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Examples_of_Rover_inspect.md

Rover inspect example

This is some examples of Rover::DataFrame#inspect in ruby.

This feature is experimental, not impremented in official release.

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R_datasets_datasets.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "2d370acd-d8bc-4a96-a235-dd065ac3c711",
"metadata": {},
"source": [
"## R datasets in Red Datasets"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "4b0e312c-129a-44d9-be1b-7520281a7628",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\"ruby 3.1.1p18 (2022-02-18 revision 53f5fc4236) [x86_64-linux]\""
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"RUBY_DESCRIPTION"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "cfbbfb0f-12a5-4053-ac43-dde3d9ff9eb5",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{:Datasets=>\"0.1.4\", :Rover=>\"0.3.0\"}"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"require 'datasets'\n",
"require 'rover' # require 'rover-df' is also ok\n",
"# Unreleased fork version\n",
"{Datasets: Datasets::VERSION, Rover: Rover::VERSION}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c31cf75c-20cf-4cdf-8153-b62fbbdc8625",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1745"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get a list for R-dataset\n",
"dataset_list = Datasets::RdatasetsList.new\n",
"dataset_list.count"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "95c93c22-890a-41ac-a3f8-68e331f7624d",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{\"AER\"=>107, \"asaur\"=>8, \"boot\"=>49, \"carData\"=>63, \"causaldata\"=>33, \"cluster\"=>9, \"COUNT\"=>21, \"crch\"=>1, \"DAAG\"=>121, \"datasets\"=>84, \"dplyr\"=>5, \"dragracer\"=>3, \"drc\"=>35, \"Ecdat\"=>134, \"evir\"=>7, \"forecast\"=>5, \"fpp2\"=>38, \"gap\"=>18, \"geepack\"=>9, \"ggplot2\"=>11, \"ggplot2movies\"=>1, \"gt\"=>6, \"HistData\"=>46, \"HLMdiag\"=>4, \"HSAUR\"=>41, \"hwde\"=>2, \"ISLR\"=>13, \"KMsurv\"=>27, \"lattice\"=>7, \"lme4\"=>11, \"lmec\"=>1, \"MASS\"=>87, \"mediation\"=>6, \"mi\"=>2, \"mosaicData\"=>31, \"mstate\"=>8, \"multgee\"=>2, \"nycflights13\"=>5, \"openintro\"=>206, \"palmerpenguins\"=>1, \"plm\"=>14, \"plyr\"=>2, \"pscl\"=>17, \"psych\"=>23, \"quantreg\"=>7, \"reshape2\"=>3, \"robustbase\"=>40, \"rpart\"=>6, \"sandwich\"=>4, \"sem\"=>6, \"Stat2Data\"=>211, \"stevedata\"=>71, \"survival\"=>14, \"texmex\"=>7, \"tidyr\"=>15, \"validate\"=>4, \"vcd\"=>33}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Show the packages and its count\n",
"dataset_list.map(&:package).tally"
]
},
{
"cell_type": "markdown",
"id": "22775590-c41d-427f-94f4-d6108843df02",
"metadata": {},
"source": [
"### Package 'datasets'"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "16b4bff3-f8a1-462b-992e-8f406cd98a6e",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1] ability.cov, Ability and Intelligence Tests\n",
"Rover::DataFrame : 6 observations(rows) of 8 variables(columns).\n",
"Variables : 8 numeric\n",
"# key type level data_preview\n",
"1 :cov.general float64 6 [24.641, 5.991, 33.52, 6.023, 20.755, ...]\n",
"2 :cov.picture float64 6 [5.991, 6.7, 18.137, 1.782, 4.936, ...]\n",
"3 :cov.blocks float64 6 [33.52, 18.137, 149.831, 19.424, 31.43, ...]\n",
"4 :cov.maze float64 6 [6.023, 1.782, 19.424, 12.711, 4.757, ...]\n",
"5 :cov.reading float64 6 [20.755, 4.936, 31.43, 4.757, 52.604, ...]\n",
"6 :cov.vocab float64 6 [29.701, 7.204, 50.753, 9.075, 66.762, ...]\n",
"7 :center int64 1 {0=>6}\n",
"8 :n.obs int64 1 {112=>6}\n",
"\n",
"[2] airmiles, Passenger Miles on Commercial US Airlines, 1937-1960\n",
"Rover::DataFrame : 24 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time int64 24 [1937, 1938, 1939, 1940, 1941, ...]\n",
"2 :value int64 24 [412, 480, 683, 1052, 1385, ...]\n",
"\n",
"[3] AirPassengers, Monthly Airline Passenger Numbers 1949-1960\n",
"Rover::DataFrame : 144 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time float64 144 [1949.0, 1949.08333333333, 1949.16666666667, 1949.25, 1949.33333333333, ...]\n",
"2 :value int64 118 [112, 118, 132, 129, 121, ...]\n",
"\n",
"[4] airquality, New York Air Quality Measurements\n",
"Rover::DataFrame : 153 observations(rows) of 6 variables(columns).\n",
"Variables : 4 numeric, 2 objects\n",
"# key type level data_preview\n",
"1 :Ozone object 68 [41, 36, 12, 18, NA, ...]\n",
"2 :Solar.R object 118 [190, 118, 149, 313, NA, ...]\n",
"3 :Wind float64 31 [7.4, 8.0, 12.6, 11.5, 14.3, ...]\n",
"4 :Temp int64 40 [67, 72, 74, 62, 56, ...]\n",
"5 :Month int64 5 {5=>31, 6=>30, 7=>31, 8=>31, 9=>30}\n",
"6 :Day int64 31 [1, 2, 3, 4, 5, ...]\n",
"\n",
"[5] anscombe, Anscombe's Quartet of 'Identical' Simple Linear Regressions\n",
"Rover::DataFrame : 11 observations(rows) of 8 variables(columns).\n",
"Variables : 8 numeric\n",
"# key type level data_preview\n",
"1 :x1 int64 11 [10, 8, 13, 9, 11, ...]\n",
"2 :x2 int64 11 [10, 8, 13, 9, 11, ...]\n",
"3 :x3 int64 11 [10, 8, 13, 9, 11, ...]\n",
"4 :x4 int64 2 {8=>10, 19=>1}\n",
"5 :y1 float64 11 [8.04, 6.95, 7.58, 8.81, 8.33, ...]\n",
"6 :y2 float64 11 [9.14, 8.14, 8.74, 8.77, 9.26, ...]\n",
"7 :y3 float64 11 [7.46, 6.77, 12.74, 7.11, 7.81, ...]\n",
"8 :y4 float64 11 [6.58, 5.76, 7.71, 8.84, 8.47, ...]\n",
"\n",
"[6] attenu, The Joyner-Boore Attenuation Data\n",
"Rover::DataFrame : 182 observations(rows) of 5 variables(columns).\n",
"Variables : 4 numeric, 1 object\n",
"# key type level data_preview\n",
"1 :event int64 23 [1, 2, 2, 2, 2, ...]\n",
"2 :mag float64 17 [7.0, 7.4, 7.4, 7.4, 7.4, ...]\n",
"3 :station object 118 [117, 1083, 1095, 283, 135, ...]\n",
"4 :dist float64 153 [12.0, 148.0, 42.0, 85.0, 107.0, ...]\n",
"5 :accel float64 120 [0.359, 0.014, 0.196, 0.135, 0.062, ...]\n",
"\n",
"[7] attitude, The Chatterjee-Price Attitude Data\n",
"Rover::DataFrame : 30 observations(rows) of 7 variables(columns).\n",
"Variables : 7 numeric\n",
"# key type level data_preview\n",
"1 :rating int64 22 [43, 63, 71, 61, 81, ...]\n",
"2 :complaints int64 23 [51, 64, 70, 63, 78, ...]\n",
"3 :privileges int64 24 [30, 51, 68, 45, 56, ...]\n",
"4 :learning int64 23 [39, 54, 69, 47, 66, ...]\n",
"5 :raises int64 21 [61, 63, 76, 54, 71, ...]\n",
"6 :critical int64 21 [92, 73, 86, 84, 83, ...]\n",
"7 :advance int64 20 [45, 47, 48, 35, 47, ...]\n",
"\n",
"[8] austres, Quarterly Time Series of the Number of Australian Residents\n",
"Rover::DataFrame : 89 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time float64 89 [1971.25, 1971.5, 1971.75, 1972.0, 1972.25, ...]\n",
"2 :value float64 89 [13067.3, 13130.5, 13198.4, 13254.2, 13303.7, ...]\n",
"\n",
"[9] BJsales, Sales Data with Leading Indicator\n",
"Rover::DataFrame : 150 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time int64 150 [1, 2, 3, 4, 5, ...]\n",
"2 :value float64 123 [200.1, 199.5, 199.4, 198.9, 199.0, ...]\n",
"\n",
"[10] BOD, Biochemical Oxygen Demand\n",
"Rover::DataFrame : 6 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :Time int64 6 [1, 2, 3, 4, 5, ...]\n",
"2 :demand float64 6 [8.3, 10.3, 19.0, 16.0, 15.6, ...]\n",
"\n",
"[11] cars, Speed and Stopping Distances of Cars\n",
"Rover::DataFrame : 50 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :speed int64 19 [4, 4, 7, 7, 8, ...]\n",
"2 :dist int64 35 [2, 10, 4, 22, 16, ...]\n",
"\n",
"[12] ChickWeight, Weight versus age of chicks on different diets\n",
"Rover::DataFrame : 578 observations(rows) of 4 variables(columns).\n",
"Variables : 4 numeric\n",
"# key type level data_preview\n",
"1 :weight int64 212 [42, 51, 59, 64, 76, ...]\n",
"2 :Time int64 12 [0, 2, 4, 6, 8, ...]\n",
"3 :Chick int64 50 [1, 1, 1, 1, 1, ...]\n",
"4 :Diet int64 4 {1=>220, 2=>120, 3=>120, 4=>118}\n",
"\n",
"[13] chickwts, Chicken Weights by Feed Type\n",
"Rover::DataFrame : 71 observations(rows) of 2 variables(columns).\n",
"Variables : 1 numeric, 1 object\n",
"# key type level data_preview\n",
"1 :weight int64 66 [179, 160, 136, 227, 217, ...]\n",
"2 :feed object 6 [horsebean, horsebean, horsebean, horsebean, horsebean, ...]\n",
"\n",
"[14] CO2, Carbon Dioxide Uptake in Grass Plants\n",
"Rover::DataFrame : 84 observations(rows) of 5 variables(columns).\n",
"Variables : 2 numeric, 3 objects\n",
"# key type level data_preview\n",
"1 :Plant object 12 [Qn1, Qn1, Qn1, Qn1, Qn1, ...]\n",
"2 :Type object 2 {\"Quebec\"=>42, \"Mississippi\"=>42}\n",
"3 :Treatment object 2 {\"nonchilled\"=>42, \"chilled\"=>42}\n",
"4 :conc int64 7 [95, 175, 250, 350, 500, ...]\n",
"5 :uptake float64 76 [16.0, 30.4, 34.8, 37.2, 35.3, ...]\n",
"\n",
"[15] co2, Mauna Loa Atmospheric CO2 Concentration\n",
"Rover::DataFrame : 468 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time float64 468 [1959.0, 1959.08333333334, 1959.16666666668, 1959.25000000002, 1959.33333333336, ...]\n",
"2 :value float64 451 [315.42, 316.31, 316.5, 317.56, 318.13, ...]\n",
"\n",
"[16] crimtab, Student's 3000 Criminals Data\n",
"Rover::DataFrame : 924 observations(rows) of 3 variables(columns).\n",
"Variables : 3 numeric\n",
"# key type level data_preview\n",
"1 :Var1 float64 42 [9.4, 9.5, 9.6, 9.7, 9.8, ...]\n",
"2 :Var2 float64 22 [142.24, 142.24, 142.24, 142.24, 142.24, ...]\n",
"3 :Freq int64 45 [0, 0, 0, 0, 0, ...]\n",
"\n",
"[17] discoveries, Yearly Numbers of Important Discoveries\n",
"Rover::DataFrame : 100 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time int64 100 [1860, 1861, 1862, 1863, 1864, ...]\n",
"2 :value int64 12 [5, 3, 0, 2, 0, ...]\n",
"\n",
"[18] DNase, Elisa assay of DNase\n",
"Rover::DataFrame : 176 observations(rows) of 3 variables(columns).\n",
"Variables : 3 numeric\n",
"# key type level data_preview\n",
"1 :Run int64 11 [1, 1, 1, 1, 1, ...]\n",
"2 :conc float64 8 [0.04882812, 0.04882812, 0.1953125, 0.1953125, 0.390625, ...]\n",
"3 :density float64 161 [0.017, 0.018, 0.121, 0.124, 0.206, ...]\n",
"\n",
"[19] esoph, Smoking, Alcohol and (O)esophageal Cancer\n",
"Rover::DataFrame : 88 observations(rows) of 5 variables(columns).\n",
"Variables : 2 numeric, 3 objects\n",
"# key type level data_preview\n",
"1 :agegp object 6 [25-34, 25-34, 25-34, 25-34, 25-34, ...]\n",
"2 :alcgp object 4 {\"0-39g/day\"=>23, \"40-79\"=>23, \"80-119\"=>21, \"120+\"=>21}\n",
"3 :tobgp object 4 {\"0-9g/day\"=>24, \"10-19\"=>24, \"20-29\"=>20, \"30+\"=>20}\n",
"4 :ncases int64 10 [0, 0, 0, 0, 0, ...]\n",
"5 :ncontrols int64 27 [40, 10, 6, 5, 27, ...]\n",
"\n",
"[20] euro, Conversion Rates of Euro Currencies\n",
"Rover::DataFrame : 11 observations(rows) of 1 variable(column).\n",
"Variable : 1 numeric\n",
"# key type level data_preview\n",
"1 :dat float64 10 [13.7603, 40.3399, 1.95583, 166.386, 5.94573, ...]\n",
"\n",
"[21] EuStockMarkets, Daily Closing Prices of Major European Stock Indices, 1991-1998\n",
"Rover::DataFrame : 1860 observations(rows) of 4 variables(columns).\n",
"Variables : 4 numeric\n",
"# key type level data_preview\n",
"1 :DAX float64 1774 [1628.75, 1613.63, 1606.51, 1621.04, 1618.16, ...]\n",
"2 :SMI float64 1725 [1678.1, 1688.5, 1678.6, 1684.1, 1686.6, ...]\n",
"3 :CAC float64 1617 [1772.8, 1750.5, 1718.0, 1708.1, 1723.1, ...]\n",
"4 :FTSE float64 1729 [2443.6, 2460.2, 2448.2, 2470.4, 2484.7, ...]\n",
"\n",
"[22] faithful, Old Faithful Geyser Data\n",
"Rover::DataFrame : 272 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :eruptions float64 126 [3.6, 1.8, 3.333, 2.283, 4.533, ...]\n",
"2 :waiting int64 51 [79, 54, 74, 62, 85, ...]\n",
"\n",
"[23] Formaldehyde, Determination of Formaldehyde\n",
"Rover::DataFrame : 6 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :carb float64 6 [0.1, 0.3, 0.5, 0.6, 0.7, ...]\n",
"2 :optden float64 6 [0.086, 0.269, 0.446, 0.538, 0.626, ...]\n",
"\n",
"[24] freeny, Freeny's Revenue Data\n",
"Rover::DataFrame : 39 observations(rows) of 5 variables(columns).\n",
"Variables : 5 numeric\n",
"# key type level data_preview\n",
"1 :y float64 39 [8.79236, 8.79137, 8.81486, 8.81301, 8.90751, ...]\n",
"2 :lag.quarterly.revenue float64 39 [8.79636, 8.79236, 8.79137, 8.81486, 8.81301, ...]\n",
"3 :price.index float64 38 [4.70997, 4.70217, 4.68944, 4.68558, 4.64019, ...]\n",
"4 :income.level float64 39 [5.8211, 5.82558, 5.83112, 5.84046, 5.85036, ...]\n",
"5 :market.potential float64 39 [12.9699, 12.9733, 12.9774, 12.9806, 12.9831, ...]\n",
"\n",
"[25] HairEyeColor, Hair and Eye Color of Statistics Students\n",
"Rover::DataFrame : 32 observations(rows) of 4 variables(columns).\n",
"Variables : 1 numeric, 3 objects\n",
"# key type level data_preview\n",
"1 :Hair object 4 {\"Black\"=>8, \"Brown\"=>8, \"Red\"=>8, \"Blond\"=>8}\n",
"2 :Eye object 4 {\"Brown\"=>8, \"Blue\"=>8, \"Hazel\"=>8, \"Green\"=>8}\n",
"3 :Sex object 2 {\"Male\"=>16, \"Female\"=>16}\n",
"4 :Freq int64 22 [32, 53, 10, 3, 11, ...]\n",
"\n",
"[26] Harman23.cor, Harman Example 2.3\n",
"Rover::DataFrame : 8 observations(rows) of 10 variables(columns).\n",
"Variables : 10 numeric\n",
"# key type level data_preview\n",
" 1 :cov.height float64 8 [1.0, 0.846, 0.805, 0.859, 0.473, ...]\n",
" 2 :cov.arm.span float64 8 [0.846, 1.0, 0.881, 0.826, 0.376, ...]\n",
" 3 :cov.forearm float64 8 [0.805, 0.881, 1.0, 0.801, 0.38, ...]\n",
" 4 :cov.lower.leg float64 8 [0.859, 0.826, 0.801, 1.0, 0.436, ...]\n",
" 5 :cov.weight float64 8 [0.473, 0.376, 0.38, 0.436, 1.0, ...]\n",
" 6 :cov.bitro.diameter float64 8 [0.398, 0.326, 0.319, 0.329, 0.762, ...]\n",
" 7 :cov.chest.girth float64 8 [0.301, 0.277, 0.237, 0.327, 0.73, ...]\n",
" 8 :cov.chest.width float64 8 [0.382, 0.415, 0.345, 0.365, 0.629, ...]\n",
" 9 :center int64 1 {0=>8}\n",
"10 :n.obs int64 1 {305=>8}\n",
"\n",
"[27] Harman74.cor, Harman Example 7.4\n",
"Rover::DataFrame : 24 observations(rows) of 26 variables(columns).\n",
"Variables : 26 numeric\n",
"# key type level data_preview\n",
" 1 :cov.VisualPerception float64 24 [1.0, 0.318, 0.403, 0.468, 0.321, ...]\n",
" 2 :cov.Cubes float64 23 [0.318, 1.0, 0.317, 0.23, 0.285, ...]\n",
" 3 :cov.PaperFormBoard float64 23 [0.403, 0.317, 1.0, 0.305, 0.247, ...]\n",
" 4 :cov.Flags float64 22 [0.468, 0.23, 0.305, 1.0, 0.227, ...]\n",
" 5 :cov.GeneralInformation float64 23 [0.321, 0.285, 0.247, 0.227, 1.0, ...]\n",
" 6 :cov.PargraphComprehension float64 23 [0.335, 0.234, 0.268, 0.327, 0.622, ...]\n",
" 7 :cov.SentenceCompletion float64 24 [0.304, 0.157, 0.223, 0.335, 0.656, ...]\n",
" 8 :cov.WordClassification float64 24 [0.332, 0.157, 0.382, 0.391, 0.578, ...]\n",
" 9 :cov.WordMeaning float64 22 [0.326, 0.195, 0.184, 0.325, 0.723, ...]\n",
"10 :cov.Addition float64 24 [0.116, 0.057, -0.075, 0.099, 0.311, ...]\n",
"11 :cov.Code float64 23 [0.308, 0.15, 0.091, 0.11, 0.344, ...]\n",
"12 :cov.CountingDots float64 24 [0.314, 0.145, 0.14, 0.16, 0.215, ...]\n",
"13 :cov.StraightCurvedCapitals float64 24 [0.489, 0.239, 0.321, 0.327, 0.344, ...]\n",
"14 :cov.WordRecognition float64 24 [0.125, 0.103, 0.177, 0.066, 0.28, ...]\n",
"15 :cov.NumberRecognition float64 24 [0.238, 0.131, 0.065, 0.127, 0.229, ...]\n",
"16 :cov.FigureRecognition float64 24 [0.414, 0.272, 0.263, 0.322, 0.187, ...]\n",
"17 :cov.ObjectNumber float64 22 [0.176, 0.005, 0.177, 0.187, 0.208, ...]\n",
"18 :cov.NumberFigure float64 23 [0.368, 0.255, 0.211, 0.251, 0.263, ...]\n",
"19 :cov.FigureWord float64 22 [0.27, 0.112, 0.312, 0.137, 0.19, ...]\n",
"20 :cov.Deduction float64 24 [0.365, 0.292, 0.297, 0.339, 0.398, ...]\n",
"21 :cov.NumericalPuzzles float64 24 [0.369, 0.306, 0.165, 0.349, 0.318, ...]\n",
"22 :cov.ProblemReasoning float64 24 [0.413, 0.232, 0.25, 0.38, 0.441, ...]\n",
"23 :cov.SeriesCompletion float64 24 [0.474, 0.348, 0.383, 0.335, 0.435, ...]\n",
"24 :cov.ArithmeticProblems float64 24 [0.282, 0.211, 0.203, 0.248, 0.42, ...]\n",
"25 :center int64 1 {0=>24}\n",
"26 :n.obs int64 1 {145=>24}\n",
"\n",
"[28] Indometh, Pharmacokinetics of Indomethacin\n",
"Rover::DataFrame : 66 observations(rows) of 3 variables(columns).\n",
"Variables : 3 numeric\n",
"# key type level data_preview\n",
"1 :Subject int64 6 [1, 1, 1, 1, 1, ...]\n",
"2 :time float64 11 [0.25, 0.5, 0.75, 1.0, 1.25, ...]\n",
"3 :conc float64 49 [1.5, 0.94, 0.78, 0.48, 0.37, ...]\n",
"\n",
"[29] infert, Infertility after Spontaneous and Induced Abortion\n",
"Rover::DataFrame : 248 observations(rows) of 8 variables(columns).\n",
"Variables : 7 numeric, 1 object\n",
"# key type level data_preview\n",
"1 :education object 3 {\"0-5yrs\"=>12, \"6-11yrs\"=>120, \"12+ yrs\"=>116}\n",
"2 :age int64 21 [26, 42, 39, 34, 35, ...]\n",
"3 :parity int64 6 [6, 1, 6, 4, 3, ...]\n",
"4 :induced int64 3 {1=>68, 2=>37, 0=>143}\n",
"5 :case int64 2 {1=>83, 0=>165}\n",
"6 :spontaneous int64 3 {2=>36, 0=>141, 1=>71}\n",
"7 :stratum int64 83 [1, 2, 3, 4, 5, ...]\n",
"8 :pooled.stratum int64 63 [3, 1, 4, 2, 32, ...]\n",
"\n",
"[30] InsectSprays, Effectiveness of Insect Sprays\n",
"Rover::DataFrame : 72 observations(rows) of 2 variables(columns).\n",
"Variables : 1 numeric, 1 object\n",
"# key type level data_preview\n",
"1 :count int64 24 [10, 7, 20, 14, 14, ...]\n",
"2 :spray object 6 [A, A, A, A, A, ...]\n",
"\n",
"[31] iris, Edgar Anderson's Iris Data\n",
"Rover::DataFrame : 150 observations(rows) of 5 variables(columns).\n",
"Variables : 4 numeric, 1 object\n",
"# key type level data_preview\n",
"1 :Sepal.Length float64 35 [5.1, 4.9, 4.7, 4.6, 5.0, ...]\n",
"2 :Sepal.Width float64 23 [3.5, 3.0, 3.2, 3.1, 3.6, ...]\n",
"3 :Petal.Length float64 43 [1.4, 1.4, 1.3, 1.5, 1.4, ...]\n",
"4 :Petal.Width float64 22 [0.2, 0.2, 0.2, 0.2, 0.2, ...]\n",
"5 :Species object 3 {\"setosa\"=>50, \"versicolor\"=>50, \"virginica\"=>50}\n",
"\n",
"[32] iris3, Edgar Anderson's Iris Data\n",
"Rover::DataFrame : 50 observations(rows) of 12 variables(columns).\n",
"Variables : 12 numeric\n",
"# key type level data_preview\n",
" 1 :Sepal L..Setosa float64 15 [5.1, 4.9, 4.7, 4.6, 5.0, ...]\n",
" 2 :Sepal W..Setosa float64 16 [3.5, 3.0, 3.2, 3.1, 3.6, ...]\n",
" 3 :Petal L..Setosa float64 9 [1.4, 1.4, 1.3, 1.5, 1.4, ...]\n",
" 4 :Petal W..Setosa float64 6 [0.2, 0.2, 0.2, 0.2, 0.2, ...]\n",
" 5 :Sepal L..Versicolor float64 21 [7.0, 6.4, 6.9, 5.5, 6.5, ...]\n",
" 6 :Sepal W..Versicolor float64 14 [3.2, 3.2, 3.1, 2.3, 2.8, ...]\n",
" 7 :Petal L..Versicolor float64 19 [4.7, 4.5, 4.9, 4.0, 4.6, ...]\n",
" 8 :Petal W..Versicolor float64 9 [1.4, 1.5, 1.5, 1.3, 1.5, ...]\n",
" 9 :Sepal L..Virginica float64 21 [6.3, 5.8, 7.1, 6.3, 6.5, ...]\n",
"10 :Sepal W..Virginica float64 13 [3.3, 2.7, 3.0, 2.9, 3.0, ...]\n",
"11 :Petal L..Virginica float64 20 [6.0, 5.1, 5.9, 5.6, 5.8, ...]\n",
"12 :Petal W..Virginica float64 12 [2.5, 1.9, 2.1, 1.8, 2.2, ...]\n",
"\n",
"[33] islands, Areas of the World's Major Landmasses\n",
"Rover::DataFrame : 48 observations(rows) of 1 variable(column).\n",
"Variable : 1 numeric\n",
"# key type level data_preview\n",
"1 :dat int64 38 [11506, 5500, 16988, 2968, 16, ...]\n",
"\n",
"[34] JohnsonJohnson, Quarterly Earnings per Johnson & Johnson Share\n",
"Rover::DataFrame : 84 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time float64 84 [1960.0, 1960.25, 1960.5, 1960.75, 1961.0, ...]\n",
"2 :value float64 68 [0.71, 0.63, 0.85, 0.44, 0.61, ...]\n",
"\n",
"[35] LakeHuron, Level of Lake Huron 1875-1972\n",
"Rover::DataFrame : 98 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time int64 98 [1875, 1876, 1877, 1878, 1879, ...]\n",
"2 :value float64 86 [580.38, 581.86, 580.97, 580.8, 579.79, ...]\n",
"\n",
"[36] lh, Luteinizing Hormone in Blood Samples\n",
"Rover::DataFrame : 48 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time int64 48 [1, 2, 3, 4, 5, ...]\n",
"2 :value float64 20 [2.4, 2.4, 2.4, 2.2, 2.1, ...]\n",
"\n",
"[37] LifeCycleSavings, Intercountry Life-Cycle Savings Data\n",
"Rover::DataFrame : 50 observations(rows) of 5 variables(columns).\n",
"Variables : 5 numeric\n",
"# key type level data_preview\n",
"1 :sr float64 49 [11.43, 12.07, 13.17, 5.75, 12.88, ...]\n",
"2 :pop15 float64 50 [29.35, 23.32, 23.8, 41.89, 42.19, ...]\n",
"3 :pop75 float64 46 [2.87, 4.41, 4.43, 1.67, 0.83, ...]\n",
"4 :dpi float64 50 [2329.68, 1507.99, 2108.47, 189.13, 728.47, ...]\n",
"5 :ddpi float64 48 [2.87, 3.93, 3.82, 0.22, 4.56, ...]\n",
"\n",
"[38] Loblolly, Growth of Loblolly pine trees\n",
"Rover::DataFrame : 84 observations(rows) of 3 variables(columns).\n",
"Variables : 3 numeric\n",
"# key type level data_preview\n",
"1 :height float64 84 [4.51, 10.89, 28.72, 41.74, 52.7, ...]\n",
"2 :age int64 6 [3, 5, 10, 15, 20, ...]\n",
"3 :Seed int64 14 [301, 301, 301, 301, 301, ...]\n",
"\n",
"[39] longley, Longley's Economic Regression Data\n",
"Rover::DataFrame : 16 observations(rows) of 7 variables(columns).\n",
"Variables : 7 numeric\n",
"# key type level data_preview\n",
"1 :GNP.deflator float64 16 [83.0, 88.5, 88.2, 89.5, 96.2, ...]\n",
"2 :GNP float64 16 [234.289, 259.426, 258.054, 284.599, 328.975, ...]\n",
"3 :Unemployed float64 16 [235.6, 232.5, 368.2, 335.1, 209.9, ...]\n",
"4 :Armed.Forces float64 16 [159.0, 145.6, 161.6, 165.0, 309.9, ...]\n",
"5 :Population float64 16 [107.608, 108.632, 109.773, 110.929, 112.075, ...]\n",
"6 :Year int64 16 [1947, 1948, 1949, 1950, 1951, ...]\n",
"7 :Employed float64 16 [60.323, 61.122, 60.171, 61.187, 63.221, ...]\n",
"\n",
"[40] lynx, Annual Canadian Lynx trappings 1821-1934\n",
"Rover::DataFrame : 114 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time int64 114 [1821, 1822, 1823, 1824, 1825, ...]\n",
"2 :value int64 110 [269, 321, 585, 871, 1475, ...]\n",
"\n",
"[41] morley, Michelson Speed of Light Data\n",
"Rover::DataFrame : 100 observations(rows) of 3 variables(columns).\n",
"Variables : 3 numeric\n",
"# key type level data_preview\n",
"1 :Expt int64 5 {1=>20, 2=>20, 3=>20, 4=>20, 5=>20}\n",
"2 :Run int64 20 [1, 2, 3, 4, 5, ...]\n",
"3 :Speed int64 30 [850, 740, 900, 1070, 930, ...]\n",
"\n",
"[42] mtcars, Motor Trend Car Road Tests\n",
"Rover::DataFrame : 32 observations(rows) of 11 variables(columns).\n",
"Variables : 11 numeric\n",
"# key type level data_preview\n",
" 1 :mpg float64 25 [21.0, 21.0, 22.8, 21.4, 18.7, ...]\n",
" 2 :cyl int64 3 {6=>7, 4=>11, 8=>14}\n",
" 3 :disp float64 27 [160.0, 160.0, 108.0, 258.0, 360.0, ...]\n",
" 4 :hp int64 22 [110, 110, 93, 110, 175, ...]\n",
" 5 :drat float64 22 [3.9, 3.9, 3.85, 3.08, 3.15, ...]\n",
" 6 :wt float64 29 [2.62, 2.875, 2.32, 3.215, 3.44, ...]\n",
" 7 :qsec float64 30 [16.46, 17.02, 18.61, 19.44, 17.02, ...]\n",
" 8 :vs int64 2 {0=>18, 1=>14}\n",
" 9 :am int64 2 {1=>13, 0=>19}\n",
"10 :gear int64 3 {4=>12, 3=>15, 5=>5}\n",
"11 :carb int64 6 [4, 4, 1, 1, 2, ...]\n",
"\n",
"[43] nhtemp, Average Yearly Temperatures in New Haven\n",
"Rover::DataFrame : 60 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time int64 60 [1912, 1913, 1914, 1915, 1916, ...]\n",
"2 :value float64 34 [49.9, 52.3, 49.4, 51.1, 49.4, ...]\n",
"\n",
"[44] Nile, Flow of the River Nile\n",
"Rover::DataFrame : 100 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time int64 100 [1871, 1872, 1873, 1874, 1875, ...]\n",
"2 :value int64 85 [1120, 1160, 963, 1210, 1160, ...]\n",
"\n",
"[45] nottem, Average Monthly Temperatures at Nottingham, 1920-1939\n",
"Rover::DataFrame : 240 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time float64 240 [1920.0, 1920.08333333333, 1920.16666666667, 1920.25, 1920.33333333333, ...]\n",
"2 :value float64 149 [40.6, 40.8, 44.4, 46.7, 54.1, ...]\n",
"\n",
"[46] npk, Classical N, P, K Factorial Experiment\n",
"Rover::DataFrame : 24 observations(rows) of 5 variables(columns).\n",
"Variables : 5 numeric\n",
"# key type level data_preview\n",
"1 :block int64 6 [1, 1, 1, 1, 2, ...]\n",
"2 :N int64 2 {0=>12, 1=>12}\n",
"3 :P int64 2 {1=>12, 0=>12}\n",
"4 :K int64 2 {1=>12, 0=>12}\n",
"5 :yield float64 21 [49.5, 62.8, 46.8, 57.0, 59.8, ...]\n",
"\n",
"[47] occupationalStatus, Occupational Status of Fathers and their Sons\n",
"Rover::DataFrame : 64 observations(rows) of 3 variables(columns).\n",
"Variables : 3 numeric\n",
"# key type level data_preview\n",
"1 :origin int64 8 [1, 2, 3, 4, 5, ...]\n",
"2 :destination int64 8 [1, 1, 1, 1, 1, ...]\n",
"3 :Freq int64 44 [50, 16, 12, 11, 2, ...]\n",
"\n",
"[48] Orange, Growth of Orange Trees\n",
"Rover::DataFrame : 35 observations(rows) of 3 variables(columns).\n",
"Variables : 3 numeric\n",
"# key type level data_preview\n",
"1 :Tree int64 5 {1=>7, 2=>7, 3=>7, 4=>7, 5=>7}\n",
"2 :age int64 7 [118, 484, 664, 1004, 1231, ...]\n",
"3 :circumference int64 30 [30, 58, 87, 115, 120, ...]\n",
"\n",
"[49] OrchardSprays, Potency of Orchard Sprays\n",
"Rover::DataFrame : 64 observations(rows) of 4 variables(columns).\n",
"Variables : 3 numeric, 1 object\n",
"# key type level data_preview\n",
"1 :decrease int64 48 [57, 95, 8, 69, 92, ...]\n",
"2 :rowpos int64 8 [1, 2, 3, 4, 5, ...]\n",
"3 :colpos int64 8 [1, 1, 1, 1, 1, ...]\n",
"4 :treatment object 8 [D, E, B, H, G, ...]\n",
"\n",
"[50] PlantGrowth, Results from an Experiment on Plant Growth\n",
"Rover::DataFrame : 30 observations(rows) of 2 variables(columns).\n",
"Variables : 1 numeric, 1 object\n",
"# key type level data_preview\n",
"1 :weight float64 29 [4.17, 5.58, 5.18, 6.11, 4.5, ...]\n",
"2 :group object 3 {\"ctrl\"=>10, \"trt1\"=>10, \"trt2\"=>10}\n",
"\n",
"[51] precip, Annual Precipitation in US Cities\n",
"Rover::DataFrame : 70 observations(rows) of 1 variable(column).\n",
"Variable : 1 numeric\n",
"# key type level data_preview\n",
"1 :dat float64 62 [67.0, 54.7, 7.0, 48.5, 14.0, ...]\n",
"\n",
"[52] presidents, Quarterly Approval Ratings of US Presidents\n",
"Rover::DataFrame : 120 observations(rows) of 2 variables(columns).\n",
"Variables : 1 numeric, 1 object\n",
"# key type level data_preview\n",
"1 :time float64 120 [1945.0, 1945.25, 1945.5, 1945.75, 1946.0, ...]\n",
"2 :value object 50 [NA, 87, 82, 75, 63, ...]\n",
"\n",
"[53] pressure, Vapor Pressure of Mercury as a Function of Temperature\n",
"Rover::DataFrame : 19 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :temperature int64 19 [0, 20, 40, 60, 80, ...]\n",
"2 :pressure float64 19 [0.0002, 0.0012, 0.006, 0.03, 0.09, ...]\n",
"\n",
"[54] Puromycin, Reaction Velocity of an Enzymatic Reaction\n",
"Rover::DataFrame : 23 observations(rows) of 3 variables(columns).\n",
"Variables : 2 numeric, 1 object\n",
"# key type level data_preview\n",
"1 :conc float64 6 [0.02, 0.02, 0.06, 0.06, 0.11, ...]\n",
"2 :rate int64 23 [76, 47, 97, 107, 123, ...]\n",
"3 :state object 2 {\"treated\"=>12, \"untreated\"=>11}\n",
"\n",
"[55] quakes, Locations of Earthquakes off Fiji\n",
"Rover::DataFrame : 1000 observations(rows) of 5 variables(columns).\n",
"Variables : 5 numeric\n",
"# key type level data_preview\n",
"1 :lat float64 721 [-20.42, -20.62, -26.0, -17.97, -20.42, ...]\n",
"2 :long float64 605 [181.62, 181.03, 184.1, 181.66, 181.96, ...]\n",
"3 :depth int64 422 [562, 650, 42, 626, 649, ...]\n",
"4 :mag float64 22 [4.8, 4.2, 5.4, 4.1, 4.0, ...]\n",
"5 :stations int64 102 [41, 15, 43, 19, 11, ...]\n",
"\n",
"[56] randu, Random Numbers from Congruential Generator RANDU\n",
"Rover::DataFrame : 400 observations(rows) of 3 variables(columns).\n",
"Variables : 3 numeric\n",
"# key type level data_preview\n",
"1 :x float64 400 [3.1e-05, 0.044495, 0.82244, 0.322291, 0.393595, ...]\n",
"2 :y float64 400 [0.000183, 0.155732, 0.873416, 0.648545, 0.826873, ...]\n",
"3 :z float64 400 [0.000824, 0.533939, 0.838542, 0.990648, 0.418881, ...]\n",
"\n",
"[57] rivers, Lengths of Major North American Rivers\n",
"Rover::DataFrame : 141 observations(rows) of 1 variable(column).\n",
"Variable : 1 numeric\n",
"# key type level data_preview\n",
"1 :dat int64 114 [735, 320, 325, 392, 524, ...]\n",
"\n",
"[58] rock, Measurements on Petroleum Rock Samples\n",
"Rover::DataFrame : 48 observations(rows) of 4 variables(columns).\n",
"Variables : 4 numeric\n",
"# key type level data_preview\n",
"1 :area int64 47 [4990, 7002, 7558, 7352, 7943, ...]\n",
"2 :peri float64 47 [2791.9, 3892.6, 3930.66, 3869.32, 3948.54, ...]\n",
"3 :shape float64 47 [0.0903296, 0.148622, 0.183312, 0.117063, 0.122417, ...]\n",
"4 :perm float64 12 [6.3, 6.3, 6.3, 6.3, 17.1, ...]\n",
"\n",
"[59] Seatbelts, Road Casualties in Great Britain 1969-84\n",
"Rover::DataFrame : 192 observations(rows) of 8 variables(columns).\n",
"Variables : 8 numeric\n",
"# key type level data_preview\n",
"1 :DriversKilled int64 82 [107, 97, 102, 87, 119, ...]\n",
"2 :drivers int64 172 [1687, 1508, 1507, 1385, 1632, ...]\n",
"3 :front int64 170 [867, 825, 806, 814, 991, ...]\n",
"4 :rear int64 150 [269, 265, 319, 407, 454, ...]\n",
"5 :kms int64 191 [9059, 7685, 9963, 10955, 11823, ...]\n",
"6 :PetrolPrice float64 189 [0.102971811805368, 0.102362995884646, 0.102062490635914, 0.100873300511862, 0.101019672891934, ...]\n",
"7 :VanKilled int64 16 [12, 6, 12, 8, 10, ...]\n",
"8 :law int64 2 {0=>169, 1=>23}\n",
"\n",
"[60] sleep, Student's Sleep Data\n",
"Rover::DataFrame : 20 observations(rows) of 3 variables(columns).\n",
"Variables : 3 numeric\n",
"# key type level data_preview\n",
"1 :extra float64 17 [0.7, -1.6, -0.2, -1.2, -0.1, ...]\n",
"2 :group int64 2 {1=>10, 2=>10}\n",
"3 :ID int64 10 [1, 2, 3, 4, 5, ...]\n",
"\n",
"[61] stackloss, Brownlee's Stack Loss Plant Data\n",
"Rover::DataFrame : 21 observations(rows) of 4 variables(columns).\n",
"Variables : 4 numeric\n",
"# key type level data_preview\n",
"1 :Air.Flow int64 7 [80, 80, 75, 62, 62, ...]\n",
"2 :Water.Temp int64 9 [27, 27, 25, 24, 22, ...]\n",
"3 :Acid.Conc. int64 11 [89, 88, 90, 87, 87, ...]\n",
"4 :stack.loss int64 14 [42, 37, 37, 28, 18, ...]\n",
"\n",
"[62] sunspot.month, Monthly Sunspot Data, from 1749 to \"Present\"\n",
"Rover::DataFrame : 3177 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time float64 3177 [1749.0, 1749.08333333333, 1749.16666666667, 1749.25, 1749.33333333333, ...]\n",
"2 :value float64 1221 [58.0, 62.6, 70.0, 55.7, 85.0, ...]\n",
"\n",
"[63] sunspot.year, Yearly Sunspot Data, 1700-1988\n",
"Rover::DataFrame : 289 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time int64 289 [1700, 1701, 1702, 1703, 1704, ...]\n",
"2 :value float64 239 [5.0, 11.0, 16.0, 23.0, 36.0, ...]\n",
"\n",
"[64] sunspots, Monthly Sunspot Numbers, 1749-1983\n",
"Rover::DataFrame : 2820 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time float64 2820 [1749.0, 1749.08333333333, 1749.16666666667, 1749.25, 1749.33333333334, ...]\n",
"2 :value float64 1140 [58.0, 62.6, 70.0, 55.7, 85.0, ...]\n",
"\n",
"[65] swiss, Swiss Fertility and Socioeconomic Indicators (1888) Data\n",
"Rover::DataFrame : 47 observations(rows) of 6 variables(columns).\n",
"Variables : 6 numeric\n",
"# key type level data_preview\n",
"1 :Fertility float64 46 [80.2, 83.1, 92.5, 85.8, 76.9, ...]\n",
"2 :Agriculture float64 47 [17.0, 45.1, 39.7, 36.5, 43.5, ...]\n",
"3 :Examination int64 22 [15, 6, 5, 12, 17, ...]\n",
"4 :Education int64 19 [12, 9, 5, 7, 15, ...]\n",
"5 :Catholic float64 46 [9.96, 84.84, 93.4, 33.77, 5.16, ...]\n",
"6 :Infant.Mortality float64 37 [22.2, 22.2, 20.2, 20.3, 20.6, ...]\n",
"\n",
"[66] Theoph, Pharmacokinetics of Theophylline\n",
"Rover::DataFrame : 132 observations(rows) of 5 variables(columns).\n",
"Variables : 5 numeric\n",
"# key type level data_preview\n",
"1 :Subject int64 12 [1, 1, 1, 1, 1, ...]\n",
"2 :Wt float64 11 [79.6, 79.6, 79.6, 79.6, 79.6, ...]\n",
"3 :Dose float64 10 [4.02, 4.02, 4.02, 4.02, 4.02, ...]\n",
"4 :Time float64 78 [0.0, 0.25, 0.57, 1.12, 2.02, ...]\n",
"5 :conc float64 115 [0.74, 2.84, 6.57, 10.5, 9.66, ...]\n",
"\n",
"[67] Titanic, Survival of passengers on the Titanic\n",
"Rover::DataFrame : 32 observations(rows) of 5 variables(columns).\n",
"Variables : 1 numeric, 4 objects\n",
"# key type level data_preview\n",
"1 :Class object 4 {\"1st\"=>8, \"2nd\"=>8, \"3rd\"=>8, \"Crew\"=>8}\n",
"2 :Sex object 2 {\"Male\"=>16, \"Female\"=>16}\n",
"3 :Age object 2 {\"Child\"=>16, \"Adult\"=>16}\n",
"4 :Survived object 2 {\"No\"=>16, \"Yes\"=>16}\n",
"5 :Freq int64 22 [0, 0, 35, 0, 0, ...]\n",
"\n",
"[68] ToothGrowth, The Effect of Vitamin C on Tooth Growth in Guinea Pigs\n",
"Rover::DataFrame : 60 observations(rows) of 3 variables(columns).\n",
"Variables : 2 numeric, 1 object\n",
"# key type level data_preview\n",
"1 :len float64 43 [4.2, 11.5, 7.3, 5.8, 6.4, ...]\n",
"2 :supp object 2 {\"VC\"=>30, \"OJ\"=>30}\n",
"3 :dose float64 3 {0.5=>20, 1.0=>20, 2.0=>20}\n",
"\n",
"[69] treering, Yearly Treering Data, -6000-1979\n",
"Rover::DataFrame : 7980 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time int64 7980 [-6000, -5999, -5998, -5997, -5996, ...]\n",
"2 :value float64 1429 [1.345, 1.077, 1.545, 1.319, 1.413, ...]\n",
"\n",
"[70] trees, Diameter, Height and Volume for Black Cherry Trees\n",
"Rover::DataFrame : 31 observations(rows) of 3 variables(columns).\n",
"Variables : 3 numeric\n",
"# key type level data_preview\n",
"1 :Girth float64 27 [8.3, 8.6, 8.8, 10.5, 10.7, ...]\n",
"2 :Height int64 21 [70, 65, 63, 72, 81, ...]\n",
"3 :Volume float64 30 [10.3, 10.3, 10.2, 16.4, 18.8, ...]\n",
"\n",
"[71] UCBAdmissions, Student Admissions at UC Berkeley\n",
"Rover::DataFrame : 24 observations(rows) of 4 variables(columns).\n",
"Variables : 1 numeric, 3 objects\n",
"# key type level data_preview\n",
"1 :Admit object 2 {\"Admitted\"=>12, \"Rejected\"=>12}\n",
"2 :Gender object 2 {\"Male\"=>12, \"Female\"=>12}\n",
"3 :Dept object 6 [A, A, A, A, B, ...]\n",
"4 :Freq int64 23 [512, 313, 89, 19, 353, ...]\n",
"\n",
"[72] UKDriverDeaths, Road Casualties in Great Britain 1969-84\n",
"Rover::DataFrame : 192 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time float64 192 [1969.0, 1969.08333333333, 1969.16666666667, 1969.25, 1969.33333333333, ...]\n",
"2 :value int64 172 [1687, 1508, 1507, 1385, 1632, ...]\n",
"\n",
"[73] UKgas, UK Quarterly Gas Consumption\n",
"Rover::DataFrame : 108 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time float64 108 [1960.0, 1960.25, 1960.5, 1960.75, 1961.0, ...]\n",
"2 :value float64 93 [160.1, 129.7, 84.8, 120.1, 160.1, ...]\n",
"\n",
"[74] USAccDeaths, Accidental Deaths in the US 1973-1978\n",
"Rover::DataFrame : 72 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time float64 72 [1973.0, 1973.08333333333, 1973.16666666667, 1973.25, 1973.33333333333, ...]\n",
"2 :value int64 71 [9007, 8106, 8928, 9137, 10017, ...]\n",
"\n",
"[75] USArrests, Violent Crime Rates by US State\n",
"Rover::DataFrame : 50 observations(rows) of 4 variables(columns).\n",
"Variables : 4 numeric\n",
"# key type level data_preview\n",
"1 :Murder float64 43 [13.2, 10.0, 8.1, 8.8, 9.0, ...]\n",
"2 :Assault int64 45 [236, 263, 294, 190, 276, ...]\n",
"3 :UrbanPop int64 36 [58, 48, 80, 50, 91, ...]\n",
"4 :Rape float64 48 [21.2, 44.5, 31.0, 19.5, 40.6, ...]\n",
"\n",
"[76] USJudgeRatings, Lawyers' Ratings of State Judges in the US Superior Court\n",
"Rover::DataFrame : 43 observations(rows) of 12 variables(columns).\n",
"Variables : 12 numeric\n",
"# key type level data_preview\n",
" 1 :CONT float64 24 [5.7, 6.8, 7.2, 6.8, 7.3, ...]\n",
" 2 :INTG float64 23 [7.9, 8.9, 8.1, 8.8, 6.4, ...]\n",
" 3 :DMNR float64 26 [7.7, 8.8, 7.8, 8.5, 4.3, ...]\n",
" 4 :DILG float64 26 [7.3, 8.5, 7.8, 8.8, 6.5, ...]\n",
" 5 :CFMG float64 23 [7.1, 7.8, 7.5, 8.3, 6.0, ...]\n",
" 6 :DECI float64 24 [7.4, 8.1, 7.6, 8.5, 6.2, ...]\n",
" 7 :PREP float64 26 [7.1, 8.0, 7.5, 8.7, 5.7, ...]\n",
" 8 :FAMI float64 27 [7.1, 8.0, 7.5, 8.7, 5.7, ...]\n",
" 9 :ORAL float64 28 [7.1, 7.8, 7.3, 8.4, 5.1, ...]\n",
"10 :WRIT float64 25 [7.0, 7.9, 7.4, 8.5, 5.3, ...]\n",
"11 :PHYS float64 23 [8.3, 8.5, 7.9, 8.8, 5.5, ...]\n",
"12 :RTEN float64 27 [7.8, 8.7, 7.8, 8.7, 4.8, ...]\n",
"\n",
"[77] USPersonalExpenditure, Personal Expenditure Data\n",
"Rover::DataFrame : 5 observations(rows) of 5 variables(columns).\n",
"Variables : 5 numeric\n",
"# key type level data_preview\n",
"1 :1940 float64 5 {22.2=>1, 10.5=>1, 3.53=>1, 1.04=>1, 0.341=>1}\n",
"2 :1945 float64 5 {44.5=>1, 15.5=>1, 5.76=>1, 1.98=>1, 0.974=>1}\n",
"3 :1950 float64 5 {59.6=>1, 29.0=>1, 9.71=>1, 2.45=>1, 1.8=>1}\n",
"4 :1955 float64 5 {73.2=>1, 36.5=>1, 14.0=>1, 3.4=>1, 2.6=>1}\n",
"5 :1960 float64 5 {86.8=>1, 46.2=>1, 21.1=>1, 5.4=>1, 3.64=>1}\n",
"\n",
"[78] uspop, Populations Recorded by the US Census\n",
"Rover::DataFrame : 19 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time int64 19 [1790, 1800, 1810, 1820, 1830, ...]\n",
"2 :value float64 19 [3.93, 5.31, 7.24, 9.64, 12.9, ...]\n",
"\n",
"[79] VADeaths, Death Rates in Virginia (1940)\n",
"Rover::DataFrame : 5 observations(rows) of 4 variables(columns).\n",
"Variables : 4 numeric\n",
"# key type level data_preview\n",
"1 :Rural Male float64 5 {11.7=>1, 18.1=>1, 26.9=>1, 41.0=>1, 66.0=>1}\n",
"2 :Rural Female float64 5 {8.7=>1, 11.7=>1, 20.3=>1, 30.9=>1, 54.3=>1}\n",
"3 :Urban Male float64 5 {15.4=>1, 24.3=>1, 37.0=>1, 54.6=>1, 71.1=>1}\n",
"4 :Urban Female float64 5 {8.4=>1, 13.6=>1, 19.3=>1, 35.1=>1, 50.0=>1}\n",
"\n",
"[80] volcano, Topographic Information on Auckland's Maunga Whau Volcano\n",
"Rover::DataFrame : 87 observations(rows) of 61 variables(columns).\n",
"Variables : 61 numeric\n",
"# key type level data_preview\n",
" 1 :V1 int64 27 [100, 101, 102, 103, 104, ...]\n",
" 2 :V2 int64 30 [100, 101, 102, 103, 104, ...]\n",
" 3 :V3 int64 33 [101, 102, 103, 104, 105, ...]\n",
" 4 :V4 int64 35 [101, 102, 103, 104, 105, ...]\n",
" 5 :V5 int64 33 [101, 102, 103, 104, 105, ...]\n",
" 6 :V6 int64 35 [101, 102, 103, 104, 105, ...]\n",
" 7 :V7 int64 36 [101, 102, 103, 104, 105, ...]\n",
" 8 :V8 int64 33 [100, 101, 102, 103, 104, ...]\n",
" 9 :V9 int64 37 [100, 101, 102, 103, 104, ...]\n",
"10 :V10 int64 35 [100, 101, 102, 103, 103, ...]\n",
"11 :V11 int64 38 [101, 102, 103, 103, 104, ...]\n",
"12 :V12 int64 40 [101, 102, 103, 104, 104, ...]\n",
"13 :V13 int64 43 [102, 103, 104, 104, 105, ...]\n",
"14 :V14 int64 42 [102, 103, 104, 104, 105, ...]\n",
"15 :V15 int64 38 [102, 103, 104, 105, 105, ...]\n",
"16 :V16 int64 43 [102, 103, 104, 105, 106, ...]\n",
"17 :V17 int64 41 [103, 104, 105, 106, 107, ...]\n",
"18 :V18 int64 40 [104, 105, 106, 107, 108, ...]\n",
"19 :V19 int64 43 [103, 104, 105, 106, 108, ...]\n",
"20 :V20 int64 46 [102, 103, 104, 106, 108, ...]\n",
"21 :V21 int64 45 [101, 102, 104, 106, 109, ...]\n",
"22 :V22 int64 48 [101, 102, 105, 107, 110, ...]\n",
"23 :V23 int64 48 [102, 103, 106, 108, 112, ...]\n",
"24 :V24 int64 55 [103, 105, 107, 110, 114, ...]\n",
"25 :V25 int64 56 [104, 106, 108, 111, 115, ...]\n",
"26 :V26 int64 51 [104, 106, 110, 114, 118, ...]\n",
"27 :V27 int64 54 [105, 107, 111, 117, 121, ...]\n",
"28 :V28 int64 62 [107, 109, 113, 118, 122, ...]\n",
"29 :V29 int64 61 [107, 110, 114, 117, 121, ...]\n",
"30 :V30 int64 59 [107, 110, 115, 119, 123, ...]\n",
"31 :V31 int64 64 [108, 110, 114, 120, 128, ...]\n",
"32 :V32 int64 64 [108, 110, 115, 121, 131, ...]\n",
"33 :V33 int64 62 [110, 111, 116, 122, 129, ...]\n",
"34 :V34 int64 64 [110, 112, 118, 124, 130, ...]\n",
"35 :V35 int64 57 [110, 113, 119, 125, 131, ...]\n",
"36 :V36 int64 62 [110, 114, 119, 126, 131, ...]\n",
"37 :V37 int64 54 [110, 116, 121, 127, 132, ...]\n",
"38 :V38 int64 61 [110, 115, 121, 127, 132, ...]\n",
"39 :V39 int64 53 [110, 114, 120, 126, 131, ...]\n",
"40 :V40 int64 53 [110, 112, 118, 124, 130, ...]\n",
"41 :V41 int64 54 [108, 110, 116, 122, 128, ...]\n",
"42 :V42 int64 50 [108, 110, 114, 120, 126, ...]\n",
"43 :V43 int64 53 [108, 110, 112, 117, 122, ...]\n",
"44 :V44 int64 45 [107, 109, 111, 116, 119, ...]\n",
"45 :V45 int64 47 [107, 108, 110, 113, 115, ...]\n",
"46 :V46 int64 45 [108, 109, 110, 111, 114, ...]\n",
"47 :V47 int64 48 [108, 109, 110, 110, 112, ...]\n",
"48 :V48 int64 54 [108, 109, 110, 110, 110, ...]\n",
"49 :V49 int64 50 [108, 109, 109, 110, 110, ...]\n",
"50 :V50 int64 54 [108, 108, 109, 109, 110, ...]\n",
"51 :V51 int64 50 [107, 108, 109, 109, 110, ...]\n",
"52 :V52 int64 51 [107, 108, 109, 109, 110, ...]\n",
"53 :V53 int64 42 [107, 108, 108, 109, 109, ...]\n",
"54 :V54 int64 40 [107, 107, 108, 108, 109, ...]\n",
"55 :V55 int64 36 [106, 107, 107, 108, 108, ...]\n",
"56 :V56 int64 33 [106, 106, 107, 107, 107, ...]\n",
"57 :V57 int64 29 [105, 106, 106, 107, 107, ...]\n",
"58 :V58 int64 26 [105, 105, 106, 106, 107, ...]\n",
"59 :V59 int64 22 [104, 105, 105, 106, 106, ...]\n",
"60 :V60 int64 20 [104, 104, 105, 105, 106, ...]\n",
"61 :V61 int64 17 [103, 104, 104, 105, 105, ...]\n",
"\n",
"[81] warpbreaks, The Number of Breaks in Yarn during Weaving\n",
"Rover::DataFrame : 54 observations(rows) of 3 variables(columns).\n",
"Variables : 1 numeric, 2 objects\n",
"# key type level data_preview\n",
"1 :breaks int64 31 [26, 30, 54, 25, 70, ...]\n",
"2 :wool object 2 {\"A\"=>27, \"B\"=>27}\n",
"3 :tension object 3 {\"L\"=>18, \"M\"=>18, \"H\"=>18}\n",
"\n",
"[82] women, Average Heights and Weights for American Women\n",
"Rover::DataFrame : 15 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :height int64 15 [58, 59, 60, 61, 62, ...]\n",
"2 :weight int64 15 [115, 117, 120, 123, 126, ...]\n",
"\n",
"[83] WorldPhones, The World's Telephones\n",
"Rover::DataFrame : 7 observations(rows) of 7 variables(columns).\n",
"Variables : 7 numeric\n",
"# key type level data_preview\n",
"1 :N.Amer int64 7 [45939, 60423, 64721, 68484, 71799, ...]\n",
"2 :Europe int64 7 [21574, 29990, 32510, 35218, 37598, ...]\n",
"3 :Asia int64 7 [2876, 4708, 5230, 6662, 6856, ...]\n",
"4 :S.Amer int64 7 [1815, 2568, 2695, 2845, 3000, ...]\n",
"5 :Oceania int64 7 [1646, 2366, 2526, 2691, 2868, ...]\n",
"6 :Africa int64 7 [89, 1411, 1546, 1663, 1769, ...]\n",
"7 :Mid.Amer int64 7 [555, 733, 773, 836, 911, ...]\n",
"\n",
"[84] WWWusage, Internet Usage per Minute\n",
"Rover::DataFrame : 100 observations(rows) of 2 variables(columns).\n",
"Variables : 2 numeric\n",
"# key type level data_preview\n",
"1 :time int64 100 [1, 2, 3, 4, 5, ...]\n",
"2 :value int64 57 [88, 84, 85, 85, 84, ...]\n",
"\n"
]
}
],
"source": [
"package = 'datasets'\n",
"\n",
"dataset_list.filter(package: package).each.with_index(1) do |ds, i|\n",
" puts \"[#{i}] #{ds.dataset}, #{ds.title}\"\n",
" dataset = Datasets::Rdatasets.new(package, ds.dataset)\n",
" df = Rover::DataFrame.new(dataset.to_table.to_h)\n",
" p df\n",
"end; nil"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Ruby 3.1.1",
"language": "ruby",
"name": "ruby"
},
"language_info": {
"file_extension": ".rb",
"mimetype": "application/x-ruby",
"name": "ruby",
"version": "3.1.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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