 1 1 1 1 1 1 ...
#> $ unsp : Factor w/ 2 levels "0","1": 1 1 2 2 1 2 2 1 1 1 ...
#> --------------
#> pluton : 'data.frame': 45 obs. of 4 variables:
#> $ Pu238: num 0.126 0.133 0.127 0.156 0.503 ...
#> $ Pu239: num 75.8 75.5 75.2 78.9 73.3 ...
#> $ Pu240: num 21.2 21.4 21.7 18.4 20.2 ...
#> $ Pu241: num 2.18 2.24 2.31 1.91 4.13 ...
#> --------------
#> ruspini : 'data.frame': 75 obs. of 2 variables:
#> $ x: int 4 5 10 9 13 13 12 15 18 19 ...
#> $ y: int 53 63 59 77 49 69 88 75 61 65 ...
#> --------------
#> votes.repub : 'data.frame': 50 obs. of 31 variables:
#> $ X1856: num NA NA NA NA 18.8 ...
#> $ X1860: num NA NA NA NA 33 ...
#> $ X1864: num NA NA NA NA 58.6 ...
#> $ X1868: num 51.4 NA NA 53.7 50.2 ...
#> $ X1872: num 53.2 NA NA 52.2 56.4 ...
#> $ X1876: num 40 NA NA 39.9 50.9 ...
#> $ X1880: num 37 NA NA 39.5 48.9 ...
#> $ X1884: num 38.4 NA NA 40.5 52.1 ...
#> $ X1888: num 32.3 NA NA 38.1 50 ...
#> $ X1892: num 3.95 NA NA 32.01 43.76 ...
#> $ X1896: num 28.1 NA NA 25.1 49.1 ...
#> $ X1900: num 34.7 NA NA 35 54.5 ...
#> $ X1904: num 20.6 NA NA 40.2 61.9 ...
#> $ X1908: num 24.4 NA NA 37.3 55.5 ...
#> $ X1912: num 8.26 NA 12.74 19.73 0.58 ...
#> $ X1916: num 22 NA 35.4 28 46.3 ...
#> $ X1920: num 31 NA 55.4 38.7 66.2 ...
#> $ X1924: num 27 NA 41.3 29.3 57.2 ...
#> $ X1928: num 48.5 NA 57.6 39.3 64.7 ...
#> $ X1932: num 14.2 NA 30.5 12.9 37.4 ...
#> $ X1936: num 12.8 NA 26.9 17.9 31.7 ...
#> $ X1940: num 14.3 NA 36 20.9 41.4 ...
#> $ X1944: num 18.2 NA 40.9 29.8 43 ...
#> $ X1948: num 19 NA 43.8 21 47.1 ...
#> $ X1952: num 35 NA 58.4 43.8 56.4 ...
#> $ X1956: num 39.4 NA 61 45.8 55.4 ...
#> $ X1960: num 41.8 50.9 55.5 43.1 50.1 ...
#> $ X1964: num 69.5 34.1 50.4 43.9 40.9 38.7 32.2 39.1 48.9 54.1 ...
#> $ X1968: num 14 45.3 54.8 30.8 47.8 50.5 44.3 45.1 40.5 30.4 ...
#> $ X1972: num 72.4 58.1 64.7 68.9 55 62.6 58.6 59.6 71.9 75 ...
#> $ X1976: num 43.5 62.9 58.6 35 50.9 ...
#> --------------
#> xclara : 'data.frame': 3000 obs. of 2 variables:
#> $ V1: num 2.07 17.94 1.08 11.12 23.71 ...
#> $ V2: num -3.24 15.78 7.32 14.41 2.56 ...
#> --------------
#>
#> All data sets in R package 'rpart' :
#> -------------------------- =====
#>
#> car.test.frame : 'data.frame': 60 obs. of 8 variables:
#> $ Price : int 8895 7402 6319 6635 6599 8672 7399 7254 9599 5866 ...
#> $ Country : Factor w/ 8 levels "France","Germany",..: 8 8 5 4 3 6 4 5 3 3 ...
#> $ Reliability: int 4 2 4 5 5 4 5 1 5 NA ...
#> $ Mileage : int 33 33 37 32 32 26 33 28 25 34 ...
#> $ Type : Factor w/ 6 levels "Compact","Large",..: 4 4 4 4 4 4 4 4 4 4 ...
#> $ Weight : int 2560 2345 1845 2260 2440 2285 2275 2350 2295 1900 ...
#> $ Disp. : int 97 114 81 91 113 97 97 98 109 73 ...
#> $ HP : int 113 90 63 92 103 82 90 74 90 73 ...
#> --------------
#> car90 : 'data.frame': 111 obs. of 34 variables:
#> $ Country : Factor w/ 10 levels "Brazil","England",..: 5 5 4 4 4 4 10 10 10 NA ...
#> $ Disp : num 112 163 141 121 152 209 151 231 231 189 ...
#> $ Disp2 : num 1.8 2.7 2.3 2 2.5 3.5 2.5 3.8 3.8 3.1 ...
#> $ Eng.Rev : num 2935 2505 2775 2835 2625 ...
#> $ Front.Hd : num 3.5 2 2.5 4 2 3 4 6 5 5.5 ...
#> $ Frt.Leg.Room: num 41.5 41.5 41.5 42 42 42 42 42 41 41 ...
#> $ Frt.Shld : num 53 55.5 56.5 52.5 52 54.5 56.5 58.5 59 58 ...
#> $ Gear.Ratio : num 3.26 2.95 3.27 3.25 3.02 2.8 NA NA NA NA ...
#> $ Gear2 : num 3.21 3.02 3.25 3.25 2.99 2.85 2.84 1.99 1.99 2.33 ...
#> $ HP : num 130 160 130 108 168 208 110 165 165 101 ...
#> $ HP.revs : num 6000 5900 5500 5300 5800 5700 5200 4800 4800 4400 ...
#> $ Height : num 47.5 50 51.5 50.5 49.5 51 49.5 50.5 51 50.5 ...
#> $ Length : num 177 191 193 176 175 186 189 197 197 192 ...
#> $ Luggage : num 16 14 17 10 12 12 16 16 16 15 ...
#> $ Mileage : num NA 20 NA 27 NA NA 21 NA 23 NA ...
#> $ Model2 : Factor w/ 21 levels ""," Turbo 4 (3)",..: 1 1 1 1 1 1 1 14 13 1 ...
#> $ Price : num 11950 24760 26900 18900 24650 ...
#> $ Rear.Hd : num 1.5 2 3 1 1 2.5 2.5 4.5 3.5 3.5 ...
#> $ Rear.Seating: num 26.5 28.5 31 28 25.5 27 28 30.5 28.5 27.5 ...
#> $ RearShld : num 52 55.5 55 52 51.5 55.5 56 58.5 58.5 56.5 ...
#> $ Reliability : Ord.factor w/ 5 levels "Much worse"<"worse"<..: 5 5 NA NA 4 NA 3 3 3 NA ...
#> $ Rim : Factor w/ 6 levels "R12","R13","R14",..: 3 4 4 3 3 4 3 3 3 3 ...
#> $ Sratio.m : num NA NA NA NA NA NA NA NA NA NA ...
#> $ Sratio.p : num 0.86 0.96 0.97 0.71 0.88 0.78 0.76 0.83 0.87 0.88 ...
#> $ Steering : Factor w/ 3 levels "manual","power",..: 2 2 2 2 2 2 2 2 2 2 ...
#> $ Tank : num 13.2 18 21.1 15.9 16.4 21.1 15.7 18 18 16.5 ...
#> $ Tires : Factor w/ 30 levels "145","145/80",..: 16 20 20 8 17 28 13 23 23 22 ...
#> $ Trans1 : Factor w/ 4 levels "","man.4","man.5",..: 3 3 3 3 3 3 1 1 1 1 ...
#> $ Trans2 : Factor w/ 4 levels "","auto.3","auto.4",..: 3 3 2 2 3 3 2 3 3 3 ...
#> $ Turning : num 37 42 39 35 35 39 41 43 42 41 ...
#> $ Type : Factor w/ 6 levels "Compact","Large",..: 4 3 3 1 1 3 3 2 2 NA ...
#> $ Weight : num 2700 3265 2935 2670 2895 ...
#> $ Wheel.base : num 102 109 106 100 101 109 105 111 111 108 ...
#> $ Width : num 67 69 71 67 65 69 69 72 72 71 ...
#> --------------
#> cu.summary : 'data.frame': 117 obs. of 5 variables:
#> $ Price : num 11950 6851 6995 8895 7402 ...
#> $ Country : Factor w/ 10 levels "Brazil","England",..: 5 5 10 10 10 7 5 6 6 7 ...
#> $ Reliability: Ord.factor w/ 5 levels "Much worse"<"worse"<..: 5 NA 1 4 2 4 NA 5 5 2 ...
#> $ Mileage : num NA NA NA 33 33 37 NA NA 32 NA ...
#> $ Type : Factor w/ 6 levels "Compact","Large",..: 4 4 4 4 4 4 4 4 4 4 ...
#> --------------
#> kyphosis : 'data.frame': 81 obs. of 4 variables:
#> $ Kyphosis: Factor w/ 2 levels "absent","present": 1 1 2 1 1 1 1 1 1 2 ...
#> $ Age : int 71 158 128 2 1 1 61 37 113 59 ...
#> $ Number : int 3 3 4 5 4 2 2 3 2 6 ...
#> $ Start : int 5 14 5 1 15 16 17 16 16 12 ...
#> --------------
#> solder :
#> solder : 'data.frame': 900 obs. of 6 variables:
#> $ Opening: Factor w/ 3 levels "L","M","S": 1 1 1 1 1 1 1 1 1 1 ...
#> $ Solder : Factor w/ 2 levels "Thick","Thin": 1 1 1 1 1 1 1 1 1 1 ...
#> $ Mask : Factor w/ 5 levels "A1.5","A3","A6",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ PadType: Factor w/ 10 levels "D4","D6","D7",..: 9 9 9 1 1 1 4 4 4 2 ...
#> $ Panel : Factor w/ 3 levels "1","2","3": 1 2 3 1 2 3 1 2 3 1 ...
#> $ skips : num 0 0 0 0 0 0 0 0 0 0 ...
#> solder.balance : 'data.frame': 720 obs. of 6 variables:
#> $ Opening: Factor w/ 3 levels "L","M","S": 1 1 1 1 1 1 1 1 1 1 ...
#> $ Solder : Factor w/ 2 levels "Thick","Thin": 1 1 1 1 1 1 1 1 1 1 ...
#> $ Mask : Factor w/ 4 levels "A1.5","A3","B3",..: 1 1 1 1 1 1 1 1 1 1 ...
#> $ PadType: Factor w/ 10 levels "D4","D6","D7",..: 9 9 9 1 1 1 4 4 4 2 ...
#> $ Panel : int 1 2 3 1 2 3 1 2 3 1 ...
#> $ skips : int 0 0 0 0 0 0 0 0 0 0 ...
#> --------------
#> stagec : 'data.frame': 146 obs. of 8 variables:
#> $ pgtime : num 6.1 9.4 5.2 3.2 1.9 4.8 5.8 7.3 3.7 15.9 ...
#> $ pgstat : int 0 0 1 1 1 0 0 0 1 0 ...
#> $ age : int 64 62 59 62 64 69 75 71 73 64 ...
#> $ eet : int 2 1 2 2 2 1 2 2 2 2 ...
#> $ g2 : num 10.26 NA 9.99 3.57 22.56 ...
#> $ grade : int 2 3 3 2 4 3 2 3 3 3 ...
#> $ gleason: int 4 8 7 4 8 7 NA 7 6 7 ...
#> $ ploidy : Factor w/ 3 levels "diploid","tetraploid",..: 1 3 1 1 2 1 2 3 1 2 ...
#> --------------
#>
#> All data sets in R package 'sfsmisc' :
#> -------------------------- =======
#>
#> potatoes : 'data.frame': 64 obs. of 5 variables:
#> $ pos : Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 1 1 1 1 1 ...
#> $ treat : Factor w/ 16 levels "A","B","C","D",..: 1 2 3 4 5 6 7 8 9 10 ...
#> $ nitrogen: Factor w/ 4 levels "0","1","2","4": 1 1 1 1 2 2 2 2 3 3 ...
#> $ potash : Factor w/ 4 levels "0","1","2","4": 1 2 3 4 1 2 3 4 1 2 ...
#> $ yield : num 318 363 368 382 314 ...
#> --------------
#>
#> All data sets in R package 'graphics' :
#> -------------------------- ========
#>
#>
#> All data sets in R package 'grDevices' :
#> -------------------------- =========
#>
#>
#> All data sets in R package 'utils' :
#> -------------------------- =====
#>
#>
#> All data sets in R package 'methods' :
#> -------------------------- =======
#>