Fix Rdatasets#each to change "NA" to nil.
- This is the data preview after this fix applied.
- 265 datasets are fixed.
# [package, dataset] pairs
[["AER", "BenderlyZwick"], ["AER", "GrowthDJ"], ["AER", "GSS7402"], ["AER", "MASchools"], ["AER", "STAR"], ["AER", "USMacroG"], ["AER", "USMacroSWM"], ["AER", "USSeatBelts"], ["asaur", "hepatoCellular"], ["boot", "neuro"], ["boot", "urine"], ["carData", "Chile"], ["carData", "Davis"], ["carData", "Freedman"], ["carData", "GSSvocab"], ["carData", "Hartnagel"], ["carData", "UN"], ["carData", "UN98"], ["causaldata", "abortion"], ["causaldata", "adult_services"], ["causaldata", "auto"], ["causaldata", "close_college"], ["causaldata", "nhefs"], ["causaldata", "nhefs_complete"], ["causaldata", "scorecard"], ["causaldata", "social_insure"], ["causaldata", "thornton_hiv"], ["cluster", "animals"], ["cluster", "plantTraits"], ["cluster", "votes.repub"], ["COUNT", "loomis"], ["COUNT", "ships"], ["DAAG", "bomregions"], ["DAAG", "bomregions2011"], ["DAAG", "bomregions2012"], ["DAAG", "cuckoohosts"], ["DAAG", "measles"], ["DAAG", "nassCDS"], ["DAAG", "nswdemo"], ["DAAG", "possum"], ["DAAG", "poxetc"], ["DAAG", "rainforest"], ["DAAG", "socsupport"], ["datasets", "airquality"], ["datasets", "presidents"], ["dplyr", "starwars"], ["dplyr", "storms"], ["dragracer", "rpdr_contep"], ["Ecdat", "Accident"], ["Ecdat", "bankingCrises"], ["Ecdat", "breaches"], ["Ecdat", "Garch"], ["Ecdat", "Hstarts"], ["Ecdat", "MCAS"], ["Ecdat", "nuclearWeaponStates"], ["Ecdat", "Orange"], ["Ecdat", "PSID"], ["Ecdat", "RetSchool"], ["Ecdat", "terrorism"], ["Ecdat", "USclassifiedDocuments"], ["Ecdat", "USGDPpresidents"], ["Ecdat", "UStaxWords"], ["fpp2", "melsyd"], ["gap", "meyer"], ["gap", "mr"], ["gap", "PD"], ["geepack", "dietox"], ["geepack", "muscatine"], ["ggplot2", "msleep"], ["ggplot2", "txhousing"], ["gt", "countrypops"], ["gt", "exibble"], ["gt", "sza"], ["HistData", "Cavendish"], ["HistData", "Fingerprints"], ["HistData", "OldMaps"], ["HistData", "Snow.dates"], ["HistData", "Virginis"], ["HSAUR", "BtheB"], ["ISLR", "Hitters"], ["KMsurv", "bcdeter"], ["lmec", "UTIdata"], ["MASS", "biopsy"], ["MASS", "Pima.tr2"], ["MASS", "survey"], ["mi", "CHAIN"], ["mi", "nlsyV"], ["mosaicData", "Gestation"], ["mosaicData", "HELPmiss"], ["mosaicData", "HELPrct"], ["mosaicData", "Marriage"], ["mosaicData", "SnowGR"], ["mosaicData", "Weather"], ["multgee", "arthritis"], ["multgee", "housing"], ["nycflights13", "flights"], ["nycflights13", "weather"], ["openintro", "acs12"], ["openintro", "ames"], ["openintro", "babies"], ["openintro", "births"], ["openintro", "births14"], ["openintro", "blizzard_salary"], ["openintro", "census"], ["openintro", "china"], ["openintro", "cia_factbook"], ["openintro", "cle_sac"], ["openintro", "email_test"], ["openintro", "exclusive_relationship"], ["openintro", "fastfood"], ["openintro", "get_it_dunn_run"], ["openintro", "gss2010"], ["openintro", "heart_transplant"], ["openintro", "hfi"], ["openintro", "husbands_wives"], ["openintro", "loan50"], ["openintro", "loans_full_schema"], ["openintro", "mammals"], ["openintro", "mlb_teams"], ["openintro", "mn_police_use_of_force"], ["openintro", "mtl"], ["openintro", "ncbirths"], ["openintro", "piracy"], ["openintro", "pm25_2011_durham"], ["openintro", "possum"], ["openintro", "president"], ["openintro", "prius_mpg"], ["openintro", "smoking"], ["openintro", "snowfall"], ["openintro", "sowc_demographics"], ["openintro", "sowc_maternal_newborn"], ["openintro", "sp500"], ["openintro", "speed_gender_height"], ["openintro", "world_pop"], ["palmerpenguins", "penguins"], ["plyr", "baseball"], ["pscl", "AustralianElections"], ["pscl", "ca2006"], ["pscl", "politicalInformation"], ["pscl", "state.info"], ["psych", "bfi"], ["psych", "sat.act"], ["ratdat", "complete"], ["ratdat", "complete_old"], ["ratdat", "surveys"], ["reshape2", "smiths"], ["robustbase", "airmay"], ["rpart", "car.test.frame"], ["rpart", "cu.summary"], ["rpart", "stagec"], ["sandwich", "Investment"], ["sandwich", "PublicSchools"], ["sem", "HS.data"], ["sem", "Tests"], ["Stat2Data", "AppleStock"], ["Stat2Data", "CreditRisk"], ["Stat2Data", "Day1Survey"], ["Stat2Data", "Faces"], ["Stat2Data", "Goldenrod"], ["Stat2Data", "GrinnellHouses"], ["Stat2Data", "Handwriting"], ["Stat2Data", "HawkTail2"], ["Stat2Data", "Hoops"], ["Stat2Data", "MathPlacement"], ["Stat2Data", "MedGPA"], ["Stat2Data", "NCbirths"], ["Stat2Data", "Overdrawn"], ["Stat2Data", "Pines"], ["Stat2Data", "Political"], ["Stat2Data", "Pollster08"], ["Stat2Data", "WeightLossIncentive"], ["Stat2Data", "YouthRisk"], ["Stat2Data", "YouthRisk2007"], ["Stat2Data", "YouthRisk2009"], ["stevedata", "anes_prochoice"], ["stevedata", "anes_vote84"], ["stevedata", "CFT15"], ["stevedata", "DST"], ["stevedata", "eq_passengercars"], ["stevedata", "ESS9GB"], ["stevedata", "ESSBE5"], ["stevedata", "gss_abortion"], ["stevedata", "gss_spending"], ["stevedata", "gss_wages"], ["stevedata", "mvprod"], ["stevedata", "nesarc_drinkspd"], ["stevedata", "recessions"], ["stevedata", "SBCD"], ["stevedata", "sealevels"], ["stevedata", "sugar_price"], ["stevedata", "thatcher_approval"], ["stevedata", "therms"], ["stevedata", "turnips"], ["stevedata", "TV16"], ["stevedata", "usa_chn_gdp_forecasts"], ["stevedata", "usa_migration"], ["stevedata", "wvs_ccodes"], ["stevedata", "wvs_immig"], ["stevedata", "wvs_justifbribe"], ["stevedata", "wvs_usa_abortion"], ["stevedata", "wvs_usa_educat"], ["stevedata", "yugo_sales"], ["survival", "cancer"], ["survival", "pbc"], ["survival", "rhDNase"], ["survival", "transplant"], ["survival", "udca"], ["tidyr", "smiths"], ["tidyr", "us_rent_income"], ["tidyr", "who"], ["validate", "retailers"], ["validate", "SBS2000"], ["vcd", "Bundesliga"], ["wooldridge", "athlet1"], ["wooldridge", "athlet2"], ["wooldridge", "beveridge"], ["wooldridge", "big9salary"], ["wooldridge", "bwght"], ["wooldridge", "bwght2"], ["wooldridge", "card"], ["wooldridge", "catholic"], ["wooldridge", "cement"], ["wooldridge", "consump"], ["wooldridge", "countymurders"], ["wooldridge", "cps91"], ["wooldridge", "crime2"], ["wooldridge", "crime3"], ["wooldridge", "crime4"], ["wooldridge", "discrim"], ["wooldridge", "earns"], ["wooldridge", "econmath"], ["wooldridge", "ezanders"], ["wooldridge", "ezunem"], ["wooldridge", "fertil2"], ["wooldridge", "fertil3"], ["wooldridge", "fish"], ["wooldridge", "gpa3"], ["wooldridge", "happiness"], ["wooldridge", "hseinv"], ["wooldridge", "injury"], ["wooldridge", "intdef"], ["wooldridge", "intqrt"], ["wooldridge", "inven"], ["wooldridge", "jtrain"], ["wooldridge", "lawsch85"], ["wooldridge", "loanapp"], ["wooldridge", "lowbrth"], ["wooldridge", "mathpnl"], ["wooldridge", "minwage"], ["wooldridge", "mlb1"], ["wooldridge", "murder"], ["wooldridge", "nbasal"], ["wooldridge", "nyse"], ["wooldridge", "okun"], ["wooldridge", "pension"], ["wooldridge", "phillips"], ["wooldridge", "prminwge"], ["wooldridge", "rental"], ["wooldridge", "school93_98"], ["wooldridge", "traffic2"], ["wooldridge", "volat"], ["wooldridge", "vote2"], ["wooldridge", "voucher"], ["wooldridge", "wage2"], ["wooldridge", "wageprc"]]Rdatasets: AER: BenderlyZwick, Benderly and Zwick Data: Inflation, Growth and Stock Returns
RedAmber::DataFrame : 31 x 5 Vectors
Vectors : 5 numeric
# key type level data_preview
1 :returns double 26 [nil, nil, 53.0, 31.2, 3.7, ... ], 3 nils
2 :growth double 23 [nil, nil, 6.7, 2.1, 1.8, ... ], 3 nils
3 :inflation double 27 [nil, nil, -0.4, 0.4, 2.9, ... ], 3 nils
4 :growth2 double 25 [3.9, 4.0, -1.3, 5.6, 2.1, ... ]
5 :inflation2 double 25 [2.2, 2.1, 0.6, 1.3, 1.9, ... ]Rdatasets: AER: GrowthDJ, Determinants of Economic Growth
RedAmber::DataFrame : 121 x 10 Vectors
Vectors : 7 numeric, 3 strings
# key type level data_preview
1 :oil string 2 {"no"=>98, "yes"=>23}
2 :inter string 2 {"yes"=>75, "no"=>46}
3 :oecd string 2 {"no"=>99, "yes"=>22}
4 :gdp60 uint32 115 [2485, 1588, 1116, 959, 529, ... ], 5 nils
5 :gdp85 uint16 106 [4371, 1171, 1071, 3671, 857, ... ], 13 nils
6 :gdpgrowth double 63 [4.8, 0.8, 2.2, 8.6, 2.9, ... ], 4 nils
7 :popgrowth double 37 [2.6, 2.1, 2.4, 3.2, 0.9, ... ], 14 nils
8 :invest double 98 [24.1, 5.8, 10.8, 28.3, 12.7, ... ]
9 :school double 75 [4.5, 1.8, 1.8, 2.9, 0.4, ... ], 3 nils
10 :literacy60 uint8 59 [10, 5, 5, nil, 2, ... ], 18 nilsRdatasets: AER: GSS7402, US General Social Survey 1974-2002
RedAmber::DataFrame : 9120 x 10 Vectors
Vectors : 6 numeric, 4 strings
# key type level data_preview
1 :kids uint8 9 [0, 1, 1, 2, 2, ... ]
2 :age uint8 72 [25, 30, 55, 57, 71, ... ]
3 :education uint8 21 [14, 13, 2, 16, 12, ... ]
4 :year uint16 8 [2002, 2002, 2002, 2002, 2002, ... ]
5 :siblings uint8 27 [1, 4, 1, 1, 6, ... ]
6 :agefirstbirth uint8 34 [nil, 19, 27, 22, 29, ... ], 5808 nils
7 :ethnicity string 2 {"cauc"=>7335, "other"=>1785}
8 :city16 string 2 {"no"=>5246, "yes"=>3874}
9 :lowincome16 string 2 {"no"=>7182, "yes"=>1938}
10 :immigrant string 2 {"no"=>8122, "yes"=>998}Rdatasets: AER: MASchools, Massachusetts Test Score Data
RedAmber::DataFrame : 220 x 16 Vectors
Vectors : 15 numeric, 1 string
# key type level data_preview
1 :district uint16 220 [1, 2, 3, 5, 7, ... ]
2 :municipality string 220 ["Abington", "Acton", "Acushnet", "Agawam", "Amesbury", ... ]
3 :expreg uint16 207 [4201, 4129, 3627, 4015, 4273, ... ]
4 :expspecial double 220 [7375.68994140625, 8573.990234375, 8081.72021484375, 8181.3701171875, 7037.22021484375, ... ]
5 :expbil uint32 47 [0, 0, 0, 0, 0, ... ]
6 :expocc uint16 40 [0, 0, 0, 0, 0, ... ]
7 :exptot uint16 209 [4646, 4930, 4281, 4826, 4824, ... ]
8 :scratio double 94 [16.6000003814697, 5.69999980926514, 7.5, 8.60000038146973, 6.09999990463257, ... ], 9 nils
9 :special double 105 [14.6000003814697, 17.3999996185303, 12.1000003814697, 21.1000003814697, 16.7999992370605, ... ]
10 :lunch double 154 [11.8000001907349, 2.5, 14.1000003814697, 12.1000003814697, 17.3999996185303, ... ]
11 :stratio double 83 [19.0, 22.6000003814697, 19.2999992370605, 17.8999996185303, 17.5, ... ]
12 :income double 216 [16.379, 25.792, 14.04, 16.111, 15.423, ... ]
13 :score4 uint16 63 [714, 731, 704, 704, 701, ... ]
14 :score8 uint16 73 [691, nil, 693, 691, 699, ... ], 40 nils
15 :salary double 191 [34.3600006103516, 38.0629997253418, 32.4910011291504, 33.1059989929199, 34.4365005493164, ... ], 25 nils
16 :english double 92 [0.0, 1.24610590934753, 0.0, 0.322580635547638, 0.0, ... ]Rdatasets: AER: STAR, Project STAR: Student-Teacher Achievement Ratio
RedAmber::DataFrame : 11598 x 47 Vectors
Vectors : 20 numeric, 27 strings
# key type level data_preview
1 :gender string 3 {"female"=>5456, "male"=>6122, nil=>20}
2 :ethnicity string 7 ["afam", "cauc", "afam", "cauc", "afam", ... ], 145 nils
3 :birth string 22 ["1979 Q3", "1980 Q1", "1979 Q4", "1979 Q4", "1980 Q1", ... ], 70 nils
4 :stark string 4 {nil=>5273, "small"=>1900, "regular+aide"=>2231, "regular"=>2194}
5 :star1 string 4 {nil=>4769, "small"=>1925, "regular+aide"=>2320, "regular"=>2584}
6 :star2 string 4 {nil=>4758, "small"=>2016, "regular+aide"=>2495, "regular"=>2329}
7 :star3 string 4 {"regular"=>2085, "small"=>2174, "regular+aide"=>2543, nil=>4796}
8 :readk uint16 94 [nil, 447, 450, nil, 439, ... ], 5809 nils
9 :read1 uint16 87 [nil, 507, 579, nil, nil, ... ], 5202 nils
10 :read2 uint16 94 [nil, 568, 588, nil, nil, ... ], 5521 nils
11 :read3 uint16 91 [580, 587, 644, 686, nil, ... ], 5598 nils
12 :mathk uint16 39 [nil, 473, 536, nil, 463, ... ], 5727 nils
13 :math1 uint16 68 [nil, 538, 592, nil, nil, ... ], 4998 nils
14 :math2 uint16 88 [nil, 579, 579, nil, nil, ... ], 5533 nils
15 :math3 uint16 95 [564, 593, 639, 667, nil, ... ], 5521 nils
16 :lunchk string 3 {nil=>5296, "non-free"=>3250, "free"=>3052}
17 :lunch1 string 3 {nil=>4947, "free"=>3430, "non-free"=>3221}
18 :lunch2 string 3 {nil=>5102, "non-free"=>3160, "free"=>3336}
19 :lunch3 string 3 {"free"=>3293, "non-free"=>3227, nil=>5078}
20 :schoolk string 5 {nil=>5273, "rural"=>2917, "suburban"=>1412, "inner-city"=>1428, "urban"=>568}
... 27 more Vectors ...Rdatasets: AER: USMacroG, US Macroeconomic Data (1950-2000, Greene)
RedAmber::DataFrame : 204 x 12 Vectors
Vectors : 12 numeric
# key type level data_preview
1 :gdp double 203 [1610.5, 1658.8, 1723.0, 1753.9, 1773.5, ... ]
2 :consumption double 204 [1058.9, 1075.9, 1131.0, 1097.6, 1122.8, ... ]
3 :invest double 202 [198.1, 220.4, 239.7, 271.8, 242.9, ... ]
4 :government double 202 [361.0, 366.4, 359.6, 382.5, 421.9, ... ]
5 :dpi double 203 [1186.1, 1178.1, 1196.5, 1210.0, 1207.9, ... ]
6 :cpi double 190 [70.6, 71.4, 73.2, 74.9, 77.3, ... ]
7 :m1 double 201 [110.2, 111.75, 112.95, 113.93, 115.08, ... ]
8 :tbill double 186 [1.12, 1.17, 1.23, 1.35, 1.4, ... ]
9 :unemp double 61 [6.4, 5.6, 4.6, 4.2, 3.5, ... ]
10 :population double 204 [149.461, 150.26, 151.064, 151.871, 152.393, ... ]
11 :inflation double 194 [nil, 4.5071, 9.959, 9.1834, 12.616, ... ], 1 nil
12 :interest double 204 [nil, -3.3404, -8.729, -7.8301, -11.216, ... ], 1 nilRdatasets: AER: USMacroSWM, Monthly US Macroeconomic Data (1947-2004, Stock & Watson)
RedAmber::DataFrame : 696 x 4 Vectors
Vectors : 4 numeric
# key type level data_preview
1 :production double 616 [17.04, 17.14, 17.24, 17.1, 17.17, ... ]
2 :oil double 23 [nil, nil, nil, nil, nil, ... ], 12 nils
3 :cpi double 609 [21.48, 21.62, 22.0, 22.0, 21.95, ... ]
4 :expenditure double 533 [nil, nil, nil, nil, nil, ... ], 144 nilsRdatasets: AER: USSeatBelts, Effects of Mandatory Seat Belt Laws in the US
RedAmber::DataFrame : 765 x 12 Vectors
Vectors : 6 numeric, 6 strings
# key type level data_preview
1 :state string 51 ["AK", "AK", "AK", "AK", "AK", ... ]
2 :year uint16 15 [1983, 1984, 1985, 1986, 1987, ... ]
3 :miles uint32 758 [3358, 3589, 3840, 4008, 3900, ... ]
4 :fatalities double 765 [0.0446694456040859, 0.0373363047838211, 0.0330729149281979, 0.0251996014267206, 0.0194871798157692, ... ]
5 :seatbelt double 246 [nil, nil, nil, nil, nil, ... ], 209 nils
6 :speed65 string 2 {"no"=>271, "yes"=>494}
7 :speed70 string 2 {"no"=>711, "yes"=>54}
8 :drinkage string 2 {"yes"=>677, "no"=>88}
9 :alcohol string 2 {"no"=>676, "yes"=>89}
10 :income uint16 750 [17973, 18093, 18925, 18466, 18021, ... ]
11 :age double 764 [28.2349662780762, 28.343542098999, 28.3728160858154, 28.3966522216797, 28.4532508850098, ... ]
12 :enforce string 3 {"no"=>293, "secondary"=>379, "primary"=>93}Rdatasets: asaur: hepatoCellular, hepatoCellular
RedAmber::DataFrame : 227 x 48 Vectors
Vectors : 48 numeric
# key type level data_preview
1 :Number uint8 227 [1, 2, 3, 4, 5, ... ]
2 :Age uint8 52 [57, 58, 65, 54, 71, ... ]
3 :Gender uint8 2 {0=>30, 1=>197}
4 :HBsAg uint8 2 {1=>209, 0=>18}
5 :Cirrhosis uint8 2 {1=>142, 0=>85}
6 :ALT uint8 2 {1=>138, 2=>89}
7 :AST uint8 2 {2=>96, 1=>131}
8 :AFP uint8 2 {2=>149, 1=>78}
9 :Tumorsize uint8 2 {2=>131, 1=>96}
10 :Tumordifferentiation uint8 2 {1=>129, 2=>98}
11 :Vascularinvasion uint8 2 {0=>186, 1=>41}
12 :Tumormultiplicity uint8 2 {1=>170, 2=>57}
13 :Capsulation uint8 2 {0=>52, 1=>175}
14 :TNM uint8 2 {2=>105, 1=>122}
15 :BCLC uint8 2 {1=>136, 2=>91}
16 :OS uint8 75 [83, 81, 79, 76, 7, ... ]
17 :Death uint8 2 {0=>130, 1=>97}
18 :RFS uint8 68 [13, 81, 79, 76, 3, ... ]
19 :Recurrence uint8 2 {1=>143, 0=>84}
20 :CXCL17T double 226 [113.9472382, 54.07154185, 22.18883123, 8.442809305, 8.271131328, ... ]
... 28 more Vectors ...Rdatasets: boot: neuro, Neurophysiological Point Process Data
RedAmber::DataFrame : 469 x 6 Vectors
Vectors : 6 numeric
# key type level data_preview
1 :V1 double 40 [nil, nil, nil, nil, nil, ... ], 429 nils
2 :V2 double 359 [-203.7, -203.0, -249.0, -231.5, nil, ... ], 51 nils
3 :V3 double 405 [-84.1, -97.8, -92.1, -97.5, -130.1, ... ]
4 :V4 double 291 [18.5, 25.8, 27.8, 27.0, 25.8, ... ], 1 nil
5 :V5 double 378 [nil, 134.7, 177.1, 150.3, 160.0, ... ], 24 nils
6 :V6 double 83 [nil, nil, nil, nil, nil, ... ], 379 nilsRdatasets: boot: urine, Urine Analysis Data
RedAmber::DataFrame : 79 x 7 Vectors
Vectors : 7 numeric
# key type level data_preview
1 :r uint8 2 {0=>45, 1=>34}
2 :gravity double 29 [1.021, 1.017, 1.008, 1.011, 1.005, ... ]
3 :ph double 70 [4.91, 5.74, 7.2, 5.51, 6.52, ... ]
4 :osmo uint16 77 [725, 577, 321, 408, 187, ... ], 1 nil
5 :cond double 64 [nil, 20.0, 14.9, 12.6, 7.5, ... ], 1 nil
6 :urea uint16 73 [443, 296, 101, 224, 91, ... ]
7 :calc double 75 [2.45, 4.49, 2.36, 2.15, 1.16, ... ]Rdatasets: carData: Chile, Voting Intentions in the 1988 Chilean Plebiscite
RedAmber::DataFrame : 2700 x 8 Vectors
Vectors : 4 numeric, 4 strings
# key type level data_preview
1 :region string 5 {"N"=>322, "C"=>600, "S"=>718, "SA"=>960, "M"=>100}
2 :population uint32 10 [175000, 175000, 175000, 175000, 175000, ... ]
3 :sex string 2 {"M"=>1321, "F"=>1379}
4 :age uint8 54 [65, 29, 38, 49, 23, ... ], 1 nil
5 :education string 4 {"P"=>1107, "PS"=>462, "S"=>1120, nil=>11}
6 :income uint32 8 [35000, 7500, 15000, 35000, 35000, ... ], 98 nils
7 :statusquo double 2093 [1.0082, -1.29617, 1.23072, -1.03163, -1.10496, ... ], 17 nils
8 :vote string 5 {"Y"=>868, "N"=>889, "U"=>588, nil=>168, "A"=>187}Rdatasets: carData: Davis, Self-Reports of Height and Weight
RedAmber::DataFrame : 200 x 5 Vectors
Vectors : 4 numeric, 1 string
# key type level data_preview
1 :sex string 2 {"M"=>88, "F"=>112}
2 :weight uint8 54 [77, 58, 53, 68, 59, ... ]
3 :height uint8 43 [182, 161, 161, 177, 157, ... ]
4 :repwt uint8 54 [77, 51, 54, 70, 59, ... ], 17 nils
5 :repht uint8 33 [180, 159, 158, 175, 155, ... ], 17 nilsRdatasets: carData: Freedman, Crowding and Crime in U. S. Metropolitan Areas
RedAmber::DataFrame : 110 x 4 Vectors
Vectors : 4 numeric
# key type level data_preview
1 :population uint16 99 [675, 713, nil, 534, 1261, ... ], 10 nils
2 :nonwhite double 84 [7.3, 2.6, 3.3, 0.8, 1.4, ... ]
3 :density uint16 99 [746, 322, nil, 491, 1612, ... ], 10 nils
4 :crime uint16 109 [2602, 1388, 5018, 1182, 3341, ... ]Rdatasets: carData: GSSvocab, Data from the General Social Survey (GSS) from the National Opinion Research Center of the University of Chicago.
RedAmber::DataFrame : 28867 x 8 Vectors
Vectors : 4 numeric, 4 strings
# key type level data_preview
1 :year uint16 20 [1978, 1978, 1978, 1978, 1978, ... ]
2 :gender string 2 {"female"=>16385, "male"=>12482}
3 :nativeBorn string 3 {"yes"=>26224, "no"=>2556, nil=>87}
4 :ageGroup string 6 ["50-59", "60+", "30-39", "50-59", "40-49", ... ], 94 nils
5 :educGroup string 6 ["12 yrs", "<12 yrs", "<12 yrs", "12 yrs", "12 yrs", ... ], 81 nils
6 :vocab uint8 12 [10, 6, 4, 9, 6, ... ], 1348 nils
7 :age uint8 73 [52, 74, 35, 50, 41, ... ], 94 nils
8 :educ uint8 22 [12, 9, 10, 12, 12, ... ], 81 nilsRdatasets: carData: Hartnagel, Canadian Crime-Rates Time Series
RedAmber::DataFrame : 38 x 8 Vectors
Vectors : 8 numeric
# key type level data_preview
1 :year uint16 38 [1931, 1932, 1933, 1934, 1935, ... ]
2 :tfr uint16 38 [3200, 3084, 2864, 2803, 2755, ... ]
3 :partic uint16 30 [234, 234, 235, 237, 238, ... ]
4 :degrees double 36 [12.4, 12.9, 13.9, 13.6, 13.2, ... ]
5 :fconvict double 36 [77.1, 92.9, 98.3, 88.1, 79.4, ... ]
6 :ftheft double 35 [nil, nil, nil, nil, 20.4, ... ], 4 nils
7 :mconvict double 37 [778.7, 745.7, 768.3, 733.6, 765.7, ... ]
8 :mtheft double 33 [nil, nil, nil, nil, 247.1, ... ], 4 nilsRdatasets: carData: UN, National Statistics from the United Nations, Mostly From 2009-2011
RedAmber::DataFrame : 213 x 7 Vectors
Vectors : 5 numeric, 2 strings
# key type level data_preview
1 :region string 9 ["Asia", "Europe", "Africa", nil, "Africa", ... ], 14 nils
2 :group string 4 {"other"=>115, "africa"=>53, nil=>14, "oecd"=>31}
3 :fertility double 194 [5.968, 1.525, 2.142, nil, 5.135, ... ], 14 nils
4 :ppgdp double 200 [499.0, 3677.2, 4473.0, nil, 4321.9, ... ], 14 nils
5 :lifeExpF double 193 [49.49, 80.4, 75.0, nil, 53.17, ... ], 14 nils
6 :pctUrban uint8 81 [23, 53, 67, nil, 59, ... ], 14 nils
7 :infantMortality double 206 [124.535, 16.561, 21.458, 11.2938865700958, 96.191, ... ], 6 nilsRdatasets: carData: UN98, United Nations Social Indicators Data 1998]
RedAmber::DataFrame : 207 x 13 Vectors
Vectors : 12 numeric, 1 string
# key type level data_preview
1 :region string 5 {"Asia"=>50, "Europe"=>44, "Africa"=>55, "America"=>41, "Oceania"=>17}
2 :tfr double 151 [6.9, 2.6, 3.81, nil, nil, ... ], 10 nils
3 :contraception uint8 72 [nil, nil, 52, nil, nil, ... ], 63 nils
4 :educationMale double 55 [nil, nil, 11.1, nil, nil, ... ], 131 nils
5 :educationFemale double 58 [nil, nil, 9.9, nil, nil, ... ], 131 nils
6 :lifeMale double 143 [45.0, 68.0, 67.5, 68.0, nil, ... ], 11 nils
7 :lifeFemale double 144 [46.0, 74.0, 70.3, 73.0, nil, ... ], 11 nils
8 :infantMortality uint8 96 [154, 32, 44, 11, nil, ... ], 6 nils
9 :GDPperCapita uint16 189 [2848, 863, 1531, nil, nil, ... ], 10 nils
10 :economicActivityMale double 121 [87.5, nil, 76.4, 58.8, nil, ... ], 42 nils
11 :economicActivityFemale double 140 [7.2, nil, 7.8, 42.4, nil, ... ], 42 nils
12 :illiteracyMale double 134 [52.8, nil, 26.1, 0.264, nil, ... ], 47 nils
13 :illiteracyFemale double 144 [85.0, nil, 51.0, 0.36, nil, ... ], 47 nilsRdatasets: causaldata: abortion, Data on abortion legalization and sexually transmitted infections
RedAmber::DataFrame : 19584 x 22 Vectors
Vectors : 22 numeric
# key type level data_preview
1 :fip uint8 51 [1, 1, 1, 1, 1, ... ]
2 :age uint8 6 [30, 15, 20, 20, 20, ... ]
3 :race uint8 2 {2=>9792, 1=>9792}
4 :year uint16 16 [1985, 1985, 1985, 1985, 1985, ... ]
5 :sex uint8 2 {1=>9792, 2=>9792}
6 :totpop uint32 9269 [78805, 224003, 94113, 252076, 94113, ... ]
7 :ir double 9287 [371.475799560547, 51.3892517089844, 390.875671386719, 100.836799621582, 390.875671386719, ... ]
8 :crack double 816 [0.21743780374527, 0.21743780374527, 0.21743780374527, 0.21743780374527, 0.21743780374527, ... ]
9 :alcohol double 211 [1.89999997615814, 1.89999997615814, 1.89999997615814, 1.89999997615814, 1.89999997615814, ... ]
10 :income uint16 794 [11566, 11566, 11566, 11566, 11566, ... ]
11 :ur double 517 [8.61666679382324, 8.61666679382324, 8.61666679382324, 8.61666679382324, 8.61666679382324, ... ]
12 :poverty double 177 [20.6000003814697, 20.6000003814697, 20.6000003814697, 20.6000003814697, 20.6000003814697, ... ]
13 :repeal uint8 2 {0=>17664, 1=>1920}
14 :acc double 788 [0.679504454135895, 0.679504454135895, 0.679504454135895, 0.679504454135895, 0.679504454135895, ... ]
15 :wht uint8 2 {0=>9792, 1=>9792}
16 :male uint8 2 {1=>9792, 0=>9792}
17 :lnr double 9331 [7.8808798789978, 6.3621654510498, 8.91779327392578, 5.90699529647827, 9.07200908660889, ... ], 1663 nils
18 :t uint8 16 [1, 1, 1, 1, 1, ... ]
19 :younger uint8 2 {0=>16320, 1=>3264}
20 :fa uint8 103 [nil, 1, nil, nil, nil, ... ], 13056 nils
... 2 more Vectors ...Rdatasets: causaldata: adult_services, Data from a survey of internet-mediated sex workers
RedAmber::DataFrame : 1787 x 31 Vectors
Vectors : 31 numeric
# key type level data_preview
1 :id uint16 500 [243, 397, 598, 28, 28, ... ], 60 nils
2 :session uint8 5 {2=>459, 4=>373, 1=>484, 3=>411, nil=>60}
3 :age uint8 44 [27, 28, 50, 41, 41, ... ], 86 nils
4 :age_cl double 73 [30.0, 56.0, 52.0, 72.0, 46.0, ... ], 142 nils
5 :appearance_cl uint8 11 [5, 5, 6, 5, 8, ... ], 111 nils
6 :bmi double 249 [nil, 28.9719314575195, 21.453857421875, 24.0283203125, 24.0283203125, ... ], 103 nils
7 :schooling uint8 5 {11=>72, 16=>776, 12=>164, 14=>712, nil=>63}
8 :asq_cl double 73 [900.0, 3136.0, 2704.0, 5184.0, 2116.0, ... ], 142 nils
9 :provider_second uint8 3 {1=>1630, 2=>87, nil=>70}
10 :asian_cl uint8 3 {0=>1577, nil=>126, 1=>84}
11 :black_cl uint8 3 {1=>85, 0=>1576, nil=>126}
12 :hispanic_cl uint8 3 {0=>1611, nil=>126, 1=>50}
13 :othrace_cl uint8 3 {0=>1617, 1=>44, nil=>126}
14 :reg uint8 3 {0=>754, 1=>968, nil=>65}
15 :hot uint8 3 {0=>907, 1=>807, nil=>73}
16 :massage_cl uint8 3 {0=>1076, 1=>643, nil=>68}
17 :lnw double 176 [nil, 5.52146100997925, 5.29831743240356, 5.92692613601685, 5.11599588394165, ... ], 122 nils
18 :llength double 70 [2.70805025100708, 4.78749179840088, 5.19295692443848, 3.68887948989868, 4.49980974197388, ... ], 70 nils
19 :unsafe uint8 2 {0=>885, 1=>902}
20 :asian uint8 3 {0=>1692, 1=>35, nil=>60}
... 11 more Vectors ...Rdatasets: causaldata: auto, Automobile data from Stata
RedAmber::DataFrame : 74 x 12 Vectors
Vectors : 11 numeric, 1 string
# key type level data_preview
1 :make string 74 ["AMC Concord", "AMC Pacer", "AMC Spirit", "Buick Century", "Buick Electra", ... ]
2 :price uint16 74 [4099, 4749, 3799, 4816, 7827, ... ]
3 :mpg uint8 21 [22, 17, 22, 20, 15, ... ]
4 :rep78 uint8 6 [3, 3, nil, 3, 4, ... ], 5 nils
5 :headroom double 8 [2.5, 3.0, 3.0, 4.5, 4.0, ... ]
6 :trunk uint8 18 [11, 11, 12, 16, 20, ... ]
7 :weight uint16 64 [2930, 3350, 2640, 3250, 4080, ... ]
8 :length uint8 47 [186, 173, 168, 196, 222, ... ]
9 :turn uint8 18 [40, 40, 35, 40, 43, ... ]
10 :displacement uint16 31 [121, 258, 121, 196, 350, ... ]
11 :gear_ratio double 36 [3.57999992370605, 2.52999997138977, 3.07999992370605, 2.9300000667572, 2.41000008583069, ... ]
12 :foreign uint8 2 {0=>52, 1=>22}Rdatasets: causaldata: close_college, Data from Card (1995) to estimate the effect of college education on earnings
RedAmber::DataFrame : 3010 x 8 Vectors
Vectors : 8 numeric
# key type level data_preview
1 :nearc4 uint8 2 {0=>957, 1=>2053}
2 :educ uint8 18 [7, 12, 12, 11, 12, ... ]
3 :black uint8 2 {1=>703, 0=>2307}
4 :smsa uint8 2 {1=>2146, 0=>864}
5 :south uint8 2 {0=>1795, 1=>1215}
6 :married uint8 7 [1, 1, 1, 1, 1, ... ], 7 nils
7 :exper uint8 24 [16, 9, 16, 10, 16, ... ]
8 :lwage double 755 [6.30627489089966, 6.17586708068848, 6.58063888549805, 5.52146100997925, 6.59167385101318, ... ]Rdatasets: causaldata: nhefs, National Health and Nutrition Examination Survey Data I Epidemiologic Follow-up Study
RedAmber::DataFrame : 1629 x 67 Vectors
Vectors : 67 numeric
# key type level data_preview
1 :seqn uint16 1629 [233, 235, 244, 245, 252, ... ]
2 :qsmk uint8 2 {0=>1201, 1=>428}
3 :death uint8 2 {0=>1311, 1=>318}
4 :yrdth uint8 11 [nil, nil, nil, 85, nil, ... ], 1311 nils
5 :modth uint8 13 [nil, nil, nil, 2, nil, ... ], 1307 nils
6 :dadth uint8 32 [nil, nil, nil, 14, nil, ... ], 1307 nils
7 :sbp uint8 108 [175, 123, 115, 148, 118, ... ], 77 nils
8 :dbp uint8 70 [96, 80, 75, 78, 77, ... ], 81 nils
9 :sex uint8 2 {0=>799, 1=>830}
10 :age uint8 49 [42, 36, 56, 68, 40, ... ]
11 :race uint8 2 {1=>215, 0=>1414}
12 :income uint8 13 [19, 18, 15, 15, 18, ... ], 62 nils
13 :marital uint8 6 [2, 2, 3, 3, 2, ... ]
14 :school uint8 17 [7, 9, 11, 5, 11, ... ]
15 :education uint8 5 {1=>311, 2=>351, 3=>659, 5=>182, 4=>126}
16 :ht double 384 [174.1875, 159.375, 168.5, 170.1875, 181.875, ... ]
17 :wt71 double 527 [79.04, 58.63, 56.81, 59.42, 87.09, ... ]
18 :wt82 double 176 [68.94604024, 61.23496995, 66.22448602, 64.41011654, 92.07925111, ... ], 63 nils
19 :wt82_71 double 1511 [-10.09395976, 2.60496995, 9.41448602, 4.99011654, 4.98925111, ... ], 63 nils
20 :birthplace uint8 50 [47, 42, 51, 37, 42, ... ], 92 nils
... 47 more Vectors ...Rdatasets: causaldata: nhefs_complete, Complete-Data National Health and Nutrition Examination Survey Data I Epidemiologic Follow-up Study
RedAmber::DataFrame : 1566 x 67 Vectors
Vectors : 67 numeric
# key type level data_preview
1 :seqn uint16 1566 [233, 235, 244, 245, 252, ... ]
2 :qsmk uint8 2 {0=>1163, 1=>403}
3 :death uint8 2 {0=>1275, 1=>291}
4 :yrdth uint8 11 [nil, nil, nil, 85, nil, ... ], 1275 nils
5 :modth uint8 13 [nil, nil, nil, 2, nil, ... ], 1271 nils
6 :dadth uint8 32 [nil, nil, nil, 14, nil, ... ], 1271 nils
7 :sbp uint8 108 [175, 123, 115, 148, 118, ... ], 29 nils
8 :dbp uint8 70 [96, 80, 75, 78, 77, ... ], 33 nils
9 :sex uint8 2 {0=>762, 1=>804}
10 :age uint8 49 [42, 36, 56, 68, 40, ... ]
11 :race uint8 2 {1=>206, 0=>1360}
12 :income uint8 13 [19, 18, 15, 15, 18, ... ], 59 nils
13 :marital uint8 6 [2, 2, 3, 3, 2, ... ]
14 :school uint8 17 [7, 9, 11, 5, 11, ... ]
15 :education uint8 5 {1=>291, 2=>340, 3=>637, 5=>177, 4=>121}
16 :ht double 378 [174.1875, 159.375, 168.5, 170.1875, 181.875, ... ]
17 :wt71 double 514 [79.04, 58.63, 56.81, 59.42, 87.09, ... ]
18 :wt82 double 175 [68.94604024, 61.23496995, 66.22448602, 64.41011654, 92.07925111, ... ]
19 :wt82_71 double 1510 [-10.09395976, 2.60496995, 9.41448602, 4.99011654, 4.98925111, ... ]
20 :birthplace uint8 50 [47, 42, 51, 37, 42, ... ], 90 nils
... 47 more Vectors ...Rdatasets: causaldata: scorecard, Earnings and Loan Repayment in US Four-Year Colleges
RedAmber::DataFrame : 48445 x 8 Vectors
Vectors : 6 numeric, 2 strings
# key type level data_preview
1 :unitid uint32 7424 [100654, 100663, 100690, 100706, 100724, ... ]
2 :inst_name string 7231 ["Alabama A & M University", "University of Alabama at Birmingham", "Amridge University", "University of Alabama in Huntsville", "Alabama State University", ... ]
3 :state_abbr string 59 ["AL", "AL", "AL", "AL", "AL", ... ]
4 :pred_degree_awarded_ipeds uint8 3 {3=>16462, 2=>11310, 1=>20673}
5 :year uint16 8 [2007, 2007, 2007, 2007, 2007, ... ]
6 :earnings_med uint32 814 [36600, 40800, nil, 49300, 30500, ... ], 15706 nils
7 :count_not_working uint16 1245 [116, 366, 6, 122, 210, ... ], 15801 nils
8 :count_working uint32 4134 [1139, 2636, 25, 975, 1577, ... ], 14772 nilsRdatasets: causaldata: social_insure, Data from "Social Networks and the Decision to Insure"
RedAmber::DataFrame : 1410 x 13 Vectors
Vectors : 11 numeric, 2 strings
# key type level data_preview
1 :address string 166 ["beilian2", "beilian2", "beilian2", "beilian2", "beilian2", ... ]
2 :village string 44 ["beilian", "beilian", "beilian", "beilian", "beilian", ... ]
3 :takeup_survey uint8 2 {0=>756, 1=>654}
4 :age uint8 69 [62, 63, 44, 76, 52, ... ], 4 nils
5 :agpop uint8 17 [2, 5, 3, 6, 6, ... ], 6 nils
6 :ricearea_2010 double 167 [10.0, 15.0, 7.5, nil, 11.0, ... ], 9 nils
7 :disaster_prob double 22 [30.0, 100.0, 20.0, 50.0, 0.0, ... ]
8 :male uint8 3 {1=>1270, 0=>137, nil=>3}
9 :default uint8 2 {1=>683, 0=>727}
10 :intensive uint8 2 {0=>717, 1=>693}
11 :risk_averse double 6 [0.0, 0.0, 0.0, 0.60000002, 0.19999999, ... ]
12 :literacy uint8 3 {0=>287, 1=>1102, nil=>21}
13 :pre_takeup_rate double 76 [0.071428575, 0.071428575, 0.071428575, 0.071428575, 0.071428575, ... ]Rdatasets: causaldata: thornton_hiv, Data from HIV information experiment in Thornton (2008)
RedAmber::DataFrame : 4820 x 7 Vectors
Vectors : 7 numeric
# key type level data_preview
1 :villnum uint8 125 [1, 1, 1, 1, 1, ... ], 27 nils
2 :got uint8 3 {1=>2016, nil=>1926, 0=>878}
3 :distvct double 2492 [2.71892142295837, 2.83503913879395, 2.4857132434845, 2.83503913879395, 1.83713066577911, ... ]
4 :tinc double 28 [2.08031988143921, nil, 1.8911999464035, nil, 0.0945599973201752, ... ], 1919 nils
5 :any uint8 3 {1=>2222, nil=>1919, 0=>679}
6 :age uint8 71 [22, 44, 19, 30, 53, ... ], 441 nils
7 :hiv2004 int8 4 {0=>2695, nil=>1926, 1=>185, -1=>14}Rdatasets: cluster: animals, Attributes of Animals
RedAmber::DataFrame : 20 x 6 Vectors
Vectors : 6 numeric
# key type level data_preview
1 :war uint8 2 {1=>10, 2=>10}
2 :fly uint8 2 {1=>16, 2=>4}
3 :ver uint8 2 {1=>6, 2=>14}
4 :end uint8 3 {1=>12, 2=>6, nil=>2}
5 :gro uint8 3 {2=>11, 1=>6, nil=>3}
6 :hai uint8 2 {1=>11, 2=>9}Rdatasets: cluster: plantTraits, Plant Species Traits Data
RedAmber::DataFrame : 136 x 31 Vectors
Vectors : 31 numeric
# key type level data_preview
1 :pdias double 95 [96.84, 110.72, 0.06, 0.08, 1.48, ... ], 36 nils
2 :longindex double 61 [0.0, 0.0, 0.666666667, 0.488888889, 0.476190476, ... ], 25 nils
3 :durflow uint8 9 [2, 3, 3, 2, 3, ... ]
4 :height uint8 8 [7, 8, 2, 2, 2, ... ]
5 :begflow uint8 9 [5, 4, 6, 7, 5, ... ]
6 :mycor uint8 4 {2=>68, 1=>19, 0=>10, nil=>39}
7 :vegaer uint8 4 {0=>117, 2=>12, 1=>5, nil=>2}
8 :vegsout uint8 4 {0=>96, 1=>19, 2=>19, nil=>2}
9 :autopoll uint8 4 {0=>73, 1=>26, 3=>21, 2=>16}
10 :insects uint8 5 {4=>17, 0=>41, 3=>62, 2=>5, 1=>11}
11 :wind uint8 5 {0=>95, 4=>17, 3=>20, 1=>2, 2=>2}
12 :lign uint8 2 {1=>43, 0=>93}
13 :piq uint8 2 {0=>119, 1=>17}
14 :ros uint8 3 {0=>120, 1=>12, nil=>4}
15 :semiros uint8 3 {0=>97, 1=>35, nil=>4}
16 :leafy uint8 3 {1=>85, 0=>47, nil=>4}
17 :suman uint8 2 {0=>117, 1=>19}
18 :winan uint8 2 {0=>124, 1=>12}
19 :monocarp uint8 2 {0=>127, 1=>9}
20 :polycarp uint8 2 {1=>117, 0=>19}
... 11 more Vectors ...Rdatasets: cluster: votes.repub, Votes for Republican Candidate in Presidential Elections
RedAmber::DataFrame : 50 x 31 Vectors
Vectors : 31 numeric
# key type level data_preview
1 :X1856 double 21 [nil, nil, nil, nil, 18.77, ... ], 30 nils
2 :X1860 double 24 [nil, nil, nil, nil, 32.96, ... ], 27 nils
3 :X1864 double 26 [nil, nil, nil, nil, 58.63, ... ], 25 nils
4 :X1868 double 34 [51.44, nil, nil, 53.73, 50.24, ... ], 17 nils
5 :X1872 double 38 [53.19, nil, nil, 52.17, 56.38, ... ], 13 nils
6 :X1876 double 38 [40.02, nil, nil, 39.88, 50.88, ... ], 13 nils
7 :X1880 double 39 [36.98, nil, nil, 39.55, 48.92, ... ], 12 nils
8 :X1884 double 38 [38.44, nil, nil, 40.5, 52.08, ... ], 12 nils
9 :X1888 double 39 [32.28, nil, nil, 38.07, 49.95, ... ], 12 nils
10 :X1892 double 43 [3.95, nil, nil, 32.01, 43.76, ... ], 8 nils
11 :X1896 double 45 [28.13, nil, nil, 25.11, 49.13, ... ], 6 nils
12 :X1900 double 45 [34.67, nil, nil, 35.04, 54.48, ... ], 6 nils
13 :X1904 double 45 [20.65, nil, nil, 40.25, 61.9, ... ], 6 nils
14 :X1908 double 44 [24.38, nil, nil, 37.31, 55.46, ... ], 5 nils
15 :X1912 double 47 [8.26, nil, 12.74, 19.73, 0.58, ... ], 3 nils
16 :X1916 double 48 [21.97, nil, 35.37, 28.01, 46.26, ... ], 2 nils
17 :X1920 double 49 [30.98, nil, 55.41, 38.73, 66.24, ... ], 2 nils
18 :X1924 double 48 [27.01, nil, 41.26, 29.28, 57.21, ... ], 2 nils
19 :X1928 double 49 [48.49, nil, 57.57, 39.33, 64.7, ... ], 2 nils
20 :X1932 double 49 [14.15, nil, 30.53, 12.91, 37.4, ... ], 2 nils
... 11 more Vectors ...Rdatasets: COUNT: loomis, loomis
RedAmber::DataFrame : 410 x 11 Vectors
Vectors : 11 numeric
# key type level data_preview
1 :anvisits uint16 48 [nil, nil, nil, nil, nil, ... ], 26 nils
2 :gender uint8 3 {1=>245, 2=>155, nil=>10}
3 :income uint8 5 {4=>144, 2=>97, nil=>29, 1=>53, 3=>87}
4 :income1 uint8 3 {0=>328, nil=>29, 1=>53}
5 :income2 uint8 3 {0=>284, 1=>97, nil=>29}
6 :income3 uint8 3 {0=>294, nil=>29, 1=>87}
7 :income4 uint8 3 {1=>144, 0=>237, nil=>29}
8 :travel uint8 4 {nil=>43, 3=>130, 2=>142, 1=>95}
9 :travel1 uint8 3 {nil=>43, 0=>272, 1=>95}
10 :travel2 uint8 3 {nil=>43, 0=>225, 1=>142}
11 :travel3 uint8 3 {nil=>43, 1=>130, 0=>237}Rdatasets: COUNT: ships, ships
RedAmber::DataFrame : 40 x 7 Vectors
Vectors : 7 numeric
# key type level data_preview
1 :accident uint8 17 [0, 0, 3, 4, 6, ... ], 6 nils
2 :op uint8 2 {0=>20, 1=>20}
3 :"co.65.69" uint8 2 {0=>30, 1=>10}
4 :"co.70.74" uint8 2 {0=>30, 1=>10}
5 :"co.75.79" uint8 2 {0=>30, 1=>10}
6 :service uint16 34 [127, 63, 1095, 1095, 1512, ... ], 6 nils
7 :ship uint8 5 {1=>8, 2=>8, 3=>8, 4=>8, 5=>8}Rdatasets: DAAG: bomregions, Australian and Related Historical Annual Climate Data, by region
RedAmber::DataFrame : 109 x 22 Vectors
Vectors : 22 numeric
# key type level data_preview
1 :Year uint16 109 [1900, 1901, 1902, 1903, 1904, ... ]
2 :eastAVt double 86 [nil, nil, nil, nil, nil, ... ], 10 nils
3 :seAVt double 89 [nil, nil, nil, nil, nil, ... ], 10 nils
4 :southAVt double 83 [nil, nil, nil, nil, nil, ... ], 10 nils
5 :swAVt double 88 [nil, nil, nil, nil, nil, ... ], 10 nils
6 :westAVt double 88 [nil, nil, nil, nil, nil, ... ], 10 nils
7 :northAVt double 84 [nil, nil, nil, nil, nil, ... ], 10 nils
8 :mdbAVt double 86 [nil, nil, nil, nil, nil, ... ], 10 nils
9 :auAVt double 82 [nil, nil, nil, nil, nil, ... ], 10 nils
10 :eastRain double 109 [429.98, 500.12, 315.33, 694.09, 564.86, ... ]
11 :seRain double 109 [603.39, 510.89, 420.77, 628.07, 550.98, ... ]
12 :southRain double 108 [375.39, 314.01, 283.64, 420.83, 388.11, ... ]
13 :swRain double 109 [738.28, 558.98, 541.85, 729.44, 711.39, ... ]
14 :westRain double 109 [399.9, 323.07, 362.57, 377.11, 417.96, ... ]
15 :northRain double 109 [360.29, 475.92, 344.86, 601.27, 603.84, ... ]
16 :mdbRain double 109 [412.67, 364.65, 255.85, 524.88, 448.4, ... ]
17 :auRain double 109 [368.73, 401.72, 317.18, 518.59, 504.65, ... ]
18 :SOI double 109 [-5.55, 0.991666666666667, 0.458333333333333, 4.93333333333333, 4.35, ... ]
19 :co2mlo double 51 [nil, nil, nil, nil, nil, ... ], 59 nils
20 :co2law double 73 [295.8, 296.1, 296.5, 296.8, 297.2, ... ], 30 nils
... 2 more Vectors ...Rdatasets: DAAG: bomregions2011, Australian and Related Historical Annual Climate Data, by region
RedAmber::DataFrame : 112 x 22 Vectors
Vectors : 22 numeric
# key type level data_preview
1 :Year uint16 112 [1900, 1901, 1902, 1903, 1904, ... ]
2 :eastAVt double 78 [nil, nil, nil, nil, nil, ... ], 10 nils
3 :seAVt double 76 [nil, nil, nil, nil, nil, ... ], 10 nils
4 :southAVt double 75 [nil, nil, nil, nil, nil, ... ], 10 nils
5 :swAVt double 79 [nil, nil, nil, nil, nil, ... ], 10 nils
6 :westAVt double 80 [nil, nil, nil, nil, nil, ... ], 10 nils
7 :northAVt double 74 [nil, nil, nil, nil, nil, ... ], 10 nils
8 :mdbAVt double 70 [nil, nil, nil, nil, nil, ... ], 10 nils
9 :auAVt double 76 [nil, nil, nil, nil, nil, ... ], 10 nils
10 :eastRain double 112 [453.33, 530.68, 330.49, 727.58, 596.4, ... ]
11 :seRain double 112 [654.62, 568.21, 462.3, 688.05, 606.53, ... ]
12 :southRain double 111 [375.24, 322.03, 287.95, 431.21, 395.46, ... ]
13 :swRain double 112 [789.97, 582.94, 566.51, 777.12, 768.53, ... ]
14 :westRain double 112 [383.62, 301.37, 343.9, 360.95, 402.97, ... ]
15 :northRain double 112 [369.1, 478.01, 335.81, 607.08, 613.28, ... ]
16 :mdbRain double 111 [418.89, 372.09, 258.11, 534.63, 457.26, ... ]
17 :auRain double 112 [373.38, 406.65, 314.46, 526.58, 513.16, ... ]
18 :SOI double 112 [-5.55, 0.991666666666667, 0.458333333333333, 4.93333333333333, 4.35, ... ]
19 :co2mlo double 54 [nil, nil, nil, nil, nil, ... ], 59 nils
20 :co2law double 73 [295.8, 296.1, 296.5, 296.8, 297.2, ... ], 33 nils
... 2 more Vectors ...Rdatasets: DAAG: bomregions2012, Australian and Related Historical Annual Climate Data, by region
RedAmber::DataFrame : 113 x 22 Vectors
Vectors : 22 numeric
# key type level data_preview
1 :Year uint16 113 [1900, 1901, 1902, 1903, 1904, ... ]
2 :eastAVt double 74 [nil, nil, nil, nil, nil, ... ], 10 nils
3 :seAVt double 75 [nil, nil, nil, nil, nil, ... ], 10 nils
4 :southAVt double 81 [nil, nil, nil, nil, nil, ... ], 10 nils
5 :swAVt double 81 [nil, nil, nil, nil, nil, ... ], 10 nils
6 :westAVt double 72 [nil, nil, nil, nil, nil, ... ], 10 nils
7 :northAVt double 82 [nil, nil, nil, nil, nil, ... ], 10 nils
8 :mdbAVt double 85 [nil, nil, nil, nil, nil, ... ], 10 nils
9 :auAVt double 77 [nil, nil, nil, nil, nil, ... ], 10 nils
10 :eastRain double 113 [453.33, 530.68, 330.49, 727.58, 596.4, ... ]
11 :seRain double 113 [654.62, 568.21, 462.3, 688.05, 606.53, ... ]
12 :southRain double 112 [375.24, 322.03, 287.95, 431.21, 395.46, ... ]
13 :swRain double 113 [789.97, 582.94, 566.51, 777.12, 768.53, ... ]
14 :westRain double 113 [383.62, 301.37, 343.9, 360.95, 402.97, ... ]
15 :northRain double 113 [369.1, 478.01, 335.81, 607.08, 613.28, ... ]
16 :mdbRain double 112 [418.89, 372.09, 258.11, 534.63, 457.26, ... ]
17 :auRain double 113 [373.38, 406.65, 314.46, 526.58, 513.16, ... ]
18 :SOI double 113 [-5.55, 0.991666666666667, 0.458333333333333, 4.93333333333333, 4.35, ... ]
19 :co2mlo double 55 [nil, nil, nil, nil, nil, ... ], 59 nils
20 :co2law double 73 [295.8, 296.1, 296.5, 296.8, 297.2, ... ], 34 nils
... 2 more Vectors ...Rdatasets: DAAG: cuckoohosts, Comparison of cuckoo eggs with host eggs
RedAmber::DataFrame : 10 x 12 Vectors
Vectors : 12 numeric
# key type level data_preview
1 :clength double 8 [22.3, 23.1, 22.5, 22.6, 23.1, ... ]
2 :"cl.sd" double 10 [0.89, 1.01, 0.66, 0.9, 0.85, ... ]
3 :cbreadth double 6 [16.7, 16.8, 16.4, 16.6, 16.6, ... ]
4 :"cb.sd" double 9 [0.38, 0.52, 0.53, 0.45, 0.44, ... ]
5 :cnum uint8 8 [45, 14, 16, 26, 15, ... ]
6 :hlength double 8 [19.7, 20.1, 20.2, 20.75, 20.0, ... ], 3 nils
7 :"hl.sd" double 8 [1.25, 0.81, 0.86, 1.44, 0.7, ... ], 3 nils
8 :hbreadth double 8 [14.6, 14.7, 15.4, 14.67, 15.1, ... ], 3 nils
9 :"hb.sd" double 8 [0.56, 14.7, 15.4, 0.37, 0.48, ... ], 2 nils
10 :hnum uint8 7 [74, 26, 57, 16, 27, ... ], 4 nils
11 :match uint8 7 [56, 1, 7, 26, 11, ... ]
12 :nomatch uint8 9 [6, 19, 11, 3, 4, ... ]Rdatasets: DAAG: measles, Deaths in London from measles
RedAmber::DataFrame : 311 x 2 Vectors
Vectors : 2 numeric
# key type level data_preview
1 :time uint16 311 [1629, 1630, 1631, 1632, 1633, ... ]
2 :value uint16 214 [42, 2, 3, 80, 21, ... ], 62 nilsRdatasets: DAAG: nassCDS, Airbag and other influences on accident fatalities
RedAmber::DataFrame : 26217 x 15 Vectors
Vectors : 7 numeric, 8 strings
# key type level data_preview
1 :dvcat string 5 {"25-39"=>8214, "10-24"=>12848, "40-54"=>2977, "55+"=>1492, "1-9km/h"=>686}
2 :weight double 10131 [25.0689999999013, 25.0689999999013, 32.3789999997243, 495.443999998271, 25.0689999999013, ... ]
3 :dead string 2 {"alive"=>25037, "dead"=>1180}
4 :airbag string 2 {"none"=>11798, "airbag"=>14419}
5 :seatbelt string 2 {"belted"=>18573, "none"=>7644}
6 :frontal uint8 2 {1=>16866, 0=>9351}
7 :sex string 2 {"f"=>12248, "m"=>13969}
8 :ageOFocc uint8 82 [26, 72, 69, 53, 32, ... ]
9 :yearacc uint16 6 [1997, 1997, 1997, 1997, 1997, ... ]
10 :yearVeh uint16 46 [1990, 1995, 1988, 1995, 1988, ... ], 1 nil
11 :abcat string 3 {"unavail"=>11798, "deploy"=>8836, "nodeploy"=>5583}
12 :occRole string 2 {"driver"=>20601, "pass"=>5616}
13 :deploy uint8 2 {0=>17381, 1=>8836}
14 :injSeverity uint8 8 [3, 1, 4, 1, 3, ... ], 153 nils
15 :caseid string 9409 ["2:3:1", "2:3:2", "2:5:1", "2:10:1", "2:11:1", ... ]Rdatasets: DAAG: nswdemo, Labour Training Evaluation Data
RedAmber::DataFrame : 722 x 10 Vectors
Vectors : 10 numeric
# key type level data_preview
1 :trt uint8 2 {0=>425, 1=>297}
2 :age uint8 35 [23, 26, 22, 34, 18, ... ]
3 :educ uint8 14 [10, 12, 9, 9, 9, ... ]
4 :black uint8 2 {1=>578, 0=>144}
5 :hisp uint8 2 {0=>646, 1=>76}
6 :marr uint8 2 {0=>605, 1=>117}
7 :nodeg uint8 2 {1=>563, 0=>159}
8 :re74 double 116 [0.0, 0.0, 0.0, nil, 0.0, ... ], 277 nils
9 :re75 double 424 [0.0, 0.0, 0.0, 4368.413, 0.0, ... ]
10 :re78 double 524 [0.0, 12383.68, 0.0, 14051.16, 10740.08, ... ]Rdatasets: DAAG: possum, Possum Measurements
RedAmber::DataFrame : 104 x 14 Vectors
Vectors : 12 numeric, 2 strings
# key type level data_preview
1 :case uint8 104 [1, 2, 3, 4, 5, ... ]
2 :site uint8 7 [1, 1, 1, 1, 1, ... ]
3 :Pop string 2 {"Vic"=>46, "other"=>58}
4 :sex string 2 {"m"=>61, "f"=>43}
5 :age uint8 10 [8, 6, 6, 6, 2, ... ], 2 nils
6 :hdlngth double 71 [94.1, 92.5, 94.0, 93.2, 91.5, ... ]
7 :skullw double 64 [60.4, 57.6, 60.0, 57.1, 56.3, ... ]
8 :totlngth double 34 [89.0, 91.5, 95.5, 92.0, 85.5, ... ]
9 :taill double 19 [36.0, 36.5, 39.0, 38.0, 36.0, ... ]
10 :footlgth double 76 [74.5, 72.5, 75.4, 76.1, 71.0, ... ], 1 nil
11 :earconch double 69 [54.5, 51.2, 51.9, 52.2, 53.2, ... ]
12 :eye double 35 [15.2, 16.0, 15.5, 15.2, 15.1, ... ]
13 :chest double 19 [28.0, 28.5, 30.0, 28.0, 28.5, ... ]
14 :belly double 24 [36.0, 33.0, 34.0, 34.0, 33.0, ... ]Rdatasets: DAAG: poxetc, Deaths from various causes, in London from 1629 till 1881, with gaps
RedAmber::DataFrame : 253 x 5 Vectors
Vectors : 5 numeric
# key type level data_preview
1 :fpox uint16 222 [72, 40, 58, 531, 72, ... ], 19 nils
2 :measles uint16 192 [42, 2, 3, 80, 21, ... ], 33 nils
3 :all uint32 233 [8814, 10471, 8458, 9539, 8427, ... ], 19 nils
4 :fpox2all double 232 [8.17, 3.82, 6.85, 55.66, 8.54, ... ], 19 nils
5 :measles2all double 211 [4.47, 0.19, 0.35, 8.38, 2.49, ... ], 33 nilsRdatasets: DAAG: rainforest, Rainforest Data
RedAmber::DataFrame : 65 x 7 Vectors
Vectors : 6 numeric, 1 string
# key type level data_preview
1 :dbh uint8 26 [6, 23, 20, 23, 24, ... ]
2 :wood uint16 56 [nil, 353, 208, 445, 590, ... ], 1 nil
3 :bark uint8 5 {nil=>61, 105=>1, 78=>1, 8=>1, 13=>1}
4 :root uint8 12 [6, 135, nil, nil, nil, ... ], 52 nils
5 :rootsk double 12 [0.3, 13.0, nil, nil, nil, ... ], 52 nils
6 :branch uint8 34 [nil, 35, 41, 50, nil, ... ], 22 nils
7 :species string 4 {"Acacia mabellae"=>16, "C. fraseri"=>12, "Acmena smithii"=>26, "B. myrtifolia"=>11}Rdatasets: DAAG: socsupport, Social Support Data
RedAmber::DataFrame : 95 x 20 Vectors
Vectors : 12 numeric, 8 strings
# key type level data_preview
1 :gender string 2 {"male"=>24, "female"=>71}
2 :age string 5 {"21-24"=>35, "18-20"=>44, "25-30"=>6, "40+"=>4, "31-40"=>6}
3 :country string 2 {"australia"=>85, "other"=>10}
4 :marital string 3 {"other"=>10, "single"=>76, "married"=>9}
5 :livewith string 6 ["partner", "partner", "residences", "parents", "friends", ... ]
6 :employment string 5 {"employed part-time"=>45, "parental support"=>19, "govt assistance"=>14, "employed fulltime"=>2, "other"=>15}
7 :firstyr string 2 {"other"=>71, "first year"=>24}
8 :enrolment string 3 {"full-time"=>79, "part-time"=>12, ""=>4}
9 :emotional uint8 17 [22, 21, 21, 19, 16, ... ]
10 :emotionalsat uint8 15 [23, 20, 18, 19, 19, ... ], 1 nil
11 :tangible uint8 17 [17, 12, 16, 20, 11, ... ], 1 nil
12 :tangiblesat uint8 13 [18, 10, 16, 17, 15, ... ], 3 nils
13 :affect uint8 12 [15, 10, 15, 11, 6, ... ]
14 :affectsat uint8 12 [15, 6, 15, 11, 10, ... ]
15 :psi uint8 11 [12, 9, 13, 13, 11, ... ]
16 :psisat uint8 9 [13, 6, 12, 12, 12, ... ]
17 :esupport uint8 14 [13, 12, 14, 15, 9, ... ], 1 nil
18 :psupport uint8 16 [11, 7, 13, 15, 7, ... ], 2 nils
19 :supsources uint8 13 [13, 10, 14, 15, 9, ... ], 2 nils
20 :BDI uint8 29 [5, 8, 16, 0, 9, ... ]Rdatasets: datasets: airquality, New York Air Quality Measurements
RedAmber::DataFrame : 153 x 6 Vectors
Vectors : 6 numeric
# key type level data_preview
1 :Ozone uint8 68 [41, 36, 12, 18, nil, ... ], 37 nils
2 :"Solar.R" uint16 118 [190, 118, 149, 313, nil, ... ], 7 nils
3 :Wind double 31 [7.4, 8.0, 12.6, 11.5, 14.3, ... ]
4 :Temp uint8 40 [67, 72, 74, 62, 56, ... ]
5 :Month uint8 5 {5=>31, 6=>30, 7=>31, 8=>31, 9=>30}
6 :Day uint8 31 [1, 2, 3, 4, 5, ... ]Rdatasets: datasets: presidents, Quarterly Approval Ratings of US Presidents
RedAmber::DataFrame : 120 x 2 Vectors
Vectors : 2 numeric
# key type level data_preview
1 :time double 120 [1945.0, 1945.25, 1945.5, 1945.75, 1946.0, ... ]
2 :value uint8 50 [nil, 87, 82, 75, 63, ... ], 6 nilsRdatasets: dplyr: starwars, Starwars characters
RedAmber::DataFrame : 87 x 11 Vectors
Vectors : 3 numeric, 8 strings
# key type level data_preview
1 :name string 87 ["Luke Skywalker", "C-3PO", "R2-D2", "Darth Vader", "Leia Organa", ... ]
2 :height uint16 46 [172, 167, 96, 202, 150, ... ], 6 nils
3 :mass double 39 [77.0, 75.0, 32.0, 136.0, 49.0, ... ], 28 nils
4 :hair_color string 13 ["blond", nil, nil, "none", "brown", ... ], 5 nils
5 :skin_color string 31 ["fair", "gold", "white, blue", "white", "light", ... ]
6 :eye_color string 15 ["blue", "yellow", "red", "yellow", "brown", ... ]
7 :birth_year double 37 [19.0, 112.0, 33.0, 41.9, 19.0, ... ], 44 nils
8 :sex string 5 {"male"=>60, "none"=>6, "female"=>16, "hermaphroditic"=>1, nil=>4}
9 :gender string 3 {"masculine"=>66, "feminine"=>17, nil=>4}
10 :homeworld string 49 ["Tatooine", "Tatooine", "Naboo", "Tatooine", "Alderaan", ... ], 10 nils
11 :species string 38 ["Human", "Droid", "Droid", "Human", "Human", ... ], 4 nilsRdatasets: dplyr: storms, Storm tracks data
RedAmber::DataFrame : 10010 x 13 Vectors
Vectors : 11 numeric, 2 strings
# key type level data_preview
1 :name string 198 ["Amy", "Amy", "Amy", "Amy", "Amy", ... ]
2 :year uint16 41 [1975, 1975, 1975, 1975, 1975, ... ]
3 :month uint8 10 [6, 6, 6, 6, 6, ... ]
4 :day uint8 31 [27, 27, 27, 27, 28, ... ]
5 :hour uint8 24 [0, 6, 12, 18, 0, ... ]
6 :lat double 403 [27.5, 28.5, 29.5, 30.5, 31.5, ... ]
7 :long int8 99 [-79, -79, -79, -79, -78, ... ]
8 :status string 3 {"tropical depression"=>2545, "tropical storm"=>4374, "hurricane"=>3091}
9 :category int8 7 [-1, -1, -1, -1, -1, ... ]
10 :wind uint8 31 [25, 25, 25, 25, 25, ... ]
11 :pressure uint16 124 [1013, 1013, 1013, 1013, 1012, ... ]
12 :ts_diameter double 109 [nil, nil, nil, nil, nil, ... ], 6528 nils
13 :hu_diameter double 35 [nil, nil, nil, nil, nil, ... ], 6528 nilsRdatasets: dragracer: rpdr_contep, RuPaul's Drag Race Episode-Contestant Data
RedAmber::DataFrame : 2096 x 11 Vectors
Vectors : 8 numeric, 3 strings
# key type level data_preview
1 :season string 13 ["S01", "S01", "S01", "S01", "S01", ... ]
2 :rank uint8 16 [1, 2, 3, 4, 5, ... ], 14 nils
3 :missc uint8 3 {0=>1924, 1=>158, nil=>14}
4 :contestant string 166 ["BeBe Zahara Benet", "Nina Flowers", "Rebecca Glasscock", "Shannel", "Ongina", ... ]
5 :episode uint8 16 [1, 1, 1, 1, 1, ... ]
6 :outcome string 20 ["SAFE", "WIN", "LOW", "SAFE", "HIGH", ... ], 798 nils
7 :eliminated uint8 3 {0=>1178, 1=>120, nil=>798}
8 :participant uint8 2 {1=>1298, 0=>798}
9 :minichalw uint8 3 {0=>1232, nil=>798, 1=>66}
10 :finale uint8 2 {0=>1926, 1=>170}
11 :penultimate uint8 2 {0=>2056, 1=>40}Rdatasets: Ecdat: Accident, Ship Accidents
RedAmber::DataFrame : 40 x 5 Vectors
Vectors : 2 numeric, 3 strings
# key type level data_preview
1 :type string 5 {"A"=>8, "B"=>8, "C"=>8, "D"=>8, "E"=>8}
2 :constr string 4 {"C6064"=>9, "C6569"=>10, "C7074"=>10, "C7579"=>11}
3 :operate string 2 {"O6074"=>19, "O7579"=>21}
4 :months uint16 34 [127, 63, 1095, 1095, 1512, ... ], 6 nils
5 :acc uint8 17 [0, 0, 3, 4, 6, ... ], 6 nilsRdatasets: Ecdat: bankingCrises, Countries in Banking Crises
RedAmber::DataFrame : 211 x 71 Vectors
Vectors : 71 numeric
# key type level data_preview
1 :year uint16 211 [1800, 1801, 1802, 1803, 1804, ... ]
2 :Algeria uint8 2 {0=>208, 1=>3}
3 :Angola uint8 2 {0=>204, 1=>7}
4 :Argentina uint8 2 {0=>196, 1=>15}
5 :Australia uint8 2 {0=>202, 1=>9}
6 :Austria uint8 2 {0=>204, 1=>7}
7 :Belgium uint8 2 {0=>196, 1=>15}
8 :Bolivia uint8 2 {0=>204, 1=>7}
9 :Brazil uint8 2 {0=>195, 1=>16}
10 :Canada uint8 2 {0=>201, 1=>10}
11 :CentralAfricanRep uint8 2 {0=>192, 1=>19}
12 :Chile uint8 2 {0=>200, 1=>11}
13 :China uint8 2 {0=>185, 1=>26}
14 :Colombia uint8 2 {0=>203, 1=>8}
15 :CostaRica uint8 2 {0=>207, 1=>4}
16 :CoteDIvoire uint8 2 {0=>207, 1=>4}
17 :Denmark uint8 2 {0=>194, 1=>17}
18 :DominicanRepublic uint8 2 {0=>209, 1=>2}
19 :Ecuador uint8 2 {0=>205, 1=>6}
20 :Egypt uint8 2 {0=>201, 1=>10}
... 51 more Vectors ...Rdatasets: Ecdat: breaches, Cyber Security Breaches
RedAmber::DataFrame : 1055 x 13 Vectors
Vectors : 3 numeric, 7 strings, 3 temporal
# key type level data_preview
1 :Number uint16 1055 [0, 1, 2, 3, 4, ... ]
2 :Name_of_Covered_Entity string 967 ["Brooke Army Medical Center", "Mid America Kidney Stone Association, LLC", "Alaska Department of Health and Social Services", "Health Services for Children with Special Needs, Inc.", "L. Douglas Carlson, M.D.", ... ]
3 :State string 52 ["TX", "MO", "AK", "DC", "CA", ... ]
4 :Business_Associate_Involved string 232 ["", "", "", "", "", ... ]
5 :Individuals_Affected uint32 809 [1000, 1000, 501, 3800, 5257, ... ]
6 :Date_of_Breach string 800 ["10/16/2009", "9/22/2009", "10/12/2009", "10/9/2009", "9/27/2009", ... ]
7 :Type_of_Breach string 29 ["Theft", "Theft", "Theft", "Loss", "Theft", ... ]
8 :Location_of_Breached_Information string 41 ["Paper", "Network Server", "Other Portable Electronic Device, Other", "Laptop", "Desktop Computer", ... ]
9 :Date_Posted_or_Updated date64 43 [#<DateTime: 2014-06-30T09:00:00+09:00 ((2456839j,0s,0n),+32400s,2299161j)>, #<DateTime: 2014-05-30T09:00:00+09:00 ((2456808j,0s,0n),+32400s,2299161j)>, ... ]
10 :Summary string 142 ["A binder containing the protected health information (PHI) of up to 1,272 individuals was stolen from a staff member's vehicle. The PHI included names, telephone numbers, detailed treatment notes, and possibly social security numbers. In response to the breach, the covered entity (CE) sanctioned the workforce member and developed a new policy requiring on-call staff members to submit any information created during their shifts to the main office instead of adding it to the binder. Following OCR's investigation, the CE notified the local media about the breach.", "Five desktop computers containing unencrypted electronic protected health information (e-PHI) were stolen from the covered entity (CE). Originally, the CE reported that over 500 persons were involved, but subsequent investigation showed that about 260 persons were involved. The ePHI included demographic and financial information. The CE provided breach notification to affected individuals and HHS. Following the breach, the CE improved physical security by installing motion detectors and alarm systems security monitoring. It improved technical safeguards by installing enhanced antivirus and encryption software. As a result of OCR's investigation the CE updated its computer password policy. ", "", "A laptop was lost by an employee while in transit on public transportation. The computer contained the protected health information of 3800 individuals. The protected health information involved in the breach included names, Medicaid ID numbers, dates of birth, and primary physicians. In response to this incident, the covered entity took steps to enforce the requirements of the Privacy & Security Rules. The covered entity has installed encryption software on all employee computers, strengthened access controls including passwords, reviewed and updated security policies and procedures, and updated it risk assessment. In addition, all employees received additional security training. \n\n", "A shared Computer that was used for backup was stolen on 9/27/09 from the reception desk area of the covered entity. The Computer contained certain electronic protected health information (ePHI) of 5,257 individuals who were patients of the CE. The ePHI involved in the breach included names, dates of birth, and clinical information, but there were no social security numbers, financial information, addresses, phone numbers, or other ePHI in any of the reports on the disks or the hard drive on the stolen Computer. Following the breach, the covered entity notified all 5,257 affected individuals and the appropriate media; added technical safeguards of encryption for all ePHI stored on the USB flash drive or the CD used on the replacement computer; added physical safeguards by keeping new portable devices locked when not in use in a secure combination safe in doctor's private office or in a secure filing cabinet; and added administrative safeguards by requiring annual refresher retraining of CE staff for Privacy and Security Rules as well as requiring immediate retraining of cleaning staff in both Rules.\n\n", ... ]
11 :breach_start date64 732 [#<DateTime: 2009-10-16T09:00:00+09:00 ((2455121j,0s,0n),+32400s,2299161j)>, #<DateTime: 2009-09-22T09:00:00+09:00 ((2455097j,0s,0n),+32400s,2299161j)>, ... ]
12 :breach_end date64 122 [nil, nil, ... ]
13 :year uint16 14 [2009, 2009, 2009, 2009, 2009, ... ]Rdatasets: Ecdat: Garch, Daily Observations on Exchange Rates of the US Dollar Against Other Currencies
RedAmber::DataFrame : 1867 x 8 Vectors
Vectors : 7 numeric, 1 string
# key type level data_preview
1 :date uint32 1867 [800102, 800103, 800104, 800107, 800108, ... ]
2 :day string 5 {"wednesday"=>382, "thursday"=>374, "friday"=>376, "monday"=>355, "tuesday"=>380}
3 :dm double 1252 [0.5861, 0.5837, 0.5842, 0.5853, 0.5824, ... ]
4 :ddm double 1809 [nil, -0.00410327127264, 0.000856237743895, 0.00188114634353, -0.0049670394147, ... ], 1 nil
5 :bp double 1125 [2.249, 2.2365, 2.241, 2.2645, 2.256, ... ]
6 :cd double 929 [0.8547, 0.8552, 0.8566, 0.8538, 0.8553, ... ]
7 :dy double 1022 [0.004206, 0.004187, 0.004269, 0.004315, 0.004257, ... ]
8 :sf double 1144 [0.6365, 0.6357, 0.6355, 0.6373, 0.6329, ... ]Rdatasets: Ecdat: Hstarts, Housing Starts
RedAmber::DataFrame : 168 x 2 Vectors
Vectors : 2 numeric
# key type level data_preview
1 :hs double 167 [7.98933, 8.83961, 8.94841, 8.98907, 8.39087, ... ]
2 :hssa double 141 [nil, nil, nil, nil, nil, ... ], 24 nilsRdatasets: Ecdat: MCAS, The Massachusetts Test Score Data Set
RedAmber::DataFrame : 220 x 17 Vectors
Vectors : 15 numeric, 2 strings
# key type level data_preview
1 :code uint16 220 [1, 2, 3, 5, 7, ... ]
2 :municipa string 220 ["ABINGTON", "ACTON", "ACUSHNET", "AGAWAM", "AMESBURY", ... ]
3 :district string 220 ["Abington", "Acton", "Acushnet", "Agawam", "Amesbury", ... ]
4 :regday uint16 207 [4201, 4129, 3627, 4015, 4273, ... ]
5 :specneed double 220 [7375.68994140625, 8573.990234375, 8081.72021484375, 8181.3701171875, 7037.22021484375, ... ]
6 :bilingua uint32 47 [0, 0, 0, 0, 0, ... ]
7 :occupday uint16 40 [0, 0, 0, 0, 0, ... ]
8 :totday uint16 209 [4646, 4930, 4281, 4826, 4824, ... ]
9 :spc double 94 [16.6000003814697, 5.69999980926514, 7.5, 8.60000038146973, 6.09999990463257, ... ], 9 nils
10 :speced double 105 [14.6000003814697, 17.3999996185303, 12.1000003814697, 21.1000003814697, 16.7999992370605, ... ]
11 :lnchpct double 154 [11.8000001907349, 2.5, 14.1000003814697, 12.1000003814697, 17.3999996185303, ... ]
12 :tchratio double 83 [19.0, 22.6000003814697, 19.2999992370605, 17.8999996185303, 17.5, ... ]
13 :percap double 216 [16.379, 25.792, 14.04, 16.111, 15.423, ... ]
14 :totsc4 uint16 63 [714, 731, 704, 704, 701, ... ]
15 :totsc8 uint16 73 [691, nil, 693, 691, 699, ... ], 40 nils
16 :avgsalary double 191 [34.3600006103516, 38.0629997253418, 32.4910011291504, 33.1059989929199, 34.4365005493164, ... ], 25 nils
17 :pctel double 92 [0.0, 1.24610590934753, 0.0, 0.322580635547638, 0.0, ... ]Rdatasets: Ecdat: nuclearWeaponStates, Nations with nuclear weapons
RedAmber::DataFrame : 9 x 17 Vectors
Vectors : 12 numeric, 3 strings, 2 temporal
# key type level data_preview
1 :nation string 9 ["US", "Russia", "UK", "France", "China", ... ]
2 :ctry string 9 ["US", "RU", "GB", "FR", "CN", ... ]
3 :firstTest date64 9 [#<DateTime: 1945-07-16T09:00:00+09:00 ((2431653j,0s,0n),+32400s,2299161j)>, #<DateTime: 1949-08-29T10:00:00+10:00 ((2433158j,0s,0n),+36000s,2299161j)>, ... ]
4 :firstTestYr double 9 [1945.53698630137, 1949.65753424658, 1952.75409836066, 1960.1174863388, 1964.78961748634, ... ]
5 :yearsSinceLastFirstTest double 9 [nil, 4.12054794520554, 3.09656411408037, 7.3633879781421, 4.67213114754099, ... ], 1 nil
6 :nuclearWeapons uint16 9 [6550, 6490, 225, 300, 280, ... ]
7 :nYieldNA uint16 9 [3830, 4509, 0, 60, 46, ... ]
8 :nLowYield uint8 4 {0=>6, 92=>1, 38=>1, 78=>1}
9 :nMidYield uint8 2 {0=>8, 44=>1}
10 :nHighYield uint16 6 [2720, 1889, 225, 240, 234, ... ]
11 :popM double 9 [328.2, 146.7, 67.5, 67.0, 1427.6, ... ]
12 :popYr uint16 4 {2019=>3, 2020=>2, 2018=>3, 2017=>1}
13 :GDP_B double 9 [21439.0, 1657.0, 2744.0, 2771.0, 14140.0, ... ]
14 :GDPyr uint16 4 {2019=>5, 2020=>2, 219=>1, 2017=>1}
15 :Maddison string 9 ["USA", "SUN", "GBR", "FRA", "CHN", ... ]
16 :startNucPgm date64 9 [#<DateTime: 1942-01-19T09:00:00+09:00 ((2430379j,0s,0n),+32400s,2299161j)>, #<DateTime: 1945-08-22T09:00:00+09:00 ((2431690j,0s,0n),+32400s,2299161j)>, ... ]
17 :startNucPgmYr double 9 [1942.04931506849, 1945.63835616438, 1947.01917808219, 1956.84972677596, 1955.30684931507, ... ]Rdatasets: Ecdat: Orange, The Orange Juice Data Set
RedAmber::DataFrame : 642 x 3 Vectors
Vectors : 3 numeric
# key type level data_preview
1 :priceoj double 281 [43.3, 39.0, 39.0, 39.0, 39.0, ... ]
2 :pricefg double 373 [28.10000038, 27.89962, 27.89962, 28.0, 28.20000076, ... ]
3 :fdd uint8 21 [nil, 6, 0, 0, 0, ... ], 1 nilRdatasets: Ecdat: PSID, Panel Survey of Income Dynamics
RedAmber::DataFrame : 4856 x 8 Vectors
Vectors : 7 numeric, 1 string
# key type level data_preview
1 :intnum uint16 2929 [4, 4, 4, 4, 5, ... ]
2 :persnum uint8 49 [4, 6, 7, 173, 2, ... ]
3 :age uint8 21 [39, 35, 33, 39, 47, ... ]
4 :educatn uint8 21 [12, 12, 12, 10, 9, ... ], 1 nil
5 :earnings uint32 953 [77250, 12000, 8000, 15000, 6500, ... ]
6 :hours uint16 1278 [2940, 2040, 693, 1904, 1683, ... ]
7 :kids uint8 13 [2, 2, 1, 2, 5, ... ]
8 :married string 7 ["married", "divorced", "married", "married", "married", ... ]Rdatasets: Ecdat: RetSchool, Return to Schooling
RedAmber::DataFrame : 5225 x 17 Vectors
Vectors : 17 numeric
# key type level data_preview
1 :wage76 double 760 [nil, 1.7011, 1.5707, 1.9755, 0.9163, ... ], 2147 nils
2 :grade76 uint8 20 [nil, 7, 12, 12, 11, ... ], 1554 nils
3 :exp76 uint8 27 [nil, 16, 9, 16, 10, ... ], 1554 nils
4 :black uint8 2 {1=>1438, 0=>3787}
5 :south76 uint8 3 {nil=>1530, 0=>2230, 1=>1465}
6 :smsa76 uint8 2 {0=>2655, 1=>2570}
7 :region uint8 9 [1, 1, 1, 1, 2, ... ]
8 :smsa66 uint8 2 {1=>3448, 0=>1777}
9 :momdad14 uint8 2 {0=>1212, 1=>4013}
10 :sinmom14 uint8 2 {1=>618, 0=>4607}
11 :nodaded uint8 2 {1=>1295, 0=>3930}
12 :nomomed uint8 2 {0=>4573, 1=>652}
13 :daded double 20 [9.9372, 9.9372, 8.0, 14.0, 11.0, ... ]
14 :momed double 20 [11.0, 10.251, 8.0, 12.0, 12.0, ... ]
15 :famed uint8 9 [9, 9, 8, 2, 6, ... ]
16 :age76 uint8 11 [28, 29, 27, 34, 27, ... ]
17 :col4 uint8 2 {0=>1610, 1=>3615}Rdatasets: Ecdat: terrorism, Global Terrorism Database yearly summaries
RedAmber::DataFrame : 46 x 25 Vectors
Vectors : 23 numeric, 2 strings
# key type level data_preview
1 :year uint16 46 [1970, 1971, 1972, 1973, 1974, ... ]
2 :methodology string 4 {"PGIS"=>28, "CETIS"=>10, "ISVG"=>4, "START"=>4}
3 :method string 4 {"p"=>28, "c"=>10, "i"=>4, "s"=>4}
4 :incidents uint16 46 [651, 470, 494, 473, 580, ... ]
5 :"incidents.us" uint16 38 [468, 247, 64, 58, 94, ... ], 1 nil
6 :suicide uint16 27 [0, 0, 0, 0, 0, ... ], 1 nil
7 :"suicide.us" uint8 5 {0=>40, nil=>1, 1=>3, 4=>1, 2=>1}
8 :nkill uint16 46 [171, 173, 566, 370, 542, ... ]
9 :"nkill.us" uint16 36 [28, 15, 12, 73, 17, ... ]
10 :nwound uint16 46 [192, 82, 222, 495, 754, ... ]
11 :"nwound.us" uint16 42 [132, 48, 23, 87, 63, ... ]
12 :"pNA.nkill" double 46 [0.064516, 0.13617, 0.093117, 0.103594, 0.1, ... ], 1 nil
13 :"pNA.nkill.us" double 46 [0.379416, 0.682979, 0.880567, 0.911205, 0.841379, ... ], 1 nil
14 :"pNA.nwound" double 46 [0.079877, 0.308511, 0.668016, 0.484144, 0.394828, ... ], 1 nil
15 :"pNA.nwound.us" double 46 [0.380952, 0.682979, 0.88664, 0.911205, 0.856897, ... ], 1 nil
16 :worldPopulation double 46 [3682487.691, 3757734.668, 3833594.894, 3909722.12, 3985733.775, ... ]
17 :USpopulation double 46 [209485.807, 211357.912, 213219.515, 215092.9, 217001.865, ... ]
18 :worldDeathRate double 46 [11.966, 11.731, 11.635, 11.392, 11.285, ... ]
19 :USdeathRate double 15 [9.5, 9.3, 9.4, 9.3, 9.1, ... ]
20 :worldDeaths double 46 [44064647.710506, 44081985.390308, 44603876.59169, 44539554.39104, 44979005.650875, ... ]
... 5 more Vectors ...Rdatasets: Ecdat: USclassifiedDocuments, Official Secrecy of the United States Government
RedAmber::DataFrame : 29 x 5 Vectors
Vectors : 5 numeric
# key type level data_preview
1 :year uint16 29 [1980, 1982, 1984, 1986, 1988, ... ]
2 :OCAuthorities uint16 19 [7149, 6943, 6900, 6756, 6654, ... ], 11 nils
3 :OCActivity uint32 25 [nil, nil, nil, nil, nil, ... ], 5 nils
4 :TenYrDeclass double 15 [nil, nil, nil, nil, nil, ... ], 12 nils
5 :DerivClassActivity uint32 18 [nil, nil, nil, nil, nil, ... ], 12 nilsRdatasets: Ecdat: USGDPpresidents, US GDP per capita with presidents and wars
RedAmber::DataFrame : 262 x 12 Vectors
Vectors : 9 numeric, 3 strings
# key type level data_preview
1 :Year uint16 262 [1610, 1620, 1630, 1640, 1650, ... ]
2 :CPI double 180 [nil, nil, nil, nil, nil, ... ], 17 nils
3 :GDPdeflator double 201 [nil, nil, nil, nil, nil, ... ], 33 nils
4 :"population.K" double 248 [0.35, 2.302, 4.646, 26.634, 50.368, ... ], 15 nils
5 :realGDPperCapita double 230 [nil, nil, nil, nil, nil, ... ], 33 nils
6 :executive string 54 ["JamesI", "JamesI", "CharlesI", "CharlesI", "Cromwell", ... ]
7 :war string 11 ["", "", "", "", "", ... ]
8 :battleDeaths double 46 [nil, nil, nil, nil, nil, ... ], 17 nils
9 :battleDeathsPMP double 49 [nil, nil, nil, nil, nil, ... ], 32 nils
10 :Keynes uint8 2 {0=>221, 1=>41}
11 :unemployment double 102 [nil, nil, nil, nil, nil, ... ], 43 nils
12 :unempSource string 5 {nil=>43, "Lebergott"=>90, "Romer"=>41, "Coen"=>9, "BLS"=>79}Rdatasets: Ecdat: UStaxWords, Number of Words in US Tax Law
RedAmber::DataFrame : 7 x 10 Vectors
Vectors : 10 numeric
# key type level data_preview
1 :year uint16 7 [1955, 1965, 1975, 1985, 1995, ... ]
2 :IncomeTaxCode uint16 7 [172, 243, 395, 776, 1060, ... ], 1 nil
3 :otherTaxCode uint16 7 [237, 305, 363, 556, 731, ... ], 1 nil
4 :EntireTaxCode uint16 7 [409, 548, 758, 1332, 1791, ... ], 1 nil
5 :IncomeTaxRegulations uint16 7 [547, 1638, 2456, 3594, 4663, ... ], 1 nil
6 :otherTaxRegulations uint16 7 [440, 565, 692, 813, 1198, ... ], 1 nil
7 :EntireTaxRegulations uint16 7 [987, 2203, 3148, 4407, 5861, ... ], 1 nil
8 :IncomeTaxCodeAndRegs uint16 7 [718, 1881, 2851, 4369, 5722, ... ], 1 nil
9 :otherTaxCodeAndRegs uint16 7 [678, 870, 1055, 1370, 1930, ... ], 1 nil
10 :EntireTaxCodeAndRegs uint16 7 [1396, 2751, 3906, 5739, 7652, ... ]Rdatasets: fpp2: melsyd, Total weekly air passenger numbers on Ansett airline flights between Melbourne and Sydney, 1987-1992.
RedAmber::DataFrame : 283 x 3 Vectors
Vectors : 3 numeric
# key type level data_preview
1 :"First.Class" double 242 [1.912, 1.848, 1.856, 2.142, 2.118, ... ], 1 nil
2 :"Business.Class" double 166 [nil, nil, nil, nil, nil, ... ], 107 nils
3 :"Economy.Class" double 277 [20.167, 20.161, 19.993, 20.986, 20.497, ... ], 1 nilRdatasets: gap: meyer, Internal functions for gap
RedAmber::DataFrame : 306 x 5 Vectors
Vectors : 5 numeric
# key type level data_preview
1 :animal uint16 306 [1, 2, 3, 4, 5, ... ]
2 :sire uint8 13 [nil, nil, nil, nil, nil, ... ], 24 nils
3 :dam uint8 37 [nil, nil, nil, nil, nil, ... ], 24 nils
4 :generation uint8 2 {1=>162, 2=>144}
5 :y uint16 61 [nil, nil, nil, nil, nil, ... ], 24 nilsRdatasets: gap: mr, Internal functions for gap
RedAmber::DataFrame : 9 x 7 Vectors
Vectors : 6 numeric, 1 string
# key type level data_preview
1 :SNP string 9 ["rs188743906", "rs2289779", "rs117804300", "rs7033492", "rs10793962", ... ]
2 :"b.LIF.R" double 9 [0.6804, -0.0788, -0.2281, -0.0968, 0.2098, ... ]
3 :"SE.LIF.R" double 9 [0.1104, 0.0134, 0.039, 0.0147, 0.0212, ... ]
4 :"b.FEV1" double 9 [0.00177, 0.00104, -0.00392, -0.00585, 0.00378, ... ]
5 :"SE.FEV1" double 9 [0.0166, 0.00261, 0.00855, 0.00269, 0.00536, ... ]
6 :"b.CAD" double 9 [nil, -0.007543, 0.109372, 0.022793, -0.014567, ... ], 1 nil
7 :"SE.CAD" double 9 [nil, 0.0092258, 0.0362219, 0.0119903, 0.0138196, ... ], 1 nilRdatasets: gap: PD, Internal functions for gap
RedAmber::DataFrame : 825 x 22 Vectors
Vectors : 6 numeric, 16 strings
# key type level data_preview
1 :lab string 825 ["002-001", "008-015", "019-000", "030-001", "038-000", ... ]
2 :apoe string 5 {""=>396, "3/4"=>79, "3/3"=>299, "2/3"=>50, "4/4"=>1}
3 :rs10506151 string 4 {"2/2"=>537, ""=>70, "1/1"=>18, "1/2"=>200}
4 :rs10784486 string 4 {"1/2"=>350, "2/2"=>317, ""=>70, "1/1"=>88}
5 :rs1365763 string 4 {"2/2"=>536, ""=>68, "1/2"=>199, "1/1"=>22}
6 :rs1388598 string 4 {"2/2"=>688, ""=>75, "1/2"=>59, "1/1"=>3}
7 :rs1491938 string 4 {"1/2"=>356, ""=>72, "1/1"=>141, "2/2"=>256}
8 :rs1491941 string 4 {"1/2"=>319, "2/2"=>364, ""=>70, "1/1"=>72}
9 :m770 string 4 {""=>69, "1/1"=>79, "2/2"=>374, "1/2"=>303}
10 :int4 string 4 {""=>78, "1/2"=>383, "2/2"=>178, "1/1"=>186}
11 :snca string 8 ["", "", "", "", "", ... ]
12 :abc string 3 {"Control"=>235, "PD+"=>230, "PD-"=>360}
13 :diag string 3 {""=>782, "PRO"=>40, "POS"=>3}
14 :sex string 2 {"F"=>390, "M"=>435}
15 :race string 3 {""=>787, "AI"=>20, "B"=>18}
16 :aon uint8 62 [nil, nil, nil, nil, nil, ... ], 261 nils
17 :comments string 11 ["", "", "", "", "", ... ]
18 :pd uint8 2 {0=>235, 1=>590}
19 :apoe234 int8 4 {nil=>396, 1=>80, 0=>299, -1=>50}
20 :apoe2 uint8 3 {nil=>396, 0=>379, 1=>50}
... 2 more Vectors ...Rdatasets: geepack: dietox, Growth curves of pigs in a 3x3 factorial experiment
RedAmber::DataFrame : 861 x 8 Vectors
Vectors : 6 numeric, 2 strings
# key type level data_preview
1 :Pig uint16 72 [4601, 4601, 4601, 4601, 4601, ... ]
2 :Evit string 3 {"Evit000"=>276, "Evit200"=>299, "Evit100"=>286}
3 :Cu string 3 {"Cu000"=>274, "Cu035"=>300, "Cu175"=>287}
4 :Litter uint8 21 [1, 1, 1, 1, 1, ... ]
5 :Start double 54 [26.5, 26.5, 26.5, 26.5, 26.5, ... ]
6 :Weight double 545 [26.5, 27.59999, 36.5, 40.29999, 49.09998, ... ]
7 :Feed double 606 [nil, 5.200005, 17.6, 28.5, 45.200001, ... ], 72 nils
8 :Time uint8 12 [1, 2, 3, 4, 5, ... ]Rdatasets: geepack: muscatine, Data on Obesity from the Muscatine Coronary Risk Factor Study.
RedAmber::DataFrame : 14568 x 7 Vectors
Vectors : 5 numeric, 2 strings
# key type level data_preview
1 :id uint16 4856 [1, 1, 1, 2, 2, ... ]
2 :gender string 2 {"M"=>7458, "F"=>7110}
3 :base_age uint8 5 {6=>2805, 8=>3042, 10=>3075, 12=>2811, 14=>2835}
4 :age uint8 7 [6, 8, 10, 6, 8, ... ]
5 :occasion uint8 3 {1=>4856, 2=>4856, 3=>4856}
6 :obese string 3 {"yes"=>2112, "no"=>7744, nil=>4712}
7 :numobese uint8 3 {1=>2112, 0=>7744, nil=>4712}Rdatasets: ggplot2: msleep, An updated and expanded version of the mammals sleep dataset
RedAmber::DataFrame : 83 x 11 Vectors
Vectors : 6 numeric, 5 strings
# key type level data_preview
1 :name string 83 ["Cheetah", "Owl monkey", "Mountain beaver", "Greater short-tailed shrew", "Cow", ... ]
2 :genus string 77 ["Acinonyx", "Aotus", "Aplodontia", "Blarina", "Bos", ... ]
3 :vore string 5 {"carni"=>19, "omni"=>20, "herbi"=>32, nil=>7, "insecti"=>5}
4 :order string 19 ["Carnivora", "Primates", "Rodentia", "Soricomorpha", "Artiodactyla", ... ]
5 :conservation string 7 ["lc", nil, "nt", "lc", "domesticated", ... ], 29 nils
6 :sleep_total double 65 [12.1, 17.0, 14.4, 14.9, 4.0, ... ]
7 :sleep_rem double 33 [nil, 1.8, 2.4, 2.3, 0.7, ... ], 22 nils
8 :sleep_cycle double 23 [nil, nil, nil, 0.133333333, 0.666666667, ... ], 51 nils
9 :awake double 65 [11.9, 7.0, 9.6, 9.1, 20.0, ... ]
10 :brainwt double 54 [nil, 0.0155, nil, 0.00029, 0.423, ... ], 27 nils
11 :bodywt double 82 [50.0, 0.48, 1.35, 0.019, 600.0, ... ]Rdatasets: ggplot2: txhousing, Housing sales in TX
RedAmber::DataFrame : 8602 x 9 Vectors
Vectors : 8 numeric, 1 string
# key type level data_preview
1 :city string 46 ["Abilene", "Abilene", "Abilene", "Abilene", "Abilene", ... ]
2 :year uint16 16 [2000, 2000, 2000, 2000, 2000, ... ]
3 :month uint8 12 [1, 2, 3, 4, 5, ... ]
4 :sales uint16 1712 [72, 98, 130, 98, 141, ... ], 568 nils
5 :volume double 7495 [5380000.0, 6505000.0, 9285000.0, 9730000.0, 10590000.0, ... ], 568 nils
6 :median double 1538 [71400.0, 58700.0, 58100.0, 68600.0, 67300.0, ... ], 616 nils
7 :listings uint16 3703 [701, 746, 784, 785, 794, ... ], 1424 nils
8 :inventory double 296 [6.3, 6.6, 6.8, 6.9, 6.8, ... ], 1467 nils
9 :date double 187 [2000.0, 2000.08333333333, 2000.16666666667, 2000.25, 2000.33333333333, ... ]Rdatasets: gt: countrypops, Yearly populations of countries from 1960 to 2017
RedAmber::DataFrame : 12470 x 5 Vectors
Vectors : 2 numeric, 3 strings
# key type level data_preview
1 :country_name string 215 ["Aruba", "Aruba", "Aruba", "Aruba", "Aruba", ... ]
2 :country_code_2 string 215 ["AW", "AW", "AW", "AW", "AW", ... ], 58 nils
3 :country_code_3 string 215 ["ABW", "ABW", "ABW", "ABW", "ABW", ... ]
4 :year uint16 58 [1960, 1961, 1962, 1963, 1964, ... ]
5 :population uint32 12322 [54211, 55438, 56225, 56695, 57032, ... ], 107 nilsRdatasets: gt: exibble, A toy example tibble for testing with gt: exibble
RedAmber::DataFrame : 8 x 9 Vectors
Vectors : 2 numeric, 6 strings, 1 temporal
# key type level data_preview
1 :num double 8 [0.1111, 2.222, 33.33, 444.4, 5550.0, ... ], 1 nil
2 :char string 8 ["apricot", "banana", "coconut", "durian", nil, ... ], 1 nil
3 :fctr string 8 ["one", "two", "three", "four", "five", ... ]
4 :date date64 8 [#<DateTime: 2015-01-15T09:00:00+09:00 ((2457038j,0s,0n),+32400s,2299161j)>, #<DateTime: 2015-02-15T09:00:00+09:00 ((2457069j,0s,0n),+32400s,2299161j)>, ... ]
5 :time string 8 ["13:35", "14:40", "15:45", "16:50", "17:55", ... ], 1 nil
6 :datetime string 8 ["2018-01-01 02:22", "2018-02-02 14:33", "2018-03-03 03:44", "2018-04-04 15:55", "2018-05-05 04:00", ... ], 1 nil
7 :currency double 8 [49.95, 17.95, 1.39, 65100.0, 1325.81, ... ], 1 nil
8 :row string 8 ["row_1", "row_2", "row_3", "row_4", "row_5", ... ]
9 :group string 2 {"grp_a"=>4, "grp_b"=>4}Rdatasets: gt: sza, Twice hourly solar zenith angles by month & latitude
RedAmber::DataFrame : 816 x 4 Vectors
Vectors : 3 numeric, 1 string
# key type level data_preview
1 :latitude uint8 4 {20=>204, 30=>204, 40=>204, 50=>204}
2 :month string 12 ["jan", "jan", "jan", "jan", "jan", ... ]
3 :tst double 17 [256.0, 280.0, 320.0, 344.0, 384.0, ... ]
4 :sza double 414 [nil, nil, nil, nil, nil, ... ], 215 nilsRdatasets: HistData: Cavendish, Cavendish's Determinations of the Density of the Earth
RedAmber::DataFrame : 29 x 3 Vectors
Vectors : 3 numeric
# key type level data_preview
1 :density double 27 [5.5, 5.61, 4.88, 5.07, 5.26, ... ]
2 :density2 double 27 [5.5, 5.61, 5.88, 5.07, 5.26, ... ]
3 :density3 double 22 [nil, nil, nil, nil, nil, ... ], 6 nilsRdatasets: HistData: Fingerprints, Waite's data on Patterns in Fingerprints
RedAmber::DataFrame : 36 x 3 Vectors
Vectors : 3 numeric
# key type level data_preview
1 :Whorls uint8 6 [0, 1, 2, 3, 4, ... ]
2 :Loops uint8 6 [0, 0, 0, 0, 0, ... ]
3 :count uint8 22 [78, 106, 130, 125, 104, ... ], 15 nilsRdatasets: HistData: OldMaps, Latitudes and Longitudes of 39 Points in 11 Old Maps
RedAmber::DataFrame : 468 x 6 Vectors
Vectors : 5 numeric, 1 string
# key type level data_preview
1 :point uint8 39 [1, 1, 1, 1, 1, ... ]
2 :col uint8 12 [1, 2, 3, 4, 5, ... ]
3 :name string 11 ["Actual", "Coronelli", "Del'Isle", "Popple", "Belin", ... ]
4 :year uint16 10 [nil, 1688, 1703, 1733, 1744, ... ], 39 nils
5 :lat double 281 [41.68, 41.62, -41.12, -41.03, 41.28, ... ]
6 :long double 350 [82.85, 87.67, -83.03, -85.1, 82.42, ... ]Rdatasets: HistData: Snow.dates, John Snow's Map and Data on the 1854 London Cholera Outbreak
RedAmber::DataFrame : 44 x 3 Vectors
Vectors : 2 numeric, 1 temporal
# key type level data_preview
1 :date date64 44 [#<DateTime: 1854-08-19T09:00:00+09:00 ((2398450j,0s,0n),+32400s,2299161j)>, #<DateTime: 1854-08-20T09:00:00+09:00 ((2398451j,0s,0n),+32400s,2299161j)>, ... ]
2 :attacks uint8 18 [1, 1, 1, 0, 1, ... ]
3 :deaths uint8 19 [1, 0, 2, 0, 0, ... ]Rdatasets: HistData: Virginis, John F. W. Herschel's Data on the Orbit of the Twin Stars gamma _Virginis_
RedAmber::DataFrame : 18 x 6 Vectors
Vectors : 4 numeric, 2 strings
# key type level data_preview
1 :year double 18 [1718.19, 1718.2, 1720.31, 1756.0, 1780.06, ... ]
2 :posangle double 14 [160.8666667, 160.8666667, 139.1166667, 144.3666667, nil, ... ], 4 nils
3 :distance double 10 [nil, nil, 7.49, 6.5, 5.7, ... ], 9 nils
4 :weight double 6 [4.0, 4.0, 0.1, 4.0, 4.0, ... ]
5 :notes string 15 ["Pound", "Bradley", "Cassini very uncertain", "Mayer", "H. Catal. 1782", ... ]
6 :authority string 9 ["Pound", "Bradley", "Cassini", "Mayer", "H", ... ]Rdatasets: HSAUR: BtheB, Beat the Blues Data
RedAmber::DataFrame : 100 x 8 Vectors
Vectors : 5 numeric, 3 strings
# key type level data_preview
1 :drug string 2 {"No"=>56, "Yes"=>44}
2 :length string 2 {">6m"=>51, "<6m"=>49}
3 :treatment string 2 {"TAU"=>48, "BtheB"=>52}
4 :"bdi.pre" uint8 40 [29, 32, 25, 21, 26, ... ]
5 :"bdi.2m" uint8 38 [2, 16, 20, 17, 23, ... ], 3 nils
6 :"bdi.4m" uint8 34 [2, 24, nil, 16, nil, ... ], 27 nils
7 :"bdi.6m" uint8 34 [nil, 17, nil, 10, nil, ... ], 42 nils
8 :"bdi.8m" uint8 25 [nil, 20, nil, 9, nil, ... ], 48 nilsRdatasets: ISLR: Hitters, Baseball Data
RedAmber::DataFrame : 322 x 20 Vectors
Vectors : 17 numeric, 3 strings
# key type level data_preview
1 :AtBat uint16 247 [293, 315, 479, 496, 321, ... ]
2 :Hits uint8 144 [66, 81, 130, 141, 87, ... ]
3 :HmRun uint8 36 [1, 7, 18, 20, 10, ... ]
4 :Runs uint8 96 [30, 24, 66, 65, 39, ... ]
5 :RBI uint8 103 [29, 38, 72, 78, 42, ... ]
6 :Walks uint8 89 [14, 39, 76, 37, 30, ... ]
7 :Years uint8 22 [1, 14, 3, 11, 2, ... ]
8 :CAtBat uint16 314 [293, 3449, 1624, 5628, 396, ... ]
9 :CHits uint16 288 [66, 835, 457, 1575, 101, ... ]
10 :CHmRun uint16 146 [1, 69, 63, 225, 12, ... ]
11 :CRuns uint16 261 [30, 321, 224, 828, 48, ... ]
12 :CRBI uint16 262 [29, 414, 266, 838, 46, ... ]
13 :CWalks uint16 248 [14, 375, 263, 354, 33, ... ]
14 :League string 2 {"A"=>175, "N"=>147}
15 :Division string 2 {"E"=>157, "W"=>165}
16 :PutOuts uint16 232 [446, 632, 880, 200, 805, ... ]
17 :Assists uint16 161 [33, 43, 82, 11, 40, ... ]
18 :Errors uint8 29 [20, 10, 14, 3, 4, ... ]
19 :Salary double 151 [nil, 475.0, 480.0, 500.0, 91.5, ... ], 59 nils
20 :NewLeague string 2 {"A"=>176, "N"=>146}Rdatasets: KMsurv: bcdeter, data from Section 1.18
RedAmber::DataFrame : 95 x 3 Vectors
Vectors : 3 numeric
# key type level data_preview
1 :lower uint8 37 [0, 0, 0, 4, 5, ... ]
2 :upper uint8 35 [5, 7, 8, 11, 11, ... ], 37 nils
3 :treat uint8 2 {1=>46, 2=>49}Rdatasets: lmec: UTIdata, Data set for Unstructured Treatment Interruption Study
RedAmber::DataFrame : 373 x 5 Vectors
Vectors : 4 numeric, 1 string
# key type level data_preview
1 :Patid string 72 ["C1", "C1", "C1", "C1", "C1", ... ]
2 :"Days.after.TI" int16 237 [-96, 69, 73, 149, 231, ... ]
3 :Fup uint8 8 [0, 12, 1, 3, 6, ... ]
4 :RNA uint32 313 [14920, 21758, 17535, 12409, 7140, ... ], 11 nils
5 :RNAcens uint8 3 {0=>340, 2=>7, 1=>26}Rdatasets: MASS: biopsy, Biopsy Data on Breast Cancer Patients
RedAmber::DataFrame : 699 x 11 Vectors
Vectors : 10 numeric, 1 string
# key type level data_preview
1 :ID uint32 645 [1000025, 1002945, 1015425, 1016277, 1017023, ... ]
2 :V1 uint8 10 [5, 5, 3, 6, 4, ... ]
3 :V2 uint8 10 [1, 4, 1, 8, 1, ... ]
4 :V3 uint8 10 [1, 4, 1, 8, 1, ... ]
5 :V4 uint8 10 [1, 5, 1, 1, 3, ... ]
6 :V5 uint8 10 [2, 7, 2, 3, 2, ... ]
7 :V6 uint8 11 [1, 10, 2, 4, 1, ... ], 16 nils
8 :V7 uint8 10 [3, 3, 3, 3, 3, ... ]
9 :V8 uint8 10 [1, 2, 1, 7, 1, ... ]
10 :V9 uint8 9 [1, 1, 1, 1, 1, ... ]
11 :class string 2 {"benign"=>458, "malignant"=>241}Rdatasets: MASS: Pima.tr2, Diabetes in Pima Indian Women
RedAmber::DataFrame : 300 x 8 Vectors
Vectors : 7 numeric, 1 string
# key type level data_preview
1 :npreg uint8 15 [5, 7, 5, 0, 0, ... ]
2 :glu uint8 108 [86, 195, 77, 165, 107, ... ]
3 :bp uint8 39 [68, 70, 82, 76, 60, ... ], 13 nils
4 :skin uint8 46 [28, 33, 41, 43, 25, ... ], 98 nils
5 :bmi double 158 [30.2, 25.1, 35.8, 47.9, 26.4, ... ], 3 nils
6 :ped double 249 [0.364, 0.163, 0.156, 0.259, 0.133, ... ]
7 :age uint8 44 [24, 55, 35, 26, 23, ... ]
8 :type string 2 {"No"=>194, "Yes"=>106}Rdatasets: MASS: survey, Student Survey Data
RedAmber::DataFrame : 237 x 12 Vectors
Vectors : 5 numeric, 7 strings
# key type level data_preview
1 :Sex string 3 {"Female"=>118, "Male"=>118, nil=>1}
2 :"Wr.Hnd" double 61 [18.5, 19.5, 18.0, 18.8, 20.0, ... ], 1 nil
3 :"NW.Hnd" double 69 [18.0, 20.5, 13.3, 18.9, 20.0, ... ], 1 nil
4 :"W.Hnd" string 3 {"Right"=>218, "Left"=>18, nil=>1}
5 :Fold string 3 {"R on L"=>120, "L on R"=>99, "Neither"=>18}
6 :Pulse uint8 44 [92, 104, 87, nil, 35, ... ], 45 nils
7 :Clap string 4 {"Left"=>39, "Neither"=>50, "Right"=>147, nil=>1}
8 :Exer string 3 {"Some"=>98, "None"=>24, "Freq"=>115}
9 :Smoke string 5 {"Never"=>189, "Regul"=>17, "Occas"=>19, "Heavy"=>11, nil=>1}
10 :Height double 68 [173.0, 177.8, nil, 160.0, 165.0, ... ], 28 nils
11 :"M.I" string 3 {"Metric"=>141, "Imperial"=>68, nil=>28}
12 :Age double 88 [18.25, 17.583, 16.917, 20.333, 23.667, ... ]Rdatasets: mi: CHAIN, Subset of variables from the CHAIN project
RedAmber::DataFrame : 532 x 7 Vectors
Vectors : 7 numeric
# key type level data_preview
1 :log_virus double 97 [9.996476859, 0.0, nil, nil, 7.090076836, ... ], 179 nils
2 :age uint8 48 [29, 38, 47, 53, 42, ... ], 24 nils
3 :income uint8 11 [5, 5, 6, 2, 2, ... ], 38 nils
4 :healthy double 493 [34.18271255, 58.09812546, 21.87600327, 18.67593765, 54.09996414, ... ], 24 nils
5 :mental uint8 3 {1=>138, 0=>370, nil=>24}
6 :damage uint8 6 [4, 5, 1, 5, 4, ... ], 63 nils
7 :treatment uint8 4 {1=>117, 2=>160, 0=>231, nil=>24}Rdatasets: mi: nlsyV, National Longitudinal Survey of Youth Extract
RedAmber::DataFrame : 400 x 7 Vectors
Vectors : 7 numeric
# key type level data_preview
1 :"ppvtr.36" uint8 77 [105, 91, 89, 85, 66, ... ], 75 nils
2 :first uint8 2 {1=>174, 0=>226}
3 :"b.marr" uint8 3 {1=>274, 0=>114, nil=>12}
4 :income uint32 261 [21446, 12125, 13560, 24500, 3304, ... ], 82 nils
5 :momage uint8 17 [20, 22, 22, 28, 20, ... ]
6 :momed uint8 5 {2=>135, 3=>81, 1=>118, 4=>26, nil=>40}
7 :momrace uint8 4 {3=>148, 1=>55, nil=>117, 2=>80}Rdatasets: mosaicData: Gestation, Data from the Child Health and Development Studies
RedAmber::DataFrame : 1236 x 23 Vectors
Vectors : 10 numeric, 12 strings, 1 temporal
# key type level data_preview
1 :id uint16 1236 [15, 20, 58, 61, 72, ... ]
2 :pluralty string 1 {"single fetus"=>1236}
3 :outcome string 1 {"live birth"=>1236}
4 :date date64 358 [#<DateTime: 1964-11-11T09:00:00+09:00 ((2438711j,0s,0n),+32400s,2299161j)>, #<DateTime: 1965-02-07T09:00:00+09:00 ((2438799j,0s,0n),+32400s,2299161j)>, ... ]
5 :gestation uint16 107 [284, 282, 279, nil, 282, ... ], 13 nils
6 :sex string 1 {"male"=>1236}
7 :wt uint8 107 [120, 113, 128, 123, 108, ... ]
8 :parity uint8 13 [1, 2, 1, 2, 1, ... ]
9 :race string 6 ["asian", "white", "white", "white", "white", ... ], 13 nils
10 :age uint8 31 [27, 33, 28, 36, 23, ... ], 2 nils
11 :ed string 8 ["College graduate", "College graduate", "HS graduate--no other schooling", "College graduate", "College graduate", ... ], 1 nil
12 :ht uint8 20 [62, 64, 64, 69, 67, ... ], 22 nils
13 :"wt.1" uint8 106 [100, 135, 115, 190, 125, ... ], 36 nils
14 :drace string 5 {"asian"=>39, "white"=>860, "black"=>250, nil=>46, "mex"=>41}
15 :dage uint8 42 [31, 38, 32, 43, 24, ... ], 7 nils
16 :ded string 8 ["College graduate", "College graduate", "8th -12th grade - did not graduate", "HS+some college", "College graduate", ... ], 13 nils
17 :dht uint8 20 [65, 70, nil, 68, nil, ... ], 492 nils
18 :dwt uint16 86 [110, 148, nil, 197, nil, ... ], 499 nils
19 :marital string 5 {"married"=>1208, nil=>2, "legally separated"=>15, "never married"=>6, "divorced"=>5}
20 :inc string 11 ["2500-5000", "10000-12500", "5000-7500", "20000-22500", "2500-5000", ... ], 124 nils
... 3 more Vectors ...Rdatasets: mosaicData: HELPmiss, Health Evaluation and Linkage to Primary Care
RedAmber::DataFrame : 470 x 28 Vectors
Vectors : 19 numeric, 9 strings
# key type level data_preview
1 :age uint8 43 [37, 37, 26, 39, 32, ... ]
2 :anysub string 3 {"yes"=>196, nil=>216, "no"=>58}
3 :cesd uint8 58 [49, 30, 39, 15, 39, ... ]
4 :d1 uint8 22 [3, 22, 0, 2, 12, ... ]
5 :daysanysub uint16 117 [177, 2, 3, 189, 2, ... ], 218 nils
6 :dayslink uint16 183 [225, nil, 365, 343, 57, ... ], 23 nils
7 :drugrisk uint8 22 [0, 0, 20, 0, 0, ... ], 2 nils
8 :e2b uint8 13 [nil, nil, nil, 1, 1, ... ], 248 nils
9 :female uint8 2 {0=>359, 1=>111}
10 :sex string 2 {"male"=>359, "female"=>111}
11 :g1b string 2 {"yes"=>134, "no"=>336}
12 :homeless string 2 {"housed"=>251, "homeless"=>219}
13 :i1 uint8 65 [13, 56, 0, 5, 10, ... ]
14 :i2 uint8 87 [26, 62, 0, 5, 13, ... ]
15 :id uint16 470 [1, 2, 3, 4, 5, ... ]
16 :indtot uint8 39 [39, 43, 41, 28, 38, ... ], 14 nils
17 :link string 3 {"yes"=>167, nil=>23, "no"=>280}
18 :mcs double 469 [25.1119899749756, 26.6703071594238, 6.76292324066162, 43.9678802490234, 21.6757545471191, ... ], 2 nils
19 :pcs double 469 [58.413688659668, 36.0369415283203, 74.8063278198242, 61.9316787719727, 37.3455848693848, ... ], 2 nils
20 :pss_fr uint8 15 [0, 1, 13, 11, 10, ... ]
... 8 more Vectors ...Rdatasets: mosaicData: HELPrct, Health Evaluation and Linkage to Primary Care
RedAmber::DataFrame : 453 x 30 Vectors
Vectors : 21 numeric, 9 strings
# key type level data_preview
1 :age uint8 42 [37, 37, 26, 39, 32, ... ]
2 :anysubstatus uint8 3 {1=>190, nil=>207, 0=>56}
3 :anysub string 3 {"yes"=>190, nil=>207, "no"=>56}
4 :cesd uint8 58 [49, 30, 39, 15, 39, ... ]
5 :d1 uint8 21 [3, 22, 0, 2, 12, ... ]
6 :daysanysub uint16 114 [177, 2, 3, 189, 2, ... ], 209 nils
7 :dayslink uint16 181 [225, nil, 365, 343, 57, ... ], 22 nils
8 :drugrisk uint8 22 [0, 0, 20, 0, 0, ... ], 1 nil
9 :e2b uint8 13 [nil, nil, nil, 1, 1, ... ], 239 nils
10 :female uint8 2 {0=>346, 1=>107}
11 :sex string 2 {"male"=>346, "female"=>107}
12 :g1b string 2 {"yes"=>127, "no"=>326}
13 :homeless string 2 {"housed"=>244, "homeless"=>209}
14 :i1 uint8 63 [13, 56, 0, 5, 10, ... ]
15 :i2 uint8 85 [26, 62, 0, 5, 13, ... ]
16 :id uint16 453 [1, 2, 3, 4, 5, ... ]
17 :indtot uint8 38 [39, 43, 41, 28, 38, ... ]
18 :linkstatus uint8 3 {1=>163, nil=>22, 0=>268}
19 :link string 3 {"yes"=>163, nil=>22, "no"=>268}
20 :mcs double 453 [25.1119899749756, 26.6703071594238, 6.76292324066162, 43.9678802490234, 21.6757545471191, ... ]
... 10 more Vectors ...Rdatasets: mosaicData: Marriage, Marriage records
RedAmber::DataFrame : 98 x 15 Vectors
Vectors : 6 numeric, 6 strings, 3 temporal
# key type level data_preview
1 :bookpageID string 49 ["B230p539", "B230p677", "B230p766", "B230p892", "B230p994", ... ]
2 :appdate date64 49 [#<DateTime: 1996-10-29T09:00:00+09:00 ((2450386j,0s,0n),+32400s,2299161j)>, #<DateTime: 1996-11-12T09:00:00+09:00 ((2450400j,0s,0n),+32400s,2299161j)>, ... ]
3 :ceremonydate date64 49 [#<DateTime: 1996-11-09T09:00:00+09:00 ((2450397j,0s,0n),+32400s,2299161j)>, #<DateTime: 1996-11-12T09:00:00+09:00 ((2450400j,0s,0n),+32400s,2299161j)>, ... ]
4 :delay uint8 16 [11, 0, 8, 5, 5, ... ]
5 :officialTitle string 9 ["CIRCUIT JUDGE ", "MARRIAGE OFFICIAL", "MARRIAGE OFFICIAL", "MINISTER", "MINISTER", ... ]
6 :person string 2 {"Groom"=>49, "Bride"=>49}
7 :dob date64 98 [#<DateTime: 2064-04-11T09:00:00+09:00 ((2475022j,0s,0n),+32400s,2299161j)>, #<DateTime: 2064-08-06T09:00:00+09:00 ((2475139j,0s,0n),+32400s,2299161j)>, ... ]
8 :age double 97 [32.60273973, 32.29041096, 34.79178082, 40.57808219, 30.02191781, ... ]
9 :race string 4 {"White"=>74, "Hispanic"=>1, "Black"=>22, "American Indian"=>1}
10 :prevcount uint8 5 {0=>46, 1=>36, 3=>5, 2=>10, 5=>1}
11 :prevconc string 3 {nil=>48, "Divorce"=>43, "Death"=>7}
12 :hs uint8 5 {12=>83, 10=>5, 9=>4, 11=>5, 8=>1}
13 :college uint8 9 [7, 0, 3, 4, 0, ... ], 10 nils
14 :dayOfBirth uint16 88 [102, 219, 51, 141, 348, ... ]
15 :sign string 12 ["Aries", "Leo", "Pisces", "Gemini", "Saggitarius", ... ]Rdatasets: mosaicData: SnowGR, Snowfall data for Grand Rapids, MI
RedAmber::DataFrame : 119 x 15 Vectors
Vectors : 15 numeric
# key type level data_preview
1 :SeasonStart uint16 119 [1893, 1894, 1895, 1896, 1897, ... ]
2 :SeasonEnd uint16 119 [1894, 1895, 1896, 1897, 1898, ... ]
3 :Jul uint8 1 {0=>119}
4 :Aug uint8 1 {0=>119}
5 :Sep uint8 1 {0=>119}
6 :Oct double 19 [0.0, 0.0, 0.4, 0.2, 0.0, ... ]
7 :Nov double 79 [8.0, 7.5, 23.2, 8.0, 1.4, ... ]
8 :Dec double 98 [24.9, 5.3, 15.0, 8.0, 8.0, ... ]
9 :Jan double 98 [12.5, 21.5, nil, 4.9, 15.5, ... ], 1 nil
10 :Feb double 95 [6.8, 8.0, 8.5, 11.2, 29.5, ... ], 1 nil
11 :Mar double 92 [4.8, 22.5, 2.0, 12.0, 0.0, ... ], 2 nils
12 :Apr double 54 [2.0, 0.0, 0.0, 0.0, 0.0, ... ], 1 nil
13 :May double 10 [0.0, 0.0, 0.0, 0.0, 0.0, ... ], 1 nil
14 :Jun uint8 2 {0=>118, nil=>1}
15 :Total double 109 [59.0, 64.8, 49.1, 44.3, 54.4, ... ], 1 nilRdatasets: mosaicData: Weather, Weather
RedAmber::DataFrame : 3655 x 25 Vectors
Vectors : 21 numeric, 3 strings, 1 temporal
# key type level data_preview
1 :city string 5 {"Auckland"=>731, "Mumbai"=>731, "Beijing"=>731, "Chicago"=>731, "San Diego"=>731}
2 :date date64 731 [#<DateTime: 2016-01-01T09:00:00+09:00 ((2457389j,0s,0n),+32400s,2299161j)>, #<DateTime: 2016-01-02T09:00:00+09:00 ((2457390j,0s,0n),+32400s,2299161j)>, ... ]
3 :year uint16 2 {2016=>1830, 2017=>1825}
4 :month uint8 12 [1, 1, 1, 1, 1, ... ]
5 :day uint8 31 [1, 2, 3, 4, 5, ... ]
6 :high_temp uint8 92 [68, 68, 77, 73, 69, ... ]
7 :avg_temp uint8 89 [65, 66, 72, 66, 62, ... ]
8 :low_temp int8 90 [62, 64, 66, 60, 55, ... ]
9 :high_dewpt int8 92 [64, 64, 70, 66, 55, ... ]
10 :avg_dewpt int8 94 [60, 63, 67, 60, 52, ... ]
11 :low_dewpt int8 102 [55, 61, 64, 54, 48, ... ]
12 :high_humidity uint8 72 [100, 100, 100, 100, 82, ... ]
13 :avg_humidity uint8 87 [82, 94, 91, 76, 69, ... ]
14 :low_humidity uint8 88 [68, 88, 74, 53, 56, ... ]
15 :high_hg double 130 [30.15, 30.04, 29.8, 30.12, 30.21, ... ]
16 :avg_hg double 137 [30.09, 29.9, 29.73, 29.9, 30.14, ... ]
17 :low_hg double 133 [30.01, 29.8, 29.68, 29.77, 30.09, ... ]
18 :high_vis uint8 19 [6, 6, 6, 6, 6, ... ]
19 :avg_vis uint8 20 [6, 5, 6, 6, 6, ... ]
20 :low_vis uint8 20 [4, 1, 1, 6, 6, ... ]
... 5 more Vectors ...Rdatasets: multgee: arthritis, Rheumatoid Arthritis Clinical Trial
RedAmber::DataFrame : 906 x 7 Vectors
Vectors : 7 numeric
# key type level data_preview
1 :id uint16 302 [1, 1, 1, 2, 2, ... ]
2 :y uint8 6 [4, 5, 5, 4, 4, ... ], 18 nils
3 :sex uint8 2 {2=>657, 1=>249}
4 :age uint8 44 [54, 54, 54, 41, 41, ... ]
5 :trt uint8 2 {2=>459, 1=>447}
6 :baseline uint8 5 {2=>219, 3=>417, 4=>168, 5=>33, 1=>69}
7 :time uint8 3 {1=>302, 3=>302, 5=>302}Rdatasets: multgee: housing, Homeless Data
RedAmber::DataFrame : 1448 x 4 Vectors
Vectors : 4 numeric
# key type level data_preview
1 :id uint16 362 [1, 1, 1, 1, 2, ... ]
2 :y uint8 4 {1=>484, 2=>511, 0=>294, nil=>159}
3 :time uint8 4 {0=>362, 6=>362, 12=>362, 24=>362}
4 :sec uint8 2 {1=>724, 0=>724}Rdatasets: nycflights13: flights, Flights data
RedAmber::DataFrame : 336776 x 19 Vectors
Vectors : 14 numeric, 4 strings, 1 temporal
# key type level data_preview
1 :year uint16 1 {2013=>336776}
2 :month uint8 12 [1, 1, 1, 1, 1, ... ]
3 :day uint8 31 [1, 1, 1, 1, 1, ... ]
4 :dep_time uint16 1319 [517, 533, 542, 544, 554, ... ], 8255 nils
5 :sched_dep_time uint16 1021 [515, 529, 540, 545, 600, ... ]
6 :dep_delay int16 528 [2, 4, 2, -1, -6, ... ], 8255 nils
7 :arr_time uint16 1412 [830, 850, 923, 1004, 812, ... ], 8713 nils
8 :sched_arr_time uint16 1163 [819, 830, 850, 1022, 837, ... ]
9 :arr_delay int16 578 [11, 20, 33, -18, -25, ... ], 9430 nils
10 :carrier string 16 ["UA", "UA", "AA", "B6", "DL", ... ]
11 :flight uint16 3844 [1545, 1714, 1141, 725, 461, ... ]
12 :tailnum string 4044 ["N14228", "N24211", "N619AA", "N804JB", "N668DN", ... ], 2512 nils
13 :origin string 3 {"EWR"=>120835, "LGA"=>104662, "JFK"=>111279}
14 :dest string 105 ["IAH", "IAH", "MIA", "BQN", "ATL", ... ]
15 :air_time uint16 510 [227, 227, 160, 183, 116, ... ], 9430 nils
16 :distance uint16 214 [1400, 1416, 1089, 1576, 762, ... ]
17 :hour uint8 20 [5, 5, 5, 5, 6, ... ]
18 :minute uint8 60 [15, 29, 40, 45, 0, ... ]
19 :time_hour date64 6936 [#<DateTime: 2013-01-01T14:00:00+09:00 ((2456294j,18000s,0n),+32400s,2299161j)>, #<DateTime: 2013-01-01T14:00:00+09:00 ((2456294j,18000s,0n),+32400s,2299161j)>, ... ]Rdatasets: nycflights13: weather, Hourly weather data
RedAmber::DataFrame : 26115 x 15 Vectors
Vectors : 13 numeric, 1 string, 1 temporal
# key type level data_preview
1 :origin string 3 {"EWR"=>8703, "JFK"=>8706, "LGA"=>8706}
2 :year uint16 1 {2013=>26115}
3 :month uint8 12 [1, 1, 1, 1, 1, ... ]
4 :day uint8 31 [1, 1, 1, 1, 1, ... ]
5 :hour uint8 24 [1, 2, 3, 4, 5, ... ]
6 :temp double 174 [39.02, 39.02, 39.02, 39.92, 39.02, ... ], 1 nil
7 :dewp double 154 [26.06, 26.96, 28.04, 28.04, 28.04, ... ], 1 nil
8 :humid double 2500 [59.37, 61.63, 64.43, 62.21, 64.43, ... ], 1 nil
9 :wind_dir uint16 38 [270, 250, 240, 250, 260, ... ], 460 nils
10 :wind_speed double 37 [10.35702, 8.05546, 11.5078, 12.65858, 12.65858, ... ], 4 nils
11 :wind_gust double 38 [nil, nil, nil, nil, nil, ... ], 20778 nils
12 :precip double 59 [0.0, 0.0, 0.0, 0.0, 0.0, ... ]
13 :pressure double 469 [1012.0, 1012.3, 1012.5, 1012.2, 1011.9, ... ], 2729 nils
14 :visib double 20 [10.0, 10.0, 10.0, 10.0, 10.0, ... ]
15 :time_hour date64 8713 [#<DateTime: 2013-01-01T10:00:00+09:00 ((2456294j,3600s,0n),+32400s,2299161j)>, #<DateTime: 2013-01-01T11:00:00+09:00 ((2456294j,7200s,0n),+32400s,2299161j)>, ... ]Rdatasets: openintro: acs12, American Community Survey, 2012
RedAmber::DataFrame : 2000 x 13 Vectors
Vectors : 4 numeric, 9 strings
# key type level data_preview
1 :income uint32 267 [60000, 0, nil, 0, 0, ... ], 377 nils
2 :employment string 4 {"not in labor force"=>656, nil=>395, "employed"=>843, "unemployed"=>106}
3 :hrs_work uint8 56 [40, nil, nil, nil, nil, ... ], 1041 nils
4 :race string 4 {"white"=>1555, "other"=>152, "asian"=>87, "black"=>206}
5 :age uint8 95 [68, 88, 12, 17, 77, ... ]
6 :gender string 2 {"female"=>969, "male"=>1031}
7 :citizen string 2 {"yes"=>1882, "no"=>118}
8 :time_to_work uint8 45 [nil, nil, nil, nil, nil, ... ], 1217 nils
9 :lang string 3 {"english"=>1527, "other"=>368, nil=>105}
10 :married string 2 {"no"=>1167, "yes"=>833}
11 :edu string 4 {"college"=>359, "hs or lower"=>1439, "grad"=>144, nil=>58}
12 :disability string 2 {"no"=>1676, "yes"=>324}
13 :birth_qrtr string 4 {"jul thru sep"=>504, "jan thru mar"=>485, "oct thru dec"=>532, "apr thru jun"=>479}Rdatasets: openintro: ames, Housing prices in Ames, Iowa
RedAmber::DataFrame : 2930 x 82 Vectors
Vectors : 39 numeric, 43 strings
# key type level data_preview
1 :Order uint16 2930 [1, 2, 3, 4, 5, ... ]
2 :PID uint32 2930 [526301100, 526350040, 526351010, 526353030, 527105010, ... ]
3 :area uint16 1292 [1656, 896, 1329, 2110, 1629, ... ]
4 :price uint32 1032 [215000, 105000, 172000, 244000, 189900, ... ]
5 :"MS.SubClass" uint8 16 [20, 20, 20, 20, 60, ... ]
6 :"MS.Zoning" string 7 ["RL", "RH", "RL", "RL", "RL", ... ]
7 :"Lot.Frontage" uint16 129 [141, 80, 81, 93, 74, ... ], 490 nils
8 :"Lot.Area" uint32 1960 [31770, 11622, 14267, 11160, 13830, ... ]
9 :Street string 2 {"Pave"=>2918, "Grvl"=>12}
10 :Alley string 3 {nil=>2732, "Pave"=>78, "Grvl"=>120}
11 :"Lot.Shape" string 4 {"IR1"=>979, "Reg"=>1859, "IR2"=>76, "IR3"=>16}
12 :"Land.Contour" string 4 {"Lvl"=>2633, "HLS"=>120, "Bnk"=>117, "Low"=>60}
13 :Utilities string 3 {"AllPub"=>2927, "NoSewr"=>2, "NoSeWa"=>1}
14 :"Lot.Config" string 5 {"Corner"=>511, "Inside"=>2140, "CulDSac"=>180, "FR2"=>85, "FR3"=>14}
15 :"Land.Slope" string 3 {"Gtl"=>2789, "Mod"=>125, "Sev"=>16}
16 :Neighborhood string 28 ["NAmes", "NAmes", "NAmes", "NAmes", "Gilbert", ... ]
17 :"Condition.1" string 9 ["Norm", "Feedr", "Norm", "Norm", "Norm", ... ]
18 :"Condition.2" string 8 ["Norm", "Norm", "Norm", "Norm", "Norm", ... ]
19 :"Bldg.Type" string 5 {"1Fam"=>2425, "TwnhsE"=>233, "Twnhs"=>101, "Duplex"=>109, "2fmCon"=>62}
20 :"House.Style" string 8 ["1Story", "1Story", "1Story", "1Story", "2Story", ... ]
... 62 more Vectors ...Rdatasets: openintro: babies, The Child Health and Development Studies
RedAmber::DataFrame : 1236 x 8 Vectors
Vectors : 8 numeric
# key type level data_preview
1 :case uint16 1236 [1, 2, 3, 4, 5, ... ]
2 :bwt uint8 107 [120, 113, 128, 123, 108, ... ]
3 :gestation uint16 107 [284, 282, 279, nil, 282, ... ], 13 nils
4 :parity uint8 2 {0=>921, 1=>315}
5 :age uint8 31 [27, 33, 28, 36, 23, ... ], 2 nils
6 :height uint8 20 [62, 64, 64, 69, 67, ... ], 22 nils
7 :weight uint8 106 [100, 135, 115, 190, 125, ... ], 36 nils
8 :smoke uint8 3 {0=>742, 1=>484, nil=>10}Rdatasets: openintro: births, North Carolina births, 100 cases
RedAmber::DataFrame : 150 x 9 Vectors
Vectors : 6 numeric, 3 strings
# key type level data_preview
1 :f_age uint8 30 [31, 34, 36, 41, 42, ... ], 31 nils
2 :m_age uint8 27 [30, 36, 35, 40, 37, ... ]
3 :weeks uint8 16 [39, 39, 40, 40, 40, ... ]
4 :premature string 2 {"full term"=>129, "premie"=>21}
5 :visits uint8 19 [13, 5, 12, 13, nil, ... ], 1 nil
6 :gained uint8 55 [1, 35, 29, 30, 10, ... ], 2 nils
7 :weight double 68 [6.88, 7.69, 8.88, 9.0, 7.94, ... ]
8 :sex_baby string 2 {"male"=>82, "female"=>68}
9 :smoke string 2 {"smoker"=>50, "nonsmoker"=>100}Rdatasets: openintro: births14, US births
RedAmber::DataFrame : 1000 x 13 Vectors
Vectors : 6 numeric, 7 strings
# key type level data_preview
1 :fage uint8 39 [34, 36, 37, nil, 32, ... ], 114 nils
2 :mage uint8 31 [34, 31, 36, 16, 31, ... ]
3 :mature string 2 {"younger mom"=>841, "mature mom"=>159}
4 :weeks uint8 24 [37, 41, 37, 38, 36, ... ]
5 :premie string 2 {"full term"=>876, "premie"=>124}
6 :visits uint8 28 [14, 12, 10, nil, 12, ... ], 56 nils
7 :gained uint8 81 [28, 41, 28, 29, 48, ... ], 42 nils
8 :weight double 360 [6.96, 8.86, 7.51, 6.19, 6.75, ... ]
9 :lowbirthweight string 2 {"not low"=>919, "low"=>81}
10 :sex string 2 {"male"=>505, "female"=>495}
11 :habit string 3 {"nonsmoker"=>867, "smoker"=>114, nil=>19}
12 :marital string 2 {"married"=>594, "not married"=>406}
13 :whitemom string 2 {"white"=>765, "not white"=>235}Rdatasets: openintro: blizzard_salary, Blizzard Employee Voluntary Salary Info.
RedAmber::DataFrame : 466 x 9 Vectors
Vectors : 2 numeric, 7 strings
# key type level data_preview
1 :timestamp string 391 ["8/6/20 18:57", "8/6/20 18:56", "8/6/20 18:56", "7/31/20 16:50", "3/11/21 10:28", ... ]
2 :status string 2 {"Full Time Employee"=>449, "Contractor"=>17}
3 :current_title string 203 ["Consultant", "Engineer", "Engineer", "Customer Support", "Game Master", ... ], 15 nils
4 :current_salary double 295 [1.0, 1.0, 1.0, 16.34, 16.73, ... ], 56 nils
5 :salary_type string 3 {"year"=>402, "hour"=>63, "week"=>1}
6 :percent_incr double 59 [1.0, 1.0, 1.0, 1.0, nil, ... ], 44 nils
7 :other_info string 161 [nil, nil, nil, "Near smack dab in the middle of my pay band", nil, ... ], 297 nils
8 :location string 13 ["Irvine", "Irvine", "Irvine", "Austin", "Austin", ... ], 3 nils
9 :performance_rating string 5 {"High"=>127, "Successful"=>178, nil=>130, "Top"=>24, "Developing"=>7}Rdatasets: openintro: census, Random sample of 2000 U.S. Census Data
RedAmber::DataFrame : 500 x 8 Vectors
Vectors : 4 numeric, 4 strings
# key type level data_preview
1 :census_year uint16 1 {2000=>500}
2 :state_fips_code string 47 ["Florida", "Florida", "Florida", "Florida", "Florida", ... ]
3 :total_family_income uint32 366 [14550, 22800, 0, 23000, 48000, ... ], 15 nils
4 :age uint8 87 [44, 20, 20, 6, 55, ... ]
5 :sex string 2 {"Male"=>268, "Female"=>232}
6 :race_general string 8 ["Two major races", "White", "Black", "White", "White", ... ]
7 :marital_status string 6 ["Married/spouse present", "Never married/single", "Never married/single", "Never married/single", "Married/spouse present", ... ]
8 :total_personal_income int32 217 [0, 13000, 20000, nil, 36000, ... ], 108 nilsRdatasets: openintro: china, Child care hours
RedAmber::DataFrame : 9788 x 3 Vectors
Vectors : 3 numeric
# key type level data_preview
1 :gender uint8 2 {1=>4660, 2=>5128}
2 :edu uint8 8 [1, 5, 2, 2, 3, ... ], 2434 nils
3 :child_care int16 70 [-99, -99, -99, -99, -99, ... ], 8549 nilsRdatasets: openintro: cia_factbook, CIA Factbook Details on Countries
RedAmber::DataFrame : 259 x 11 Vectors
Vectors : 10 numeric, 1 string
# key type level data_preview
1 :country string 259 ["Russia", "Canada", "United States", "China", "Brazil", ... ]
2 :area double 249 [17098242.0, 9984670.0, 9826675.0, 9596960.0, 8514877.0, ... ], 2 nils
3 :birth_rate double 217 [11.87, 10.29, 13.42, 12.17, 14.72, ... ], 35 nils
4 :death_rate double 210 [13.83, 8.31, 8.15, 7.44, 6.54, ... ], 34 nils
5 :infant_mortality_rate double 219 [7.08, 4.71, 6.17, 14.79, 19.21, ... ], 35 nils
6 :internet_users double 201 [40853000.0, 26960000.0, 245000000.0, 389000000.0, 75982000.0, ... ], 46 nils
7 :life_exp_at_birth double 212 [70.16, 81.67, 79.56, 75.15, 73.28, ... ], 35 nils
8 :maternal_mortality_rate uint16 104 [34, 12, 21, 37, 56, ... ], 75 nils
9 :net_migration_rate double 177 [1.69, 5.66, 2.45, -0.32, -0.15, ... ], 37 nils
10 :population uint32 239 [142470272, 34834841, 318892103, 1355692576, 202656788, ... ], 21 nils
11 :population_growth_rate double 176 [-0.03, 0.76, 0.77, 0.44, 0.8, ... ], 26 nilsRdatasets: openintro: cle_sac, Cleveland and Sacramento
RedAmber::DataFrame : 500 x 8 Vectors
Vectors : 3 numeric, 5 strings
# key type level data_preview
1 :year uint16 1 {2000=>500}
2 :state string 2 {"California"=>233, "Ohio"=>267}
3 :city string 2 {"Sacramento"=>233, "Cleveland"=>267}
4 :age uint8 87 [56, 53, 17, 37, 40, ... ]
5 :sex string 2 {"Male"=>240, "Female"=>260}
6 :race string 3 {"Other"=>61, "White"=>383, "Black"=>56}
7 :marital_status string 6 ["Married / spouse present", "Married / spouse present", "Never married / single", "Never married / single", "Never married / single", ... ]
8 :personal_income uint32 234 [40240, 13600, 0, 49000, 38300, ... ], 113 nilsRdatasets: openintro: email_test, Data frame representing information about a collection of emails
RedAmber::DataFrame : 1252 x 21 Vectors
Vectors : 18 numeric, 2 strings, 1 temporal
# key type level data_preview
1 :spam uint8 2 {1=>141, 0=>1111}
2 :to_multiple uint8 2 {0=>1061, 1=>191}
3 :from uint8 2 {1=>1250, 0=>2}
4 :cc uint8 19 [0, 0, 0, 0, 0, ... ]
5 :sent_email uint8 3 {0=>165, nil=>740, 1=>347}
6 :time date64 1213 [#<DateTime: 2012-04-01T13:01:37+09:00 ((2456019j,14497s,0n),+32400s,2299161j)>, #<DateTime: 2012-04-02T04:02:05+09:00 ((2456019j,68525s,0n),+32400s,2299161j)>, ... ]
7 :image uint8 7 [0, 0, 0, 0, 0, ... ]
8 :attach uint8 9 [0, 0, 0, 0, 0, ... ]
9 :dollar uint8 26 [0, 0, 0, 0, 0, ... ]
10 :winner string 2 {"no"=>1223, "yes"=>29}
11 :inherit uint8 3 {0=>1215, 1=>35, 2=>2}
12 :viagra uint8 3 {0=>1250, 2=>1, 1=>1}
13 :password uint8 9 [0, 0, 0, 0, 0, ... ]
14 :num_char double 1180 [0.98, 0.961, 1.063, 0.619, 0.759, ... ]
15 :line_breaks uint16 479 [15, 15, 15, 10, 10, ... ]
16 :format uint8 2 {1=>944, 0=>308}
17 :re_subj uint8 2 {0=>959, 1=>293}
18 :exclaim_subj uint8 2 {0=>1132, 1=>120}
19 :urgent_subj uint8 2 {0=>1246, 1=>6}
20 :exclaim_mess uint16 51 [2, 1, 1, 1, 1, ... ]
... 1 more Vector ...Rdatasets: openintro: exclusive_relationship, Number of Exclusive Relationships
RedAmber::DataFrame : 218 x 1 Vector
Vector : 1 numeric
# key type level data_preview
1 :num uint8 11 [2, 4, 1, 4, nil, ... ], 15 nilsRdatasets: openintro: fastfood, Nutrition in fast food
RedAmber::DataFrame : 515 x 17 Vectors
Vectors : 14 numeric, 3 strings
# key type level data_preview
1 :restaurant string 8 ["Mcdonalds", "Mcdonalds", "Mcdonalds", "Mcdonalds", "Mcdonalds", ... ]
2 :item string 505 ["Artisan Grilled Chicken Sandwich", "Single Bacon Smokehouse Burger", "Double Bacon Smokehouse Burger", "Grilled Bacon Smokehouse Chicken Sandwich", "Crispy Bacon Smokehouse Chicken Sandwich", ... ]
3 :calories uint16 113 [380, 840, 1130, 750, 920, ... ]
4 :cal_fat uint16 117 [60, 410, 600, 280, 410, ... ]
5 :total_fat uint8 80 [7, 45, 67, 31, 45, ... ]
6 :sat_fat double 40 [2.0, 17.0, 27.0, 10.0, 12.0, ... ]
7 :trans_fat double 10 [0.0, 1.5, 3.0, 0.5, 0.5, ... ]
8 :cholesterol uint16 52 [95, 130, 220, 155, 120, ... ]
9 :sodium uint16 197 [1110, 1580, 1920, 1940, 1980, ... ]
10 :total_carb uint8 103 [44, 62, 63, 62, 81, ... ]
11 :fiber uint8 19 [3, 2, 3, 2, 4, ... ], 12 nils
12 :sugar uint8 31 [11, 18, 18, 18, 18, ... ]
13 :protein uint8 71 [37, 46, 70, 55, 46, ... ], 1 nil
14 :vit_a uint8 22 [4, 6, 10, 6, 6, ... ], 214 nils
15 :vit_c uint16 24 [20, 20, 20, 25, 20, ... ], 210 nils
16 :calcium uint16 27 [20, 20, 50, 20, 20, ... ], 210 nils
17 :salad string 1 {"Other"=>515}Rdatasets: openintro: get_it_dunn_run, Get it Dunn Run, Race Times
RedAmber::DataFrame : 978 x 10 Vectors
Vectors : 3 numeric, 6 strings, 1 temporal
# key type level data_preview
1 :date date64 2 [#<DateTime: 2017-05-13T09:00:00+09:00 ((2457887j,0s,0n),+32400s,2299161j)>, #<DateTime: 2017-05-13T09:00:00+09:00 ((2457887j,0s,0n),+32400s,2299161j)>, ... ]
2 :race string 2 {"5k"=>776, "half-marathon"=>202}
3 :bib_num uint16 964 [1690, 1691, 1692, 1878, 1906, ... ]
4 :first_name string 519 ["Jeff", "Julie", "Aimothy", "Ashley", "Bob", ... ]
5 :last_initial string 37 ["A", "A", "A", "A", "A", ... ]
6 :sex string 2 {"M"=>393, "F"=>585}
7 :age uint8 76 [58, 57, 47, 32, 59, ... ], 5 nils
8 :city string 182 ["Menomonie", "Menomonie", "Menomonie", "Cadott", "Boyd", ... ]
9 :state string 14 ["WI", "WI", "WI", "WI", "WI", ... ]
10 :run_time_minutes double 970 [59.473, 59.4626666666667, 36.5303333333333, 33.2705, 56.6825, ... ]Rdatasets: openintro: gss2010, 2010 General Social Survey
RedAmber::DataFrame : 2044 x 5 Vectors
Vectors : 3 numeric, 2 strings
# key type level data_preview
1 :hrsrelax uint8 19 [2, 4, nil, nil, nil, ... ], 890 nils
2 :mntlhlth uint8 23 [3, 6, nil, nil, nil, ... ], 893 nils
3 :hrs1 uint8 73 [55, 45, nil, nil, nil, ... ], 872 nils
4 :degree string 5 {"BACHELOR"=>375, "LT HIGH SCHOOL"=>305, "JUNIOR COLLEGE"=>145, "HIGH SCHOOL"=>1001, "GRADUATE"=>218}
5 :grass string 3 {nil=>785, "LEGAL"=>603, "NOT LEGAL"=>656}Rdatasets: openintro: heart_transplant, Heart Transplant Data
RedAmber::DataFrame : 103 x 8 Vectors
Vectors : 5 numeric, 3 strings
# key type level data_preview
1 :id uint8 99 [15, 43, 61, 75, 6, ... ]
2 :acceptyear uint8 8 [68, 70, 71, 72, 68, ... ]
3 :age uint8 35 [53, 43, 52, 52, 54, ... ]
4 :survived string 2 {"dead"=>75, "alive"=>28}
5 :survtime uint16 88 [1, 2, 2, 2, 3, ... ]
6 :prior string 2 {"no"=>91, "yes"=>12}
7 :transplant string 2 {"control"=>34, "treatment"=>69}
8 :wait uint16 41 [nil, nil, nil, nil, nil, ... ], 34 nilsRdatasets: openintro: hfi, Human Freedom Index
RedAmber::DataFrame : 1458 x 123 Vectors
Vectors : 120 numeric, 3 strings
# key type level data_preview
1 :year uint16 9 [2016, 2016, 2016, 2016, 2016, ... ]
2 :ISO_code string 162 ["ALB", "DZA", "AGO", "ARG", "ARM", ... ]
3 :countries string 162 ["Albania", "Algeria", "Angola", "Argentina", "Armenia", ... ]
4 :region string 10 ["Eastern Europe", "Middle East & North Africa", "Sub-Saharan Africa", "Latin America & the Caribbean", "Caucasus & Central Asia", ... ]
5 :pf_rol_procedural double 424 [6.661502941, nil, nil, 7.098483117, nil, ... ], 578 nils
6 :pf_rol_civil double 442 [4.547243777, nil, nil, 5.791960457, nil, ... ], 578 nils
7 :pf_rol_criminal double 454 [4.666508223, nil, nil, 4.343929507, nil, ... ], 578 nils
8 :pf_rol double 727 [5.291751647, 3.819566026, 3.451813885, 5.744791027, 5.003205353, ... ], 80 nils
9 :pf_ss_homicide double 943 [8.920429431, 9.456253595, 8.060260239, 7.62297422, 8.808749692, ... ], 80 nils
10 :pf_ss_disappearances_disap uint8 4 {10=>1048, 5=>188, 0=>133, nil=>89}
11 :pf_ss_disappearances_violent double 184 [10.0, 9.294029701, 10.0, 10.0, 10.0, ... ], 80 nils
12 :pf_ss_disappearances_organized double 8 [10.0, 5.0, 7.5, 7.5, 7.5, ... ], 179 nils
13 :pf_ss_disappearances_fatalities double 457 [10.0, 9.926119387, 10.0, 10.0, 9.316196301, ... ], 80 nils
14 :pf_ss_disappearances_injuries double 509 [10.0, 9.990149252, 10.0, 9.990877458, 9.93161963, ... ], 80 nils
15 :pf_ss_disappearances double 589 [10.0, 8.842059668, 8.5, 9.498175492, 9.349563186, ... ], 80 nils
16 :pf_ss_women_fgm double 43 [10.0, 10.0, 10.0, 10.0, 10.0, ... ], 172 nils
17 :pf_ss_women_missing double 6 [7.5, 7.5, 10.0, 10.0, 5.0, ... ], 120 nils
18 :pf_ss_women_inheritance_widows uint8 4 {5=>352, 0=>166, 10=>399, nil=>541}
19 :pf_ss_women_inheritance_daughters uint8 4 {5=>342, 0=>172, 10=>403, nil=>541}
20 :pf_ss_women_inheritance double 6 [5.0, 0.0, 5.0, 10.0, 10.0, ... ], 119 nils
... 103 more Vectors ...Rdatasets: openintro: husbands_wives, Great Britain: husband and wife pairs
RedAmber::DataFrame : 199 x 7 Vectors
Vectors : 7 numeric
# key type level data_preview
1 :age_husband uint8 44 [49, 25, 40, 52, 58, ... ]
2 :age_wife uint8 44 [43, 28, 30, 57, 52, ... ], 29 nils
3 :ht_husband uint16 121 [1809, 1841, 1659, 1779, 1616, ... ]
4 :ht_wife uint16 53 [1590, 1560, 1620, 1540, 1420, ... ]
5 :age_husb_at_marriage uint8 29 [25, 19, 38, 26, 30, ... ], 4 nils
6 :age_wife_at_marriage uint8 26 [19, 22, 28, 31, 24, ... ], 30 nils
7 :years_married uint8 40 [24, 6, 2, 26, 28, ... ], 4 nilsRdatasets: openintro: loan50, Loan data from Lending Club
RedAmber::DataFrame : 50 x 18 Vectors
Vectors : 11 numeric, 7 strings
# key type level data_preview
1 :state string 25 ["NJ", "CA", "SC", "CA", "OH", ... ]
2 :emp_length uint8 12 [3, 10, nil, 0, 4, ... ], 2 nils
3 :term uint8 2 {60=>14, 36=>36}
4 :homeownership string 3 {"rent"=>21, "mortgage"=>26, "own"=>3}
5 :annual_income double 36 [59000.0, 60000.0, 75000.0, 75000.0, 254000.0, ... ]
6 :verified_income string 3 {"Not Verified"=>21, "Verified"=>9, "Source Verified"=>20}
7 :debt_to_income double 50 [0.557525423728814, 1.30568333333333, 1.05628, 0.574346666666667, 0.238149606299213, ... ]
8 :total_credit_limit uint32 50 [95131, 51929, 301373, 59890, 422619, ... ]
9 :total_credit_utilized uint32 50 [32894, 78341, 79221, 43076, 60490, ... ]
10 :num_cc_carrying_balance uint8 12 [8, 2, 14, 10, 2, ... ]
11 :loan_purpose string 8 ["debt_consolidation", "credit_card", "debt_consolidation", "credit_card", "home_improvement", ... ]
12 :loan_amount uint16 34 [22000, 6000, 25000, 6000, 25000, ... ]
13 :grade string 5 {"B"=>19, "E"=>2, "D"=>8, "A"=>15, "C"=>6}
14 :interest_rate double 28 [10.9, 9.92, 26.3, 9.92, 9.43, ... ]
15 :public_record_bankrupt uint8 2 {0=>46, 1=>4}
16 :loan_status string 2 {"Current"=>44, "Fully Paid"=>6}
17 :has_second_income string 2 {"FALSE"=>42, "TRUE"=>8}
18 :total_income double 41 [59000.0, 60000.0, 75000.0, 75000.0, 254000.0, ... ]Rdatasets: openintro: loans_full_schema, Loan data from Lending Club
RedAmber::DataFrame : 10000 x 55 Vectors
Vectors : 42 numeric, 13 strings
# key type level data_preview
1 :emp_title string 4742 ["global config engineer ", "warehouse office clerk", "assembly", "customer service", "security supervisor ", ... ]
2 :emp_length uint8 12 [3, 10, 3, 1, 10, ... ], 817 nils
3 :state string 50 ["NJ", "HI", "WI", "PA", "CA", ... ]
4 :homeownership string 3 {"MORTGAGE"=>4789, "RENT"=>3858, "OWN"=>1353}
5 :annual_income double 1463 [90000.0, 40000.0, 40000.0, 30000.0, 35000.0, ... ]
6 :verified_income string 3 {"Verified"=>2290, "Not Verified"=>3594, "Source Verified"=>4116}
7 :debt_to_income double 3674 [18.01, 5.04, 21.15, 10.16, 57.96, ... ], 24 nils
8 :annual_income_joint double 597 [nil, nil, nil, nil, 57000.0, ... ], 8505 nils
9 :verification_income_joint string 4 {""=>8545, "Verified"=>345, "Not Verified"=>611, "Source Verified"=>499}
10 :debt_to_income_joint double 1190 [nil, nil, nil, nil, 37.66, ... ], 8505 nils
11 :delinq_2y uint8 12 [0, 0, 0, 0, 0, ... ]
12 :months_since_last_delinq uint8 98 [38, nil, 28, nil, nil, ... ], 5658 nils
13 :earliest_credit_line uint16 53 [2001, 1996, 2006, 2007, 2008, ... ]
14 :inquiries_last_12m uint8 26 [6, 1, 4, 0, 7, ... ]
15 :total_credit_lines uint8 78 [28, 30, 31, 4, 22, ... ]
16 :open_credit_lines uint8 45 [10, 14, 10, 4, 16, ... ]
17 :total_credit_limit uint32 9119 [70795, 28800, 24193, 25400, 69839, ... ]
18 :total_credit_utilized uint32 9497 [38767, 4321, 16000, 4997, 52722, ... ]
19 :num_collections_last_12m uint8 4 {0=>9873, 2=>9, 1=>117, 3=>1}
20 :num_historical_failed_to_pay uint8 9 [0, 1, 0, 1, 0, ... ]
... 35 more Vectors ...Rdatasets: openintro: mammals, Sleep in Mammals
RedAmber::DataFrame : 62 x 11 Vectors
Vectors : 10 numeric, 1 string
# key type level data_preview
1 :species string 62 ["Africanelephant", "Africangiantpouchedrat", "ArcticFox", "Arcticgroundsquirrel", "Asianelephant", ... ]
2 :body_wt double 60 [6654.0, 1.0, 3.385, 0.92, 2547.0, ... ]
3 :brain_wt double 59 [5712.0, 6.6, 44.5, 5.7, 4603.0, ... ]
4 :non_dreaming double 40 [nil, 6.3, nil, nil, 2.1, ... ], 14 nils
5 :dreaming double 31 [nil, 2.0, nil, nil, 1.8, ... ], 12 nils
6 :total_sleep double 45 [3.3, 8.3, 12.5, 16.5, 3.9, ... ], 4 nils
7 :life_span double 48 [38.6, 4.5, 14.0, nil, 69.0, ... ], 4 nils
8 :gestation double 50 [645.0, 42.0, 60.0, 25.0, 624.0, ... ], 4 nils
9 :predation uint8 5 {3=>12, 1=>14, 5=>14, 4=>7, 2=>15}
10 :exposure uint8 5 {5=>13, 1=>27, 2=>13, 4=>5, 3=>4}
11 :danger uint8 5 {3=>10, 1=>19, 4=>10, 5=>9, 2=>14}Rdatasets: openintro: mlb_teams, Major League Baseball Teams Data.
RedAmber::DataFrame : 2784 x 41 Vectors
Vectors : 33 numeric, 8 strings
# key type level data_preview
1 :year uint16 145 [1876, 1876, 1876, 1876, 1876, ... ]
2 :league_id string 2 {"NL"=>1504, "AL"=>1280}
3 :division_id string 4 {""=>1346, "W"=>565, "E"=>588, "C"=>285}
4 :rank uint8 12 [4, 1, 8, 2, 5, ... ]
5 :games_played uint8 85 [70, 66, 65, 69, 69, ... ]
6 :home_games uint8 48 [nil, nil, nil, nil, nil, ... ], 228 nils
7 :wins uint8 100 [39, 52, 9, 47, 30, ... ]
8 :losses uint8 103 [31, 14, 56, 21, 36, ... ]
9 :division_winner string 3 {""=>1374, "Y"=>260, "N"=>1150}
10 :wild_card_winner string 3 {""=>2010, "N"=>698, "Y"=>76}
11 :league_winner string 3 {"N"=>2493, "Y"=>263, ""=>28}
12 :world_series_winner string 3 {""=>248, "N"=>2416, "Y"=>120}
13 :runs_scored uint16 593 [471, 624, 238, 429, 280, ... ]
14 :at_bats uint16 1006 [2722, 2748, 2372, 2664, 2570, ... ]
15 :hits uint16 676 [723, 926, 555, 711, 641, ... ]
16 :doubles uint16 277 [96, 131, 51, 96, 68, ... ]
17 :triples uint8 122 [24, 32, 12, 22, 14, ... ]
18 :homeruns uint16 259 [9, 8, 4, 2, 6, ... ]
19 :walks uint16 530 [58, 70, 41, 39, 24, ... ]
20 :strikeouts_by_batters uint16 1062 [98, 45, 136, 78, 98, ... ], 16 nils
... 21 more Vectors ...Rdatasets: openintro: mn_police_use_of_force, Minneapolis police use of force data.
RedAmber::DataFrame : 12925 x 13 Vectors
Vectors : 2 numeric, 11 strings
# key type level data_preview
1 :response_datetime string 5310 ["2016/01/01 00:47:36", "2016/01/01 02:19:34", "2016/01/01 02:19:34", "2016/01/01 02:28:48", "2016/01/01 02:28:48", ... ]
2 :problem string 126 ["Assault in Progress ", "Fight ", "Fight ", "Fight ", "Fight ", ... ]
3 :is_911_call string 2 {"Yes"=>7004, "No"=>5921}
4 :primary_offense string 253 ["DASLT1", "DISCON", "DISCON", "PRIORI", "PRIORI", ... ]
5 :subject_injury string 3 {""=>9848, "No"=>1446, "Yes"=>1631}
6 :force_type string 11 ["Bodily Force", "Chemical Irritant", "Chemical Irritant", "Chemical Irritant", "Chemical Irritant", ... ]
7 :force_type_action string 41 ["Body Weight to Pin", "Personal Mace", "Personal Mace", "Crowd Control Mace", "Crowd Control Mace", ... ]
8 :race string 7 ["Black", "Black", "White", "Black", "Black", ... ], 1024 nils
9 :sex string 3 {"Male"=>10280, "Female"=>2144, nil=>501}
10 :age uint8 68 [20, 27, 23, 20, 20, ... ], 1066 nils
11 :type_resistance string 24 ["Tensed", "Verbal Non-Compliance", "Verbal Non-Compliance", "Commission of Crime", "Commission of Crime", ... ]
12 :precinct uint8 6 [1, 1, 1, 1, 1, ... ]
13 :neighborhood string 87 ["Downtown East", "Downtown West", "Downtown West", "Downtown West", "Downtown West", ... ]Rdatasets: openintro: mtl, Medial temporal lobe (MTL) and other data for 26 participants
RedAmber::DataFrame : 35 x 23 Vectors
Vectors : 19 numeric, 4 strings
# key type level data_preview
1 :subject uint16 35 [9690, 9722, 9735, 9787, 10010, ... ]
2 :sex string 2 {"M"=>10, "F"=>25}
3 :ethnic string 2 {"Caucasian"=>29, "Other"=>6}
4 :educ uint8 8 [14, 20, 14, 14, 18, ... ]
5 :e4grp string 2 {"Non-E4"=>20, "E4"=>15}
6 :age uint8 20 [66, 71, 66, 63, 71, ... ]
7 :mmse uint8 3 {30=>16, 29=>14, 28=>5}
8 :ham_d uint8 9 [4, 8, 2, 0, 0, ... ], 2 nils
9 :ham_a uint8 12 [9, 4, 0, 9, 1, ... ], 2 nils
10 :dig_sym uint8 29 [57, 47, 60, 64, 94, ... ], 4 nils
11 :delay_vp uint8 7 [8, 1, 3, 8, 8, ... ], 3 nils
12 :bfr_selective_reminding_delayed uint8 12 [11, nil, 2, 4, 5, ... ], 2 nils
13 :sitting uint8 13 [10, 11, 5, 7, 3, ... ]
14 :met_minwk double 32 [777.0, 1039.8, 795.0, 2400.0, 2358.0, ... ]
15 :ipa_qgrp string 2 {"Low"=>21, "High"=>14}
16 :aca1 double 35 [2.25275, 2.08825, 2.2096, 1.8206, 1.969, ... ]
17 :aca23dg double 34 [3.3639, 2.916, 3.15045, 2.8603, 3.0667, ... ]
18 :ae_cort double 35 [2.6358, 2.7773, 2.40835, 2.30295, 2.73345, ... ]
19 :a_fusi_cort double 35 [2.78055, 2.4982, 2.7616, 2.48635, 2.627, ... ]
20 :a_ph_cort double 35 [2.4611, 3.13505, 3.17635, 2.6916, 2.5617, ... ]
... 3 more Vectors ...Rdatasets: openintro: ncbirths, North Carolina births, 1000 cases
RedAmber::DataFrame : 1000 x 13 Vectors
Vectors : 6 numeric, 7 strings
# key type level data_preview
1 :fage uint8 38 [nil, nil, 19, 21, nil, ... ], 171 nils
2 :mage uint8 33 [13, 14, 15, 15, 15, ... ]
3 :mature string 2 {"younger mom"=>867, "mature mom"=>133}
4 :weeks uint8 24 [39, 42, 37, 41, 39, ... ], 2 nils
5 :premie string 3 {"full term"=>846, "premie"=>152, nil=>2}
6 :visits uint8 27 [10, 15, 11, 6, 9, ... ], 9 nils
7 :marital string 3 {"not married"=>386, "married"=>613, nil=>1}
8 :gained uint8 72 [38, 20, 38, 34, 27, ... ], 27 nils
9 :weight double 126 [7.63, 7.88, 6.63, 8.0, 6.38, ... ]
10 :lowbirthweight string 2 {"not low"=>889, "low"=>111}
11 :gender string 2 {"male"=>497, "female"=>503}
12 :habit string 3 {"nonsmoker"=>873, "smoker"=>126, nil=>1}
13 :whitemom string 3 {"not white"=>284, "white"=>714, nil=>2}Rdatasets: openintro: piracy, Piracy and PIPA/SOPA
RedAmber::DataFrame : 534 x 8 Vectors
Vectors : 3 numeric, 5 strings
# key type level data_preview
1 :name string 533 ["Ackerman, Gary", "Adams, Sandra", "Aderholt, Robert", "Akin, Todd", "Alexander, Rodney", ... ]
2 :party string 3 {" D"=>243, " R"=>289, " I"=>2}
3 :state string 50 ["NY", "FL", "AL", "MO", "LA", ... ]
4 :money_pro int32 327 [13350, 3500, 4779, 2500, 3500, ... ], 14 nils
5 :money_con int32 351 [14800, 5650, 23944, 8200, 2700, ... ], 35 nils
6 :years uint8 43 [30, 2, 16, 12, 10, ... ]
7 :stance string 5 {"unknown"=>294, "no"=>122, "yes"=>63, "leaning no"=>44, "undecided"=>11}
8 :chamber string 2 {"house"=>434, "senate"=>100}Rdatasets: openintro: pm25_2011_durham, Air quality for Durham, NC
RedAmber::DataFrame : 449 x 20 Vectors
Vectors : 12 numeric, 8 strings
# key type level data_preview
1 :date string 360 ["1/3/11", "1/6/11", "1/9/11", "1/10/11", "1/18/11", ... ]
2 :aqs_site_id string 1 {"37-063-0015"=>449}
3 :poc uint8 2 {1=>91, 3=>358}
4 :daily_mean_pm2_5_concentration double 410 [5.9, 10.4, 5.6, 6.2, 9.4, ... ]
5 :units string 1 {"ug/m3 LC"=>449}
6 :daily_aqi_value uint8 45 [19, 34, 18, 20, 31, ... ], 358 nils
7 :daily_obs_count uint8 7 [1, 1, 1, 1, 1, ... ]
8 :percent_complete uint8 6 [100, 100, 100, 100, 100, ... ]
9 :aqs_parameter_code uint32 2 {88101=>91, 88502=>358}
10 :aqs_parameter_desc string 2 {"PM2.5 - Local Conditions"=>91, "Acceptable PM2.5 AQI & Speciation Mass"=>358}
11 :csa_code uint16 1 {450=>449}
12 :csa_name string 1 {"Raleigh-Durham-Cary, NC"=>449}
13 :cbsa_code uint16 1 {20500=>449}
14 :cbsa_name string 1 {"Durham, NC"=>449}
15 :state_code uint8 1 {37=>449}
16 :state string 1 {"North Carolina"=>449}
17 :county_code uint8 1 {63=>449}
18 :county string 1 {"Durham"=>449}
19 :site_latitude double 1 {36.032944=>449}
20 :site_longitude double 1 {-78.905417=>449}Rdatasets: openintro: possum, Possums in Australia and New Guinea
RedAmber::DataFrame : 104 x 8 Vectors
Vectors : 6 numeric, 2 strings
# key type level data_preview
1 :site uint8 7 [1, 1, 1, 1, 1, ... ]
2 :pop string 2 {"Vic"=>46, "other"=>58}
3 :sex string 2 {"m"=>61, "f"=>43}
4 :age uint8 10 [8, 6, 6, 6, 2, ... ], 2 nils
5 :head_l double 71 [94.1, 92.5, 94.0, 93.2, 91.5, ... ]
6 :skull_w double 64 [60.4, 57.6, 60.0, 57.1, 56.3, ... ]
7 :total_l double 34 [89.0, 91.5, 95.5, 92.0, 85.5, ... ]
8 :tail_l double 19 [36.0, 36.5, 39.0, 38.0, 36.0, ... ]Rdatasets: openintro: president, United States Presidental History
RedAmber::DataFrame : 67 x 5 Vectors
Vectors : 2 numeric, 3 strings
# key type level data_preview
1 :potus string 43 ["George Washington", "John Adams", "Thomas Jefferson", "Thomas Jefferson", "James Madison", ... ]
2 :party string 8 [" ", "Federalist", "Democratic-Republican", "Democratic-Republican", "Democratic-Republican", ... ]
3 :start uint16 56 [1789, 1797, 1801, 1805, 1809, ... ]
4 :end uint16 57 [1797, 1801, 1805, 1809, 1812, ... ], 1 nil
5 :vpotus string 49 ["John Adams", "Thomas Jefferson", "Aaron Burr", "George Clinton", "George Clinton", ... ]Rdatasets: openintro: prius_mpg, User reported fuel efficiency for 2017 Toyota Prius Prime
RedAmber::DataFrame : 19 x 5 Vectors
Vectors : 3 numeric, 1 string, 1 temporal
# key type level data_preview
1 :average_mpg double 19 [128.6, 202.6, 98.4, 241.1, 148.9, ... ]
2 :state string 13 ["NV", "CA", "AL", "FL", "CA", ... ]
3 :stop_and_go double 14 [nil, 0.48, 0.33, nil, 0.8, ... ], 4 nils
4 :highway double 14 [nil, 0.52, 0.67, nil, 0.2, ... ], 4 nils
5 :last_updated date64 19 [#<DateTime: 2019-02-20T09:00:00+09:00 ((2458535j,0s,0n),+32400s,2299161j)>, #<DateTime: 2019-02-16T09:00:00+09:00 ((2458531j,0s,0n),+32400s,2299161j)>, ... ]Rdatasets: openintro: smoking, UK Smoking Data
RedAmber::DataFrame : 1691 x 12 Vectors
Vectors : 3 numeric, 9 strings
# key type level data_preview
1 :gender string 2 {"Male"=>726, "Female"=>965}
2 :age uint8 79 [38, 42, 40, 40, 39, ... ]
3 :marital_status string 5 {"Divorced"=>161, "Single"=>427, "Married"=>812, "Widowed"=>223, "Separated"=>68}
4 :highest_qualification string 8 ["No Qualification", "No Qualification", "Degree", "Degree", "GCSE/O Level", ... ]
5 :nationality string 8 ["British", "British", "English", "English", "British", ... ]
6 :ethnicity string 7 ["White", "White", "White", "White", "White", ... ]
7 :gross_income string 10 ["2,600 to 5,200", "Under 2,600", "28,600 to 36,400", "10,400 to 15,600", "2,600 to 5,200", ... ]
8 :region string 7 ["The North", "The North", "The North", "The North", "The North", ... ]
9 :smoke string 2 {"No"=>1270, "Yes"=>421}
10 :amt_weekends uint8 25 [nil, 12, nil, nil, nil, ... ], 1270 nils
11 :amt_weekdays uint8 25 [nil, 12, nil, nil, nil, ... ], 1270 nils
12 :type string 5 {""=>1270, "Packets"=>297, "Hand-Rolled"=>72, "Both/Mainly Packets"=>42, "Both/Mainly Hand-Rolled"=>10}Rdatasets: openintro: snowfall, Snowfall at Paradise, Mt. Rainier National Park
RedAmber::DataFrame : 100 x 3 Vectors
Vectors : 3 numeric
# key type level data_preview
1 :year_start uint16 100 [1920, 1921, 1922, 1923, 1924, ... ]
2 :year_end uint16 100 [1921, 1922, 1923, 1924, 1925, ... ]
3 :total_snow double 84 [671.0, 723.0, 565.0, 551.0, 674.0, ... ], 9 nilsRdatasets: openintro: sowc_demographics, SOWC Demographics Data.
RedAmber::DataFrame : 202 x 18 Vectors
Vectors : 17 numeric, 1 string
# key type level data_preview
1 :countries_and_areas string 202 ["Afghanistan", "Albania", "Algeria", "Andorra", "Angola", ... ]
2 :total_pop_2018 uint32 198 [37172, 2883, 42228, 77, 30810, ... ]
3 :under18_pop_2018 uint32 183 [18745, 635, 14416, nil, 16457, ... ], 18 nils
4 :under5_pop_2018 uint32 175 [5601, 173, 4951, nil, 5553, ... ], 18 nils
5 :pop_growth_rate_2018 double 52 [3.2, -0.5, 1.7, 0.9, 3.5, ... ]
6 :pop_growth_rate_2030 double 43 [2.1, -0.3, 1.5, 0.1, 3.1, ... ]
7 :births_2018 uint16 156 [1207, 34, 1023, nil, 1257, ... ], 18 nils
8 :fertility_2018 double 47 [4.5, 1.6, 3.0, nil, 5.5, ... ], 18 nils
9 :life_expectancy_1970 uint8 41 [37, 67, 50, nil, 41, ... ], 18 nils
10 :life_expectancy_2000 uint8 39 [56, 74, 71, nil, 47, ... ], 18 nils
11 :life_expectancy_2018 uint8 31 [64, 78, 77, nil, 61, ... ], 18 nils
12 :dependency_ratio_total uint8 62 [84, 46, 58, nil, 96, ... ], 18 nils
13 :dependency_ratio_child uint8 71 [79, 26, 47, nil, 92, ... ], 18 nils
14 :dependency_ratio_oldage uint8 34 [5, 20, 10, nil, 4, ... ], 18 nils
15 :percent_urban_2018 uint8 80 [25, 60, 73, 88, 66, ... ]
16 :pop_urban_growth_rate_2018 double 68 [4.0, 1.6, 2.8, 0.6, 5.0, ... ], 1 nil
17 :pop_urban_growth_rate_2030 double 55 [3.4, 0.9, 2.1, 0.1, 4.0, ... ], 1 nil
18 :migration_rate double 97 [-1.7, -4.9, -0.2, nil, 0.2, ... ], 18 nilsRdatasets: openintro: sowc_maternal_newborn, SOWC Maternal and Newborn Health Data.
RedAmber::DataFrame : 202 x 18 Vectors
Vectors : 17 numeric, 1 string
# key type level data_preview
1 :countries_and_areas string 202 ["Afghanistan", "Albania", "Algeria", "Andorra", "Angola", ... ]
2 :life_expectancy_female uint8 31 [66, 80, 78, nil, 64, ... ], 18 nils
3 :family_planning_1549 uint8 66 [42, 5, 77, nil, 30, ... ], 72 nils
4 :family_planning_1519 uint8 53 [21, 5, nil, nil, 15, ... ], 122 nils
5 :adolescent_birth_rate uint8 104 [77, 17, 10, 3, 163, ... ], 2 nils
6 :births_age_18 uint8 39 [20, 3, 1, nil, 38, ... ], 86 nils
7 :antenatal_care_1 uint8 33 [59, 88, 93, nil, 82, ... ], 37 nils
8 :antenatal_care_4_1549 uint8 62 [18, 78, 67, nil, 61, ... ], 55 nils
9 :antenatal_care_4_1519 uint8 51 [16, 72, nil, nil, 56, ... ], 109 nils
10 :delivery_care_attendant_1549 uint8 53 [51, 100, 97, nil, 50, ... ], 33 nils
11 :delivery_care_attendant_1519 uint8 47 [54, 100, nil, nil, 50, ... ], 110 nils
12 :delivery_care_institutional uint8 54 [48, 99, 97, nil, 46, ... ], 34 nils
13 :c_section uint8 44 [3, 31, 16, nil, 4, ... ], 44 nils
14 :postnatal_health_newborns uint8 55 [9, 86, nil, nil, 21, ... ], 115 nils
15 :postnatal_health_mothers uint8 60 [40, 88, nil, nil, 23, ... ], 109 nils
16 :maternal_deaths_2017 uint32 110 [7700, 5, 1200, nil, 3000, ... ], 18 nils
17 :maternal_mortality_ratio_2017 uint16 116 [638, 15, 112, nil, 241, ... ], 18 nils
18 :risk_maternal_death_2017 uint16 136 [33, 3800, 270, nil, 69, ... ], 18 nilsRdatasets: openintro: sp500, Financial information for 50 S&P 500 companies
RedAmber::DataFrame : 50 x 12 Vectors
Vectors : 11 numeric, 1 string
# key type level data_preview
1 :stock string 50 ["SCHW", "PBCT", "MAT", "CCL", "VIAB", ... ]
2 :market_cap uint32 50 [17700, 4340, 11210, 24050, 26110, ... ]
3 :ent_value uint32 48 [15760, 4550, 11210, 32550, 32290, ... ]
4 :trail_pe double 49 [19.89, 21.84, 15.16, 12.79, 16.18, ... ]
5 :forward_pe double 50 [16.77, 13.24, 12.48, 11.95, 9.64, ... ]
6 :ev_over_rev double 49 [3.36, 3.9, 1.79, 2.06, 2.15, ... ]
7 :profit_margin double 50 [18.42, 17.03, 12.27, 12.11, 11.56, ... ]
8 :revenue double 48 [4690.0, 1170.0, 6270.0, 15790.0, 15040.0, ... ]
9 :growth double 40 [1.2, 18.7, 1.4, 5.7, 3.2, ... ], 3 nils
10 :earn_before double 48 [nil, nil, 1220.0, 3780.0, 4090.0, ... ], 3 nils
11 :cash double 50 [35310.0, 865.3, 1370.0, 450.0, 1150.0, ... ], 1 nil
12 :debt double 50 [2000.0, 1080.0, 1560.0, 9350.0, 7790.0, ... ]Rdatasets: openintro: speed_gender_height, Speed, gender, and height of 1325 students
RedAmber::DataFrame : 1325 x 3 Vectors
Vectors : 2 numeric, 1 string
# key type level data_preview
1 :speed uint8 70 [85, 40, 87, 110, 110, ... ], 18 nils
2 :gender string 2 {"female"=>882, "male"=>443}
3 :height double 45 [69.0, 71.0, 64.0, 60.0, 70.0, ... ], 5 nilsRdatasets: openintro: world_pop, World Population Data.
RedAmber::DataFrame : 216 x 62 Vectors
Vectors : 61 numeric, 1 string
# key type level data_preview
1 :country string 216 ["Afghanistan", "Albania", "Algeria", "American Samoa", "Andorra", ... ]
2 :year_1960 uint32 215 [8996967, 1608800, 11057864, 20127, 13410, ... ], 1 nil
3 :year_1961 uint32 216 [9169406, 1659800, 11336336, 20605, 14378, ... ], 1 nil
4 :year_1962 uint32 215 [9351442, 1711319, 11619828, 21246, 15379, ... ], 1 nil
5 :year_1963 uint32 216 [9543200, 1762621, 11912800, 22029, 16407, ... ], 1 nil
6 :year_1964 uint32 216 [9744772, 1814135, 12221675, 22850, 17466, ... ], 1 nil
7 :year_1965 uint32 216 [9956318, 1864791, 12550880, 23675, 18542, ... ], 1 nil
8 :year_1966 uint32 216 [10174840, 1914573, 12902626, 24473, 19646, ... ], 1 nil
9 :year_1967 uint32 216 [10399936, 1965598, 13275020, 25235, 20760, ... ], 1 nil
10 :year_1968 uint32 216 [10637064, 2022272, 13663581, 25980, 21886, ... ], 1 nil
11 :year_1969 uint32 216 [10893772, 2081695, 14061724, 26698, 23053, ... ], 1 nil
12 :year_1970 uint32 216 [11173654, 2135479, 14464992, 27362, 24275, ... ], 1 nil
13 :year_1971 uint32 216 [11475450, 2187853, 14872253, 27982, 25571, ... ], 1 nil
14 :year_1972 uint32 215 [11791222, 2243126, 15285992, 28564, 26885, ... ], 1 nil
15 :year_1973 uint32 216 [12108963, 2296752, 15709831, 29103, 28232, ... ], 1 nil
16 :year_1974 uint32 216 [12412960, 2350124, 16149018, 29595, 29515, ... ], 1 nil
17 :year_1975 uint32 216 [12689164, 2404831, 16607706, 30045, 30705, ... ], 1 nil
18 :year_1976 uint32 216 [12943093, 2458526, 17085799, 30455, 31782, ... ], 1 nil
19 :year_1977 uint32 216 [13171294, 2513546, 17582899, 30834, 32769, ... ], 1 nil
20 :year_1978 uint32 216 [13341199, 2566266, 18102266, 31262, 33744, ... ], 1 nil
... 42 more Vectors ...Rdatasets: palmerpenguins: penguins, Size measurements for adult foraging penguins near Palmer Station, Antarctica
RedAmber::DataFrame : 344 x 8 Vectors
Vectors : 5 numeric, 3 strings
# key type level data_preview
1 :species string 3 {"Adelie"=>152, "Gentoo"=>124, "Chinstrap"=>68}
2 :island string 3 {"Torgersen"=>52, "Biscoe"=>168, "Dream"=>124}
3 :bill_length_mm double 165 [39.1, 39.5, 40.3, nil, 36.7, ... ], 2 nils
4 :bill_depth_mm double 81 [18.7, 17.4, 18.0, nil, 19.3, ... ], 2 nils
5 :flipper_length_mm uint8 56 [181, 186, 195, nil, 193, ... ], 2 nils
6 :body_mass_g uint16 95 [3750, 3800, 3250, nil, 3450, ... ], 2 nils
7 :sex string 3 {"male"=>168, "female"=>165, nil=>11}
8 :year uint16 3 {2007=>110, 2008=>114, 2009=>120}Rdatasets: plyr: baseball, Yearly batting records for all major league baseball players
RedAmber::DataFrame : 21699 x 22 Vectors
Vectors : 19 numeric, 3 strings
# key type level data_preview
1 :id string 1228 ["ansonca01", "forceda01", "mathebo01", "startjo01", "suttoez01", ... ]
2 :year uint16 137 [1871, 1871, 1871, 1871, 1871, ... ]
3 :stint uint8 4 {1=>19808, 2=>1775, 3=>111, 4=>5}
4 :team string 132 ["RC1", "WS3", "FW1", "NY2", "CL1", ... ]
5 :lg string 7 ["", "", "", "", "", ... ]
6 :g uint8 166 [25, 32, 19, 33, 29, ... ]
7 :ab uint16 687 [120, 162, 89, 161, 128, ... ]
8 :r uint8 165 [29, 45, 15, 35, 35, ... ]
9 :h uint16 245 [39, 45, 24, 58, 45, ... ]
10 :X2b uint8 62 [11, 9, 3, 5, 3, ... ]
11 :X3b uint8 29 [3, 4, 1, 1, 7, ... ]
12 :hr uint8 65 [0, 0, 0, 1, 3, ... ]
13 :rbi uint8 171 [16, 29, 10, 34, 23, ... ], 12 nils
14 :sb uint8 105 [6, 8, 2, 4, 3, ... ], 250 nils
15 :cs uint8 36 [2, 0, 1, 2, 1, ... ], 4525 nils
16 :bb uint8 154 [2, 4, 2, 3, 1, ... ]
17 :so uint8 176 [1, 0, 0, 0, 0, ... ], 1305 nils
18 :ibb uint8 45 [nil, nil, nil, nil, nil, ... ], 7528 nils
19 :hbp uint8 37 [nil, nil, nil, nil, nil, ... ], 377 nils
20 :sh uint8 49 [nil, nil, nil, nil, nil, ... ], 960 nils
... 2 more Vectors ...Rdatasets: pscl: AustralianElections, elections to Australian House of Representatives, 1949-2016
RedAmber::DataFrame : 27 x 19 Vectors
Vectors : 18 numeric, 1 temporal
# key type level data_preview
1 :date date64 27 [#<DateTime: 1949-12-10T09:00:00+09:00 ((2433261j,0s,0n),+32400s,2299161j)>, #<DateTime: 1951-04-28T09:00:00+09:00 ((2433765j,0s,0n),+32400s,2299161j)>, ... ]
2 :Seats uint8 8 [121, 121, 122, 122, 122, ... ]
3 :Uncontested uint8 4 {0=>24, 3=>1, 7=>1, 10=>1}
4 :ALPSeats uint8 24 [47, 52, 57, 47, 45, ... ]
5 :LPSeats uint8 22 [55, 52, 47, 57, 58, ... ]
6 :NPSeats uint8 12 [19, 17, 17, 18, 19, ... ]
7 :OtherSeats uint8 5 {0=>16, 1=>3, 2=>2, 5=>4, 3=>2}
8 :ALP double 27 [45.98, 47.63, 50.03, 44.63, 42.81, ... ]
9 :ALP2PP double 25 [49.0, 49.3, 50.7, 45.8, 45.9, ... ]
10 :LP double 26 [39.39, 40.62, 38.31, 39.73, 37.23, ... ]
11 :NP double 27 [10.87, 9.72, 8.52, 7.9, 9.32, ... ]
12 :DLP double 18 [0.0, 0.0, 0.0, 5.17, 9.41, ... ]
13 :Dem double 15 [0.0, 0.0, 0.0, 0.0, 0.0, ... ]
14 :Green double 9 [nil, nil, nil, nil, nil, ... ], 19 nils
15 :Hanson double 5 {0.0=>23, 8.43=>1, 4.34=>1, 0.26=>1, 1.29=>1}
16 :Com double 3 {0.0=>24, 1.0=>1, 1.2=>2}
17 :AP double 5 {0.0=>23, 0.88=>1, 2.42=>1, 2.33=>1, 0.43=>1}
18 :Informal double 27 [1.99, 1.9, 1.35, 2.88, 2.87, ... ]
19 :Turnout double 26 [95.97, 96.0, 96.09, 95.0, 95.48, ... ]Rdatasets: pscl: ca2006, California Congressional Districts in 2006
RedAmber::DataFrame : 53 x 13 Vectors
Vectors : 9 numeric, 4 strings
# key type level data_preview
1 :district uint8 53 [1, 2, 3, 4, 5, ... ]
2 :D uint32 53 [111650, 54829, 72815, 97705, 89119, ... ], 1 nil
3 :R uint32 47 [49663, 108002, 114155, 105525, 29824, ... ], 7 nils
4 :Other uint16 30 [7850, 5613, 5193, 10754, 7110, ... ], 24 nils
5 :IncParty string 2 {"D"=>33, "R"=>20}
6 :IncName string 50 ["Thompson", "Herger", "Lungren", "Doolittle", "Matsui", ... ], 2 nils
7 :open string 2 {"FALSE"=>51, "TRUE"=>2}
8 :contested string 2 {"TRUE"=>45, "FALSE"=>8}
9 :Bush2004 uint32 53 [111754, 173528, 176512, 216838, 77788, ... ]
10 :Kerry2004 uint32 53 [173926, 102254, 123671, 132267, 125378, ... ]
11 :Other2004 uint16 53 [5508, 3980, 2936, 4119, 2172, ... ]
12 :Bush2000 uint32 53 [98506, 150196, 142946, 172169, 66011, ... ]
13 :Gore2000 uint32 53 [131376, 81861, 107690, 104437, 113987, ... ]Rdatasets: pscl: politicalInformation, Interviewer ratings of respondent levels of political information
RedAmber::DataFrame : 1807 x 8 Vectors
Vectors : 3 numeric, 5 strings
# key type level data_preview
1 :y string 6 ["Fairly High", "Average", "Very High", "Average", "Fairly High", ... ], 7 nils
2 :collegeDegree string 2 {"Yes"=>724, "No"=>1083}
3 :female string 2 {"No"=>790, "Yes"=>1017}
4 :age uint8 80 [49, 35, 57, 63, 40, ... ], 9 nils
5 :homeOwn string 2 {"Yes"=>1205, "No"=>602}
6 :govt string 2 {"No"=>1596, "Yes"=>211}
7 :length double 1432 [58.4000015258789, 46.1500015258789, 89.5199966430664, 92.629997253418, 58.8499984741211, ... ], 8 nils
8 :id uint8 115 [1, 2, 3, 4, 4, ... ]Rdatasets: pscl: state.info, information about the American states needed for U.S. Congress
RedAmber::DataFrame : 51 x 3 Vectors
Vectors : 2 numeric, 1 string
# key type level data_preview
1 :icpsr uint8 51 [1, 2, 3, 4, 5, ... ]
2 :state string 51 ["Connecticut", "Maine", "Massachusetts", "New Hampshire", "Rhode Island", ... ]
3 :year uint16 35 [1788, 1820, 1788, 1788, 1790, ... ], 1 nilRdatasets: psych: bfi, 25 Personality items representing 5 factors
RedAmber::DataFrame : 2800 x 28 Vectors
Vectors : 28 numeric
# key type level data_preview
1 :A1 uint8 7 [2, 2, 5, 4, 2, ... ], 16 nils
2 :A2 uint8 7 [4, 4, 4, 4, 3, ... ], 27 nils
3 :A3 uint8 7 [3, 5, 5, 6, 3, ... ], 26 nils
4 :A4 uint8 7 [4, 2, 4, 5, 4, ... ], 19 nils
5 :A5 uint8 7 [4, 5, 4, 5, 5, ... ], 16 nils
6 :C1 uint8 7 [2, 5, 4, 4, 4, ... ], 21 nils
7 :C2 uint8 7 [3, 4, 5, 4, 4, ... ], 24 nils
8 :C3 uint8 7 [3, 4, 4, 3, 5, ... ], 20 nils
9 :C4 uint8 7 [4, 3, 2, 5, 3, ... ], 26 nils
10 :C5 uint8 7 [4, 4, 5, 5, 2, ... ], 16 nils
11 :E1 uint8 7 [3, 1, 2, 5, 2, ... ], 23 nils
12 :E2 uint8 7 [3, 1, 4, 3, 2, ... ], 16 nils
13 :E3 uint8 7 [3, 6, 4, 4, 5, ... ], 25 nils
14 :E4 uint8 7 [4, 4, 4, 4, 4, ... ], 9 nils
15 :E5 uint8 7 [4, 3, 5, 4, 5, ... ], 21 nils
16 :N1 uint8 7 [3, 3, 4, 2, 2, ... ], 22 nils
17 :N2 uint8 7 [4, 3, 5, 5, 3, ... ], 21 nils
18 :N3 uint8 7 [2, 3, 4, 2, 4, ... ], 11 nils
19 :N4 uint8 7 [2, 5, 2, 4, 4, ... ], 36 nils
20 :N5 uint8 7 [3, 5, 3, 1, 3, ... ], 29 nils
... 8 more Vectors ...Rdatasets: psych: sat.act, 3 Measures of ability: SATV, SATQ, ACT
RedAmber::DataFrame : 700 x 6 Vectors
Vectors : 6 numeric
# key type level data_preview
1 :gender uint8 2 {2=>453, 1=>247}
2 :education uint8 6 [3, 3, 3, 4, 2, ... ]
3 :age uint8 48 [19, 23, 20, 27, 33, ... ]
4 :ACT uint8 23 [24, 35, 21, 26, 31, ... ]
5 :SATV uint16 70 [500, 600, 480, 550, 600, ... ]
6 :SATQ uint16 73 [500, 500, 470, 520, 550, ... ], 13 nilsRdatasets: ratdat: complete, Complete survey data.
RedAmber::DataFrame : 35549 x 13 Vectors
Vectors : 7 numeric, 6 strings
# key type level data_preview
1 :record_id uint16 35549 [1, 2, 3, 4, 5, ... ]
2 :month uint8 12 [7, 7, 7, 7, 7, ... ]
3 :day uint8 31 [16, 16, 16, 16, 16, ... ]
4 :year uint16 26 [1977, 1977, 1977, 1977, 1977, ... ]
5 :plot_id uint8 24 [2, 3, 2, 7, 3, ... ]
6 :species_id string 49 ["NL", "NL", "DM", "DM", "DM", ... ]
7 :sex string 3 {"M"=>17348, "F"=>15690, ""=>2511}
8 :hindfoot_length uint8 57 [32, 33, 37, 36, 35, ... ], 4111 nils
9 :weight uint16 256 [nil, nil, nil, nil, nil, ... ], 3266 nils
10 :genus string 27 ["Neotoma", "Neotoma", "Dipodomys", "Dipodomys", "Dipodomys", ... ], 763 nils
11 :species string 41 ["albigula", "albigula", "merriami", "merriami", "merriami", ... ], 763 nils
12 :taxa string 5 {"Rodent"=>34247, nil=>763, "Rabbit"=>75, "Bird"=>450, "Reptile"=>14}
13 :plot_type string 5 {"Control"=>15660, "Long-term Krat Exclosure"=>5259, "Rodent Exclosure"=>4744, "Spectab exclosure"=>3931, "Short-term Krat Exclosure"=>5955}Rdatasets: ratdat: complete_old, Complete survey data from 1977 to 1989.
RedAmber::DataFrame : 16878 x 13 Vectors
Vectors : 7 numeric, 6 strings
# key type level data_preview
1 :record_id uint16 16878 [1, 2, 3, 4, 5, ... ]
2 :month uint8 12 [7, 7, 7, 7, 7, ... ]
3 :day uint8 31 [16, 16, 16, 16, 16, ... ]
4 :year uint16 13 [1977, 1977, 1977, 1977, 1977, ... ]
5 :plot_id uint8 24 [2, 3, 2, 7, 3, ... ]
6 :species_id string 36 ["NL", "NL", "DM", "DM", "DM", ... ]
7 :sex string 3 {"M"=>8260, "F"=>7318, ""=>1300}
8 :hindfoot_length uint8 52 [32, 33, 37, 36, 35, ... ], 2733 nils
9 :weight uint16 253 [nil, nil, nil, nil, nil, ... ], 1692 nils
10 :genus string 24 ["Neotoma", "Neotoma", "Dipodomys", "Dipodomys", "Dipodomys", ... ], 357 nils
11 :species string 31 ["albigula", "albigula", "merriami", "merriami", "merriami", ... ], 357 nils
12 :taxa string 5 {"Rodent"=>16148, nil=>357, "Rabbit"=>69, "Bird"=>300, "Reptile"=>4}
13 :plot_type string 5 {"Control"=>7213, "Long-term Krat Exclosure"=>2019, "Rodent Exclosure"=>2764, "Spectab exclosure"=>1907, "Short-term Krat Exclosure"=>2975}Rdatasets: ratdat: surveys, Survey data.
RedAmber::DataFrame : 35549 x 9 Vectors
Vectors : 7 numeric, 2 strings
# key type level data_preview
1 :record_id uint16 35549 [1, 2, 3, 4, 5, ... ]
2 :month uint8 12 [7, 7, 7, 7, 7, ... ]
3 :day uint8 31 [16, 16, 16, 16, 16, ... ]
4 :year uint16 26 [1977, 1977, 1977, 1977, 1977, ... ]
5 :plot_id uint8 24 [2, 3, 2, 7, 3, ... ]
6 :species_id string 49 ["NL", "NL", "DM", "DM", "DM", ... ]
7 :sex string 3 {"M"=>17348, "F"=>15690, ""=>2511}
8 :hindfoot_length uint8 57 [32, 33, 37, 36, 35, ... ], 4111 nils
9 :weight uint16 256 [nil, nil, nil, nil, nil, ... ], 3266 nilsRdatasets: reshape2: smiths, Demo data describing the Smiths.
RedAmber::DataFrame : 2 x 5 Vectors
Vectors : 4 numeric, 1 string
# key type level data_preview
1 :subject string 2 ["John Smith", "Mary Smith"]
2 :time uint8 1 {1=>2}
3 :age uint8 2 [33, nil], 1 nil
4 :weight uint8 2 [90, nil], 1 nil
5 :height double 2 [1.87, 1.54]Rdatasets: robustbase: airmay, Air Quality Data
RedAmber::DataFrame : 31 x 4 Vectors
Vectors : 4 numeric
# key type level data_preview
1 :X1 uint16 28 [190, 118, 149, 313, nil, ... ], 4 nils
2 :X2 double 18 [7.4, 8.0, 12.6, 11.5, 14.3, ... ]
3 :X3 uint8 18 [67, 72, 74, 62, 56, ... ]
4 :Y uint8 22 [41, 36, 12, 18, nil, ... ], 5 nilsRdatasets: rpart: car.test.frame, Automobile Data from 'Consumer Reports' 1990
RedAmber::DataFrame : 60 x 8 Vectors
Vectors : 6 numeric, 2 strings
# key type level data_preview
1 :Price uint16 58 [8895, 7402, 6319, 6635, 6599, ... ]
2 :Country string 8 ["USA", "USA", "Korea", "Japan/USA", "Japan", ... ]
3 :Reliability uint8 6 [4, 2, 4, 5, 5, ... ], 11 nils
4 :Mileage uint8 18 [33, 33, 37, 32, 32, ... ]
5 :Type string 6 ["Small", "Small", "Small", "Small", "Small", ... ]
6 :Weight uint16 55 [2560, 2345, 1845, 2260, 2440, ... ]
7 :"Disp." uint16 32 [97, 114, 81, 91, 113, ... ]
8 :HP uint8 37 [113, 90, 63, 92, 103, ... ]Rdatasets: rpart: cu.summary, Automobile Data from 'Consumer Reports' 1990
RedAmber::DataFrame : 117 x 5 Vectors
Vectors : 2 numeric, 3 strings
# key type level data_preview
1 :Price uint16 112 [11950, 6851, 6995, 8895, 7402, ... ]
2 :Country string 10 ["Japan", "Japan", "USA", "USA", "USA", ... ]
3 :Reliability string 6 ["Much better", nil, "Much worse", "better", "worse", ... ], 32 nils
4 :Mileage uint8 19 [nil, nil, nil, 33, 33, ... ], 57 nils
5 :Type string 6 ["Small", "Small", "Small", "Small", "Small", ... ]Rdatasets: rpart: stagec, Stage C Prostate Cancer
RedAmber::DataFrame : 146 x 8 Vectors
Vectors : 7 numeric, 1 string
# key type level data_preview
1 :pgtime double 84 [6.1, 9.4, 5.2, 3.2, 1.9, ... ]
2 :pgstat uint8 2 {0=>92, 1=>54}
3 :age uint8 28 [64, 62, 59, 62, 64, ... ]
4 :eet uint8 3 {2=>108, 1=>36, nil=>2}
5 :g2 double 132 [10.26, nil, 9.99, 3.57, 22.56, ... ], 7 nils
6 :grade uint8 4 {2=>59, 3=>79, 4=>6, 1=>2}
7 :gleason uint8 9 [4, 8, 7, 4, 8, ... ], 3 nils
8 :ploidy string 3 {"diploid"=>67, "aneuploid"=>11, "tetraploid"=>68}Rdatasets: sandwich: Investment, US Investment Data
RedAmber::DataFrame : 20 x 7 Vectors
Vectors : 7 numeric
# key type level data_preview
1 :GNP double 20 [596.7, 637.7, 691.1, 756.0, 799.6, ... ]
2 :Investment double 20 [90.9, 97.4, 113.5, 125.7, 122.8, ... ]
3 :Price double 20 [0.7167, 0.7277, 0.7436, 0.7676, 0.7906, ... ]
4 :Interest double 19 [3.23, 3.55, 4.04, 4.5, 4.19, ... ]
5 :RealGNP double 20 [832.565927166178, 876.322660436993, 929.397525551372, 984.887962480459, 1011.38375917025, ... ]
6 :RealInv double 20 [126.83131017162, 133.84636526041, 152.635825712749, 163.757165190203, 155.325069567417, ... ]
7 :RealInt double 20 [nil, 2.01518766568997, 1.85503366772021, 1.27245831091986, 1.19364773319437, ... ], 1 nilRdatasets: sandwich: PublicSchools, US Expenditures for Public Schools
RedAmber::DataFrame : 51 x 2 Vectors
Vectors : 2 numeric
# key type level data_preview
1 :Expenditure uint16 46 [275, 821, 339, 275, 387, ... ], 1 nil
2 :Income uint16 50 [6247, 10851, 7374, 6183, 8850, ... ]Rdatasets: sem: HS.data, Holizinger and Swineford's Data
RedAmber::DataFrame : 301 x 32 Vectors
Vectors : 30 numeric, 2 strings
# key type level data_preview
1 :id uint16 301 [1, 2, 3, 4, 5, ... ]
2 :Gender string 2 {"Male"=>146, "Female"=>155}
3 :grade uint8 2 {7=>235, 8=>66}
4 :agey uint8 6 [13, 13, 13, 13, 12, ... ]
5 :agem uint8 12 [1, 7, 1, 2, 2, ... ]
6 :school string 2 {"Pasteur"=>156, "Grant-White"=>145}
7 :visual uint8 35 [20, 32, 27, 32, 29, ... ]
8 :cubes uint8 25 [31, 21, 21, 31, 19, ... ]
9 :paper uint8 19 [12, 12, 12, 16, 12, ... ]
10 :flags uint8 35 [3, 17, 15, 24, 7, ... ]
11 :general uint8 57 [40, 34, 20, 42, 37, ... ]
12 :paragrap uint8 20 [7, 5, 3, 8, 8, ... ]
13 :sentence uint8 25 [23, 12, 7, 18, 16, ... ]
14 :wordc uint8 31 [22, 22, 12, 21, 25, ... ]
15 :wordm uint8 40 [9, 9, 3, 17, 18, ... ]
16 :addition uint8 97 [78, 87, 75, 69, 85, ... ]
17 :code uint8 68 [74, 84, 49, 65, 63, ... ]
18 :counting uint8 84 [115, 125, 78, 106, 126, ... ]
19 :straight uint16 130 [229, 285, 159, 175, 213, ... ]
20 :wordr uint8 52 [170, 184, 170, 181, 187, ... ]
... 12 more Vectors ...Rdatasets: sem: Tests, Six Mental Tests
RedAmber::DataFrame : 32 x 6 Vectors
Vectors : 6 numeric
# key type level data_preview
1 :x1 uint8 14 [23, 29, 14, 20, 25, ... ], 2 nils
2 :x2 uint8 16 [nil, 26, 21, 18, 26, ... ], 5 nils
3 :x3 uint8 16 [16, 23, nil, 17, 22, ... ], 4 nils
4 :y1 uint8 17 [15, 22, 15, 18, nil, ... ], 2 nils
5 :y2 uint8 16 [14, 18, 16, 21, 21, ... ], 1 nil
6 :y3 uint8 14 [16, 19, 18, 19, 26, ... ], 4 nilsRdatasets: Stat2Data: AppleStock, Daily Price and Volume of Apple Stock
RedAmber::DataFrame : 66 x 4 Vectors
Vectors : 3 numeric, 1 string
# key type level data_preview
1 :Date string 66 ["7/21/2016", "7/22/2016", "7/25/2016", "7/26/2016", "7/27/2016", ... ]
2 :Price double 65 [99.43, 98.66, 97.34, 96.67, 102.95, ... ]
3 :Change double 61 [nil, -0.77, -1.32, -0.67, 6.28, ... ], 1 nil
4 :Volume double 66 [32.69, 28.218, 40.291, 53.455, 92.144, ... ]Rdatasets: Stat2Data: CreditRisk, Overdrawn Checking Account?
RedAmber::DataFrame : 450 x 4 Vectors
Vectors : 4 numeric
# key type level data_preview
1 :Age uint8 9 [19, 19, 19, 19, 19, ... ], 9 nils
2 :Sex uint8 3 {1=>252, 0=>197, nil=>1}
3 :DaysDrink uint8 31 [3, 20, 6, 10, 0, ... ], 1 nil
4 :Overdrawn uint8 3 {0=>387, 1=>60, nil=>3}Rdatasets: Stat2Data: Day1Survey, First Day Survey of Statistics Students
RedAmber::DataFrame : 43 x 13 Vectors
Vectors : 10 numeric, 3 strings
# key type level data_preview
1 :Section uint8 2 {1=>24, 2=>19}
2 :Class string 6 ["Senior", "*", "Freshman", "Freshman", "N/A", ... ]
3 :Sex string 2 {"F"=>17, "M"=>26}
4 :Distance uint16 31 [400, 450, 3000, 100, 2000, ... ], 1 nil
5 :Height uint8 15 [62, 61, 61, 72, 69, ... ]
6 :Handedness string 3 {"Right"=>38, "Left"=>4, "Ambidextrous"=>1}
7 :Coins double 26 [1.12, 29.0, 1.5, 0.07, 0.12, ... ]
8 :WhiteString uint8 18 [42, 45, 22, 40, 48, ... ]
9 :BlackString uint8 14 [6, 5, 4, 4, 7, ... ]
10 :Reading double 20 [80.0, 100.0, 100.0, 50.0, 200.0, ... ]
11 :TV double 15 [3.0, 10.0, 4.0, 25.0, 5.0, ... ]
12 :Pulse uint8 17 [71, 78, 80, 63, 63, ... ]
13 :Texting uint8 21 [3, 100, 2, 200, 100, ... ]Rdatasets: Stat2Data: Faces, Facial Attractiveness of Men
RedAmber::DataFrame : 38 x 5 Vectors
Vectors : 5 numeric
# key type level data_preview
1 :MaxGripStrength double 29 [44.0, 58.0, 57.0, 48.0, 34.0, ... ]
2 :SHR double 35 [1.352, 1.349, 1.431, 1.405, 1.307, ... ]
3 :Partners uint8 12 [36, 4, 5, 6, 3, ... ]
4 :Attractive double 20 [2.333, 2.375, 1.857, 2.125, 2.5, ... ]
5 :AgeFirstSex uint8 8 [15, 15, 14, 18, 14, ... ], 7 nilsRdatasets: Stat2Data: Goldenrod, Goldenrod Galls
RedAmber::DataFrame : 1055 x 9 Vectors
Vectors : 6 numeric, 3 strings
# key type level data_preview
1 :Gdiam03 double 203 [20.7, 21.7, 20.5, 15.0, 19.5, ... ], 293 nils
2 :Stdiam03 double 96 [3.0, 3.6, 3.5, 5.1, 3.9, ... ], 293 nils
3 :Wall03 double 119 [6.0, 7.3, 7.8, nil, 7.1, ... ], 460 nils
4 :Fate03 string 7 ["f", "f", "f", "e", "u", ... ]
5 :Gdiam04 double 162 [18.9, 23.1, 19.4, 21.8, 23.5, ... ], 2 nils
6 :Stdiam04 double 56 [3.0, 3.7, 3.9, 3.0, 3.5, ... ], 1 nil
7 :Wall04 double 106 [nil, 9.4, 6.6, 10.0, 9.2, ... ], 111 nils
8 :Fate04 string 6 ["e", "f", "b", "f", "f", ... ]
9 :Fly04 string 2 {"n"=>385, "y"=>670}Rdatasets: Stat2Data: GrinnellHouses, House Sales in Grinnell, Iowa
RedAmber::DataFrame : 929 x 15 Vectors
Vectors : 14 numeric, 1 string
# key type level data_preview
1 :Date uint16 709 [16695, 16880, 16875, 16833, 16667, ... ]
2 :Address string 806 ["1510 First Ave #112 ", "1020 Center St ", "918 Chatterton St ", "1023 & 1025 Spring St. ", "503 2nd Ave ", ... ]
3 :Bedrooms uint8 9 [2, 3, 4, 3, 3, ... ]
4 :Baths double 16 [1.0, 1.0, 1.0, 1.0, 1.0, ... ]
5 :SquareFeet uint16 527 [1120, 1224, 1540, 1154, 1277, ... ], 18 nils
6 :LotSize double 399 [nil, 0.172176309, nil, nil, 0.20661157, ... ], 188 nils
7 :YearBuilt uint16 101 [1993, 1900, 1970, 1900, 1900, ... ]
8 :YearSold uint16 11 [2005, 2006, 2006, 2006, 2005, ... ]
9 :MonthSold uint8 12 [9, 3, 3, 2, 8, ... ]
10 :DaySold uint8 31 [16, 20, 15, 1, 19, ... ]
11 :CostPerSqFt double 856 [6.25, 22.06, 18.18, 26.0, 24.08, ... ]
12 :OrigPrice uint32 385 [17000, 35000, 54000, 65000, 35000, ... ]
13 :ListPrice uint32 373 [10500, 35000, 47000, 49000, 35000, ... ]
14 :SalePrice uint32 405 [7000, 27000, 28000, 30000, 30750, ... ]
15 :SPLPPct double 593 [66.67, 77.14, 59.57, 61.22, 87.86, ... ]Rdatasets: Stat2Data: Handwriting, Guess Author's Sex from Handwriting?
RedAmber::DataFrame : 204 x 8 Vectors
Vectors : 8 numeric
# key type level data_preview
1 :Individual uint8 204 [1, 2, 3, 4, 5, ... ], 1 nil
2 :Gender uint8 3 {1=>112, 0=>91, nil=>1}
3 :Survey1 uint8 15 [72, 56, 68, 80, 76, ... ], 4 nils
4 :Survey2 uint8 15 [68, 68, 48, 80, 72, ... ], 6 nils
5 :FemaleID double 18 [75.0, 41.7, 75.0, 83.3, 83.3, ... ], 1 nil
6 :MaleID double 18 [69.2, 69.2, 61.5, 76.9, 69.2, ... ], 1 nil
7 :Both uint8 17 [68, 48, 36, 76, 68, ... ], 9 nils
8 :DIFF int8 17 [4, -12, 20, 0, 4, ... ], 9 nilsRdatasets: Stat2Data: HawkTail2, Tail Lengths of Hawks (Unstacked)
RedAmber::DataFrame : 577 x 2 Vectors
Vectors : 2 numeric
# key type level data_preview
1 :Tail_RT uint16 69 [219, 221, 235, 230, 212, ... ]
2 :Tail_SS uint8 56 [157, 130, 164, 144, 136, ... ], 316 nilsRdatasets: Stat2Data: Hoops, Grinnell College Basketball Games
RedAmber::DataFrame : 147 x 22 Vectors
Vectors : 21 numeric, 1 string
# key type level data_preview
1 :Game uint8 147 [1, 2, 3, 4, 5, ... ]
2 :Opp string 9 ["Ripon", "Beloit", "Lake Forest", "Carroll", "Monmouth", ... ]
3 :Home uint8 2 {1=>75, 0=>72}
4 :OppAtt uint8 43 [67, 77, 37, 63, 52, ... ]
5 :GrAtt uint8 53 [89, 93, 68, 89, 84, ... ]
6 :Gr3Att uint8 51 [56, 45, 20, 71, 46, ... ]
7 :GrFT uint8 43 [13, 54, 34, 37, 26, ... ]
8 :OppFT uint8 45 [38, 50, 44, 57, 40, ... ]
9 :GrRB uint8 36 [40, 45, 25, 49, 30, ... ]
10 :GrOR uint8 28 [24, 28, 16, 27, 22, ... ]
11 :OppDR uint8 28 [29, 28, 35, 27, 29, ... ]
12 :OppPoint uint8 69 [119, 135, 98, 116, 109, ... ]
13 :GrPoint uint8 63 [107, 142, 83, 112, 107, ... ]
14 :GrAss uint8 28 [24, 15, 2, 20, 17, ... ]
15 :OppTO uint8 31 [28, 35, 35, 28, 30, ... ]
16 :GrTO uint8 27 [33, 22, 19, 28, 15, ... ]
17 :GrBlocks uint8 11 [2, 1, 0, 1, 0, ... ]
18 :GrSteal uint8 23 [23, 24, 4, 13, 21, ... ]
19 :X40Point uint8 3 {0=>132, 1=>14, nil=>1}
20 :X30Point uint8 2 {1=>57, 0=>90}
... 2 more Vectors ...Rdatasets: Stat2Data: MathPlacement, Math Placement Exam Results
RedAmber::DataFrame : 2696 x 16 Vectors
Vectors : 14 numeric, 2 strings
# key type level data_preview
1 :Student uint16 2688 [625, 628, 629, 630, 634, ... ]
2 :Gender uint8 3 {0=>314, 1=>266, nil=>2116}
3 :PSATM uint8 47 [56, 57, nil, 53, nil, ... ], 1560 nils
4 :SATM uint8 45 [56, nil, 62, nil, 64, ... ], 1460 nils
5 :ACTM uint8 25 [25, 23, 27, 27, 31, ... ], 322 nils
6 :Rank uint16 259 [1, 1, 42, 6, 72, ... ], 196 nils
7 :Size uint16 623 [420, 85, 421, 75, 462, ... ], 179 nils
8 :GPAadj uint8 23 [40, 40, 38, 38, 35, ... ], 20 nils
9 :PlcmtScore int8 65 [23, 21, 20, 20, 19, ... ], 35 nils
10 :Recommends string 9 ["R0", "R0", "R0", "R0", "R0", ... ]
11 :Course uint16 11 [210, 117, 117, 117, 114, ... ]
12 :Grade string 17 ["A", "A", "A-", "B", "A", ... ]
13 :RecTaken uint8 2 {1=>1848, 0=>848}
14 :TooHigh uint8 2 {0=>1162, 1=>1534}
15 :TooLow uint8 2 {0=>2642, 1=>54}
16 :CourseSuccess uint8 3 {1=>1441, 0=>688, nil=>567}Rdatasets: Stat2Data: MedGPA, GPA and Medical School Admission
RedAmber::DataFrame : 55 x 11 Vectors
Vectors : 9 numeric, 2 strings
# key type level data_preview
1 :Accept string 2 {"D"=>25, "A"=>30}
2 :Acceptance uint8 2 {0=>25, 1=>30}
3 :Sex string 2 {"F"=>28, "M"=>27}
4 :BCPM double 43 [3.59, 3.75, 3.24, 3.74, 3.53, ... ]
5 :GPA double 40 [3.62, 3.84, 3.23, 3.69, 3.38, ... ]
6 :VR uint8 8 [11, 12, 9, 12, 9, ... ]
7 :PS uint8 9 [9, 13, 10, 11, 11, ... ]
8 :WS uint8 8 [9, 8, 5, 7, 4, ... ], 1 nil
9 :BS uint8 9 [9, 12, 9, 10, 11, ... ]
10 :MCAT uint8 15 [38, 45, 33, 40, 35, ... ]
11 :Apps uint8 17 [5, 3, 19, 5, 11, ... ]Rdatasets: Stat2Data: NCbirths, North Carolina Birth Records
RedAmber::DataFrame : 1450 x 15 Vectors
Vectors : 13 numeric, 2 strings
# key type level data_preview
1 :ID uint16 1450 [1, 2, 3, 4, 5, ... ]
2 :Plural uint8 3 {1=>1401, 2=>45, 3=>4}
3 :Sex uint8 2 {1=>744, 2=>706}
4 :MomAge uint8 30 [32, 32, 27, 27, 25, ... ]
5 :Weeks uint8 25 [40, 37, 39, 39, 39, ... ], 1 nil
6 :Marital uint8 2 {1=>950, 2=>500}
7 :RaceMom uint8 7 [1, 1, 1, 1, 1, ... ]
8 :HispMom string 6 ["N", "N", "N", "N", "N", ... ]
9 :Gained uint8 76 [38, 34, 12, 15, 32, ... ], 40 nils
10 :Smoke uint8 3 {0=>1236, 1=>209, nil=>5}
11 :BirthWeightOz uint8 131 [111, 116, 138, 136, 121, ... ]
12 :BirthWeightGm double 131 [3146.85, 3288.6, 3912.3, 3855.6, 3430.35, ... ]
13 :Low uint8 2 {0=>1325, 1=>125}
14 :Premie uint8 2 {0=>1259, 1=>191}
15 :MomRace string 4 {"white"=>906, "hispanic"=>164, "black"=>332, "other"=>48}Rdatasets: Stat2Data: Overdrawn, Overdrawn Checking Account?
RedAmber::DataFrame : 450 x 4 Vectors
Vectors : 4 numeric
# key type level data_preview
1 :Age uint8 9 [19, 19, 19, 19, 19, ... ], 9 nils
2 :Sex uint8 3 {1=>252, 0=>197, nil=>1}
3 :DaysDrink uint8 31 [3, 20, 6, 10, 0, ... ], 1 nil
4 :Overdrawn uint8 3 {0=>387, 1=>60, nil=>3}Rdatasets: Stat2Data: Pines, Measurements of Pine Tree Seedlings
RedAmber::DataFrame : 1000 x 15 Vectors
Vectors : 15 numeric
# key type level data_preview
1 :Row uint8 44 [1, 1, 1, 1, 1, ... ]
2 :Col uint8 30 [1, 2, 3, 4, 5, ... ]
3 :Hgt90 double 89 [nil, 14.0, 17.0, nil, 24.0, ... ], 182 nils
4 :Hgt96 double 330 [nil, 284.0, 387.0, nil, 294.0, ... ], 139 nils
5 :Diam96 double 70 [nil, 4.2, 7.4, nil, 3.9, ... ], 149 nils
6 :Grow96 double 179 [nil, 96.0, 110.0, nil, 70.0, ... ], 136 nils
7 :Hgt97 uint16 306 [nil, 362, 442, nil, 369, ... ], 135 nils
8 :Diam97 double 122 [nil, 6.6, 9.3, nil, 7.0, ... ], 139 nils
9 :"Spread.97" uint16 203 [nil, 162, 250, nil, 176, ... ], 136 nils
10 :Needles97 double 90 [nil, 66.0, 77.0, nil, 72.0, ... ], 135 nils
11 :Deer95 uint8 3 {nil=>129, 0=>662, 1=>209}
12 :Deer97 uint8 3 {nil=>135, 1=>92, 0=>773}
13 :Cover95 uint8 4 {0=>326, 2=>224, 1=>235, 3=>215}
14 :Fert uint8 2 {0=>493, 1=>507}
15 :Spacing uint8 2 {15=>414, 10=>586}Rdatasets: Stat2Data: Political, Political Behavior of College Students
RedAmber::DataFrame : 59 x 9 Vectors
Vectors : 9 numeric
# key type level data_preview
1 :Year uint8 4 {1=>17, 4=>11, 2=>18, 3=>13}
2 :Sex uint8 2 {1=>28, 0=>31}
3 :Vote uint8 4 {1=>14, 3=>33, 2=>8, 0=>4}
4 :Paper uint8 5 {3=>23, 1=>7, 4=>15, 2=>10, 0=>4}
5 :Edit uint8 2 {1=>33, 0=>26}
6 :TV uint8 5 {0=>23, 3=>18, 1=>3, 4=>8, 2=>7}
7 :Ethics uint8 4 {2=>26, 3=>19, 4=>11, 5=>3}
8 :Inform uint8 5 {2=>13, 3=>30, 4=>10, 5=>4, 1=>2}
9 :Participate uint8 3 {0=>22, 1=>33, nil=>4}Rdatasets: Stat2Data: Pollster08, 2008 U.S. Presidential Election Polls
RedAmber::DataFrame : 102 x 11 Vectors
Vectors : 7 numeric, 4 strings
# key type level data_preview
1 :PollTaker string 34 ["Rasmussen", "Zogby", "Diageo/Hotline", "CBS", "CNN", ... ]
2 :PollDates string 56 ["8/28-30/08", "8/29-30/08", "8/29-31/08", "8/29-31/08", "8/29-31/08", ... ]
3 :MidDate string 30 ["8/29", "8/30", "8/30", "8/30", "8/30", ... ]
4 :Days uint8 30 [1, 2, 2, 2, 2, ... ]
5 :n uint16 62 [3000, 2020, 805, 781, 927, ... ], 3 nils
6 :Pop string 3 {"LV"=>58, "RV"=>38, "A"=>6}
7 :McCain uint8 14 [46, 47, 39, 40, 48, ... ]
8 :Obama uint8 13 [49, 45, 48, 48, 49, ... ]
9 :Margin int8 17 [3, -2, 9, 8, 1, ... ]
10 :Charlie uint8 2 {0=>38, 1=>64}
11 :Meltdown uint8 2 {0=>52, 1=>50}Rdatasets: Stat2Data: WeightLossIncentive, Do Financial Incentives Improve Weight Loss?
RedAmber::DataFrame : 38 x 3 Vectors
Vectors : 2 numeric, 1 string
# key type level data_preview
1 :WeightLoss double 29 [12.5, 12.0, 1.0, -5.0, 3.0, ... ], 2 nils
2 :Group string 2 {"Control"=>19, "Incentive"=>19}
3 :Month7Loss int8 24 [-2, 7, 19, 0, -1, ... ], 5 nilsRdatasets: Stat2Data: YouthRisk, Annual survey of health-risk youth behaviors
RedAmber::DataFrame : 13387 x 6 Vectors
Vectors : 6 numeric
# key type level data_preview
1 :"ride.alc.driver" uint8 2 {1=>4177, 0=>9210}
2 :female uint8 3 {1=>6666, nil=>755, 0=>5966}
3 :grade uint8 5 {10=>3299, nil=>67, 11=>3361, 9=>3249, 12=>3411}
4 :age4 uint8 6 [15, 18, nil, 17, 17, ... ], 54 nils
5 :smoke uint8 3 {1=>6946, nil=>388, 0=>6053}
6 :DriverLicense uint8 3 {0=>4308, 1=>9025, nil=>54}Rdatasets: Stat2Data: YouthRisk2007, Riding with a Driver Who Has Been Drinking
RedAmber::DataFrame : 13387 x 6 Vectors
Vectors : 6 numeric
# key type level data_preview
1 :"ride.alc.driver" uint8 2 {1=>4177, 0=>9210}
2 :female uint8 3 {1=>6666, nil=>755, 0=>5966}
3 :grade uint8 5 {10=>3299, nil=>67, 11=>3361, 9=>3249, 12=>3411}
4 :age4 uint8 6 [15, 18, nil, 17, 17, ... ], 54 nils
5 :smoke uint8 3 {1=>6946, nil=>388, 0=>6053}
6 :DriverLicense uint8 3 {0=>4308, 1=>9025, nil=>54}Rdatasets: Stat2Data: YouthRisk2009, Youth Risk Survey
RedAmber::DataFrame : 500 x 6 Vectors
Vectors : 3 numeric, 3 strings
# key type level data_preview
1 :Sleep string 8 ["", "8 hours", "5 hours", "5 hours", "7 hours", ... ]
2 :Sleep7 uint8 3 {nil=>54, 1=>296, 0=>150}
3 :SmokeLife string 3 {"No"=>271, "Yes"=>208, ""=>21}
4 :SmokeDaily string 3 {""=>33, "Yes"=>40, "No"=>427}
5 :MarijuaEver uint8 3 {0=>310, 1=>175, nil=>15}
6 :Age uint8 5 {16=>129, 17=>116, 18=>76, 15=>130, 14=>49}Rdatasets: stevedata: anes_prochoice, Abortion Attitudes (ANES, 2012)
RedAmber::DataFrame : 5914 x 14 Vectors
Vectors : 13 numeric, 1 string
# key type level data_preview
1 :version string 1 {"ANES2012TimeSeries_version20140310"=>5914}
2 :caseid uint16 5914 [1, 2, 3, 4, 5, ... ]
3 :health uint8 4 {nil=>484, 0=>1712, 1=>1196, 2=>2522}
4 :fatal uint8 4 {nil=>496, 2=>4107, 0=>557, 1=>754}
5 :incest uint8 4 {nil=>524, 0=>1424, 1=>1186, 2=>2780}
6 :rape uint8 4 {nil=>477, 0=>812, 2=>3917, 1=>708}
7 :bd uint8 4 {nil=>510, 2=>2846, 1=>1217, 0=>1341}
8 :fin uint8 4 {nil=>474, 0=>2976, 2=>1420, 1=>1044}
9 :sex uint8 4 {nil=>474, 0=>4382, 2=>428, 1=>630}
10 :choice uint8 4 {nil=>480, 2=>2242, 0=>2201, 1=>991}
11 :pid uint8 4 {0=>2361, 1=>1845, nil=>319, 2=>1389}
12 :knowspeaker uint8 3 {nil=>404, 1=>2206, 0=>3304}
13 :addchoice uint8 18 [nil, 6, nil, 11, 12, ... ], 638 nils
14 :lchoice double 967 [nil, -0.567213023407521, -0.622893899215311, 0.319304228283107, 0.625579163506229, ... ], 440 nilsRdatasets: stevedata: anes_vote84, Simple Data for a Simple Model of Individual Voter Turnout (ANES, 1984)
RedAmber::DataFrame : 2257 x 9 Vectors
Vectors : 8 numeric, 1 string
# key type level data_preview
1 :uid uint16 2257 [1, 2, 3, 4, 5, ... ]
2 :stateabb string 31 ["FL", "GA", "GA", "NC", "OH", ... ], 268 nils
3 :vote uint8 3 {0=>650, 1=>1356, nil=>251}
4 :age uint8 75 [nil, 60, nil, nil, nil, ... ], 789 nils
5 :educ uint8 11 [5, 10, 7, 5, 7, ... ], 14 nils
6 :female uint8 2 {1=>1268, 0=>989}
7 :south uint8 2 {1=>750, 0=>1507}
8 :polint int8 4 {0=>1054, 1=>638, -1=>558, nil=>7}
9 :govrace uint8 2 {0=>1957, 1=>300}Rdatasets: stevedata: CFT15, Randomization Inference in the Regression Discontinuity Design: An Application to Party Advantages in the U.S. Senate
RedAmber::DataFrame : 1390 x 9 Vectors
Vectors : 9 numeric
# key type level data_preview
1 :state uint8 50 [1, 1, 1, 1, 1, ... ]
2 :year uint16 49 [1914, 1916, 1922, 1926, 1928, ... ]
3 :vote double 1252 [36.0975723266602, 45.4687461853027, 45.5982131958008, 48.4760627746582, 51.7468681335449, ... ], 93 nils
4 :margin double 1352 [-7.688560962677, -3.92370820045471, -6.86866044998169, -27.6680564880371, -8.25696849822998, ... ]
5 :class uint8 3 {3=>487, 1=>455, 2=>448}
6 :termshouse uint8 13 [3, 0, 0, 0, 0, ... ], 282 nils
7 :termssenate uint8 20 [6, 4, 7, 3, 1, ... ], 282 nils
8 :population double 1333 [1233000.0, 1294000.0, 1431000.0, 1531000.0, 1577000.0, ... ]
9 :treatment uint8 2 {0=>640, 1=>750}Rdatasets: stevedata: DST, Casualties/Fatalities in the U.S. for Drunk-Driving, Suicide, and Terrorism
RedAmber::DataFrame : 49 x 5 Vectors
Vectors : 5 numeric
# key type level data_preview
1 :year uint16 49 [1970, 1971, 1972, 1973, 1974, ... ]
2 :nkill uint16 27 [35, 24, 10, 45, 24, ... ]
3 :terrtotal uint16 37 [197, 83, 45, 78, 86, ... ]
4 :suicides uint16 39 [nil, nil, nil, nil, nil, ... ], 11 nils
5 :ddfat uint16 38 [nil, nil, nil, nil, nil, ... ], 12 nilsRdatasets: stevedata: eq_passengercars, Export Quality Data for Passenger Cars, 1963-2014
RedAmber::DataFrame : 60424 x 6 Vectors
Vectors : 3 numeric, 3 strings
# key type level data_preview
1 :country string 166 ["Australia", "Australia", "Australia", "Australia", "Australia", ... ]
2 :ccode uint16 166 [900, 900, 900, 900, 900, ... ], 364 nils
3 :category string 7 ["Export Quality Index", "Export quality 95 percent interval - lower bound", "Export quality 95 percent interval - upper bound", "Unit value of exports", "Unit value 95 percent interval - lower bound", ... ]
4 :type string 1 {"51. Transport equipment, Passenger cars"=>60424}
5 :year uint16 52 [1963, 1963, 1963, 1963, 1963, ... ]
6 :value double 17734 [1.0516917, 0.90175551, 1.0537851, 0.77125996, 0.52688563, ... ], 18515 nilsRdatasets: stevedata: ESS9GB, British Attitudes Toward Immigration (2018-19)
RedAmber::DataFrame : 1905 x 19 Vectors
Vectors : 14 numeric, 3 strings, 2 temporal
# key type level data_preview
1 :name string 1 {"ESS9e01_2"=>1905}
2 :essround uint8 1 {9=>1905}
3 :edition double 1 {1.2=>1905}
4 :idno uint16 1905 [39, 135, 142, 146, 164, ... ]
5 :cntry string 1 {"GB"=>1905}
6 :region string 12 ["West Midlands (England)", "South West (England)", "South East (England)", "Northern Ireland", "Northern Ireland", ... ]
7 :brncntr uint8 1 {1=>1905}
8 :stintrvw date64 158 [#<DateTime: 2018-09-11T09:00:00+09:00 ((2458373j,0s,0n),+32400s,2299161j)>, #<DateTime: 2018-09-07T09:00:00+09:00 ((2458369j,0s,0n),+32400s,2299161j)>, ... ]
9 :endintrvw date64 159 [#<DateTime: 2018-09-11T09:00:00+09:00 ((2458373j,0s,0n),+32400s,2299161j)>, #<DateTime: 2018-09-07T09:00:00+09:00 ((2458369j,0s,0n),+32400s,2299161j)>, ... ]
10 :imbgeco uint8 12 [8, 0, 5, 5, 5, ... ], 18 nils
11 :imueclt uint8 12 [8, 2, 4, 7, 5, ... ], 28 nils
12 :imwbcnt uint8 12 [8, 5, 5, 7, 3, ... ], 29 nils
13 :immigsent uint8 32 [24, 7, 14, 19, 13, ... ], 55 nils
14 :agea uint8 77 [90, 61, 62, 66, 68, ... ], 12 nils
15 :female uint8 2 {0=>874, 1=>1031}
16 :eduyrs uint8 29 [9, 10, 10, 15, 12, ... ], 12 nils
17 :uempla uint8 2 {0=>1867, 1=>38}
18 :hinctnta uint8 11 [nil, 7, 2, 5, 3, ... ], 290 nils
19 :lrscale uint8 12 [5, 5, 3, 6, nil, ... ], 179 nilsRdatasets: stevedata: ESSBE5, Trust in the Police in Belgium (European Social Survey, Round 5)
RedAmber::DataFrame : 1704 x 10 Vectors
Vectors : 9 numeric, 1 string
# key type level data_preview
1 :essround uint8 1 {5=>1704}
2 :edition double 1 {3.4=>1704}
3 :idno uint32 1704 [100102, 100106, 100202, 100204, 100209, ... ]
4 :cntry string 1 {"BE"=>1704}
5 :trstplc uint8 12 [7, 4, 10, 5, 8, ... ], 3 nils
6 :agea uint8 78 [22, 43, 19, 23, 58, ... ]
7 :female uint8 2 {0=>820, 1=>884}
8 :eduyrs uint8 29 [15, 15, 13, 15, 15, ... ], 36 nils
9 :hincfel uint8 5 {2=>691, 3=>267, 1=>659, 4=>80, nil=>7}
10 :plcpvcr uint8 12 [5, 5, 5, 4, 8, ... ], 14 nilsRdatasets: stevedata: gss_abortion, Abortion Opinions in the General Social Survey
RedAmber::DataFrame : 64814 x 18 Vectors
Vectors : 15 numeric, 3 strings
# key type level data_preview
1 :id uint16 4510 [1, 2, 3, 4, 5, ... ]
2 :year uint16 32 [1972, 1972, 1972, 1972, 1972, ... ]
3 :age uint8 73 [23, 70, 48, 27, 61, ... ], 228 nils
4 :race string 3 {"White"=>52033, "Black"=>9187, "Other"=>3594}
5 :sex string 2 {"Female"=>36200, "Male"=>28614}
6 :hispaniccat uint8 30 [nil, nil, nil, nil, nil, ... ], 38164 nils
7 :educ uint8 22 [16, 10, 12, 17, 12, ... ], 177 nils
8 :partyid string 9 ["Ind,Near Dem", "Not Str Democrat", "Independent", "Not Str Democrat", "Strong Democrat", ... ], 418 nils
9 :relactiv uint8 12 [nil, nil, nil, nil, nil, ... ], 45581 nils
10 :abany uint8 3 {nil=>28020, 1=>15234, 0=>21560}
11 :abdefect uint8 3 {1=>35176, 0=>8976, nil=>20662}
12 :abnomore uint8 3 {1=>19662, 0=>24410, nil=>20742}
13 :abhlth uint8 3 {1=>39749, nil=>20454, 0=>4611}
14 :abpoor uint8 3 {1=>20790, 0=>23233, nil=>20791}
15 :abrape uint8 3 {1=>35746, nil=>20892, 0=>8176}
16 :absingle uint8 3 {1=>19436, 0=>24584, nil=>20794}
17 :pid uint8 8 [2, 1, 3, 1, 0, ... ], 1490 nils
18 :hispanic uint8 3 {nil=>38164, 0=>23555, 1=>3095}Rdatasets: stevedata: gss_spending, Attitudes Toward National Spending in the General Social Survey (2018)
RedAmber::DataFrame : 2348 x 33 Vectors
Vectors : 33 numeric
# key type level data_preview
1 :year uint16 1 {2018=>2348}
2 :id uint16 2348 [1, 2, 3, 4, 5, ... ]
3 :age uint8 73 [43, 74, 42, 63, 71, ... ], 7 nils
4 :sex uint8 2 {0=>1052, 1=>1296}
5 :educ uint8 22 [14, 10, 16, 16, 18, ... ], 3 nils
6 :degree uint8 5 {2=>196, 1=>1178, 3=>465, 4=>247, 0=>262}
7 :race uint8 3 {1=>1693, 2=>385, 3=>270}
8 :rincom16 uint8 27 [nil, nil, 22, 23, nil, ... ], 985 nils
9 :partyid uint8 9 [5, 2, 4, 2, 6, ... ], 33 nils
10 :polviews uint8 8 [6, nil, 5, 4, 7, ... ], 101 nils
11 :xnorcsiz uint8 10 [6, 6, 6, 6, 6, ... ]
12 :news uint8 6 [4, nil, 5, 1, nil, ... ], 789 nils
13 :wrkstat uint8 9 [3, 5, 1, 1, 5, ... ], 2 nils
14 :natspac int8 4 {-1=>549, 0=>1043, 1=>494, nil=>262}
15 :natenvir int8 4 {0=>539, 1=>1553, -1=>184, nil=>72}
16 :natheal int8 4 {1=>1641, 0=>479, -1=>174, nil=>54}
17 :natcity int8 4 {1=>815, 0=>798, -1=>465, nil=>270}
18 :natcrime int8 4 {1=>1386, nil=>99, 0=>664, -1=>199}
19 :natdrug int8 4 {1=>1554, nil=>87, 0=>500, -1=>207}
20 :nateduc int8 4 {1=>1789, 0=>403, -1=>134, nil=>22}
... 13 more Vectors ...Rdatasets: stevedata: gss_wages, The Gender Pay Gap in the General Social Survey
RedAmber::DataFrame : 61697 x 11 Vectors
Vectors : 6 numeric, 5 strings
# key type level data_preview
1 :year uint16 30 [1974, 1974, 1974, 1974, 1974, ... ]
2 :realrinc double 603 [4935.0, 43178.0, nil, nil, 18505.0, ... ], 23810 nils
3 :age uint8 73 [21, 41, 83, 69, 58, ... ], 219 nils
4 :occ10 uint16 539 [5620, 2040, nil, nil, 5820, ... ], 3561 nils
5 :occrecode string 12 ["Office and Administrative Support", "Professional", nil, nil, "Office and Administrative Support", ... ], 3561 nils
6 :prestg10 uint8 62 [25, 66, nil, nil, 37, ... ], 4186 nils
7 :childs uint8 10 [0, 3, 2, 2, 0, ... ], 189 nils
8 :wrkstat string 9 ["School", "Full-Time", "Housekeeper", "Housekeeper", "Full-Time", ... ], 21 nils
9 :gender string 2 {"Male"=>27106, "Female"=>34591}
10 :educcat string 6 ["High School", "Bachelor", "Less Than High School", "Less Than High School", "High School", ... ], 135 nils
11 :maritalcat string 6 ["Married", "Married", "Widowed", "Widowed", "Never Married", ... ], 27 nilsRdatasets: stevedata: mvprod, Motor Vehicle Production by Country, 1950-2019
RedAmber::DataFrame : 1206 x 3 Vectors
Vectors : 2 numeric, 1 string
# key type level data_preview
1 :country string 67 ["Algeria", "Argentina", "Australia", "Austria", "Azerbaijan", ... ]
2 :year uint16 18 [1950, 1950, 1950, 1950, 1950, ... ]
3 :value uint32 800 [nil, nil, 58000, nil, nil, ... ], 307 nilsRdatasets: stevedata: nesarc_drinkspd, The Usual Daily Drinking Habits of Americans (NESARC, 2001-2)
RedAmber::DataFrame : 43093 x 8 Vectors
Vectors : 8 numeric
# key type level data_preview
1 :idnum uint16 43093 [1, 2, 3, 4, 5, ... ]
2 :ethrace2a uint8 5 {5=>8308, 2=>8245, 1=>24507, 4=>1332, 3=>701}
3 :region uint8 4 {4=>9737, 3=>16156, 2=>8991, 1=>8209}
4 :age uint8 81 [23, 28, 81, 18, 36, ... ]
5 :sex uint8 2 {0=>18518, 1=>24575}
6 :marital uint8 6 [6, 1, 3, 6, 1, ... ]
7 :educ uint8 4 {2=>12547, 1=>7849, 4=>10034, 3=>12663}
8 :s2aq8b uint8 31 [nil, 1, nil, nil, nil, ... ], 16147 nilsRdatasets: stevedata: recessions, United States Recessions, 1855-present
RedAmber::DataFrame : 35 x 8 Vectors
Vectors : 6 numeric, 2 temporal
# key type level data_preview
1 :peak date64 35 [nil, #<DateTime: 1857-06-01T09:00:00+09:00 ((2399467j,0s,0n),+32400s,2299161j)>, ... ]
2 :trough date64 35 [#<DateTime: 1854-12-01T09:00:00+09:00 ((2398554j,0s,0n),+32400s,2299161j)>, #<DateTime: 1858-12-01T09:00:00+09:00 ((2400015j,0s,0n),+32400s,2299161j)>, ... ]
3 :peakq uint8 5 {nil=>2, 2=>8, 3=>10, 1=>9, 4=>6}
4 :troughq uint8 5 {4=>12, 3=>5, 1=>8, 2=>9, nil=>1}
5 :p2t uint8 17 [nil, 18, 8, 32, 18, ... ], 2 nils
6 :prev_t2p uint8 27 [nil, 30, 22, 46, 18, ... ], 1 nil
7 :tfpt uint8 27 [nil, 48, 30, 78, 36, ... ], 2 nils
8 :pfpp uint8 29 [nil, nil, 40, 54, 50, ... ], 2 nilsRdatasets: stevedata: SBCD, Systemic Banking Crises Database II
RedAmber::DataFrame : 547 x 4 Vectors
Vectors : 2 numeric, 2 strings
# key type level data_preview
1 :country string 157 ["Albania", "Albania", "Albania", "Albania", "Algeria", ... ]
2 :type string 4 {"banking"=>151, "currency"=>240, "debt"=>80, "debtrestructuring"=>76}
3 :year uint16 46 [1994, 1997, 1990, 1992, 1990, ... ], 1 nil
4 :month uint8 13 [nil, 1, nil, nil, nil, ... ], 324 nilsRdatasets: stevedata: sealevels, Global Average Absolute Sea Level Change, 1880–2015
RedAmber::DataFrame : 136 x 5 Vectors
Vectors : 5 numeric
# key type level data_preview
1 :year uint16 136 [1880, 1881, 1882, 1883, 1884, ... ]
2 :adjlev double 129 [0.0, 0.220472441, -0.440944881, -0.232283464, 0.590551181, ... ], 2 nils
3 :lb double 130 [-0.952755905, -0.732283464, -1.346456692, -1.129921259, -0.283464567, ... ], 2 nils
4 :ub double 128 [0.952755905, 1.173228345, 0.464566929, 0.66535433, 1.464566928, ... ], 2 nils
5 :adjlev_noaa double 24 [nil, nil, nil, nil, nil, ... ], 113 nilsRdatasets: stevedata: sugar_price, IMF Primary Commodity Price Data for Sugar
RedAmber::DataFrame : 1316 x 3 Vectors
Vectors : 1 numeric, 1 string, 1 temporal
# key type level data_preview
1 :date date64 499 [#<DateTime: 1980-01-01T09:00:00+09:00 ((2444240j,0s,0n),+32400s,2299161j)>, #<DateTime: 1980-02-01T09:00:00+09:00 ((2444271j,0s,0n),+32400s,2299161j)>, ... ]
2 :category string 3 {"United States"=>499, "Global"=>499, "Europe"=>318}
3 :value double 1075 [nil, nil, nil, nil, nil, ... ], 240 nilsRdatasets: stevedata: thatcher_approval, Margaret Thatcher Satisfaction Ratings, 1980-1990
RedAmber::DataFrame : 125 x 9 Vectors
Vectors : 7 numeric, 1 string, 1 temporal
# key type level data_preview
1 :year uint16 11 [1980, 1980, 1980, 1980, 1980, ... ]
2 :poll_date string 97 ["January", "February", "March", "April", "June", ... ]
3 :date date64 125 [#<DateTime: 1980-01-01T09:00:00+09:00 ((2444240j,0s,0n),+32400s,2299161j)>, #<DateTime: 1980-02-01T09:00:00+09:00 ((2444271j,0s,0n),+32400s,2299161j)>, ... ]
4 :govt_sat uint8 35 [30, 26, 28, 33, 32, ... ], 1 nil
5 :govt_dis uint8 37 [59, 63, 62, 57, 59, ... ], 1 nil
6 :thatcher_sat uint8 33 [39, 37, 37, 43, 42, ... ]
7 :thatcher_dis uint8 34 [50, 55, 56, 50, 52, ... ]
8 :opp_sat uint8 32 [35, 38, 44, 41, 37, ... ], 4 nils
9 :opp_dis uint8 41 [41, 42, 34, 37, 43, ... ], 4 nilsRdatasets: stevedata: therms, Thermometer Ratings for Donald Trump and Barack Obama
RedAmber::DataFrame : 3080 x 2 Vectors
Vectors : 2 numeric
# key type level data_preview
1 :fttrump1 uint8 73 [80, 85, nil, 80, 0, ... ], 7 nils
2 :ftobama1 uint8 81 [75, 15, nil, 50, 100, ... ], 8 nilsRdatasets: stevedata: turnips, Turnip prices in Animal Crossing (New Horizons)
RedAmber::DataFrame : 662 x 3 Vectors
Vectors : 1 numeric, 1 string, 1 temporal
# key type level data_preview
1 :date date64 357 [#<DateTime: 2021-04-11T09:00:00+09:00 ((2459316j,0s,0n),+32400s,2299161j)>, #<DateTime: 2021-04-12T09:00:00+09:00 ((2459317j,0s,0n),+32400s,2299161j)>, ... ]
2 :time string 3 {"5:00 a.m."=>51, "8:00 a.m."=>306, "12:00 p.m."=>305}
3 :price uint16 169 [96, 92, 87, 82, 79, ... ], 5 nilsRdatasets: stevedata: TV16, The Individual Correlates of the Trump Vote in 2016
RedAmber::DataFrame : 64600 x 21 Vectors
Vectors : 19 numeric, 2 strings
# key type level data_preview
1 :uid uint16 64600 [1, 2, 3, 4, 5, ... ]
2 :state string 51 ["New Hampshire", "Louisiana", "Missouri", "Alabama", "Colorado", ... ]
3 :votetrump uint8 3 {1=>18755, nil=>19668, 0=>26177}
4 :age uint8 80 [47, 22, 52, 28, 34, ... ]
5 :female uint8 2 {1=>35069, 0=>29531}
6 :collegeed uint8 2 {0=>41206, 1=>23394}
7 :racef string 8 ["White", "White", "Black", "Black", "White", ... ]
8 :famincr uint8 13 [nil, 6, 4, 1, 7, ... ], 6521 nils
9 :ideo uint8 6 [3, 3, 5, 4, 2, ... ], 4785 nils
10 :pid7na uint8 8 [5, 4, 1, 4, 2, ... ], 2121 nils
11 :bornagain uint8 3 {0=>46371, nil=>43, 1=>18186}
12 :religimp uint8 5 {3=>17275, nil=>34, 4=>23864, 1=>13429, 2=>9998}
13 :churchatd uint8 7 [1, nil, 4, 3, 1, ... ], 740 nils
14 :prayerfreq uint8 8 [3, nil, 5, 5, 2, ... ], 1513 nils
15 :angryracism uint8 6 [2, 1, nil, nil, 2, ... ], 11766 nils
16 :whiteadv uint8 6 [3, 4, nil, nil, 1, ... ], 11763 nils
17 :fearraces uint8 6 [1, 1, nil, nil, 1, ... ], 11825 nils
18 :racerare uint8 6 [3, 1, nil, nil, 1, ... ], 11822 nils
19 :lrelig double 241 [-0.191680628922646, nil, 0.573060093870443, 0.0693560313642333, -1.13017488472139, ... ], 4 nils
20 :lcograc double 36 [0.475293749529462, -0.185681754630724, nil, nil, -1.20408468609332, ... ], 11702 nils
... 1 more Vector ...Rdatasets: stevedata: usa_chn_gdp_forecasts, United States-China GDP and GDP Forecasts, 1960-2050
RedAmber::DataFrame : 182 x 12 Vectors
Vectors : 11 numeric, 1 string
# key type level data_preview
1 :country string 2 {"United States"=>91, "China"=>91}
2 :year uint16 91 [1960, 1961, 1962, 1963, 1964, ... ]
3 :p_gdp double 182 [3151799280242.55, 3305832621422.74, 3470474973513.13, 3645694931211.11, 3827591772815.76, ... ]
4 :p_lo80 double 182 [2865567607300.35, 3029880523098.27, 3188577791689.53, 3358056220332.44, 3532547090469.35, ... ]
5 :p_hi80 double 182 [3413431355674.18, 3600511845471.7, 3750138102953.06, 3921134540920.59, 4101902003761.61, ... ]
6 :gdp double 121 [3173051074322.84, 3246031249032.26, 3444039155223.23, 3595576878053.05, 3804120336980.13, ... ], 62 nils
7 :f_gdp double 63 [nil, nil, nil, nil, nil, ... ], 120 nils
8 :f_lo80 double 63 [nil, nil, nil, nil, nil, ... ], 120 nils
9 :f_hi80 double 63 [nil, nil, nil, nil, nil, ... ], 120 nils
10 :f_lo95 double 63 [nil, nil, nil, nil, nil, ... ], 120 nils
11 :f_hi95 double 63 [nil, nil, nil, nil, nil, ... ], 120 nils
12 :oecd_ltgdpf double 123 [nil, nil, nil, nil, nil, ... ], 60 nilsRdatasets: stevedata: usa_migration, U.S. Inbound/Outbound Migration Data, 1990-2017
RedAmber::DataFrame : 3535 x 5 Vectors
Vectors : 2 numeric, 3 strings
# key type level data_preview
1 :year uint16 7 [1990, 1995, 2000, 2005, 2010, ... ]
2 :country string 1 {"United States of America"=>3535}
3 :category string 2 {"Inbound"=>1645, "Outbound"=>1890}
4 :area string 273 ["Total", "Total", "Total", "Total", "Total", ... ]
5 :count uint32 2294 [23251026, 28451053, 34814053, 39258293, 44183643, ... ], 1080 nilsRdatasets: stevedata: wvs_ccodes, Syncing Word Values Survey Country Codes with CoW Codes
RedAmber::DataFrame : 112 x 3 Vectors
Vectors : 2 numeric, 1 string
# key type level data_preview
1 :s003 uint16 112 [8, 12, 20, 31, 32, ... ]
2 :country string 111 ["Albania", "Algeria", "Andorra", "Azerbaijan", "Argentina", ... ]
3 :ccode uint16 106 [339, 615, 232, 373, 160, ... ], 5 nilsRdatasets: stevedata: wvs_immig, Attitudes about Immigration in the World Values Survey
RedAmber::DataFrame : 310388 x 6 Vectors
Vectors : 5 numeric, 1 string
# key type level data_preview
1 :s002 uint8 4 {3=>77818, 4=>59030, 5=>83975, 6=>89565}
2 :s003 uint16 100 [8, 8, 8, 8, 8, ... ]
3 :country string 98 ["Albania", "Albania", "Albania", "Albania", "Albania", ... ], 2396 nils
4 :s020 uint16 22 [1998, 1998, 1998, 1998, 1998, ... ]
5 :uid uint32 310388 [38145, 38146, 38147, 38148, 38149, ... ]
6 :e143 uint8 5 {1=>22261, 2=>74797, nil=>125109, 3=>67764, 4=>20457}Rdatasets: stevedata: wvs_justifbribe, Attitudes about the Justifiability of Bribe-Taking in the World Values Survey
RedAmber::DataFrame : 348532 x 6 Vectors
Vectors : 5 numeric, 1 string
# key type level data_preview
1 :s002 uint8 6 [1, 1, 1, 1, 1, ... ]
2 :s003 uint16 100 [32, 32, 32, 32, 32, ... ]
3 :country string 98 ["Argentina", "Argentina", "Argentina", "Argentina", "Argentina", ... ], 2396 nils
4 :s020 uint16 28 [1984, 1984, 1984, 1984, 1984, ... ]
5 :uid uint32 348532 [1, 2, 3, 4, 5, ... ]
6 :f117 uint8 11 [1, 1, 1, 4, 5, ... ], 15532 nilsRdatasets: stevedata: wvs_usa_abortion, Attitudes on the Justifiability of Abortion in the United States (World Values Survey, 1982-2011)
RedAmber::DataFrame : 10387 x 16 Vectors
Vectors : 16 numeric
# key type level data_preview
1 :wvsccode uint16 1 {840=>10387}
2 :wave uint8 6 [1, 1, 1, 1, 1, ... ]
3 :year uint16 6 [1982, 1982, 1982, 1982, 1982, ... ]
4 :aj uint8 11 [5, 5, nil, 1, 5, ... ], 299 nils
5 :age uint8 80 [40, 43, 18, 18, 22, ... ], 31 nils
6 :collegeed uint8 3 {nil=>4173, 0=>4598, 1=>1616}
7 :female uint8 3 {0=>4938, 1=>5409, nil=>40}
8 :unemployed uint8 3 {0=>9591, nil=>165, 1=>631}
9 :ideology uint8 11 [8, nil, 10, nil, nil, ... ], 811 nils
10 :satisfinancial uint8 11 [5, 3, 2, 6, 5, ... ], 70 nils
11 :postma4 int8 4 {nil=>2537, 0=>4797, 1=>1681, -1=>1372}
12 :cai int8 5 {0=>3345, -2=>1024, -1=>2427, 1=>2564, 2=>1027}
13 :trustmostpeople uint8 3 {1=>4134, 0=>6058, nil=>195}
14 :godimportant uint8 11 [10, 10, 8, 10, 5, ... ], 132 nils
15 :respectauthority int8 4 {1=>7366, 0=>2122, nil=>122, -1=>777}
16 :nationalpride uint8 3 {1=>7209, nil=>247, 0=>2931}Rdatasets: stevedata: wvs_usa_educat, Education Categories for the United States in the World Values Survey
RedAmber::DataFrame : 42 x 6 Vectors
Vectors : 3 numeric, 3 strings
# key type level data_preview
1 :x025 uint8 9 [1, 1, 1, 2, 2, ... ], 11 nils
2 :x025cswvs uint32 32 [840102, 840115, 840116, 840001, 840103, ... ]
3 :n uint16 41 [14, 5, 78, 47, 27, ... ]
4 :x025meaning string 9 ["Inadequately completed elementary education", "Inadequately completed elementary education", "Inadequately completed elementary education", "Completed (compulsory) elementary education", "Completed (compulsory) elementary education", ... ], 11 nils
5 :x025cswvsmeaning string 31 ["US: 1st, 2nd, 3rd, or 4th grade", "US: No formal education", "US: Incomplete primary school", "US: Less than high school/Early childhood education and kind", "US: 5th or 6th grade", ... ]
6 :educat string 7 ["No Formal Education, Incomplete Primary Education", "No Formal Education, Incomplete Primary Education", "No Formal Education, Incomplete Primary Education", "Complete Primary Education", "Complete Primary Education", ... ]Rdatasets: stevedata: yugo_sales, Yugo Sales in the United States, 1985-1992
RedAmber::DataFrame : 24 x 3 Vectors
Vectors : 2 numeric, 1 string
# key type level data_preview
1 :year uint16 8 [1985, 1986, 1987, 1988, 1989, ... ]
2 :car string 3 {"Hyundai Excel"=>8, "Yugo"=>8, "Toyota Tercel"=>8}
3 :sales uint32 23 [nil, 168882, 263610, 264282, 148563, ... ], 1 nilRdatasets: survival: cancer, NCCTG Lung Cancer Data
RedAmber::DataFrame : 228 x 10 Vectors
Vectors : 10 numeric
# key type level data_preview
1 :inst uint8 19 [3, 3, 3, 5, 1, ... ], 1 nil
2 :time uint16 186 [306, 455, 1010, 210, 883, ... ]
3 :status uint8 2 {2=>165, 1=>63}
4 :age uint8 42 [74, 68, 56, 57, 60, ... ]
5 :sex uint8 2 {1=>138, 2=>90}
6 :"ph.ecog" uint8 5 {1=>113, 0=>63, 2=>50, nil=>1, 3=>1}
7 :"ph.karno" uint8 7 [90, 90, 90, 90, 100, ... ], 1 nil
8 :"pat.karno" uint8 9 [100, 90, 90, 60, 90, ... ], 3 nils
9 :"meal.cal" uint16 61 [1175, 1225, nil, 1150, nil, ... ], 47 nils
10 :"wt.loss" int8 54 [nil, 15, 15, 11, 0, ... ], 14 nilsRdatasets: survival: pbc, Mayo Clinic Primary Biliary Cholangitis Data
RedAmber::DataFrame : 418 x 20 Vectors
Vectors : 19 numeric, 1 string
# key type level data_preview
1 :id uint16 418 [1, 2, 3, 4, 5, ... ]
2 :time uint16 399 [400, 4500, 1012, 1925, 1504, ... ]
3 :status uint8 3 {2=>161, 0=>232, 1=>25}
4 :trt uint8 3 {1=>158, 2=>154, nil=>106}
5 :age double 344 [58.7652292950034, 56.4462696783025, 70.072553045859, 54.7405886379192, 38.1054072553046, ... ]
6 :sex string 2 {"f"=>374, "m"=>44}
7 :ascites uint8 3 {1=>24, 0=>288, nil=>106}
8 :hepato uint8 3 {1=>160, 0=>152, nil=>106}
9 :spiders uint8 3 {1=>90, 0=>222, nil=>106}
10 :edema double 3 {1.0=>20, 0.0=>354, 0.5=>44}
11 :bili double 98 [14.5, 1.1, 1.4, 1.8, 3.4, ... ]
12 :chol uint16 202 [261, 302, 176, 244, 279, ... ], 134 nils
13 :albumin double 154 [2.6, 4.14, 3.48, 2.54, 3.53, ... ]
14 :copper uint16 159 [156, 54, 210, 64, 143, ... ], 108 nils
15 :"alk.phos" double 296 [1718.0, 7394.8, 516.0, 6121.8, 671.0, ... ], 106 nils
16 :ast double 180 [137.95, 113.52, 96.1, 60.63, 113.15, ... ], 106 nils
17 :trig uint16 147 [172, 88, 55, 92, 72, ... ], 136 nils
18 :platelet uint16 244 [190, 221, 151, 183, 136, ... ], 11 nils
19 :protime double 49 [12.2, 10.6, 12.0, 10.3, 10.9, ... ], 2 nils
20 :stage uint8 5 {4=>144, 3=>155, 2=>92, 1=>21, nil=>6}Rdatasets: survival: rhDNase, rhDNASE data set
RedAmber::DataFrame : 767 x 8 Vectors
Vectors : 6 numeric, 2 temporal
# key type level data_preview
1 :id uint16 647 [1, 2, 3, 4, 5, ... ]
2 :inst uint8 51 [1, 1, 1, 1, 1, ... ]
3 :trt uint8 2 {1=>374, 0=>393}
4 :"entry.dt" date64 67 [#<DateTime: 1992-03-20T09:00:00+09:00 ((2448702j,0s,0n),+32400s,2299161j)>, #<DateTime: 1992-03-24T09:00:00+09:00 ((2448706j,0s,0n),+32400s,2299161j)>, ... ]
5 :"end.dt" date64 92 [#<DateTime: 1992-09-04T09:00:00+09:00 ((2448870j,0s,0n),+32400s,2299161j)>, #<DateTime: 1992-09-09T09:00:00+09:00 ((2448875j,0s,0n),+32400s,2299161j)>, ... ]
6 :fev double 37 [28.8, 64.0, 67.2, 57.6, 57.6, ... ]
7 :ivstart int16 158 [nil, nil, 65, nil, nil, ... ], 400 nils
8 :ivstop uint8 150 [nil, nil, 75, nil, nil, ... ], 400 nilsRdatasets: survival: transplant, Liver transplant waiting list
RedAmber::DataFrame : 815 x 6 Vectors
Vectors : 3 numeric, 3 strings
# key type level data_preview
1 :age uint8 56 [47, 55, 52, 40, 70, ... ], 18 nils
2 :sex string 2 {"m"=>447, "f"=>368}
3 :abo string 4 {"B"=>103, "A"=>325, "O"=>346, "AB"=>41}
4 :year uint16 10 [1994, 1991, 1996, 1995, 1996, ... ]
5 :futime uint16 397 [1197, 28, 85, 231, 1271, ... ]
6 :event string 4 {"death"=>66, "ltx"=>636, "censored"=>76, "withdraw"=>37}Rdatasets: survival: udca, Data from a trial of usrodeoxycholic acid
RedAmber::DataFrame : 170 x 15 Vectors
Vectors : 5 numeric, 10 temporal
# key type level data_preview
1 :id uint8 170 [1, 2, 3, 4, 5, ... ]
2 :trt uint8 2 {1=>86, 0=>84}
3 :"entry.dt" date64 141 [#<DateTime: 1988-04-21T09:00:00+09:00 ((2447273j,0s,0n),+32400s,2299161j)>, #<DateTime: 1988-04-27T09:00:00+09:00 ((2447279j,0s,0n),+32400s,2299161j)>, ... ]
4 :"last.dt" date64 60 [#<DateTime: 1993-06-30T09:00:00+09:00 ((2449169j,0s,0n),+32400s,2299161j)>, #<DateTime: 1993-06-30T09:00:00+09:00 ((2449169j,0s,0n),+32400s,2299161j)>, ... ]
5 :stage uint8 2 {1=>117, 0=>53}
6 :bili double 48 [1.0, 1.7, 0.5, 1.4, 1.1, ... ]
7 :riskscore double 48 [5.1, 4.2, 3.4, 5.0, 4.3, ... ], 1 nil
8 :"death.dt" date64 17 [nil, nil, ... ]
9 :"tx.dt" date64 13 [nil, nil, ... ]
10 :"hprogress.dt" date64 21 [nil, nil, ... ]
11 :"varices.dt" date64 25 [nil, nil, ... ]
12 :"ascites.dt" date64 7 [nil, nil, ... ]
13 :"enceph.dt" date64 5 [nil, nil, ... ]
14 :"double.dt" date64 17 [nil, nil, ... ]
15 :"worsen.dt" date64 17 [nil, #<DateTime: 1992-04-22T09:00:00+09:00 ((2448735j,0s,0n),+32400s,2299161j)>, ... ]Rdatasets: tidyr: smiths, Some data about the Smith family
RedAmber::DataFrame : 2 x 5 Vectors
Vectors : 4 numeric, 1 string
# key type level data_preview
1 :subject string 2 ["John Smith", "Mary Smith"]
2 :time uint8 1 {1=>2}
3 :age uint8 2 [33, nil], 1 nil
4 :weight uint8 2 [90, nil], 1 nil
5 :height double 2 [1.87, 1.54]Rdatasets: tidyr: us_rent_income, US rent and income data
RedAmber::DataFrame : 104 x 5 Vectors
Vectors : 3 numeric, 2 strings
# key type level data_preview
1 :GEOID double 52 [1.0, 1.0, 2.0, 2.0, 4.0, ... ]
2 :NAME string 52 ["Alabama", "Alabama", "Alaska", "Alaska", "Arizona", ... ]
3 :variable string 2 {"income"=>52, "rent"=>52}
4 :estimate uint16 101 [24476, 747, 32940, 1200, 27517, ... ], 1 nil
5 :moe uint16 59 [136, 3, 508, 13, 148, ... ], 1 nilRdatasets: tidyr: who, World Health Organization TB data
RedAmber::DataFrame : 7240 x 60 Vectors
Vectors : 57 numeric, 3 strings
# key type level data_preview
1 :country string 219 ["Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", ... ]
2 :iso2 string 219 ["AF", "AF", "AF", "AF", "AF", ... ], 34 nils
3 :iso3 string 219 ["AFG", "AFG", "AFG", "AFG", "AFG", ... ]
4 :year uint16 34 [1980, 1981, 1982, 1983, 1984, ... ]
5 :new_sp_m014 uint16 416 [nil, nil, nil, nil, nil, ... ], 4067 nils
6 :new_sp_m1524 uint32 1089 [nil, nil, nil, nil, nil, ... ], 4031 nils
7 :new_sp_m2534 uint32 1279 [nil, nil, nil, nil, nil, ... ], 4034 nils
8 :new_sp_m3544 uint32 1205 [nil, nil, nil, nil, nil, ... ], 4021 nils
9 :new_sp_m4554 uint32 1072 [nil, nil, nil, nil, nil, ... ], 4017 nils
10 :new_sp_m5564 uint16 901 [nil, nil, nil, nil, nil, ... ], 4022 nils
11 :new_sp_m65 uint32 846 [nil, nil, nil, nil, nil, ... ], 4031 nils
12 :new_sp_f014 uint16 452 [nil, nil, nil, nil, nil, ... ], 4066 nils
13 :new_sp_f1524 uint16 1023 [nil, nil, nil, nil, nil, ... ], 4046 nils
14 :new_sp_f2534 uint16 1086 [nil, nil, nil, nil, nil, ... ], 4040 nils
15 :new_sp_f3544 uint16 926 [nil, nil, nil, nil, nil, ... ], 4041 nils
16 :new_sp_f4554 uint16 798 [nil, nil, nil, nil, nil, ... ], 4036 nils
17 :new_sp_f5564 uint16 664 [nil, nil, nil, nil, nil, ... ], 4045 nils
18 :new_sp_f65 uint16 663 [nil, nil, nil, nil, nil, ... ], 4043 nils
19 :new_sn_m014 uint16 263 [nil, nil, nil, nil, nil, ... ], 6195 nils
20 :new_sn_m1524 uint16 304 [nil, nil, nil, nil, nil, ... ], 6210 nils
... 40 more Vectors ...Rdatasets: validate: retailers, data on Dutch supermarkets
RedAmber::DataFrame : 60 x 10 Vectors
Vectors : 9 numeric, 1 string
# key type level data_preview
1 :size string 4 {"sc0"=>9, "sc3"=>26, "sc1"=>7, "sc2"=>18}
2 :"incl.prob" double 3 {0.02=>16, 0.14=>26, 0.05=>18}
3 :staff uint8 24 [75, 9, nil, nil, nil, ... ], 6 nils
4 :turnover uint32 57 [nil, 1607, 6886, 3861, nil, ... ], 4 nils
5 :"other.rev" int32 22 [nil, nil, -33, 13, 37, ... ], 36 nils
6 :"total.rev" uint32 59 [1130, 1607, 6919, 3874, 5602, ... ], 2 nils
7 :"staff.costs" uint32 50 [nil, 131, 324, 290, 314, ... ], 10 nils
8 :"total.costs" uint32 56 [18915, 1544, 6493, 3600, 5530, ... ], 5 nils
9 :profit int32 52 [20045, 63, 426, 274, 72, ... ], 5 nils
10 :vat uint16 49 [nil, nil, nil, nil, nil, ... ], 12 nilsRdatasets: validate: SBS2000, data on Dutch supermarkets
RedAmber::DataFrame : 60 x 11 Vectors
Vectors : 9 numeric, 2 strings
# key type level data_preview
1 :id string 60 ["RET01", "RET02", "RET03", "RET04", "RET05", ... ]
2 :size string 4 {"sc0"=>9, "sc3"=>26, "sc1"=>7, "sc2"=>18}
3 :"incl.prob" double 3 {0.02=>16, 0.14=>26, 0.05=>18}
4 :staff uint8 24 [75, 9, nil, nil, nil, ... ], 6 nils
5 :turnover uint32 57 [nil, 1607, 6886, 3861, nil, ... ], 4 nils
6 :"other.rev" int32 22 [nil, nil, -33, 13, 37, ... ], 36 nils
7 :"total.rev" uint32 59 [1130, 1607, 6919, 3874, 5602, ... ], 2 nils
8 :"staff.costs" uint32 50 [nil, 131, 324, 290, 314, ... ], 10 nils
9 :"total.costs" uint32 56 [18915, 1544, 6493, 3600, 5530, ... ], 5 nils
10 :profit int32 52 [20045, 63, 426, 274, 72, ... ], 5 nils
11 :vat uint16 49 [nil, nil, nil, nil, nil, ... ], 12 nilsRdatasets: vcd: Bundesliga, Ergebnisse der Fussball-Bundesliga
RedAmber::DataFrame : 14018 x 7 Vectors
Vectors : 4 numeric, 2 strings, 1 temporal
# key type level data_preview
1 :HomeTeam string 52 ["Werder Bremen", "Hertha BSC Berlin", "Preussen Muenster", "Eintracht Frankfurt", "Karlsruher SC", ... ]
2 :AwayTeam string 52 ["Borussia Dortmund", "1. FC Nuernberg", "Hamburger SV", "1. FC Kaiserslautern", "Meidericher SV", ... ]
3 :HomeGoals uint8 13 [3, 1, 1, 1, 1, ... ]
4 :AwayGoals uint8 10 [2, 1, 1, 1, 4, ... ]
5 :Round uint8 38 [1, 1, 1, 1, 1, ... ]
6 :Year uint16 46 [1963, 1963, 1963, 1963, 1963, ... ]
7 :Date date64 3396 [#<DateTime: 1963-08-24T19:30:00+09:00 ((2438266j,37800s,0n),+32400s,2299161j)>, #<DateTime: 1963-08-24T19:30:00+09:00 ((2438266j,37800s,0n),+32400s,2299161j)>, ... ]Rdatasets: wooldridge: athlet1, athlet1
RedAmber::DataFrame : 118 x 23 Vectors
Vectors : 22 numeric, 1 string
# key type level data_preview
1 :year uint16 2 {1992=>59, 1993=>59}
2 :apps uint16 115 [6245, 7677, 13327, 19860, 10422, ... ]
3 :top25 uint8 45 [49, 58, 57, 57, 37, ... ], 25 nils
4 :ver500 uint8 48 [nil, nil, 36, 36, 28, ... ], 30 nils
5 :mth500 uint8 41 [nil, nil, 58, 58, 58, ... ], 30 nils
6 :stufac uint8 18 [20, 15, 16, 16, 20, ... ]
7 :bowl uint8 2 {1=>54, 0=>64}
8 :btitle uint8 2 {0=>104, 1=>14}
9 :finfour uint8 2 {0=>111, 1=>7}
10 :lapps double 115 [8.73953628540039, 8.94598388671875, 9.4975471496582, 9.89646339416504, 9.25167465209961, ... ]
11 :d93 uint8 2 {0=>59, 1=>59}
12 :avg500 double 62 [nil, nil, 47.0, 47.0, 43.0, ... ], 30 nils
13 :cfinfour int8 4 {nil=>59, 0=>54, -1=>2, 1=>3}
14 :clapps double 58 [nil, 0.206447601318359, nil, 0.398916244506836, nil, ... ], 59 nils
15 :cstufac int8 12 [nil, -5, nil, 0, nil, ... ], 59 nils
16 :cbowl int8 4 {nil=>59, 0=>41, 1=>9, -1=>9}
17 :cavg500 double 21 [nil, nil, nil, 0.0, nil, ... ], 77 nils
18 :cbtitle int8 4 {nil=>1, 0=>93, 1=>12, -1=>12}
19 :lapps_1 double 60 [nil, 8.73953628540039, nil, 9.4975471496582, nil, ... ], 59 nils
20 :school string 59 ["alabama", "alabama", "arizona", "arizona", "arizona state", ... ]
... 3 more Vectors ...Rdatasets: wooldridge: athlet2, athlet2
RedAmber::DataFrame : 30 x 10 Vectors
Vectors : 10 numeric
# key type level data_preview
1 :dscore int8 23 [10, -14, 23, 8, -12, ... ]
2 :dinstt int16 30 [-409, nil, -654, -222, -10, ... ], 1 nil
3 :doutstt int16 26 [-4679, -66, -637, 456, 208, ... ]
4 :htpriv uint8 2 {0=>26, 1=>4}
5 :vtpriv uint8 2 {0=>27, 1=>3}
6 :dapps int16 30 [-1038, -7051, 6209, -129, 794, ... ]
7 :htwrd uint8 2 {1=>21, 0=>9}
8 :vtwrd uint8 2 {1=>17, 0=>13}
9 :dwinrec int8 3 {0=>14, 1=>10, -1=>6}
10 :dpriv int8 3 {0=>23, 1=>4, -1=>3}Rdatasets: wooldridge: beveridge, beveridge
RedAmber::DataFrame : 135 x 8 Vectors
Vectors : 7 numeric, 1 temporal
# key type level data_preview
1 :month date64 135 [#<DateTime: 2000-12-01T09:00:00+09:00 ((2451880j,0s,0n),+32400s,2299161j)>, #<DateTime: 2001-01-01T09:00:00+09:00 ((2451911j,0s,0n),+32400s,2299161j)>, ... ]
2 :urate double 46 [3.8, 4.1, 4.1, 4.1, 4.2, ... ]
3 :vrate double 23 [4.0, 4.1, 3.7, 3.7, 3.6, ... ]
4 :t uint8 135 [1, 2, 3, 4, 5, ... ]
5 :urate_1 double 47 [nil, 3.79999995231628, 4.09999990463257, 4.09999990463257, 4.09999990463257, ... ], 1 nil
6 :vrate_1 double 24 [nil, 4.0, 4.09999990463257, 3.70000004768372, 3.70000004768372, ... ], 1 nil
7 :curate double 13 [nil, 0.300000011920929, 0.0, 0.0, 0.100000001490116, ... ], 1 nil
8 :cvrate double 11 [nil, 0.100000001490116, -0.400000005960464, 0.0, -0.100000001490116, ... ], 1 nilRdatasets: wooldridge: big9salary, big9salary
RedAmber::DataFrame : 786 x 30 Vectors
Vectors : 30 numeric
# key type level data_preview
1 :id uint16 262 [101, 101, 101, 102, 102, ... ]
2 :year uint8 3 {92=>262, 95=>262, 99=>262}
3 :salary uint32 616 [nil, nil, 107100, 79420, 88239, ... ], 107 nils
4 :pubindx double 558 [30.5400009155273, 30.9599990844727, 40.4500007629395, 33.5400009155273, 33.8899993896484, ... ]
5 :totpge double 518 [92.6669998168945, 107.169998168945, 186.5, 127.5, 133.0, ... ], 39 nils
6 :assist uint8 3 {0=>558, 1=>66, nil=>162}
7 :assoc uint8 3 {0=>517, nil=>162, 1=>107}
8 :prof uint8 3 {1=>430, 0=>193, nil=>163}
9 :chair uint8 3 {0=>645, 1=>35, nil=>106}
10 :top20phd uint8 3 {0=>165, 1=>540, nil=>81}
11 :yearphd uint8 45 [73, 73, 73, 76, 76, ... ], 87 nils
12 :female uint8 3 {0=>690, 1=>63, nil=>33}
13 :osu uint8 2 {0=>693, 1=>93}
14 :iowa uint8 2 {0=>711, 1=>75}
15 :indiana uint8 2 {1=>63, 0=>723}
16 :purdue uint8 2 {0=>726, 1=>60}
17 :msu uint8 2 {0=>678, 1=>108}
18 :minn uint8 2 {0=>705, 1=>81}
19 :mich uint8 2 {0=>678, 1=>108}
20 :wisc uint8 2 {0=>687, 1=>99}
... 10 more Vectors ...Rdatasets: wooldridge: bwght, bwght
RedAmber::DataFrame : 1388 x 14 Vectors
Vectors : 14 numeric
# key type level data_preview
1 :faminc double 27 [13.5, 7.5, 0.5, 15.5, 27.5, ... ]
2 :cigtax double 28 [16.5, 16.5, 16.5, 16.5, 16.5, ... ]
3 :cigprice double 46 [122.300003051758, 122.300003051758, 122.300003051758, 122.300003051758, 122.300003051758, ... ]
4 :bwght uint16 116 [109, 133, 129, 126, 134, ... ]
5 :fatheduc uint8 19 [12, 6, nil, 12, 14, ... ], 196 nils
6 :motheduc uint8 18 [12, 12, 12, 12, 12, ... ], 1 nil
7 :parity uint8 6 [1, 2, 2, 2, 2, ... ]
8 :male uint8 2 {1=>723, 0=>665}
9 :white uint8 2 {1=>1089, 0=>299}
10 :cigs uint8 18 [0, 0, 0, 0, 0, ... ]
11 :lbwght double 116 [4.6913480758667, 4.8903489112854, 4.85981225967407, 4.83628177642822, 4.89784002304077, ... ]
12 :bwghtlbs double 116 [6.8125, 8.3125, 8.0625, 7.875, 8.375, ... ]
13 :packs double 18 [0.0, 0.0, 0.0, 0.0, 0.0, ... ]
14 :lfaminc double 27 [2.60268974304199, 2.01490306854248, -0.6931471824646, 2.74083995819092, 3.31418609619141, ... ]Rdatasets: wooldridge: bwght2, bwght2
RedAmber::DataFrame : 1832 x 23 Vectors
Vectors : 23 numeric
# key type level data_preview
1 :mage uint8 29 [26, 29, 33, 28, 23, ... ]
2 :meduc uint8 15 [12, 12, 12, 17, 13, ... ], 30 nils
3 :monpre uint8 11 [2, 2, 1, 5, 2, ... ], 5 nils
4 :npvis uint8 33 [12, 12, 12, 8, 6, ... ], 68 nils
5 :fage uint8 43 [34, 32, 36, 32, 24, ... ], 6 nils
6 :feduc uint8 14 [16, 12, 16, 17, 16, ... ], 47 nils
7 :bwght uint16 557 [3060, 3730, 2530, 3289, 3590, ... ]
8 :omaps uint8 12 [9, 8, 8, 8, 6, ... ], 3 nils
9 :fmaps uint8 8 [9, 9, 9, 9, 8, ... ], 3 nils
10 :cigs uint8 19 [0, nil, 0, 0, 0, ... ], 110 nils
11 :drink uint8 7 [0, nil, 0, 0, 0, ... ], 115 nils
12 :lbw uint8 2 {0=>1802, 1=>30}
13 :vlbw uint8 2 {0=>1819, 1=>13}
14 :male uint8 2 {1=>941, 0=>891}
15 :mwhte uint8 2 {0=>208, 1=>1624}
16 :mblck uint8 2 {0=>1723, 1=>109}
17 :moth uint8 2 {1=>99, 0=>1733}
18 :fwhte uint8 2 {0=>202, 1=>1630}
19 :fblck uint8 2 {0=>1725, 1=>107}
20 :foth uint8 2 {1=>95, 0=>1737}
... 3 more Vectors ...Rdatasets: wooldridge: card, card
RedAmber::DataFrame : 3010 x 34 Vectors
Vectors : 34 numeric
# key type level data_preview
1 :id uint16 3010 [2, 3, 4, 5, 6, ... ]
2 :nearc2 uint8 2 {0=>1683, 1=>1327}
3 :nearc4 uint8 2 {0=>957, 1=>2053}
4 :educ uint8 18 [7, 12, 12, 11, 12, ... ]
5 :age uint8 11 [29, 27, 34, 27, 34, ... ]
6 :fatheduc uint8 20 [nil, 8, 14, 11, 8, ... ], 690 nils
7 :motheduc uint8 20 [nil, 8, 12, 12, 7, ... ], 353 nils
8 :weight uint32 348 [158413, 380166, 367470, 380166, 367470, ... ]
9 :momdad14 uint8 2 {1=>2376, 0=>634}
10 :sinmom14 uint8 2 {0=>2707, 1=>303}
11 :step14 uint8 2 {0=>2893, 1=>117}
12 :reg661 uint8 2 {1=>140, 0=>2870}
13 :reg662 uint8 2 {0=>2526, 1=>484}
14 :reg663 uint8 2 {0=>2421, 1=>589}
15 :reg664 uint8 2 {0=>2817, 1=>193}
16 :reg665 uint8 2 {0=>2383, 1=>627}
17 :reg666 uint8 2 {0=>2721, 1=>289}
18 :reg667 uint8 2 {0=>2679, 1=>331}
19 :reg668 uint8 2 {0=>2925, 1=>85}
20 :reg669 uint8 2 {0=>2738, 1=>272}
... 14 more Vectors ...Rdatasets: wooldridge: catholic, catholic
RedAmber::DataFrame : 7430 x 13 Vectors
Vectors : 13 numeric
# key type level data_preview
1 :id uint32 7430 [124902, 124915, 124916, 124932, 124944, ... ]
2 :read12 double 2981 [61.41, 58.34, 59.33, 49.59, 57.62, ... ]
3 :math12 double 3114 [49.77, 59.84, 50.38, 45.03, 54.26, ... ]
4 :female uint8 2 {0=>3586, 1=>3844}
5 :asian uint8 2 {0=>7046, 1=>384}
6 :hispan uint8 2 {0=>6661, 1=>769}
7 :black uint8 2 {0=>6905, 1=>525}
8 :motheduc double 8 [14.0, 14.0, 14.0, 12.0, 12.0, ... ]
9 :fatheduc double 8 [12.0, 14.0, 11.0, 14.0, 12.0, ... ]
10 :lfaminc double 15 [10.30895, 10.30895, 10.30895, 10.30895, 10.65726, ... ]
11 :hsgrad uint8 3 {1=>5554, nil=>1460, 0=>416}
12 :cathhs uint8 2 {0=>6978, 1=>452}
13 :parcath uint8 2 {1=>2570, 0=>4860}Rdatasets: wooldridge: cement, cement
RedAmber::DataFrame : 312 x 30 Vectors
Vectors : 30 numeric
# key type level data_preview
1 :year uint16 26 [1964, 1964, 1964, 1964, 1964, ... ]
2 :month uint8 12 [1, 2, 3, 4, 5, ... ]
3 :prccem uint16 136 [nil, nil, nil, nil, nil, ... ], 11 nils
4 :ipcem double 307 [0.474249988794327, 0.531229972839355, 0.642549991607666, 0.825850009918213, 1.0271999835968, ... ], 2 nils
5 :prcpet double 163 [13.3999996185303, 13.3999996185303, 13.3999996185303, 13.3999996185303, 13.3999996185303, ... ]
6 :rresc uint32 310 [115401, 115118, 123663, 116178, 111034, ... ]
7 :rnonc uint32 312 [142180, 144190, 145577, 150793, 149259, ... ]
8 :ip double 249 [44.5999984741211, 45.9000015258789, 46.2000007629395, 46.9000015258789, 47.0999984741211, ... ]
9 :rdefs double 184 [nil, 1.66340005397797, 1.66260004043579, 1.66260004043579, 1.66100001335144, ... ], 5 nils
10 :milemp uint16 221 [2687, 2696, 2693, 2694, 2690, ... ], 5 nils
11 :gprc double 165 [nil, nil, nil, nil, nil, ... ], 12 nils
12 :gcem double 310 [nil, 0.113460436463356, 0.190249606966972, 0.250968545675278, 0.218178749084473, ... ], 3 nils
13 :gprcpet double 181 [nil, 0.0, 0.0, 0.0, 0.0, ... ], 1 nil
14 :gres double 312 [nil, -0.00245533022098243, 0.0716024339199066, -0.0624366253614426, -0.0452870354056358, ... ], 1 nil
15 :gnon double 312 [nil, 0.0140380142256618, 0.00957328174263239, 0.0352028794586658, -0.0102249830961227, ... ], 1 nil
16 :gip double 308 [nil, 0.0287313256412745, 0.00651466427370906, 0.0150378933176398, 0.00425526080653071, ... ], 1 nil
17 :gdefs double 205 [nil, nil, -0.000481066468637437, 0.0, -0.000962827762123197, ... ], 6 nils
18 :gmilemp double 290 [nil, 0.00334386341273785, -0.00111337925773114, 0.000371264148270711, -0.00148588442243636, ... ], 6 nils
19 :jan uint8 2 {1=>26, 0=>286}
20 :feb uint8 2 {0=>286, 1=>26}
... 10 more Vectors ...Rdatasets: wooldridge: consump, consump
RedAmber::DataFrame : 37 x 24 Vectors
Vectors : 24 numeric
# key type level data_preview
1 :year uint16 37 [1959, 1960, 1961, 1962, 1963, ... ]
2 :i3 double 37 [3.41000008583069, 2.9300000667572, 2.38000011444092, 2.77999997138977, 3.16000008583069, ... ]
3 :inf double 30 [0.699999988079071, 1.70000004768372, 1.0, 1.0, 1.29999995231628, ... ]
4 :rdisp double 37 [1530.09997558594, 1565.40002441406, 1615.80004882812, 1693.69995117188, 1755.5, ... ]
5 :rnondc double 37 [606.299987792969, 615.400024414062, 626.700012207031, 646.5, 660.0, ... ]
6 :rserv double 37 [687.400024414062, 717.400024414062, 746.5, 783.400024414062, 818.700012207031, ... ]
7 :pop uint32 37 [177830, 180671, 183691, 186538, 189242, ... ]
8 :y double 37 [8604.28515625, 8664.3681640625, 8796.2939453125, 9079.6513671875, 9276.482421875, ... ]
9 :rcons double 37 [1293.69995117188, 1332.80004882812, 1373.19995117188, 1429.90002441406, 1478.69995117188, ... ]
10 :c double 37 [7274.92529296875, 7376.9453125, 7475.59716796875, 7665.46240234375, 7813.80419921875, ... ]
11 :r3 double 36 [2.71000003814697, 1.23000001907349, 1.38000011444092, 1.77999997138977, 1.8600001335144, ... ]
12 :lc double 37 [8.89218902587891, 8.90611457824707, 8.91939926147461, 8.94447994232178, 8.96364688873291, ... ]
13 :ly double 37 [9.06001567840576, 9.06697463989258, 9.08208560943604, 9.11379146575928, 9.13523769378662, ... ]
14 :gc double 37 [nil, 0.0139255523681641, 0.0132846832275391, 0.025080680847168, 0.0191669464111328, ... ], 1 nil
15 :gy double 37 [nil, 0.00695896148681641, 0.015110969543457, 0.0317058563232422, 0.0214462280273438, ... ], 1 nil
16 :gc_1 double 36 [nil, nil, 0.0139255523681641, 0.0132846832275391, 0.025080680847168, ... ], 2 nils
17 :gy_1 double 36 [nil, nil, 0.00695896148681641, 0.015110969543457, 0.0317058563232422, ... ], 2 nils
18 :r3_1 double 36 [nil, 2.71000003814697, 1.23000001907349, 1.38000011444092, 1.77999997138977, ... ], 1 nil
19 :lc_ly double 37 [-0.167826652526855, -0.160860061645508, -0.162686347961426, -0.1693115234375, -0.171590805053711, ... ]
20 :lc_ly_1 double 37 [nil, -0.167826652526855, -0.160860061645508, -0.162686347961426, -0.1693115234375, ... ], 1 nil
... 4 more Vectors ...Rdatasets: wooldridge: countymurders, countymurders
RedAmber::DataFrame : 37349 x 20 Vectors
Vectors : 20 numeric
# key type level data_preview
1 :arrests uint16 311 [2, 3, 2, 7, 3, ... ], 504 nils
2 :countyid uint16 2197 [1001, 1001, 1001, 1001, 1001, ... ]
3 :density double 23374 [54.05, 53.66, 53.75, 53.78, 53.91, ... ]
4 :popul uint32 24313 [32216, 31984, 32036, 32056, 32128, ... ]
5 :perc1019 double 9997 [20.63, 20.19, 19.66, 19.1, 18.54, ... ]
6 :perc2029 double 10957 [15.28, 15.55, 15.73, 15.88, 15.92, ... ]
7 :percblack double 12958 [22.33, 22.07, 21.8, 21.53, 21.26, ... ]
8 :percmale double 9988 [40.25, 40.36, 40.42, 40.47, 40.51, ... ]
9 :rpcincmaint double 7298 [167.67, 167.99, 166.63, 176.53, 166.25, ... ], 3 nils
10 :rpcpersinc double 32069 [8780.8, 8232.8, 8327.61, 8545.55, 8965.16, ... ], 3 nils
11 :rpcunemins double 3968 [29.16, 43.92, 71.41, 72.22, 40.36, ... ], 3 nils
12 :year uint16 17 [1980, 1981, 1982, 1983, 1984, ... ]
13 :murders uint16 356 [2, 1, 3, 7, 2, ... ]
14 :murdrate double 18077 [0.6208096, 0.3126563, 0.9364465, 2.183679, 0.62251, ... ]
15 :arrestrate double 17259 [0.6208095, 0.937969, 0.6242977, 2.183679, 0.933765, ... ], 504 nils
16 :statefips uint8 46 [1, 1, 1, 1, 1, ... ]
17 :countyfips uint16 274 [1, 1, 1, 1, 1, ... ]
18 :execs uint8 8 [0, 0, 0, 0, 0, ... ]
19 :lpopul double 24295 [10.38022, 10.37299, 10.37462, 10.37524, 10.37748, ... ]
20 :execrate double 206 [0.0, 0.0, 0.0, 0.0, 0.0, ... ]Rdatasets: wooldridge: cps91, cps91
RedAmber::DataFrame : 5634 x 24 Vectors
Vectors : 24 numeric
# key type level data_preview
1 :husage uint8 65 [42, 26, 56, 35, 42, ... ]
2 :husunion uint8 3 {0=>3184, nil=>1486, 1=>964}
3 :husearns uint16 673 [568, 600, 1500, 0, 450, ... ]
4 :huseduc uint8 19 [14, 14, 14, 12, 11, ... ]
5 :husblck uint8 2 {0=>5299, 1=>335}
6 :hushisp uint8 2 {0=>5261, 1=>373}
7 :hushrs uint8 84 [40, 0, 40, 40, 45, ... ]
8 :kidge6 uint8 2 {1=>1733, 0=>3901}
9 :earns double 613 [290.0, 654.0, 100.0, 0.0, 0.0, ... ]
10 :age uint8 42 [43, 26, 49, 35, 43, ... ]
11 :black uint8 2 {0=>5311, 1=>323}
12 :educ uint8 19 [14, 14, 12, 10, 13, ... ]
13 :hispanic uint8 2 {0=>5238, 1=>396}
14 :union uint8 3 {0=>3024, nil=>2076, 1=>534}
15 :faminc uint32 15 [45000, 45000, 55000, 55000, 27500, ... ]
16 :husexp uint8 69 [22, 6, 36, 17, 25, ... ]
17 :exper uint8 51 [23, 6, 31, 19, 24, ... ]
18 :kidlt6 uint8 2 {0=>4060, 1=>1574}
19 :hours uint8 49 [0, 0, 15, 0, 0, ... ]
20 :expersq uint16 51 [529, 36, 961, 361, 576, ... ]
... 4 more Vectors ...Rdatasets: wooldridge: crime2, crime2
RedAmber::DataFrame : 92 x 34 Vectors
Vectors : 34 numeric
# key type level data_preview
1 :pop uint32 92 [229528, 246815, 814054, 933177, 374974, ... ]
2 :crimes uint32 92 [17136, 17306, 75654, 83960, 31352, ... ]
3 :unem double 60 [8.19999980926514, 3.70000004768372, 8.10000038146973, 5.40000009536743, 9.0, ... ]
4 :officers uint16 90 [326, 321, 1621, 1803, 633, ... ]
5 :pcinc uint16 92 [8532, 12155, 7551, 11363, 8343, ... ]
6 :west uint8 2 {1=>28, 0=>64}
7 :nrtheast uint8 2 {0=>78, 1=>14}
8 :south uint8 2 {0=>62, 1=>30}
9 :year uint8 2 {82=>46, 87=>46}
10 :area double 46 [44.5999984741211, 44.5999984741211, 375.0, 375.0, 49.7999992370605, ... ]
11 :d87 uint8 2 {0=>46, 1=>46}
12 :popden double 92 [5146.36767578125, 5533.96875, 2170.810546875, 2488.47192382812, 7529.5986328125, ... ]
13 :crmrte double 92 [74.6575622558594, 70.1172943115234, 92.9348678588867, 89.9722137451172, 83.6111297607422, ... ]
14 :offarea double 91 [7.30941724777222, 7.19730949401855, 4.32266664505005, 4.80800008773804, 12.710844039917, ... ]
15 :lawexpc double 91 [850.859924316406, 2262.43994140625, 875.080017089844, 1069.64001464844, 1121.89990234375, ... ]
16 :polpc double 92 [1.42030596733093, 1.30056929588318, 1.99126839637756, 1.93210935592651, 1.68811702728271, ... ]
17 :lpop double 92 [12.3437805175781, 12.4163942337036, 13.6097822189331, 13.7463502883911, 12.8346118927002, ... ]
18 :loffic double 90 [5.7868971824646, 5.7714409828186, 7.39079856872559, 7.4972071647644, 6.45047044754028, ... ]
19 :lpcinc double 92 [9.05157947540283, 9.40549564361572, 8.92943572998047, 9.3381175994873, 9.02917861938477, ... ]
20 :llawexpc double 91 [6.74624729156494, 7.72419929504395, 6.77431535720825, 6.97507762908936, 7.02277898788452, ... ]
... 14 more Vectors ...Rdatasets: wooldridge: crime3, crime3
RedAmber::DataFrame : 106 x 12 Vectors
Vectors : 12 numeric
# key type level data_preview
1 :district uint8 53 [1, 1, 2, 2, 3, ... ]
2 :year uint8 2 {72=>53, 78=>53}
3 :crime double 103 [49.5400009155273, 71.3199996948242, 14.7700004577637, 17.8500003814697, 17.3500003814697, ... ]
4 :clrprc1 uint8 46 [22, 15, 51, 39, 33, ... ]
5 :clrprc2 uint8 47 [23, 17, 62, 40, 34, ... ]
6 :d78 uint8 2 {0=>53, 1=>53}
7 :avgclr double 65 [22.5, 16.0, 56.5, 39.5, 33.5, ... ]
8 :lcrime double 103 [3.90278053283691, 4.26717662811279, 2.69259810447693, 2.88200354576111, 2.85359263420105, ... ]
9 :clcrime double 54 [nil, 0.364396095275879, nil, 0.18940544128418, nil, ... ], 53 nils
10 :cavgclr double 39 [nil, -6.5, nil, -17.0, nil, ... ], 53 nils
11 :cclrprc1 int8 31 [nil, -7, nil, -12, nil, ... ], 53 nils
12 :cclrprc2 int8 33 [nil, -6, nil, -22, nil, ... ], 53 nilsRdatasets: wooldridge: crime4, crime4
RedAmber::DataFrame : 630 x 59 Vectors
Vectors : 59 numeric
# key type level data_preview
1 :county uint8 90 [1, 1, 1, 1, 1, ... ]
2 :year uint8 7 [81, 82, 83, 84, 85, ... ]
3 :crmrte double 629 [0.0398848988115788, 0.0383449010550976, 0.0303048007190228, 0.0347259007394314, 0.0365730002522469, ... ]
4 :prbarr double 622 [0.289696007966995, 0.338111013174057, 0.330448985099792, 0.36252498626709, 0.325394988059998, ... ]
5 :prbconv double 619 [0.402061998844147, 0.433005005121231, 0.525703012943268, 0.604705989360809, 0.578723013401031, ... ]
6 :prbpris double 539 [0.472222000360489, 0.506992995738983, 0.479705005884171, 0.520103991031647, 0.497058987617493, ... ]
7 :avgsen double 453 [5.6100001335144, 5.59000015258789, 5.80000019073486, 6.8899998664856, 6.55000019073486, ... ]
8 :polpc double 628 [0.00178677996154875, 0.0017665900522843, 0.00183576997369528, 0.00188588001765311, 0.0019243600545451, ... ]
9 :density double 553 [2.30715942382812, 2.33025407791138, 2.341801404953, 2.34642028808594, 2.36489605903625, ... ]
10 :taxpc double 630 [25.6976299285889, 24.8742523193359, 26.4514427185059, 26.8423480987549, 28.1403369903564, ... ]
11 :west uint8 2 {0=>483, 1=>147}
12 :central uint8 2 {1=>238, 0=>392}
13 :urban uint8 2 {0=>574, 1=>56}
14 :pctmin80 double 90 [20.2187004089355, 20.2187004089355, 20.2187004089355, 20.2187004089355, 20.2187004089355, ... ]
15 :wcon double 575 [206.48030090332, 212.754196166992, 219.780212402344, 223.423797607422, 243.756240844727, ... ]
16 :wtuc double 570 [333.620880126953, 369.296417236328, 1394.80346679688, 398.860412597656, 358.783020019531, ... ]
17 :wtrd double 625 [182.3330078125, 189.541412353516, 196.639465332031, 200.562911987305, 206.882736206055, ... ]
18 :wfir double 535 [272.449188232422, 300.878753662109, 309.969635009766, 350.086334228516, 383.070739746094, ... ]
19 :wser double 610 [215.733489990234, 231.576705932617, 240.156845092773, 252.447677612305, 261.086120605469, ... ]
20 :wmfg double 616 [229.119995117188, 240.330001831055, 269.700012207031, 281.739990234375, 298.880004882812, ... ]
... 39 more Vectors ...Rdatasets: wooldridge: discrim, discrim
RedAmber::DataFrame : 410 x 37 Vectors
Vectors : 37 numeric
# key type level data_preview
1 :psoda double 41 [1.12000000476837, 1.05999994277954, 1.05999994277954, 1.12000000476837, 1.12000000476837, ... ], 8 nils
2 :pfries double 46 [1.05999994277954, 0.910000026226044, 0.910000026226044, 1.01999998092651, nil, ... ], 17 nils
3 :pentree double 73 [1.01999998092651, 0.949999988079071, 0.980000019073486, 1.05999994277954, 0.490000009536743, ... ], 12 nils
4 :wagest double 33 [4.25, 4.75, 4.25, 5.0, 5.0, ... ], 20 nils
5 :nmgrs double 13 [3.0, 3.0, 3.0, 4.0, 3.0, ... ], 6 nils
6 :nregs uint8 9 [5, 3, 5, 5, 3, ... ], 22 nils
7 :hrsopen double 23 [16.0, 16.5, 18.0, 16.0, 16.0, ... ]
8 :emp double 97 [27.5, 21.5, 30.0, 27.5, 5.0, ... ], 6 nils
9 :psoda2 double 43 [1.11000001430511, 1.04999995231628, 1.04999995231628, 1.14999997615814, 1.03999996185303, ... ], 22 nils
10 :pfries2 double 44 [1.11000001430511, 0.889999985694885, 0.939999997615814, 1.04999995231628, 1.00999999046326, ... ], 28 nils
11 :pentree2 double 77 [1.04999995231628, 0.949999988079071, 0.980000019073486, 1.04999995231628, 0.579999983310699, ... ], 24 nils
12 :wagest2 double 25 [5.05000019073486, 5.05000019073486, 5.05000019073486, 5.05000019073486, 5.05000019073486, ... ], 21 nils
13 :nmgrs2 double 11 [5.0, 4.0, 4.0, 4.0, 3.0, ... ], 6 nils
14 :nregs2 uint8 9 [5, 3, 5, 5, 3, ... ], 22 nils
15 :hrsopen2 double 24 [15.0, 17.5, 17.5, 16.0, 16.0, ... ], 11 nils
16 :emp2 double 85 [27.0, 24.5, 25.0, nil, 12.0, ... ], 13 nils
17 :compown uint8 2 {1=>141, 0=>269}
18 :chain uint8 4 {3=>99, 1=>171, 2=>80, 4=>60}
19 :density double 208 [4030.0, 4030.0, 11400.0, 8345.0, 720.0, ... ], 1 nil
20 :crmrte double 207 [0.052886601537466, 0.052886601537466, 0.0360003001987934, 0.0484232008457184, 0.0615889988839626, ... ], 1 nil
... 17 more Vectors ...Rdatasets: wooldridge: earns, earns
RedAmber::DataFrame : 41 x 14 Vectors
Vectors : 14 numeric
# key type level data_preview
1 :year uint16 41 [1947, 1948, 1949, 1950, 1951, ... ]
2 :wkearns double 41 [123.519996643066, 123.430000305176, 127.839996337891, 133.830001831055, 134.869995117188, ... ]
3 :wkhours double 29 [40.2999992370605, 40.0, 39.4000015258789, 39.7999992370605, 39.9000015258789, ... ]
4 :outphr double 41 [51.4000015258789, 53.2999992370605, 54.2000007629395, 57.7000007629395, 59.4000015258789, ... ]
5 :hrwage double 41 [3.06501245498657, 3.08575010299683, 3.24466991424561, 3.36256289482117, 3.38020014762878, ... ]
6 :lhrwage double 41 [1.12005162239075, 1.12679481506348, 1.17701363563538, 1.21270346641541, 1.21793496608734, ... ]
7 :loutphr double 41 [3.93963813781738, 3.97593641281128, 3.99268102645874, 4.05525732040405, 4.08429431915283, ... ]
8 :t uint8 41 [1, 2, 3, 4, 5, ... ]
9 :ghrwage double 41 [nil, 0.00674319267272949, 0.0502188205718994, 0.0356898307800293, 0.00523149967193604, ... ], 1 nil
10 :goutphr double 41 [nil, 0.0362982749938965, 0.0167446136474609, 0.0625762939453125, 0.0290369987487793, ... ], 1 nil
11 :ghrwge_1 double 40 [nil, nil, 0.00674319267272949, 0.0502188205718994, 0.0356898307800293, ... ], 2 nils
12 :goutph_1 double 40 [nil, nil, 0.0362982749938965, 0.0167446136474609, 0.0625762939453125, ... ], 2 nils
13 :goutph_2 double 39 [nil, nil, nil, 0.0362982749938965, 0.0167446136474609, ... ], 3 nils
14 :lwkhours double 29 [3.69635152816772, 3.68887948989868, 3.67376589775085, 3.68386697769165, 3.68637633323669, ... ]Rdatasets: wooldridge: econmath, econmath
RedAmber::DataFrame : 856 x 17 Vectors
Vectors : 17 numeric
# key type level data_preview
1 :age uint8 9 [23, 23, 21, 22, 22, ... ]
2 :work double 44 [15.0, 0.0, 25.0, 30.0, 25.0, ... ]
3 :study double 52 [10.0, 22.5, 12.0, 40.0, 15.0, ... ]
4 :econhs uint8 2 {0=>539, 1=>317}
5 :colgpa double 612 [3.4909, 2.1, 3.0851, 2.6805, 3.7454, ... ]
6 :hsgpa double 531 [3.355, 3.219, 3.306, 3.977, 3.89, ... ]
7 :acteng uint8 24 [24, 23, 21, 31, 28, ... ], 42 nils
8 :actmth uint8 24 [26, 20, 24, 28, 31, ... ], 42 nils
9 :act uint8 21 [27, 24, 21, 31, 32, ... ], 42 nils
10 :mathscr uint8 11 [10, 9, 8, 10, 8, ... ]
11 :male uint8 2 {1=>428, 0=>428}
12 :calculus uint8 2 {1=>579, 0=>277}
13 :attexc uint8 2 {0=>602, 1=>254}
14 :attgood uint8 2 {0=>354, 1=>502}
15 :fathcoll uint8 2 {1=>449, 0=>407}
16 :mothcoll uint8 2 {1=>538, 0=>318}
17 :score double 149 [84.43, 57.38, 66.39, 81.15, 95.9, ... ]Rdatasets: wooldridge: ezanders, ezanders
RedAmber::DataFrame : 108 x 25 Vectors
Vectors : 24 numeric, 1 string
# key type level data_preview
1 :month string 12 ["JAN", "FEB", "MAR", "APR", "MAY", ... ]
2 :uclms uint16 107 [18369, 17661, 16340, 16268, 19340, ... ], 1 nil
3 :ez uint8 2 {0=>48, 1=>60}
4 :year uint16 9 [1980, 1980, 1980, 1980, 1980, ... ]
5 :y81 uint8 2 {0=>96, 1=>12}
6 :y82 uint8 2 {0=>96, 1=>12}
7 :y83 uint8 2 {0=>96, 1=>12}
8 :y84 uint8 2 {0=>96, 1=>12}
9 :y85 uint8 2 {0=>96, 1=>12}
10 :y86 uint8 2 {0=>96, 1=>12}
11 :y87 uint8 2 {0=>96, 1=>12}
12 :y88 uint8 2 {0=>96, 1=>12}
13 :luclms double 107 [9.81841945648193, 9.77911376953125, 9.70137119293213, 9.69695568084717, 9.8699312210083, ... ], 1 nil
14 :jan uint8 2 {1=>9, 0=>99}
15 :feb uint8 2 {0=>99, 1=>9}
16 :mar uint8 2 {0=>99, 1=>9}
17 :apr uint8 2 {0=>99, 1=>9}
18 :may uint8 2 {0=>99, 1=>9}
19 :jun uint8 2 {0=>99, 1=>9}
20 :jul uint8 2 {0=>99, 1=>9}
... 5 more Vectors ...Rdatasets: wooldridge: ezunem, ezunem
RedAmber::DataFrame : 198 x 37 Vectors
Vectors : 37 numeric
# key type level data_preview
1 :year uint16 9 [1980, 1981, 1982, 1983, 1984, ... ]
2 :uclms uint32 198 [166746, 83561, 158146, 83572, 45949, ... ]
3 :ez uint8 2 {0=>152, 1=>46}
4 :d81 uint8 2 {0=>176, 1=>22}
5 :d82 uint8 2 {0=>176, 1=>22}
6 :d83 uint8 2 {0=>176, 1=>22}
7 :d84 uint8 2 {0=>176, 1=>22}
8 :d85 uint8 2 {0=>176, 1=>22}
9 :d86 uint8 2 {0=>176, 1=>22}
10 :d87 uint8 2 {0=>176, 1=>22}
11 :d88 uint8 2 {0=>176, 1=>22}
12 :c1 uint8 2 {1=>9, 0=>189}
13 :c2 uint8 2 {0=>189, 1=>9}
14 :c3 uint8 2 {0=>189, 1=>9}
15 :c4 uint8 2 {0=>189, 1=>9}
16 :c5 uint8 2 {0=>189, 1=>9}
17 :c6 uint8 2 {0=>189, 1=>9}
18 :c7 uint8 2 {0=>189, 1=>9}
19 :c8 uint8 2 {0=>189, 1=>9}
20 :c9 uint8 2 {0=>189, 1=>9}
... 17 more Vectors ...Rdatasets: wooldridge: fertil2, fertil2
RedAmber::DataFrame : 4361 x 27 Vectors
Vectors : 27 numeric
# key type level data_preview
1 :mnthborn uint8 12 [5, 1, 7, 11, 5, ... ]
2 :yearborn uint8 36 [64, 56, 58, 45, 45, ... ]
3 :age uint8 35 [24, 32, 30, 42, 43, ... ]
4 :electric uint8 3 {1=>611, 0=>3747, nil=>3}
5 :radio uint8 3 {1=>3059, 0=>1300, nil=>2}
6 :tv uint8 3 {1=>405, 0=>3954, nil=>2}
7 :bicycle uint8 3 {1=>1202, 0=>3156, nil=>3}
8 :educ uint8 21 [12, 13, 5, 4, 11, ... ]
9 :ceb uint8 14 [0, 3, 1, 3, 2, ... ]
10 :agefbrth uint8 29 [nil, 25, 27, 17, 24, ... ], 1088 nils
11 :children uint8 14 [0, 3, 1, 2, 2, ... ]
12 :knowmeth uint8 3 {1=>4194, 0=>160, nil=>7}
13 :usemeth uint8 3 {0=>1812, 1=>2478, nil=>71}
14 :monthfm uint8 13 [nil, 11, 6, 1, 3, ... ], 2282 nils
15 :yearfm uint8 39 [nil, 80, 83, 61, 66, ... ], 2282 nils
16 :agefm uint8 36 [nil, 24, 24, 15, 20, ... ], 2282 nils
17 :idlnchld uint8 20 [2, 3, 5, 3, 2, ... ], 120 nils
18 :heduc uint8 22 [nil, 12, 7, 11, 14, ... ], 2405 nils
19 :agesq uint16 35 [576, 1024, 900, 1764, 1849, ... ]
20 :urban uint8 2 {1=>2253, 0=>2108}
... 7 more Vectors ...Rdatasets: wooldridge: fertil3, fertil3
RedAmber::DataFrame : 72 x 24 Vectors
Vectors : 24 numeric
# key type level data_preview
1 :gfr double 67 [124.699996948242, 126.599998474121, 125.0, 123.400001525879, 121.0, ... ]
2 :pe double 69 [0.0, 0.0, 0.0, 0.0, 19.2700004577637, ... ]
3 :year uint16 72 [1913, 1914, 1915, 1916, 1917, ... ]
4 :t uint8 72 [1, 2, 3, 4, 5, ... ]
5 :tsq uint16 72 [1, 4, 9, 16, 25, ... ]
6 :pe_1 double 69 [nil, 0.0, 0.0, 0.0, 0.0, ... ], 1 nil
7 :pe_2 double 68 [nil, nil, 0.0, 0.0, 0.0, ... ], 2 nils
8 :pe_3 double 67 [nil, nil, nil, 0.0, 0.0, ... ], 3 nils
9 :pe_4 double 66 [nil, nil, nil, nil, 0.0, ... ], 4 nils
10 :pill uint8 2 {0=>50, 1=>22}
11 :ww2 uint8 2 {0=>67, 1=>5}
12 :tcu uint32 72 [1, 8, 27, 64, 125, ... ]
13 :cgfr double 60 [nil, 1.90000152587891, -1.59999847412109, -1.59999847412109, -2.40000152587891, ... ], 1 nil
14 :cpe double 70 [nil, 0.0, 0.0, 0.0, 19.2700004577637, ... ], 1 nil
15 :cpe_1 double 69 [nil, nil, 0.0, 0.0, 0.0, ... ], 2 nils
16 :cpe_2 double 68 [nil, nil, nil, 0.0, 0.0, ... ], 3 nils
17 :cpe_3 double 67 [nil, nil, nil, nil, 0.0, ... ], 4 nils
18 :cpe_4 double 66 [nil, nil, nil, nil, nil, ... ], 5 nils
19 :gfr_1 double 67 [nil, 124.699996948242, 126.599998474121, 125.0, 123.400001525879, ... ], 1 nil
20 :cgfr_1 double 59 [nil, nil, 1.90000152587891, -1.59999847412109, -1.59999847412109, ... ], 2 nils
... 4 more Vectors ...Rdatasets: wooldridge: fish, fish
RedAmber::DataFrame : 97 x 20 Vectors
Vectors : 20 numeric
# key type level data_preview
1 :prca double 88 [0.622222185134888, 0.972222208976746, 1.23333299160004, 1.92857098579407, 0.803125023841858, ... ]
2 :prcw double 84 [0.76666671037674, 1.17499995231628, 1.47500002384186, 1.625, 0.864285707473755, ... ]
3 :qtya uint16 92 [1875, 2900, 770, 927, 4220, ... ]
4 :qtyw uint16 87 [2205, 566, 1525, 943, 2665, ... ]
5 :mon uint8 2 {1=>18, 0=>79}
6 :tues uint8 2 {0=>78, 1=>19}
7 :wed uint8 2 {0=>77, 1=>20}
8 :thurs uint8 2 {0=>77, 1=>20}
9 :speed2 uint8 6 [15, 10, 10, 15, 10, ... ]
10 :wave2 double 14 [7.5, 5.0, 6.0, 6.0, 3.5, ... ]
11 :speed3 uint8 8 [20, 20, 20, 20, 20, ... ]
12 :wave3 double 13 [9.0, 7.5, 4.0, 5.0, 3.5, ... ]
13 :avgprc double 96 [0.700285971164703, 1.00533592700958, 1.39391779899597, 1.77548682689667, 0.826798677444458, ... ]
14 :totqty uint16 92 [4080, 3466, 2295, 1870, 6885, ... ]
15 :lavgprc double 96 [-0.356266498565674, 0.00532174156978726, 0.332118332386017, 0.574074625968933, -0.190194055438042, ... ]
16 :ltotqty double 92 [8.31385231018066, 8.1507568359375, 7.73848819732666, 7.53369379043579, 8.8371000289917, ... ]
17 :t uint8 97 [1, 2, 3, 4, 5, ... ]
18 :lavgp_1 double 96 [nil, -0.356266498565674, 0.00532174156978726, 0.332118332386017, 0.574074625968933, ... ], 1 nil
19 :gavgprc double 97 [nil, 0.3615882396698, 0.326796591281891, 0.241956293582916, -0.764268696308136, ... ], 1 nil
20 :gavgp_1 double 96 [nil, nil, 0.3615882396698, 0.326796591281891, 0.241956293582916, ... ], 2 nilsRdatasets: wooldridge: gpa3, gpa3
RedAmber::DataFrame : 732 x 23 Vectors
Vectors : 23 numeric
# key type level data_preview
1 :term uint8 2 {1=>366, 2=>366}
2 :sat uint16 76 [920, 920, 780, 780, 810, ... ]
3 :tothrs uint8 119 [31, 43, 28, 43, 0, ... ]
4 :cumgpa double 205 [2.25, 2.03999996185303, 2.02999997138977, 2.08999991416931, 0.0, ... ]
5 :season uint8 2 {0=>241, 1=>491}
6 :frstsem uint8 2 {0=>635, 1=>97}
7 :crsgpa double 684 [2.646399974823, 2.50769996643066, 2.86789989471436, 2.88389992713928, 2.76340007781982, ... ]
8 :verbmath double 267 [0.483869999647141, 0.483869999647141, 0.813950002193451, 0.813950002193451, 0.883719980716705, ... ]
9 :trmgpa double 171 [1.5, 2.25, 2.20000004768372, 1.60000002384186, 1.60000002384186, ... ]
10 :hssize uint16 264 [10, 10, 123, 123, 119, ... ]
11 :hsrank uint16 196 [4, 4, 102, 102, 42, ... ]
12 :id uint16 366 [22, 22, 35, 35, 36, ... ]
13 :spring uint8 2 {0=>366, 1=>366}
14 :female uint8 2 {1=>180, 0=>552}
15 :black uint8 2 {0=>578, 1=>154}
16 :white uint8 2 {0=>178, 1=>554}
17 :ctrmgpa double 217 [nil, 0.75, nil, -0.600000023841858, nil, ... ], 366 nils
18 :ctothrs uint8 16 [nil, 12, nil, 15, nil, ... ], 366 nils
19 :ccrsgpa double 362 [nil, -0.138700008392334, nil, 0.0160000324249268, nil, ... ], 366 nils
20 :ccrspop double 366 [nil, -62.25, nil, -73.25, nil, ... ], 366 nils
... 3 more Vectors ...Rdatasets: wooldridge: happiness, happiness
RedAmber::DataFrame : 17137 x 33 Vectors
Vectors : 24 numeric, 9 strings
# key type level data_preview
1 :year uint16 7 [1994, 1994, 1994, 1994, 1994, ... ]
2 :workstat string 9 ["keeping house", "working fulltime", "working fulltime", "working fulltime", "working parttime", ... ], 3 nils
3 :prestige uint8 60 [46, 22, 29, 42, 36, ... ], 854 nils
4 :divorce string 3 {nil=>7383, "no"=>7421, "yes"=>2333}
5 :widowed string 3 {"iap"=>10718, nil=>6041, "yes"=>378}
6 :educ uint8 22 [12, 12, 12, 8, 13, ... ], 44 nils
7 :reg16 string 10 ["middle atlantic", "foreign", "foreign", "foreign", "middle atlantic", ... ]
8 :babies uint8 7 [2, 0, 0, 0, 0, ... ], 101 nils
9 :preteen uint8 8 [3, 0, 0, 0, 1, ... ], 101 nils
10 :teens uint8 8 [0, 0, 0, 0, 1, ... ], 88 nils
11 :income string 13 ["$10000 - 14999", nil, "$15000 - 19999", "$15000 - 19999", "$10000 - 14999", ... ], 2092 nils
12 :region string 9 ["middle atlantic", "middle atlantic", "middle atlantic", "middle atlantic", "middle atlantic", ... ]
13 :attend string 10 ["sevrl times a yr", "every week", "more thn once wk", "once a year", "once a year", ... ], 273 nils
14 :happy string 3 {"pretty happy"=>9791, "very happy"=>5260, "not too happy"=>2086}
15 :owngun string 3 {nil=>5840, "iap"=>7180, "yes"=>4117}
16 :tvhours uint8 23 [2, 3, 1, 3, nil, ... ], 5343 nils
17 :vhappy uint8 2 {0=>11877, 1=>5260}
18 :mothfath16 uint8 3 {1=>11872, 0=>5260, nil=>5}
19 :black uint8 2 {1=>2372, 0=>14765}
20 :gwbush04 uint8 3 {nil=>15207, 0=>962, 1=>968}
... 13 more Vectors ...Rdatasets: wooldridge: hseinv, hseinv
RedAmber::DataFrame : 42 x 14 Vectors
Vectors : 14 numeric
# key type level data_preview
1 :year uint16 42 [1947, 1948, 1949, 1950, 1951, ... ]
2 :inv uint32 42 [54864, 64717, 63150, 86014, 70610, ... ]
3 :pop uint32 42 [144126, 146631, 149188, 151684, 154287, ... ]
4 :price double 40 [0.819000005722046, 0.86489999294281, 0.845600008964539, 0.876500010490417, 0.881900012493134, ... ]
5 :linv double 42 [10.9126129150391, 11.0777788162231, 11.0532684326172, 11.362265586853, 11.164927482605, ... ]
6 :lpop double 42 [11.8784427642822, 11.8956747055054, 11.9129629135132, 11.92955493927, 11.946569442749, ... ]
7 :lprice double 40 [-0.199671193957329, -0.145141392946243, -0.167708829045296, -0.131818562746048, -0.125676587224007, ... ]
8 :t uint8 42 [1, 2, 3, 4, 5, ... ]
9 :invpc double 42 [0.38066691160202, 0.441359609365463, 0.42329141497612, 0.567060470581055, 0.4576535820961, ... ]
10 :linvpc double 42 [-0.965830504894257, -0.817895293235779, -0.859694421291351, -0.567289352416992, -0.781642735004425, ... ]
11 :lprice_1 double 40 [nil, -0.199671193957329, -0.145141392946243, -0.167708829045296, -0.131818562746048, ... ], 1 nil
12 :linvpc_1 double 42 [nil, -0.965830504894257, -0.817895293235779, -0.859694421291351, -0.567289352416992, ... ], 1 nil
13 :gprice double 41 [nil, 0.0545298010110855, -0.0225674360990524, 0.0358902662992477, 0.00614197552204132, ... ], 1 nil
14 :ginvpc double 42 [nil, 0.147935211658478, -0.0417991280555725, 0.292405068874359, -0.214353382587433, ... ], 1 nilRdatasets: wooldridge: injury, injury
RedAmber::DataFrame : 7150 x 30 Vectors
Vectors : 30 numeric
# key type level data_preview
1 :durat double 134 [1.0, 1.0, 84.0, 4.0, 1.0, ... ]
2 :afchnge uint8 2 {1=>3384, 0=>3766}
3 :highearn uint8 2 {1=>2852, 0=>4298}
4 :male uint8 3 {1=>5569, 0=>1565, nil=>16}
5 :married uint8 3 {0=>2109, 1=>4744, nil=>297}
6 :hosp uint8 2 {1=>1874, 0=>5276}
7 :indust uint8 4 {3=>4085, 1=>2001, 2=>1039, nil=>25}
8 :injtype uint8 8 [1, 1, 1, 1, 1, ... ]
9 :age uint8 69 [26, 31, 37, 31, 23, ... ], 4 nils
10 :prewage double 1136 [404.950012207031, 643.825012207031, 398.125, 527.799987792969, 528.9375, ... ]
11 :totmed double 3677 [1187.5732421875, 361.078552246094, 8963.6572265625, 1099.64831542969, 372.801879882812, ... ]
12 :injdes uint16 389 [1010, 1404, 1032, 1940, 1940, ... ]
13 :benefit double 445 [246.837493896484, 246.837493896484, 246.837493896484, 246.837493896484, 211.574996948242, ... ]
14 :ky uint8 2 {1=>5626, 0=>1524}
15 :mi uint8 2 {0=>5626, 1=>1524}
16 :ldurat double 134 [0.0, 0.0, 4.43081665039062, 1.3862943649292, 0.0, ... ]
17 :afhigh uint8 2 {1=>1380, 0=>5770}
18 :lprewage double 1136 [6.0037636756897, 6.46742677688599, 5.98676586151123, 6.26871728897095, 6.27087020874023, ... ]
19 :lage double 69 [3.25809645652771, 3.43398714065552, 3.61091780662537, 3.43398714065552, 3.13549423217773, ... ], 4 nils
20 :ltotmed double 3677 [7.07966709136963, 5.88909530639648, 9.10093402862549, 7.00274562835693, 5.92104721069336, ... ]
... 10 more Vectors ...Rdatasets: wooldridge: intdef, intdef
RedAmber::DataFrame : 56 x 13 Vectors
Vectors : 13 numeric
# key type level data_preview
1 :year uint16 56 [1948, 1949, 1950, 1951, 1952, ... ]
2 :i3 double 55 [1.03999996185303, 1.10000002384186, 1.22000002861023, 1.54999995231628, 1.76999998092651, ... ]
3 :inf double 40 [8.10000038146973, -1.20000004768372, 1.29999995231628, 7.90000009536743, 1.89999997615814, ... ]
4 :rec double 29 [16.2000007629395, 14.5, 14.3999996185303, 16.1000003814697, 19.0, ... ]
5 :out double 40 [11.6000003814697, 14.3000001907349, 15.6000003814697, 14.1999998092651, 19.3999996185303, ... ]
6 :def double 48 [-4.60000038146973, -0.199999809265137, 1.20000076293945, -1.90000057220459, 0.399999618530273, ... ]
7 :i3_1 double 55 [nil, 1.03999996185303, 1.10000002384186, 1.22000002861023, 1.54999995231628, ... ], 1 nil
8 :inf_1 double 41 [nil, 8.10000038146973, -1.20000004768372, 1.29999995231628, 7.90000009536743, ... ], 1 nil
9 :def_1 double 49 [nil, -4.60000038146973, -0.199999809265137, 1.20000076293945, -1.90000057220459, ... ], 1 nil
10 :ci3 double 56 [nil, 0.0600000619888306, 0.120000004768372, 0.329999923706055, 0.220000028610229, ... ], 1 nil
11 :cinf double 50 [nil, -9.30000019073486, 2.5, 6.60000038146973, -6.0, ... ], 1 nil
12 :cdef double 44 [nil, 4.40000057220459, 1.40000057220459, -3.10000133514404, 2.30000019073486, ... ], 1 nil
13 :y77 uint8 2 {0=>29, 1=>27}Rdatasets: wooldridge: intqrt, intqrt
RedAmber::DataFrame : 124 x 23 Vectors
Vectors : 23 numeric
# key type level data_preview
1 :r3 double 116 [2.76999998092651, 2.97000002861023, 4.0, 4.59999990463257, 4.15999984741211, ... ]
2 :r6 double 119 [3.01999998092651, 3.4300000667572, 4.32000017166138, 4.67999982833862, 4.32999992370605, ... ]
3 :r12 double 112 [3.3199999332428, 3.67000007629395, 4.40999984741211, 4.75, 4.38000011444092, ... ]
4 :p3 double 116 [9931.4130859375, 9926.498046875, 9901.2587890625, 9886.615234375, 9897.349609375, ... ]
5 :p6 double 119 [9851.6474609375, 9831.845703125, 9789.1337890625, 9771.962890625, 9788.65625, ... ]
6 :hy6 double 124 [nil, 0.759777367115021, 0.706002593040466, 0.995812773704529, 1.28312730789185, ... ], 1 nil
7 :hy3 double 116 [0.690602719783783, 0.740465760231018, 0.997260272502899, 1.14684927463531, 1.03715062141418, ... ]
8 :spr63 double 92 [0.25, 0.460000038146973, 0.320000171661377, 0.0799999237060547, 0.170000076293945, ... ]
9 :hy3_1 double 116 [nil, 0.690602719783783, 0.740465760231018, 0.997260272502899, 1.14684927463531, ... ], 1 nil
10 :hy6_1 double 123 [nil, nil, 0.759777367115021, 0.706002593040466, 0.995812773704529, ... ], 2 nils
11 :spr63_1 double 92 [nil, 0.25, 0.460000038146973, 0.320000171661377, 0.0799999237060547, ... ], 1 nil
12 :hy6hy3_1 double 124 [nil, 0.0691746473312378, -0.0344631671905518, -0.00144749879837036, 0.136278033256531, ... ], 1 nil
13 :cr3 double 120 [nil, 0.200000047683716, 1.02999997138977, 0.599999904632568, -0.440000057220459, ... ], 1 nil
14 :r3_1 double 116 [nil, 2.76999998092651, 2.97000002861023, 4.0, 4.59999990463257, ... ], 1 nil
15 :chy6 double 123 [nil, nil, -0.0537747740745544, 0.289810180664062, 0.287314534187317, ... ], 2 nils
16 :chy3 double 121 [nil, 0.0498630404472351, 0.256794512271881, 0.149589002132416, -0.10969865322113, ... ], 1 nil
17 :chy6_1 double 122 [nil, nil, nil, -0.0537747740745544, 0.289810180664062, ... ], 3 nils
18 :chy3_1 double 120 [nil, nil, 0.0498630404472351, 0.256794512271881, 0.149589002132416, ... ], 2 nils
19 :cr6 double 117 [nil, 0.410000085830688, 0.890000104904175, 0.359999656677246, -0.349999904632568, ... ], 1 nil
20 :cr6_1 double 116 [nil, nil, 0.410000085830688, 0.890000104904175, 0.359999656677246, ... ], 2 nils
... 3 more Vectors ...Rdatasets: wooldridge: inven, inven
RedAmber::DataFrame : 37 x 13 Vectors
Vectors : 13 numeric
# key type level data_preview
1 :year uint16 37 [1959, 1960, 1961, 1962, 1963, ... ]
2 :i3 double 37 [3.41000008583069, 2.9300000667572, 2.38000011444092, 2.77999997138977, 3.16000008583069, ... ]
3 :inf double 30 [0.699999988079071, 1.70000004768372, 1.0, 1.0, 1.29999995231628, ... ]
4 :inven double 37 [401.399993896484, 412.0, 420.899993896484, 440.899993896484, 459.0, ... ]
5 :gdp double 37 [2212.30004882812, 2261.69995117188, 2309.80004882812, 2449.10009765625, 2554.0, ... ]
6 :r3 double 36 [2.71000003814697, 1.23000001907349, 1.38000011444092, 1.77999997138977, 1.8600001335144, ... ]
7 :cinven double 37 [nil, 10.6000061035156, 8.89999389648438, 20.0, 18.1000061035156, ... ], 1 nil
8 :cgdp double 37 [nil, 49.39990234375, 48.10009765625, 139.300048828125, 104.89990234375, ... ], 1 nil
9 :cr3 double 36 [nil, -1.48000001907349, 0.150000095367432, 0.399999856948853, 0.0800001621246338, ... ], 1 nil
10 :ci3 double 37 [nil, -0.480000019073486, -0.549999952316284, 0.399999856948853, 0.380000114440918, ... ], 1 nil
11 :cinf double 33 [nil, 1.0, -0.700000047683716, 0.0, 0.299999952316284, ... ], 1 nil
12 :ginven double 37 [nil, 0.0260649286210537, 0.0213719122111797, 0.0464228168129921, 0.0402321331202984, ... ], 1 nil
13 :ggdp double 37 [nil, 0.0220840014517307, 0.0210442412644625, 0.0585596896708012, 0.0419401079416275, ... ], 1 nilRdatasets: wooldridge: jtrain, jtrain
RedAmber::DataFrame : 471 x 30 Vectors
Vectors : 30 numeric
# key type level data_preview
1 :year uint16 3 {1987=>157, 1988=>157, 1989=>157}
2 :fcode uint32 157 [410032, 410032, 410032, 410440, 410440, ... ]
3 :employ uint16 126 [100, 131, 123, 12, 13, ... ], 31 nils
4 :sales double 260 [47000000.0, 43000000.0, 49000000.0, 1560000.0, 1970000.0, ... ], 98 nils
5 :avgsal uint16 231 [35000, 37000, 39000, 10500, 11000, ... ], 65 nils
6 :scrap double 101 [nil, nil, nil, nil, nil, ... ], 309 nils
7 :rework double 66 [nil, nil, nil, nil, nil, ... ], 348 nils
8 :tothrs uint16 67 [12, 8, 8, 12, 12, ... ], 56 nils
9 :union uint8 2 {0=>378, 1=>93}
10 :grant uint8 2 {0=>405, 1=>66}
11 :d89 uint8 2 {0=>314, 1=>157}
12 :d88 uint8 2 {0=>314, 1=>157}
13 :totrain uint16 77 [100, 50, 50, 12, 13, ... ], 6 nils
14 :hrsemp double 178 [12.0, 3.05343508720398, 3.25203251838684, 12.0, 12.0, ... ], 81 nils
15 :lscrap double 101 [nil, nil, nil, nil, nil, ... ], 309 nils
16 :lemploy double 126 [4.60517024993896, 4.8751974105835, 4.81218433380127, 2.4849066734314, 2.56494927406311, ... ], 31 nils
17 :lsales double 260 [17.6656589508057, 17.5767097473145, 17.7073307037354, 14.260196685791, 14.4935436248779, ... ], 98 nils
18 :lrework double 65 [nil, nil, nil, nil, nil, ... ], 350 nils
19 :lhrsemp double 178 [2.56494927406311, 1.39956474304199, 1.44739711284637, 2.56494927406311, 2.56494927406311, ... ], 81 nils
20 :lscrap_1 double 72 [nil, nil, nil, nil, nil, ... ], 363 nils
... 10 more Vectors ...Rdatasets: wooldridge: lawsch85, lawsch85
RedAmber::DataFrame : 156 x 21 Vectors
Vectors : 21 numeric
# key type level data_preview
1 :rank uint8 154 [128, 104, 34, 49, 95, ... ]
2 :salary uint32 108 [31400, 33098, 32870, 35000, 33606, ... ], 8 nils
3 :cost uint16 144 [8340, 6980, 16370, 17566, 8350, ... ], 6 nils
4 :LSAT uint8 25 [155, 160, 155, 157, 162, ... ], 6 nils
5 :GPA double 54 [3.15000009536743, 3.5, 3.25, 3.20000004768372, 3.38000011444092, ... ], 7 nils
6 :libvol uint16 109 [216, 256, 424, 329, 332, ... ], 1 nil
7 :faculty uint8 81 [45, 44, 78, 136, 56, ... ], 4 nils
8 :age uint8 71 [12, 113, 134, 89, 70, ... ], 45 nils
9 :clsize uint16 123 [210, 190, 270, 277, 150, ... ], 3 nils
10 :north uint8 2 {1=>32, 0=>124}
11 :south uint8 2 {0=>118, 1=>38}
12 :east uint8 2 {0=>108, 1=>48}
13 :west uint8 2 {0=>118, 1=>38}
14 :lsalary double 108 [10.3545627593994, 10.4072284698486, 10.400315284729, 10.4631032943726, 10.422459602356, ... ], 8 nils
15 :studfac double 144 [4.66666650772095, 4.31818199157715, 3.46153855323792, 2.03676462173462, 2.67857146263123, ... ], 6 nils
16 :top10 uint8 2 {0=>146, 1=>10}
17 :r11_25 uint8 2 {0=>140, 1=>16}
18 :r26_40 uint8 2 {0=>143, 1=>13}
19 :r41_60 uint8 2 {0=>138, 1=>18}
20 :llibvol double 109 [5.37527847290039, 5.5451774597168, 6.04973363876343, 5.79605770111084, 5.80513477325439, ... ], 1 nil
... 1 more Vector ...Rdatasets: wooldridge: loanapp, loanapp
RedAmber::DataFrame : 1989 x 59 Vectors
Vectors : 59 numeric
# key type level data_preview
1 :occ uint8 3 {1=>1932, 2=>51, 3=>6}
2 :loanamt uint16 294 [89, 128, 128, 66, 120, ... ]
3 :action uint8 3 {1=>1684, 3=>244, 2=>61}
4 :msa uint16 1 {1120=>1989}
5 :suffolk uint8 2 {0=>1682, 1=>307}
6 :appinc uint16 228 [72, 74, 84, 36, 59, ... ]
7 :typur uint8 8 [0, 0, 3, 0, 8, ... ]
8 :unit uint8 5 {1=>1814, 2=>110, nil=>4, 3=>50, 4=>11}
9 :married uint8 3 {0=>678, 1=>1308, nil=>3}
10 :dep uint8 10 [0, 1, 0, 0, 0, ... ], 3 nils
11 :emp uint8 4 {0=>1803, 1=>147, 6=>27, 9=>12}
12 :yjob uint8 4 {0=>1364, 1=>581, 6=>28, 9=>16}
13 :self uint8 2 {0=>1732, 1=>257}
14 :atotinc uint32 1349 [5849, 4583, 2666, 3000, 2583, ... ]
15 :cototinc double 808 [0.0, 1508.0, 4416.0, 0.0, 2358.0, ... ]
16 :hexp double 1259 [1031.0, 1391.0, 1371.0, 839.0, 1341.0, ... ]
17 :price double 458 [118.0, 160.0, 143.0, 110.0, 134.0, ... ]
18 :other double 57 [0.0, 0.0, 0.0, 0.0, 0.0, ... ]
19 :liq double 634 [34.5, 52.0, 37.0, 19.0, 31.0, ... ]
20 :rep uint8 10 [1, 3, 6, 1, 1, ... ], 9 nils
... 39 more Vectors ...Rdatasets: wooldridge: lowbrth, lowbrth
RedAmber::DataFrame : 100 x 36 Vectors
Vectors : 34 numeric, 2 strings
# key type level data_preview
1 :year uint16 2 {1987=>50, 1990=>50}
2 :lowbrth double 37 [8.0, 8.39999961853027, 4.80000019073486, 4.80000019073486, 6.40000009536743, ... ]
3 :infmort double 50 [12.1999998092651, 10.8000001907349, 10.3999996185303, 10.5, 9.5, ... ]
4 :afdcprt uint16 82 [132, 132, 19, 24, 91, ... ]
5 :popul uint16 100 [4084, 4041, 524, 550, 3400, ... ]
6 :pcinc uint16 100 [12039, 14899, 18461, 20867, 14322, ... ]
7 :physic uint16 74 [151, 158, 138, 146, 191, ... ]
8 :afdcprc double 100 [3.2321252822876, 3.26651811599731, 3.62595415115356, 4.36363649368286, 2.67647051811218, ... ]
9 :d90 uint8 2 {0=>50, 1=>50}
10 :lpcinc double 100 [9.39590644836426, 9.60904979705811, 9.82341575622559, 9.94592380523682, 9.56955242156982, ... ]
11 :cafdcprc double 51 [nil, 0.0343928337097168, nil, 0.737682342529297, nil, ... ], 50 nils
12 :clpcinc double 51 [nil, 0.213143348693848, nil, 0.12250804901123, nil, ... ], 50 nils
13 :lphysic double 74 [5.01727962493896, 5.06259489059448, 4.92725372314453, 4.98360681533813, 5.25227355957031, ... ]
14 :clphysic double 49 [nil, 0.0453152656555176, nil, 0.0563530921936035, nil, ... ], 50 nils
15 :clowbrth double 21 [nil, 0.399999618530273, nil, 0.0, nil, ... ], 50 nils
16 :cinfmort double 37 [nil, -1.39999961853027, nil, 0.100000381469727, nil, ... ], 50 nils
17 :afdcpay uint16 90 [114, 115, 567, 651, 268, ... ]
18 :afdcinc double 100 [0.946922481060028, 0.771863877773285, 3.07133960723877, 3.11975836753845, 1.87124705314636, ... ]
19 :lafdcpay double 90 [4.73619842529297, 4.74493217468262, 6.34035921096802, 6.47850942611694, 5.59098720550537, ... ]
20 :clafdcpy double 48 [nil, 0.00873374938964844, nil, 0.138150215148926, nil, ... ], 50 nils
... 16 more Vectors ...Rdatasets: wooldridge: mathpnl, mathpnl
RedAmber::DataFrame : 3850 x 52 Vectors
Vectors : 52 numeric
# key type level data_preview
1 :distid uint32 550 [1010, 1010, 1010, 1010, 1010, ... ]
2 :intid uint8 57 [4, 4, 4, 4, 4, ... ]
3 :lunch double 2150 [36.2999992370605, 39.2000007629395, 38.5999984741211, 37.4099998474121, 40.7900009155273, ... ]
4 :enrol uint32 2731 [1089, 1100, 1011, 995, 1008, ... ]
5 :ptr double 180 [nil, nil, nil, 20.3999996185303, 19.1000003814697, ... ], 1650 nils
6 :found uint16 1281 [nil, nil, nil, 5245, 5398, ... ], 1650 nils
7 :expp uint16 2534 [4227, 4809, 5214, 6019, 6155, ... ]
8 :revpp uint16 1559 [nil, nil, nil, 7186, 7374, ... ], 1650 nils
9 :avgsal uint32 3617 [31854, 34361, 32351, 44852, 46603, ... ]
10 :drop double 821 [7.0, 10.1000003814697, 12.3000001907349, 1.73000001907349, 7.36999988555908, ... ], 84 nils
11 :grad double 586 [75.1999969482422, 64.6999969482422, 58.5999984741211, 94.8000030517578, 77.4000015258789, ... ], 211 nils
12 :math4 double 704 [28.7999992370605, 32.2999992370605, 39.0999984741211, 68.0, 68.4000015258789, ... ]
13 :math7 double 698 [43.4000015258789, 49.2999992370605, 36.5, 54.7000007629395, 55.4000015258789, ... ], 24 nils
14 :choice uint16 119 [nil, nil, nil, nil, nil, ... ], 2750 nils
15 :psa uint16 103 [nil, nil, nil, 0, 0, ... ], 2591 nils
16 :year uint16 7 [1992, 1993, 1994, 1995, 1996, ... ]
17 :staff double 611 [101.199996948242, 99.6999969482422, 110.800003051758, nil, nil, ... ], 2200 nils
18 :avgben uint16 1464 [6098, 8532, 9983, nil, nil, ... ], 2205 nils
19 :y92 uint8 2 {1=>550, 0=>3300}
20 :y93 uint8 2 {0=>3300, 1=>550}
... 32 more Vectors ...Rdatasets: wooldridge: minwage, minwage
RedAmber::DataFrame : 612 x 58 Vectors
Vectors : 58 numeric
# key type level data_preview
1 :emp232 double 504 [270.600006103516, 272.399993896484, 268.600006103516, 262.600006103516, 258.0, ... ]
2 :wage232 double 298 [0.860000014305115, 0.860000014305115, 0.860000014305115, 0.860000014305115, 0.870000004768372, ... ]
3 :emp236 double 303 [53.0999984741211, 54.7999992370605, 52.9000015258789, 48.2999992370605, 47.5999984741211, ... ]
4 :wage236 double 327 [0.990000009536743, 1.0, 0.980000019073486, 0.910000026226044, 0.930000007152557, ... ]
5 :emp234 double 398 [93.4000015258789, 94.5999984741211, 95.4000015258789, 95.1999969482422, 96.3000030517578, ... ]
6 :wage234 double 333 [0.920000016689301, 0.920000016689301, 0.910000026226044, 0.920000016689301, 0.920000016689301, ... ]
7 :emp314 double 499 [253.899993896484, 256.600006103516, 256.899993896484, 253.800003051758, 245.800003051758, ... ]
8 :wage314 double 341 [0.990000009536743, 0.980000019073486, 0.990000009536743, 0.990000009536743, 0.990000009536743, ... ]
9 :emp228 double 394 [185.300003051758, 184.699996948242, 182.199996948242, 179.600006103516, 173.800003051758, ... ]
10 :wage228 double 326 [0.910000026226044, 0.939999997615814, 0.980000019073486, 0.980000019073486, 0.980000019073486, ... ]
11 :emp233 double 487 [346.5, 357.600006103516, 358.200012207031, 326.700012207031, 302.799987792969, ... ]
12 :wage233 double 325 [1.54999995231628, 1.57000005245209, 1.53999996185303, 1.41999995708466, 1.37999999523163, ... ]
13 :emp394 double 383 [79.5, 79.5, 79.5999984741211, 78.1999969482422, 76.1999969482422, ... ]
14 :wage394 double 357 [1.02999997138977, 1.03999996185303, 1.04999995231628, 1.04999995231628, 1.05999994277954, ... ]
15 :emp231 double 440 [143.699996948242, 145.800003051758, 146.600006103516, 145.899993896484, 145.699996948242, ... ]
16 :wage231 double 321 [1.26999998092651, 1.26999998092651, 1.26999998092651, 1.25, 1.25, ... ]
17 :emp226 double 306 [90.5999984741211, 90.6999969482422, 90.5999984741211, 89.4000015258789, 88.5999984741211, ... ]
18 :wage226 double 362 [1.07000005245209, 1.08000004291534, 1.10000002384186, 1.12999999523163, 1.12000000476837, ... ]
19 :emp387 double 247 [41.9000015258789, 42.4000015258789, 42.0, 41.7000007629395, 41.5999984741211, ... ]
20 :wage387 double 361 [1.07000005245209, 1.07000005245209, 1.08000004291534, 1.0900000333786, 1.10000002384186, ... ]
... 38 more Vectors ...Rdatasets: wooldridge: mlb1, mlb1
RedAmber::DataFrame : 353 x 47 Vectors
Vectors : 47 numeric
# key type level data_preview
1 :salary double 213 [6329213.0, 3375000.0, 3100000.0, 2900000.0, 1650000.0, ... ]
2 :teamsal uint32 28 [38407380, 38407380, 38407380, 38407380, 38407380, ... ]
3 :nl uint8 2 {1=>168, 0=>185}
4 :years uint8 19 [12, 8, 5, 8, 12, ... ]
5 :games uint16 307 [1705, 918, 751, 1056, 1196, ... ]
6 :atbats uint16 345 [6705, 3333, 2807, 3337, 3603, ... ]
7 :runs uint16 265 [1076, 407, 370, 405, 437, ... ]
8 :hits uint16 305 [1939, 863, 840, 816, 928, ... ]
9 :doubles uint16 192 [320, 156, 148, 143, 19, ... ]
10 :triples uint8 65 [67, 38, 18, 18, 16, ... ]
11 :hruns uint16 139 [231, 73, 46, 107, 124, ... ]
12 :rbis uint16 265 [836, 342, 355, 421, 541, ... ]
13 :bavg double 113 [289.0, 259.0, 299.0, 245.0, 258.0, ... ]
14 :bb uint16 241 [619, 137, 341, 306, 316, ... ]
15 :so uint16 274 [948, 582, 228, 653, 725, ... ]
16 :sbases uint16 131 [314, 133, 41, 15, 32, ... ]
17 :fldperc uint16 61 [989, 968, 994, 971, 977, ... ]
18 :frstbase uint8 2 {0=>308, 1=>45}
19 :scndbase uint8 2 {1=>37, 0=>316}
20 :shrtstop uint8 2 {0=>304, 1=>49}
... 27 more Vectors ...Rdatasets: wooldridge: murder, murder
RedAmber::DataFrame : 153 x 13 Vectors
Vectors : 12 numeric, 1 string
# key type level data_preview
1 :id uint8 51 [1, 1, 1, 2, 2, ... ]
2 :state string 51 ["AL", "AL", "AL", "AK", "AK", ... ]
3 :year uint8 3 {87=>51, 90=>51, 93=>51}
4 :mrdrte double 93 [9.30000019073486, 11.6000003814697, 11.6000003814697, 10.1000003814697, 7.5, ... ]
5 :exec uint8 13 [2, 5, 2, 0, 0, ... ]
6 :unem double 63 [7.80000019073486, 6.80000019073486, 7.5, 10.8000001907349, 6.90000009536743, ... ]
7 :d90 uint8 2 {0=>102, 1=>51}
8 :d93 uint8 2 {0=>102, 1=>51}
9 :cmrdrte double 81 [nil, 2.30000019073486, 0.0, nil, -2.60000038146973, ... ], 51 nils
10 :cexec int8 14 [nil, 3, -3, nil, 0, ... ], 51 nils
11 :cunem int8 9 [nil, -1, 0, nil, -3, ... ], 51 nils
12 :cexec_1 int8 10 [nil, nil, 3, nil, nil, ... ], 102 nils
13 :cunem_1 int8 9 [nil, nil, -1, nil, nil, ... ], 102 nilsRdatasets: wooldridge: nbasal, nbasal
RedAmber::DataFrame : 269 x 22 Vectors
Vectors : 22 numeric
# key type level data_preview
1 :marr uint8 2 {1=>119, 0=>150}
2 :wage double 180 [1002.5, 2030.0, 650.0, 2030.0, 755.0, ... ]
3 :exper uint8 15 [4, 5, 1, 5, 3, ... ]
4 :age uint8 18 [27, 28, 25, 28, 24, ... ]
5 :coll uint8 5 {4=>220, 0=>7, 3=>37, 2=>4, 1=>1}
6 :games uint8 58 [77, 78, 74, 47, 82, ... ]
7 :minutes uint16 254 [2867, 2789, 1149, 1178, 2096, ... ]
8 :guard uint8 2 {1=>113, 0=>156}
9 :forward uint8 2 {0=>159, 1=>110}
10 :center uint8 2 {0=>223, 1=>46}
11 :points double 159 [15.5, 13.3000001907349, 5.5, 7.30000019073486, 10.8000001907349, ... ]
12 :rebounds double 91 [3.90000009536743, 2.5, 3.29999995231628, 5.09999990463257, 4.30000019073486, ... ]
13 :assists double 72 [4.5, 8.80000019073486, 0.200000002980232, 1.5, 2.59999990463257, ... ]
14 :draft uint8 60 [19, 28, 19, 1, 24, ... ], 29 nils
15 :allstar uint8 2 {0=>238, 1=>31}
16 :avgmin double 265 [37.2337608337402, 35.7564086914062, 15.5270299911499, 25.0638294219971, 25.5609798431396, ... ]
17 :lwage double 180 [6.9102520942688, 7.61579084396362, 6.47697305679321, 7.61579084396362, 6.62671804428101, ... ]
18 :black uint8 2 {1=>217, 0=>52}
19 :children uint8 2 {0=>176, 1=>93}
20 :expersq uint16 15 [16, 25, 1, 25, 9, ... ]
... 2 more Vectors ...Rdatasets: wooldridge: nyse, nyse
RedAmber::DataFrame : 691 x 8 Vectors
Vectors : 8 numeric
# key type level data_preview
1 :price double 653 [49.75, 51.439998626709, 52.0499992370605, 52.2799987792969, 54.2400016784668, ... ]
2 :return double 691 [nil, 3.39698219299316, 1.18584883213043, 0.44188192486763, 3.74904918670654, ... ], 1 nil
3 :return_1 double 690 [nil, nil, 3.39698219299316, 1.18584883213043, 0.44188192486763, ... ], 2 nils
4 :t uint16 691 [1, 2, 3, 4, 5, ... ]
5 :price_1 double 653 [nil, 49.75, 51.439998626709, 52.0499992370605, 52.2799987792969, ... ], 1 nil
6 :price_2 double 652 [nil, nil, 49.75, 51.439998626709, 52.0499992370605, ... ], 2 nils
7 :cprice double 550 [nil, 1.68999862670898, 0.610000610351562, 0.229999542236328, 1.96000289916992, ... ], 1 nil
8 :cprice_1 double 549 [nil, nil, 1.68999862670898, 0.610000610351562, 0.229999542236328, ... ], 2 nilsRdatasets: wooldridge: okun, okun
RedAmber::DataFrame : 47 x 4 Vectors
Vectors : 4 numeric
# key type level data_preview
1 :year uint16 47 [1959, 1960, 1961, 1962, 1963, ... ]
2 :pcrgdp double 32 [7.09999990463257, 2.5, 2.29999995231628, 6.09999990463257, 4.40000009536743, ... ]
3 :unem double 32 [5.5, 5.5, 6.69999980926514, 5.5, 5.69999980926514, ... ]
4 :cunem double 29 [nil, 0.0, 1.19999980926514, -1.19999980926514, 0.199999809265137, ... ], 1 nilRdatasets: wooldridge: pension, pension
RedAmber::DataFrame : 194 x 19 Vectors
Vectors : 19 numeric
# key type level data_preview
1 :id uint16 171 [38, 152, 152, 182, 222, ... ]
2 :pyears uint8 36 [1, 6, 25, 20, 35, ... ], 3 nils
3 :prftshr uint8 2 {0=>154, 1=>40}
4 :choice uint8 2 {1=>119, 0=>75}
5 :female uint8 2 {0=>77, 1=>117}
6 :married uint8 2 {1=>147, 0=>47}
7 :age uint8 19 [64, 56, 56, 63, 67, ... ]
8 :educ uint8 11 [12, 13, 12, 12, 12, ... ]
9 :finc25 uint8 2 {0=>154, 1=>40}
10 :finc35 uint8 2 {0=>160, 1=>34}
11 :finc50 uint8 2 {1=>46, 0=>148}
12 :finc75 uint8 2 {0=>168, 1=>26}
13 :finc100 uint8 2 {0=>168, 1=>26}
14 :finc101 uint8 2 {0=>182, 1=>12}
15 :wealth89 double 164 [77.9000015258789, 154.899993896484, 154.899993896484, 232.5, 179.0, ... ]
16 :black uint8 2 {0=>172, 1=>22}
17 :stckin89 uint8 2 {1=>66, 0=>128}
18 :irain89 uint8 2 {1=>100, 0=>94}
19 :pctstck uint8 3 {0=>64, 50=>72, 100=>58}Rdatasets: wooldridge: phillips, phillips
RedAmber::DataFrame : 56 x 7 Vectors
Vectors : 7 numeric
# key type level data_preview
1 :year uint16 56 [1948, 1949, 1950, 1951, 1952, ... ]
2 :unem double 37 [3.79999995231628, 5.90000009536743, 5.30000019073486, 3.29999995231628, 3.0, ... ]
3 :inf double 40 [8.10000038146973, -1.20000004768372, 1.29999995231628, 7.90000009536743, 1.89999997615814, ... ]
4 :inf_1 double 41 [nil, 8.10000038146973, -1.20000004768372, 1.29999995231628, 7.90000009536743, ... ], 1 nil
5 :unem_1 double 37 [nil, 3.79999995231628, 5.90000009536743, 5.30000019073486, 3.29999995231628, ... ], 1 nil
6 :cinf double 50 [nil, -9.30000019073486, 2.5, 6.60000038146973, -6.0, ... ], 1 nil
7 :cunem double 37 [nil, 2.10000014305115, -0.599999904632568, -2.00000023841858, -0.299999952316284, ... ], 1 nilRdatasets: wooldridge: prminwge, prminwge
RedAmber::DataFrame : 38 x 25 Vectors
Vectors : 25 numeric
# key type level data_preview
1 :year uint16 38 [1950, 1951, 1952, 1953, 1954, ... ]
2 :avgmin double 34 [0.197999998927116, 0.209000006318092, 0.224999994039536, 0.310999989509583, 0.312999993562698, ... ]
3 :avgwage double 38 [0.398000001907349, 0.409999996423721, 0.421000003814697, 0.479999989271164, 0.508000016212463, ... ]
4 :kaitz double 35 [0.155000001192093, 0.164000004529953, 0.180000007152557, 0.229000002145767, 0.210999995470047, ... ]
5 :avgcov double 35 [0.20100000500679, 0.207000002264977, 0.225999996066093, 0.231000006198883, 0.224000006914139, ... ]
6 :covt double 8 [0.28999999165535, 0.28999999165535, 0.28999999165535, 0.28999999165535, 0.28999999165535, ... ]
7 :mfgwage double 38 [0.430000007152557, 0.449999988079071, 0.479999989271164, 0.5, 0.519999980926514, ... ]
8 :prdef double 38 [0.859000027179718, 0.880999982357025, 0.953000009059906, 0.970000028610229, 1.0, ... ]
9 :prepop double 31 [0.469999998807907, 0.449000000953674, 0.433999985456467, 0.428000003099442, 0.41499999165535, ... ]
10 :prepopf double 31 [0.469999998807907, 0.449000000953674, 0.433999985456467, 0.428000003099442, 0.41499999165535, ... ]
11 :prgnp double 38 [878.700012207031, 925.0, 1015.90002441406, 1081.30004882812, 1104.40002441406, ... ]
12 :prunemp double 30 [15.3999996185303, 16.0, 14.8000001907349, 14.5, 15.2999992370605, ... ]
13 :usgnp double 38 [1203.69995117188, 1328.19995117188, 1380.0, 1435.30004882812, 1416.19995117188, ... ]
14 :t uint8 38 [1, 2, 3, 4, 5, ... ]
15 :post74 uint8 15 [0, 0, 0, 0, 0, ... ]
16 :lprunemp double 30 [2.73436737060547, 2.7725887298584, 2.69462728500366, 2.67414855957031, 2.7278528213501, ... ]
17 :lprgnp double 38 [6.77844333648682, 6.82979393005371, 6.92353010177612, 6.98591947555542, 7.00705766677856, ... ]
18 :lusgnp double 38 [7.09315538406372, 7.19157981872559, 7.22983884811401, 7.26912927627563, 7.25573253631592, ... ]
19 :lkaitz double 35 [-1.86433017253876, -1.80788886547089, -1.71479833126068, -1.4740332365036, -1.55589711666107, ... ]
20 :lprun_1 double 30 [nil, -1.87080264091492, -1.83258152008057, -1.9105429649353, -1.93102157115936, ... ], 1 nil
... 5 more Vectors ...Rdatasets: wooldridge: rental, rental
RedAmber::DataFrame : 128 x 23 Vectors
Vectors : 23 numeric
# key type level data_preview
1 :city uint8 64 [1, 1, 2, 2, 3, ... ]
2 :year uint8 2 {80=>64, 90=>64}
3 :pop uint32 128 [75211, 77759, 106743, 141865, 36608, ... ]
4 :enroll uint32 126 [15303, 18017, 22462, 29769, 11847, ... ]
5 :rent uint16 103 [197, 342, 323, 496, 216, ... ]
6 :rnthsg uint32 127 [13475, 15660, 14580, 26895, 7026, ... ]
7 :tothsg uint32 127 [26167, 29467, 37277, 55540, 13482, ... ]
8 :avginc uint16 128 [11537, 19568, 19841, 31885, 11455, ... ]
9 :lenroll double 126 [9.63580417633057, 9.79907131195068, 10.0195798873901, 10.3012228012085, 9.3798303604126, ... ]
10 :lpop double 128 [11.2280530929565, 11.2613697052002, 11.578179359436, 11.8626308441162, 10.5080223083496, ... ]
11 :lrent double 103 [5.28320360183716, 5.83481073379517, 5.77765226364136, 6.20657587051392, 5.37527847290039, ... ]
12 :ltothsg double 127 [10.1722545623779, 10.2910261154175, 10.5261316299438, 10.924859046936, 9.50911045074463, ... ]
13 :lrnthsg double 127 [9.5085916519165, 9.65886497497559, 9.58740615844727, 10.1996955871582, 8.85737323760986, ... ]
14 :lavginc double 128 [9.35331439971924, 9.88165092468262, 9.89550590515137, 10.369891166687, 9.34618186950684, ... ]
15 :clenroll double 65 [nil, -15293.201171875, nil, -22451.69921875, nil, ... ], 64 nils
16 :clpop double 65 [nil, 0.0333166122436523, nil, 0.284451484680176, nil, ... ], 64 nils
17 :clrent double 64 [nil, 0.551607131958008, nil, 0.428923606872559, nil, ... ], 64 nils
18 :cltothsg double 65 [nil, 0.118771553039551, nil, 0.398727416992188, nil, ... ], 64 nils
19 :clrnthsg double 65 [nil, 0.150273323059082, nil, 0.612289428710938, nil, ... ], 64 nils
20 :clavginc double 65 [nil, 0.528336524963379, nil, 0.474385261535645, nil, ... ], 64 nils
... 3 more Vectors ...Rdatasets: wooldridge: school93_98, school93_98
RedAmber::DataFrame : 10668 x 18 Vectors
Vectors : 18 numeric
# key type level data_preview
1 :distid uint32 525 [34010, 34010, 34010, 34010, 34010, ... ]
2 :schid uint16 1778 [1, 1, 1, 1, 1, ... ]
3 :lunch double 5085 [39.7999992370605, 35.0, 36.5900001525879, 40.5499992370605, 49.4900016784668, ... ], 303 nils
4 :enrol uint16 954 [256, 248, 244, 247, 291, ... ], 169 nils
5 :exppp uint16 3332 [2506, 2870, 4176, 4333, 3666, ... ], 937 nils
6 :math4 double 899 [46.5999984741211, 44.7000007629395, 86.5, 84.3000030517578, 83.8000030517578, ... ], 103 nils
7 :year uint16 6 [1993, 1994, 1995, 1996, 1997, ... ]
8 :y93 uint8 2 {1=>1778, 0=>8890}
9 :y94 uint8 2 {0=>8890, 1=>1778}
10 :y95 uint8 2 {0=>8890, 1=>1778}
11 :y96 uint8 2 {0=>8890, 1=>1778}
12 :y97 uint8 2 {0=>8890, 1=>1778}
13 :y98 uint8 2 {0=>8890, 1=>1778}
14 :rexpp double 6868 [2783.48095703125, 3108.19848632812, 4397.953125, 4432.4189453125, 3666.0, ... ], 937 nils
15 :found uint16 1259 [nil, nil, 4478, 4754, 5032, ... ], 3556 nils
16 :lenrol double 954 [5.5451774597168, 5.51342868804932, 5.49716806411743, 5.50938844680786, 5.67332315444946, ... ], 169 nils
17 :lrexpp double 6848 [7.93145751953125, 8.04179859161377, 8.38889408111572, 8.39670085906982, 8.2068567276001, ... ], 937 nils
18 :lavgrexpp double 7450 [nil, 7.98814916610718, 8.23033142089844, 8.3928050994873, 8.30627727508545, ... ], 3151 nilsRdatasets: wooldridge: traffic2, traffic2
RedAmber::DataFrame : 108 x 48 Vectors
Vectors : 48 numeric
# key type level data_preview
1 :year uint16 9 [1981, 1981, 1981, 1981, 1981, ... ]
2 :totacc uint16 108 [40511, 36034, 40328, 37699, 38816, ... ]
3 :fatacc uint16 79 [365, 329, 369, 369, 355, ... ]
4 :injacc uint16 108 [15626, 14469, 16026, 15560, 16546, ... ]
5 :pdoacc uint16 107 [24520, 21236, 23933, 21770, 21915, ... ]
6 :ntotacc uint16 107 [38235, 33989, 37990, 35457, 36611, ... ]
7 :nfatacc uint16 81 [323, 294, 335, 326, 321, ... ]
8 :ninjacc uint16 108 [14743, 13620, 15105, 14654, 15569, ... ]
9 :npdoacc uint16 108 [23169, 20075, 22550, 20477, 20721, ... ]
10 :rtotacc uint16 93 [236, 200, 253, 270, 263, ... ]
11 :rfatacc uint8 23 [12, 8, 12, 14, 6, ... ]
12 :rinjacc uint16 80 [99, 101, 110, 123, 136, ... ]
13 :rpdoacc uint16 75 [125, 91, 131, 133, 121, ... ]
14 :ushigh uint16 100 [1057, 934, 1143, 1009, 1082, ... ]
15 :cntyrds uint16 103 [6838, 6057, 6803, 6314, 6746, ... ]
16 :strtes uint16 106 [3474, 3123, 3458, 3202, 3334, ... ]
17 :t uint8 108 [1, 2, 3, 4, 5, ... ]
18 :tsq uint16 108 [1, 4, 9, 16, 25, ... ]
19 :unem double 54 [7.90000009536743, 8.0, 7.40000009536743, 7.19999980926514, 6.09999990463257, ... ]
20 :spdlaw uint8 2 {0=>76, 1=>32}
... 28 more Vectors ...Rdatasets: wooldridge: volat, volat
RedAmber::DataFrame : 558 x 17 Vectors
Vectors : 17 numeric
# key type level data_preview
1 :date double 558 [1947.01000976562, 1947.02001953125, 1947.03002929688, 1947.0400390625, 1947.05004882812, ... ]
2 :sp500 double 549 [15.210000038147, 15.8000001907349, 15.1599998474121, 14.6000003814697, 14.3400001525879, ... ]
3 :divyld double 276 [4.48999977111816, 4.38000011444092, 4.6100001335144, 4.75, 5.05000019073486, ... ]
4 :i3 double 411 [0.379999995231628, 0.379999995231628, 0.379999995231628, 0.379999995231628, 0.379999995231628, ... ]
5 :ip double 376 [22.3999996185303, 22.5, 22.6000003814697, 22.5, 22.6000003814697, ... ]
6 :pcsp double 558 [nil, 46.5483322143555, -48.6076202392578, -44.3271369934082, -21.3698806762695, ... ], 1 nil
7 :rsp500 double 558 [nil, 50.9283332824707, -43.9976196289062, -39.5771369934082, -16.319881439209, ... ], 1 nil
8 :pcip double 515 [nil, 5.35716342926025, 5.33335399627686, -5.30975437164307, 5.33335399627686, ... ], 1 nil
9 :ci3 double 259 [nil, 0.0, 0.0, 0.0, 0.0, ... ], 1 nil
10 :ci3_1 double 259 [nil, nil, 0.0, 0.0, 0.0, ... ], 2 nils
11 :ci3_2 double 259 [nil, nil, nil, 0.0, 0.0, ... ], 3 nils
12 :pcip_1 double 514 [nil, nil, 5.35716342926025, 5.33335399627686, -5.30975437164307, ... ], 2 nils
13 :pcip_2 double 513 [nil, nil, nil, 5.35716342926025, 5.33335399627686, ... ], 3 nils
14 :pcip_3 double 512 [nil, nil, nil, nil, 5.35716342926025, ... ], 4 nils
15 :pcsp_1 double 557 [nil, nil, 46.5483322143555, -48.6076202392578, -44.3271369934082, ... ], 2 nils
16 :pcsp_2 double 556 [nil, nil, nil, 46.5483322143555, -48.6076202392578, ... ], 3 nils
17 :pcsp_3 double 555 [nil, nil, nil, nil, 46.5483322143555, ... ], 4 nilsRdatasets: wooldridge: vote2, vote2
RedAmber::DataFrame : 186 x 26 Vectors
Vectors : 25 numeric, 1 string
# key type level data_preview
1 :state string 36 ["AL", "AL", "AL", "AK", "AZ", ... ]
2 :district uint8 36 [2, 3, 7, 1, 2, ... ]
3 :democ uint8 2 {0=>70, 1=>116}
4 :vote90 uint8 40 [51, 74, 71, 52, 66, ... ]
5 :vote88 uint8 43 [94, 65, 68, 62, 73, ... ]
6 :inexp90 uint32 186 [596096, 176550, 238446, 564759, 112373, ... ]
7 :chexp90 uint32 186 [163663, 22989, 58952, 164732, 1445, ... ]
8 :inexp88 uint32 186 [234923, 679297, 328296, 626377, 99607, ... ]
9 :chexp88 uint32 158 [nil, 443927, 8737, 402477, 3065, ... ], 29 nils
10 :prtystr uint8 47 [62, 65, 41, 60, 55, ... ]
11 :rptchall uint8 2 {0=>150, 1=>36}
12 :tenure uint8 26 [26, 1, 4, 17, 29, ... ], 9 nils
13 :lawyer uint8 3 {1=>78, 0=>99, nil=>9}
14 :linexp90 double 186 [13.2981567382812, 12.0813598632812, 12.3818979263306, 13.2441539764404, 11.6295785903931, ... ]
15 :lchexp90 double 186 [12.0055646896362, 10.0427713394165, 10.9844789505005, 12.0120754241943, 7.27586460113525, ... ]
16 :linexp88 double 186 [12.3670129776001, 13.4288139343262, 12.7016706466675, 13.3477077484131, 11.5089874267578, ... ]
17 :lchexp88 double 158 [nil, 13.0034151077271, 9.07532215118408, 12.9053936004639, 8.02780246734619, ... ], 29 nils
18 :incshr90 double 186 [78.4585647583008, 88.4789428710938, 80.1774063110352, 77.4182281494141, 98.7304306030273, ... ]
19 :incshr88 double 158 [nil, 60.4774284362793, 97.4076690673828, 60.8810386657715, 97.014762878418, ... ], 29 nils
20 :cvote int8 42 [-43, 9, 3, -10, -7, ... ]
... 6 more Vectors ...Rdatasets: wooldridge: voucher, voucher
RedAmber::DataFrame : 990 x 19 Vectors
Vectors : 19 numeric
# key type level data_preview
1 :studyid uint16 990 [21, 26, 30, 31, 33, ... ]
2 :black uint8 2 {1=>668, 0=>322}
3 :hispanic uint8 2 {0=>836, 1=>154}
4 :female uint8 2 {1=>516, 0=>474}
5 :appyear uint8 4 {90=>453, 91=>203, 93=>152, 92=>182}
6 :mnce uint8 70 [44, 46, 20, 36, 32, ... ]
7 :select uint8 2 {1=>522, 0=>468}
8 :choice uint8 2 {0=>520, 1=>470}
9 :selectyrs uint8 5 {4=>108, 3=>150, 1=>116, 2=>148, 0=>468}
10 :choiceyrs uint8 5 {1=>211, 4=>56, 3=>111, 2=>122, 0=>490}
11 :mnce90 uint8 56 [nil, nil, nil, nil, nil, ... ], 662 nils
12 :selectyrs1 uint8 2 {0=>874, 1=>116}
13 :selectyrs2 uint8 2 {0=>842, 1=>148}
14 :selectyrs3 uint8 2 {0=>840, 1=>150}
15 :selectyrs4 uint8 2 {1=>108, 0=>882}
16 :choiceyrs1 uint8 2 {1=>211, 0=>779}
17 :choiceyrs2 uint8 2 {0=>868, 1=>122}
18 :choiceyrs3 uint8 2 {0=>879, 1=>111}
19 :choiceyrs4 uint8 2 {0=>934, 1=>56}Rdatasets: wooldridge: wage2, wage2
RedAmber::DataFrame : 935 x 17 Vectors
Vectors : 17 numeric
# key type level data_preview
1 :wage uint16 449 [769, 808, 825, 650, 562, ... ]
2 :hours uint8 37 [40, 50, 40, 40, 40, ... ]
3 :IQ uint8 80 [93, 119, 108, 96, 74, ... ]
4 :KWW uint8 42 [35, 41, 46, 32, 27, ... ]
5 :educ uint8 10 [12, 18, 14, 12, 11, ... ]
6 :exper uint8 22 [11, 11, 11, 13, 14, ... ]
7 :tenure uint8 23 [2, 16, 9, 7, 5, ... ]
8 :age uint8 11 [31, 37, 33, 32, 34, ... ]
9 :married uint8 2 {1=>835, 0=>100}
10 :black uint8 2 {0=>815, 1=>120}
11 :south uint8 2 {0=>616, 1=>319}
12 :urban uint8 2 {1=>671, 0=>264}
13 :sibs uint8 15 [1, 1, 1, 4, 10, ... ]
14 :brthord uint8 11 [2, nil, 2, 3, 6, ... ], 83 nils
15 :meduc uint8 20 [8, 14, 14, 12, 6, ... ], 78 nils
16 :feduc uint8 19 [8, 14, 14, 12, 11, ... ], 194 nils
17 :lwage double 449 [6.64509105682373, 6.69456195831299, 6.71538352966309, 6.47697257995605, 6.33150196075439, ... ]Rdatasets: wooldridge: wageprc, wageprc
RedAmber::DataFrame : 286 x 20 Vectors
Vectors : 20 numeric
# key type level data_preview
1 :price double 277 [92.5999984741211, 92.5, 92.5999984741211, 92.6999969482422, 92.6999969482422, ... ]
2 :wage double 237 [2.3199999332428, 2.3199999332428, 2.3199999332428, 2.33999991416931, 2.34999990463257, ... ]
3 :t uint16 286 [1, 2, 3, 4, 5, ... ]
4 :lprice double 277 [4.52828931808472, 4.52720880508423, 4.52828931808472, 4.52936840057373, 4.52936840057373, ... ]
5 :lwage double 237 [0.841567158699036, 0.841567158699036, 0.841567158699036, 0.850150883197784, 0.85441529750824, ... ]
6 :gprice double 279 [nil, -0.00108051300048828, 0.00108051300048828, 0.00107908248901367, 0.0, ... ], 1 nil
7 :gwage double 245 [nil, 0.0, 0.0, 0.00858372449874878, 0.00426441431045532, ... ], 1 nil
8 :gwage_1 double 244 [nil, nil, 0.0, 0.0, 0.00858372449874878, ... ], 2 nils
9 :gwage_2 double 243 [nil, nil, nil, 0.0, 0.0, ... ], 3 nils
10 :gwage_3 double 242 [nil, nil, nil, nil, 0.0, ... ], 4 nils
11 :gwage_4 double 242 [nil, nil, nil, nil, nil, ... ], 5 nils
12 :gwage_5 double 241 [nil, nil, nil, nil, nil, ... ], 6 nils
13 :gwage_6 double 240 [nil, nil, nil, nil, nil, ... ], 7 nils
14 :gwage_7 double 239 [nil, nil, nil, nil, nil, ... ], 8 nils
15 :gwage_8 double 239 [nil, nil, nil, nil, nil, ... ], 9 nils
16 :gwage_9 double 238 [nil, nil, nil, nil, nil, ... ], 10 nils
17 :gwage_10 double 237 [nil, nil, nil, nil, nil, ... ], 11 nils
18 :gwage_11 double 236 [nil, nil, nil, nil, nil, ... ], 12 nils
19 :gwage_12 double 235 [nil, nil, nil, nil, nil, ... ], 13 nils
20 :gprice_1 double 278 [nil, nil, -0.00108051300048828, 0.00108051300048828, 0.00107908248901367, ... ], 2 nils