Fix Rdatasets#each to change String datatype for mixed data of Numeric and String.
- This is the data preview after this fix applied.
- 55 datasets are fixed.
# [package, dataset] pairs
[["AER", "ResumeNames"], ["boot", "tau"], ["carData", "Blackmore"], ["causaldata", "restaurant_inspections"], ["causaldata", "ri"], ["causaldata", "training_example"], ["DAAG", "biomass"], ["DAAG", "hotspots"], ["DAAG", "rockArt"], ["datasets", "attenu"], ["dragracer", "rpdr_ep"], ["drc", "germination"], ["Ecdat", "Mathlevel"], ["Ecdat", "Schooling"], ["gap", "aldh2"], ["gap", "cnv"], ["gap", "mao"], ["ggplot2", "mpg"], ["ggplot2movies", "movies"], ["gt", "gtcars"], ["HistData", "Pyx"], ["MASS", "Cars93"], ["mosaicData", "Galton"], ["mosaicData", "HELPfull"], ["nycflights13", "airports"], ["nycflights13", "planes"], ["openintro", "birds"], ["openintro", "cards"], ["openintro", "cpu"], ["openintro", "epa2012"], ["openintro", "epa2021"], ["openintro", "kobe_basket"], ["openintro", "labor_market_discrimination"], ["openintro", "nba_players_19"], ["openintro", "playing_cards"], ["openintro", "reddit_finance"], ["openintro", "resume"], ["openintro", "seattlepets"], ["openintro", "sowc_child_mortality"], ["openintro", "sp500_seq"], ["openintro", "ssd_speed"], ["openintro", "textbooks"], ["openintro", "ucla_f18"], ["openintro", "ucla_textbooks_f18"], ["openintro", "yrbss"], ["openintro", "yrbss_samp"], ["rpart", "car90"], ["Stat2Data", "Hawks"], ["stevedata", "anes_partytherms"], ["tidyr", "billboard"], ["validate", "nace_rev2"], ["validate", "samplonomy"], ["vcd", "Baseball"], ["vcd", "Lifeboats"], ["vcd", "SpaceShuttle"]]Rdatasets: AER: ResumeNames, Are Emily and Greg More Employable Than Lakisha and Jamal?
RedAmber::DataFrame : 4870 x 27 Vectors
Vectors : 2 numeric, 25 strings
# key type level data_preview
1 :name string 36 ["Allison", "Kristen", "Lakisha", "Latonya", "Carrie", ... ]
2 :gender string 2 {"female"=>3746, "male"=>1124}
3 :ethnicity string 2 {"cauc"=>2435, "afam"=>2435}
4 :quality string 2 {"low"=>2424, "high"=>2446}
5 :call string 2 {"no"=>4478, "yes"=>392}
6 :city string 2 {"chicago"=>2704, "boston"=>2166}
7 :jobs uint8 7 [2, 3, 1, 4, 3, ... ]
8 :experience uint8 26 [6, 6, 6, 6, 22, ... ]
9 :honors string 2 {"no"=>4613, "yes"=>257}
10 :volunteer string 2 {"no"=>2866, "yes"=>2004}
11 :military string 2 {"no"=>4397, "yes"=>473}
12 :holes string 2 {"yes"=>2182, "no"=>2688}
13 :school string 2 {"no"=>2145, "yes"=>2725}
14 :email string 2 {"no"=>2536, "yes"=>2334}
15 :computer string 2 {"yes"=>3996, "no"=>874}
16 :special string 2 {"no"=>3269, "yes"=>1601}
17 :college string 2 {"yes"=>3504, "no"=>1366}
18 :minimum string 13 ["5", "5", "5", "5", "some", ... ]
19 :equal string 2 {"yes"=>1418, "no"=>3452}
20 :wanted string 6 ["supervisor", "supervisor", "supervisor", "supervisor", "secretary", ... ]
... 7 more Vectors ...Rdatasets: boot: tau, Tau Particle Decay Modes
RedAmber::DataFrame : 60 x 2 Vectors
Vectors : 1 numeric, 1 string
# key type level data_preview
1 :rate double 48 [84.0, 84.7, 84.7, 85.1, 85.2, ... ]
2 :decay string 5 {"1"=>13, "rho"=>6, "pi"=>7, "e"=>14, "mu"=>20}Rdatasets: carData: Blackmore, Exercise Histories of Eating-Disordered and Control Subjects
RedAmber::DataFrame : 945 x 4 Vectors
Vectors : 2 numeric, 2 strings
# key type level data_preview
1 :subject string 231 ["100", "100", "100", "100", "100", ... ]
2 :age double 82 [8.0, 10.0, 12.0, 14.0, 15.92, ... ]
3 :exercise double 405 [2.71, 1.94, 2.36, 1.54, 8.63, ... ]
4 :group string 2 {"patient"=>586, "control"=>359}Rdatasets: causaldata: restaurant_inspections, Data on Restaurant Inspections
RedAmber::DataFrame : 27178 x 5 Vectors
Vectors : 3 numeric, 2 strings
# key type level data_preview
1 :business_name string 1618 ["MCGINLEYS PUB", "VILLAGE INN #1", "RONNIE SUSHI 2", "FRED MEYER - RETAIL FISH", "PHO GRILL", ... ]
2 :inspection_score uint8 33 [94, 86, 80, 96, 83, ... ]
3 :Year uint16 15 [2017, 2015, 2016, 2003, 2017, ... ]
4 :NumberofLocations uint16 135 [9, 66, 79, 86, 53, ... ]
5 :Weekend string 2 {"FALSE"=>26968, "TRUE"=>210}Rdatasets: causaldata: ri, A simple simulated data set for calculating p-values
RedAmber::DataFrame : 8 x 5 Vectors
Vectors : 2 numeric, 3 strings
# key type level data_preview
1 :name string 8 ["Andy", "Ben", "Chad", "Daniel", "Edith", ... ]
2 :d uint8 2 {1=>4, 0=>4}
3 :y uint8 6 [10, 5, 16, 3, 5, ... ]
4 :y0 string 5 {"."=>4, "5"=>1, "7"=>1, "8"=>1, "10"=>1}
5 :y1 string 5 {"10"=>1, "5"=>1, "16"=>1, "3"=>1, "."=>4}Rdatasets: causaldata: training_example, Simulated data from a job training program
RedAmber::DataFrame : 25 x 9 Vectors
Vectors : 8 numeric, 1 string
# key type level data_preview
1 :unit_treat uint8 11 [1, 2, 3, 4, 5, ... ], 15 nils
2 :age_treat double 12 [18.0, 29.0, 24.0, 27.0, 33.0, ... ], 14 nils
3 :earnings_treat string 14 ["9500", "12250", "11000", "11750", "13250", ... ]
4 :unit_control double 23 [1.0, 2.0, 3.0, 4.0, 5.0, ... ], 3 nils
5 :age_control double 18 [20.0, 27.0, 21.0, 39.0, 38.0, ... ], 4 nils
6 :earnings_control double 19 [8500.0, 10075.0, 8725.0, 12775.0, 12550.0, ... ], 4 nils
7 :unit_matched uint8 11 [1, 2, 3, 4, 5, ... ], 15 nils
8 :age_matched uint8 11 [18, 29, 24, 27, 33, ... ], 15 nils
9 :earnings_matched uint16 12 [8050, 10525, 9400, 10075, 11425, ... ], 14 nilsRdatasets: DAAG: biomass, Biomass Data
RedAmber::DataFrame : 153 x 8 Vectors
Vectors : 6 numeric, 2 strings
# key type level data_preview
1 :dbh uint8 58 [90, 106, 112, 34, 130, ... ]
2 :wood uint16 109 [5528, 13650, 11200, 1000, nil, ... ], 20 nils
3 :bark uint16 18 [nil, nil, nil, nil, nil, ... ], 136 nils
4 :root double 47 [460.0, 1500.0, 1100.0, 430.0, 3000.0, ... ], 99 nils
5 :rootsk double 42 [nil, 665.0, 680.0, 40.0, 1030.0, ... ], 100 nils
6 :branch uint16 54 [nil, nil, nil, nil, nil, ... ], 77 nils
7 :species string 8 ["E. maculata", "E. pilularis", "E. pilularis", "E. pilularis", "E. maculata", ... ]
8 :fac26 string 4 {"z"=>75, "2"=>47, ""=>5, "6"=>26}Rdatasets: DAAG: hotspots, Hawaian island chain hotspot Potassium-Argon ages
RedAmber::DataFrame : 35 x 6 Vectors
Vectors : 3 numeric, 3 strings
# key type level data_preview
1 :ID string 35 ["3", "5", "6", "8", "9", ... ]
2 :name string 33 ["Mauna Kea", "Kohala", "Haleakala", "West Maui", "Lanai", ... ]
3 :distance uint16 35 [54, 100, 182, 221, 226, ... ]
4 :age double 35 [0.375, 0.43, 0.75, 1.32, 1.28, ... ]
5 :error double 20 [0.05, 0.02, 0.04, 0.04, 0.04, ... ]
6 :source string 21 ["1", "2", "3", "4", "5", ... ]Rdatasets: DAAG: rockArt, Pacific Rock Art features
RedAmber::DataFrame : 103 x 641 Vectors
Vectors : 628 numeric, 13 strings
# key type level data_preview
1 :"Site.No." double 103 [2.0, 6.0, 7.0, 7.1, 9.0, ... ]
2 :"Site.Name" string 102 ["Ramadordo", "Yaritari", "Wakuia Wai", "Wakuia Wai", "Ifa Kuruku", ... ]
3 :"Site.Code" string 55 ["AED", "*", "AEH", "AEH", "ABZ", ... ]
4 :District string 15 ["Sogeri", "Sogeri", "Sogeri", "Sogeri", "Sogeri", ... ]
5 :Island string 26 ["PNG", "PNG", "PNG", "PNG", "PNG", ... ]
6 :Country string 6 ["PNG", "PNG", "PNG", "PNG", "PNG", ... ]
7 :Technique string 2 {"E"=>67, "P"=>36}
8 :Engtech string 6 ["pecked", "n/a", "n/a", "*", "*", ... ]
9 :red uint8 3 {nil=>68, 1=>31, 0=>4}
10 :black uint8 3 {nil=>68, 0=>28, 1=>7}
11 :yellow uint8 3 {nil=>68, 0=>30, 1=>5}
12 :white uint8 3 {nil=>68, 0=>28, 1=>7}
13 :green uint8 3 {nil=>68, 0=>33, 1=>2}
14 :"red.blk" uint8 2 {nil=>68, 0=>35}
15 :"red.wh" uint8 3 {nil=>68, 0=>31, 1=>4}
16 :"red.yell" uint8 3 {nil=>68, 0=>34, 1=>1}
17 :"r.w.y" uint8 3 {nil=>68, 0=>34, 1=>1}
18 :"black.white" uint8 2 {nil=>68, 0=>35}
19 :blue uint8 3 {nil=>68, 0=>32, 1=>3}
20 :Geology string 6 ["igneous", "*", "*", "*", "*", ... ]
... 621 more Vectors ...Rdatasets: datasets: attenu, The Joyner-Boore Attenuation Data
RedAmber::DataFrame : 182 x 5 Vectors
Vectors : 4 numeric, 1 string
# key type level data_preview
1 :event uint8 23 [1, 2, 2, 2, 2, ... ]
2 :mag double 17 [7.0, 7.4, 7.4, 7.4, 7.4, ... ]
3 :station string 118 ["117", "1083", "1095", "283", "135", ... ], 16 nils
4 :dist double 153 [12.0, 148.0, 42.0, 85.0, 107.0, ... ]
5 :accel double 120 [0.359, 0.014, 0.196, 0.135, 0.062, ... ]Rdatasets: dragracer: rpdr_ep, RuPaul's Drag Race Episode Data
RedAmber::DataFrame : 175 x 22 Vectors
Vectors : 4 numeric, 17 strings, 1 temporal
# key type level data_preview
1 :season string 13 ["S01", "S01", "S01", "S01", "S01", ... ]
2 :episode uint8 16 [1, 2, 3, 4, 5, ... ]
3 :airdate date64 171 [#<DateTime: 2009-02-02T09:00:00+09:00 ((2454865j,0s,0n),+32400s,2299161j)>, #<DateTime: 2009-02-09T09:00:00+09:00 ((2454872j,0s,0n),+32400s,2299161j)>, ... ]
4 :special uint8 2 {0=>152, 1=>23}
5 :finale uint8 2 {0=>162, 1=>13}
6 :nickname string 153 ["Drag on a Dime", "Girl Group Challenge", "Queens of All Media", "Mac Viva-Glam Challenge", "Drag School of Charm", ... ]
7 :runwaytheme string 48 [nil, nil, nil, nil, nil, ... ], 128 nils
8 :numqueens uint8 14 [9, 8, 7, 6, 5, ... ], 10 nils
9 :minic string 89 [nil, "Act out emotions", nil, "30 Minutes to do partner's makeup", "Exercise/endurance challenge", ... ], 68 nils
10 :minicw1 string 73 [nil, "Ongina", nil, "Jade", "Rebecca Glasscock", ... ], 69 nils
11 :minicw2 string 30 [nil, "Akashia", nil, nil, nil, ... ], 145 nils
12 :minicw3 string 6 [nil, nil, nil, nil, nil, ... ], 170 nils
13 :bottom1 string 100 ["Akashia", "Akashia", "Akashia", "Jade", "Bebe Zahara Benet", ... ], 39 nils
14 :bottom2 string 101 ["Victoria (Porkchop) Parker", "Tammie Brown", "Shannel", "Rebecca Glasscock", "Ongina", ... ], 42 nils
15 :bottom3 string 2 {nil=>174, "Plastique Tiara"=>1}
16 :bottom4 string 2 {nil=>174, "Ra’jah O’Hara"=>1}
17 :bottom5 string 2 {nil=>174, "Scarlet Envy"=>1}
18 :bottom6 string 2 {nil=>174, "Shuga Cain"=>1}
19 :lipsyncartist string 106 ["RuPaul", "Michelle Williams", "Whitney Houston", "The Eurythmics", "Britney Spears", ... ], 27 nils
20 :lipsyncsong string 148 ["Supermodel", "We Break the Dawn", "The Greatest Love of All", "Would I Lie to You?", "Stronger", ... ], 27 nils
... 2 more Vectors ...Rdatasets: drc: germination, Germination of three crops
RedAmber::DataFrame : 192 x 5 Vectors
Vectors : 3 numeric, 2 strings
# key type level data_preview
1 :temp uint8 6 [10, 10, 10, 10, 10, ... ]
2 :species string 3 {"wheat"=>99, "mungbean"=>14, "rice"=>79}
3 :start uint8 19 [0, 1, 2, 3, 4, ... ]
4 :end string 19 ["1", "2", "3", "4", "5", ... ]
5 :germinated uint8 15 [0, 0, 0, 0, 0, ... ]Rdatasets: Ecdat: Mathlevel, Level of Calculus Attained for Students Taking Advanced Micro-economics
RedAmber::DataFrame : 609 x 8 Vectors
Vectors : 4 numeric, 4 strings
# key type level data_preview
1 :mathlevel string 7 ["170", "170", "170", "170", "170", ... ]
2 :sat uint16 34 [670, 660, 610, 620, 430, ... ]
3 :language string 2 {"no"=>539, "yes"=>70}
4 :sex string 2 {"male"=>373, "female"=>236}
5 :major string 5 {"ns"=>126, "other"=>130, "eco"=>209, "oss"=>103, "hum"=>41}
6 :mathcourse uint8 4 {1=>297, 0=>30, 2=>274, 3=>8}
7 :physiccourse uint8 3 {2=>13, 1=>405, 0=>191}
8 :chemistcourse uint8 3 {1=>497, 0=>40, 2=>72}Rdatasets: Ecdat: Schooling, Wages and Schooling
RedAmber::DataFrame : 3010 x 28 Vectors
Vectors : 11 numeric, 17 strings
# key type level data_preview
1 :smsa66 string 2 {"yes"=>1955, "no"=>1055}
2 :smsa76 string 2 {"yes"=>2146, "no"=>864}
3 :nearc2 string 2 {"no"=>1683, "yes"=>1327}
4 :nearc4 string 2 {"no"=>957, "yes"=>2053}
5 :nearc4a string 2 {"no"=>1527, "yes"=>1483}
6 :nearc4b string 2 {"no"=>2440, "yes"=>570}
7 :ed76 uint8 18 [7, 12, 12, 11, 12, ... ]
8 :ed66 uint8 19 [5, 11, 12, 11, 12, ... ]
9 :age76 uint8 11 [29, 27, 34, 27, 34, ... ]
10 :daded double 20 [9.94, 8.0, 14.0, 11.0, 8.0, ... ]
11 :nodaded string 2 {"yes"=>690, "no"=>2320}
12 :momed double 20 [10.25, 8.0, 12.0, 12.0, 7.0, ... ]
13 :nomomed string 2 {"yes"=>690, "no"=>2320}
14 :momdad14 string 2 {"yes"=>2376, "no"=>634}
15 :sinmom14 string 2 {"no"=>2707, "yes"=>303}
16 :step14 string 2 {"no"=>2893, "yes"=>117}
17 :south66 string 2 {"no"=>1763, "yes"=>1247}
18 :south76 string 2 {"no"=>1795, "yes"=>1215}
19 :lwage76 double 755 [6.306275, 6.175867, 6.580639, 5.521461, 6.591674, ... ]
20 :famed uint8 9 [9, 8, 2, 6, 8, ... ]
... 8 more Vectors ...Rdatasets: gap: aldh2, Internal functions for gap
RedAmber::DataFrame : 263 x 18 Vectors
Vectors : 17 numeric, 1 string
# key type level data_preview
1 :id string 263 ["2", "4", "6", "12", "13", ... ]
2 :y uint8 2 {1=>130, 0=>133}
3 :"D12S2070.a1" uint8 5 {4=>94, 1=>62, 2=>76, 0=>12, 5=>19}
4 :"D12S2070.a2" uint8 9 [4, 4, 4, 4, 4, ... ]
5 :"D12S839.a1" uint8 6 [3, 4, 3, 4, 5, ... ]
6 :"D12S839.a2" uint8 7 [4, 5, 5, 5, 5, ... ]
7 :"D12S821.a1" uint8 8 [3, 5, 3, 3, 5, ... ]
8 :"D12S821.a2" uint8 13 [7, 6, 5, 4, 6, ... ]
9 :"D12S1344.a1" uint8 11 [8, 3, 3, 8, 3, ... ]
10 :"D12S1344.a2" uint8 13 [10, 4, 8, 8, 3, ... ]
11 :"EXON12.a1" uint8 3 {2=>258, 0=>2, 1=>3}
12 :"EXON12.a2" uint8 3 {2=>200, 1=>61, 0=>2}
13 :"EXON1.a1" uint8 3 {2=>209, 0=>43, 1=>11}
14 :"EXON1.a2" uint8 3 {2=>125, 0=>43, 1=>95}
15 :"D12S2263.a1" uint8 12 [6, 8, 7, 6, 9, ... ]
16 :"D12S2263.a2" uint8 11 [8, 9, 10, 7, 9, ... ]
17 :"D12S1341.a1" uint8 9 [5, 3, 8, 5, 6, ... ]
18 :"D12S1341.a2" uint8 9 [8, 5, 8, 7, 6, ... ]Rdatasets: gap: cnv, Internal functions for gap
RedAmber::DataFrame : 602 x 5 Vectors
Vectors : 3 numeric, 2 strings
# key type level data_preview
1 :chr string 24 ["1", "1", "1", "1", "1", ... ]
2 :start uint32 445 [9795, 97883261, 70123190, 64938246, 16355196, ... ]
3 :end uint32 439 [107900000, 100200000, 74688347, 75289480, 18418523, ... ]
4 :band string 204 ["p21.3-p13.3", "p21.3-p21.2", "p31.1", "p31.3-p31.1", "p36.13", ... ]
5 :freq uint8 85 [1, 1, 2, 2, 1, ... ]Rdatasets: gap: mao, Internal functions for gap
RedAmber::DataFrame : 340 x 19 Vectors
Vectors : 6 numeric, 13 strings
# key type level data_preview
1 :id string 253 ["2341.0", "2342.0", "2343.0", "2344.0", "2345.0", ... ]
2 :type uint8 2 {0=>157, 1=>183}
3 :gender uint8 2 {0=>150, 1=>190}
4 :age uint8 43 [69, nil, nil, nil, nil, ... ], 188 nils
5 :aao uint8 25 [nil, nil, nil, nil, nil, ... ], 296 nils
6 :aad uint8 26 [nil, nil, nil, nil, nil, ... ], 295 nils
7 :updrs uint8 21 [nil, nil, nil, nil, nil, ... ], 301 nils
8 :maoai2 string 42 ["116", "122", "112", "114", "112/112", ... ]
9 :ai2code string 42 ["7", "10", "5", "6", "5/5", ... ]
10 :maobi2 string 30 ["179", "179", "175", "179", "181/181", ... ]
11 :bi2code string 30 ["6", "6", "4", "6", "7/7", ... ]
12 :gtbex3 string 20 ["255", "255", "255", "255", "251/255", ... ]
13 :bex3code string 20 ["4", "4", "4", "4", "2/4", ... ]
14 :maoavntr string 12 ["", "", "", "", "", ... ]
15 :vntrcode string 12 ["", "", "", "", "", ... ]
16 :vntrcod2 string 12 ["", "", "", "", "", ... ]
17 :maoa31 string 49 ["", "", "", "", "", ... ]
18 :mao31cod string 8 ["", "", "", "", "", ... ]
19 :mao31co2 string 8 ["", "", "", "", "", ... ]Rdatasets: ggplot2: mpg, Fuel economy data from 1999 to 2008 for 38 popular models of cars
RedAmber::DataFrame : 234 x 11 Vectors
Vectors : 5 numeric, 6 strings
# key type level data_preview
1 :manufacturer string 15 ["audi", "audi", "audi", "audi", "audi", ... ]
2 :model string 38 ["a4", "a4", "a4", "a4", "a4", ... ]
3 :displ double 35 [1.8, 1.8, 2.0, 2.0, 2.8, ... ]
4 :year uint16 2 {1999=>117, 2008=>117}
5 :cyl uint8 4 {4=>81, 6=>79, 8=>70, 5=>4}
6 :trans string 10 ["auto(l5)", "manual(m5)", "manual(m6)", "auto(av)", "auto(l5)", ... ]
7 :drv string 3 {"f"=>106, "4"=>103, "r"=>25}
8 :cty uint8 21 [18, 21, 20, 21, 16, ... ]
9 :hwy uint8 27 [29, 29, 31, 30, 26, ... ]
10 :fl string 5 {"p"=>52, "r"=>168, "e"=>8, "d"=>5, "c"=>1}
11 :class string 7 ["compact", "compact", "compact", "compact", "compact", ... ]Rdatasets: ggplot2movies: movies, Movie information and user ratings from IMDB.com.
RedAmber::DataFrame : 58788 x 24 Vectors
Vectors : 22 numeric, 2 strings
# key type level data_preview
1 :title string 56007 ["$", "$1000 a Touchdown", "$21 a Day Once a Month", "$40,000", "$50,000 Climax Show, The", ... ]
2 :year uint16 113 [1971, 1939, 1941, 1996, 1975, ... ]
3 :length uint16 305 [121, 71, 7, 70, 71, ... ]
4 :budget uint32 757 [nil, nil, nil, nil, nil, ... ], 53573 nils
5 :rating double 91 [6.4, 6.0, 8.2, 8.2, 3.4, ... ]
6 :votes uint32 4373 [348, 20, 5, 6, 17, ... ]
7 :r1 double 12 [4.5, 0.0, 0.0, 14.5, 24.5, ... ]
8 :r2 double 10 [4.5, 14.5, 0.0, 0.0, 4.5, ... ]
9 :r3 double 10 [4.5, 4.5, 0.0, 0.0, 0.0, ... ]
10 :r4 double 11 [4.5, 24.5, 0.0, 0.0, 14.5, ... ]
11 :r5 double 11 [14.5, 14.5, 0.0, 0.0, 14.5, ... ]
12 :r6 double 10 [24.5, 14.5, 24.5, 0.0, 4.5, ... ]
13 :r7 double 11 [24.5, 14.5, 0.0, 0.0, 0.0, ... ]
14 :r8 double 11 [14.5, 4.5, 44.5, 0.0, 0.0, ... ]
15 :r9 double 11 [4.5, 4.5, 24.5, 34.5, 0.0, ... ]
16 :r10 double 12 [4.5, 14.5, 24.5, 45.5, 24.5, ... ]
17 :mpaa string 5 {""=>53864, "R"=>3377, "PG-13"=>1003, "PG"=>528, "NC-17"=>16}
18 :Action uint8 2 {0=>54100, 1=>4688}
19 :Animation uint8 2 {0=>55098, 1=>3690}
20 :Comedy uint8 2 {1=>17271, 0=>41517}
... 4 more Vectors ...Rdatasets: gt: gtcars, Deluxe automobiles from the 2014-2017 period
RedAmber::DataFrame : 47 x 15 Vectors
Vectors : 8 numeric, 7 strings
# key type level data_preview
1 :mfr string 19 ["Ford", "Ferrari", "Ferrari", "Ferrari", "Ferrari", ... ]
2 :model string 47 ["GT", "458 Speciale", "458 Spider", "458 Italia", "488 GTB", ... ]
3 :year uint16 4 {2017=>9, 2015=>9, 2014=>2, 2016=>27}
4 :trim string 26 ["Base Coupe", "Base Coupe", "Base", "Base Coupe", "Base Coupe", ... ]
5 :bdy_style string 4 {"coupe"=>32, "convertible"=>5, "sedan"=>8, "hatchback"=>2}
6 :hp uint16 41 [647, 597, 562, 562, 661, ... ]
7 :hp_rpm uint16 22 [6250, 9000, 9000, 9000, 8000, ... ]
8 :trq uint16 37 [550, 398, 398, 398, 561, ... ]
9 :trq_rpm uint16 25 [5900, 6000, 6000, 6000, 3000, ... ], 1 nil
10 :mpg_c uint8 12 [11, 13, 13, 13, 15, ... ], 1 nil
11 :mpg_h uint8 15 [18, 17, 17, 17, 22, ... ], 1 nil
12 :drivetrain string 2 {"rwd"=>34, "awd"=>13}
13 :trsmn string 10 ["7a", "7a", "7a", "7a", "7a", ... ]
14 :ctry_origin string 5 {"United States"=>4, "Italy"=>15, "Japan"=>2, "United Kingdom"=>10, "Germany"=>16}
15 :msrp uint32 47 [447000, 291744, 263553, 233509, 245400, ... ]Rdatasets: HistData: Pyx, Trial of the Pyx
RedAmber::DataFrame : 72 x 4 Vectors
Vectors : 1 numeric, 3 strings
# key type level data_preview
1 :Bags string 9 ["1 and 2", "3", "4", "5", "6", ... ]
2 :Group string 3 {"near std"=>24, "below std"=>24, "above std"=>24}
3 :Deviation string 8 ["Below -R", "Below -R", "Below -R", "Below -R", "Below -R", ... ]
4 :count uint16 59 [34, 11, 20, 30, 32, ... ]Rdatasets: MASS: Cars93, Data from 93 Cars on Sale in the USA in 1993
RedAmber::DataFrame : 93 x 27 Vectors
Vectors : 18 numeric, 9 strings
# key type level data_preview
1 :Manufacturer string 32 ["Acura", "Acura", "Audi", "Audi", "BMW", ... ]
2 :Model string 93 ["Integra", "Legend", "90", "100", "535i", ... ]
3 :Type string 6 ["Small", "Midsize", "Compact", "Midsize", "Midsize", ... ]
4 :"Min.Price" double 79 [12.9, 29.2, 25.9, 30.8, 23.7, ... ]
5 :Price double 81 [15.9, 33.9, 29.1, 37.7, 30.0, ... ]
6 :"Max.Price" double 79 [18.8, 38.7, 32.3, 44.6, 36.2, ... ]
7 :"MPG.city" uint8 21 [25, 18, 20, 19, 22, ... ]
8 :"MPG.highway" uint8 22 [31, 25, 26, 26, 30, ... ]
9 :AirBags string 3 {"None"=>34, "Driver & Passenger"=>16, "Driver only"=>43}
10 :DriveTrain string 3 {"Front"=>67, "Rear"=>16, "4WD"=>10}
11 :Cylinders string 6 ["4", "6", "6", "6", "4", ... ]
12 :EngineSize double 26 [1.8, 3.2, 2.8, 2.8, 3.5, ... ]
13 :Horsepower uint16 57 [140, 200, 172, 172, 208, ... ]
14 :RPM uint16 24 [6300, 5500, 5500, 5500, 5700, ... ]
15 :"Rev.per.mile" uint16 78 [2890, 2335, 2280, 2535, 2545, ... ]
16 :"Man.trans.avail" string 2 {"Yes"=>61, "No"=>32}
17 :"Fuel.tank.capacity" double 38 [13.2, 18.0, 16.9, 21.1, 21.1, ... ]
18 :Passengers uint8 6 [5, 5, 5, 6, 4, ... ]
19 :Length uint8 51 [177, 195, 180, 193, 186, ... ]
20 :Wheelbase uint8 27 [102, 115, 102, 106, 109, ... ]
... 7 more Vectors ...Rdatasets: mosaicData: Galton, Galton's dataset of parent and child heights
RedAmber::DataFrame : 898 x 6 Vectors
Vectors : 4 numeric, 2 strings
# key type level data_preview
1 :family string 197 ["1", "1", "1", "1", "2", ... ]
2 :father double 34 [78.5, 78.5, 78.5, 78.5, 75.5, ... ]
3 :mother double 29 [67.0, 67.0, 67.0, 67.0, 66.5, ... ]
4 :sex string 2 {"M"=>465, "F"=>433}
5 :height double 65 [73.2, 69.2, 69.0, 69.0, 73.5, ... ]
6 :nkids uint8 12 [4, 4, 4, 4, 4, ... ]Rdatasets: mosaicData: HELPfull, Health Evaluation and Linkage to Primary Care
RedAmber::DataFrame : 1472 x 788 Vectors
Vectors : 782 numeric, 6 strings
# key type level data_preview
1 :ID uint16 470 [1, 1, 1, 1, 2, ... ]
2 :TIME uint8 5 {0=>470, 6=>254, 18=>254, 24=>277, 12=>217}
3 :NUM_INTERVALS uint8 4 {1=>1238, 2=>132, 3=>72, 4=>30}
4 :INT_TIME1 double 491 [0.0, 8.03333333333333, 15.5333333333333, 27.5666666666667, 0.0, ... ]
5 :DAYS_SINCE_BL uint16 491 [nil, 241, 466, 827, nil, ... ], 470 nils
6 :INT_TIME2 double 350 [6.0, 8.03333333333333, 7.5, 12.0333333333333, 6.0, ... ]
7 :DAYS_SINCE_PREV uint16 351 [nil, 241, 225, 361, nil, ... ], 470 nils
8 :PREV_TIME uint8 5 {nil=>470, 0=>400, 6=>216, 18=>202, 12=>184}
9 :DEAD uint8 2 {0=>1410, 1=>62}
10 :A1 uint8 3 {1=>359, nil=>1002, 2=>111}
11 :A9 uint8 17 [9, nil, nil, nil, 12, ... ], 1005 nils
12 :A10 uint8 7 [1, 2, 1, 4, 6, ... ], 7 nils
13 :A11A uint8 3 {1=>1130, 0=>322, nil=>20}
14 :A11B uint8 3 {0=>625, 1=>790, nil=>57}
15 :A11C uint8 3 {1=>1364, 0=>96, nil=>12}
16 :A11D uint8 3 {1=>804, 0=>652, nil=>16}
17 :A11E uint8 3 {1=>1001, 0=>461, nil=>10}
18 :A12B uint8 10 [6, nil, nil, nil, 6, ... ], 1005 nils
19 :A13 uint8 7 [1, 2, 1, 1, 4, ... ], 8 nils
20 :A14A uint8 3 {0=>1199, 1=>265, nil=>8}
... 768 more Vectors ...Rdatasets: nycflights13: airports, Airport metadata
RedAmber::DataFrame : 1458 x 8 Vectors
Vectors : 4 numeric, 4 strings
# key type level data_preview
1 :faa string 1458 ["04G", "06A", "06C", "06N", "09J", ... ]
2 :name string 1440 ["Lansdowne Airport", "Moton Field Municipal Airport", "Schaumburg Regional", "Randall Airport", "Jekyll Island Airport", ... ]
3 :lat double 1456 [41.1304722, 32.4605722, 41.9893408, 41.431912, 31.0744722, ... ]
4 :lon double 1458 [-80.6195833, -85.6800278, -88.1012428, -74.3915611, -81.4277778, ... ]
5 :alt int16 911 [1044, 264, 801, 523, 11, ... ]
6 :tz int8 7 [-5, -6, -6, -5, -5, ... ]
7 :dst string 3 {"A"=>1388, "U"=>47, "N"=>23}
8 :tzone string 10 ["America/New_York", "America/Chicago", "America/Chicago", "America/New_York", "America/New_York", ... ], 3 nilsRdatasets: nycflights13: planes, Plane metadata.
RedAmber::DataFrame : 3322 x 9 Vectors
Vectors : 4 numeric, 5 strings
# key type level data_preview
1 :tailnum string 3322 ["N10156", "N102UW", "N103US", "N104UW", "N10575", ... ]
2 :year uint16 47 [2004, 1998, 1999, 1999, 2002, ... ], 70 nils
3 :type string 3 {"Fixed wing multi engine"=>3292, "Fixed wing single engine"=>25, "Rotorcraft"=>5}
4 :manufacturer string 35 ["EMBRAER", "AIRBUS INDUSTRIE", "AIRBUS INDUSTRIE", "AIRBUS INDUSTRIE", "EMBRAER", ... ]
5 :model string 127 ["EMB-145XR", "A320-214", "A320-214", "A320-214", "EMB-145LR", ... ]
6 :engines uint8 4 {2=>3288, 1=>27, 4=>4, 3=>3}
7 :seats uint16 48 [55, 182, 182, 182, 55, ... ]
8 :speed uint16 14 [nil, nil, nil, nil, nil, ... ], 3299 nils
9 :engine string 6 ["Turbo-fan", "Turbo-fan", "Turbo-fan", "Turbo-fan", "Turbo-fan", ... ]Rdatasets: openintro: birds, Aircraft-Wildlife Collisions
RedAmber::DataFrame : 19302 x 17 Vectors
Vectors : 4 numeric, 13 strings
# key type level data_preview
1 :opid string 285 ["AAL", "USA", "AAL", "AAL", "AAL", ... ]
2 :operator string 285 ["AMERICAN AIRLINES", "US AIRWAYS", "AMERICAN AIRLINES", "AMERICAN AIRLINES", "AMERICAN AIRLINES", ... ]
3 :atype string 284 ["MD-80", "FK-28-4000", "B-727-200", "MD-82", "MD-82", ... ]
4 :remarks string 13737 ["NO DAMAGE", "2 BIRDS, NO DAMAGE.", nil, nil, "NO DAMAGE", ... ], 2786 nils
5 :phase_of_flt string 9 ["Descent", "Climb", "Approach", "Climb", "Climb", ... ], 1783 nils
6 :ac_mass uint8 6 [4, 4, 4, 4, 4, ... ], 1284 nils
7 :num_engs uint8 5 {2=>13817, 3=>2048, 1=>1526, 4=>604, nil=>1307}
8 :date string 3186 ["9/30/1990 0:00:00", "11/29/1993 0:00:00", "8/13/1993 0:00:00", "10/7/1993 0:00:00", "9/25/1993 0:00:00", ... ]
9 :time_of_day string 5 {"Night"=>4537, "Day"=>11170, "Dusk"=>878, "Dawn"=>640, nil=>2077}
10 :state string 59 ["IL", "MD", "TN", "VA", "SC", ... ], 871 nils
11 :height uint16 298 [7000, 10, 400, 100, 50, ... ], 3193 nils
12 :speed uint16 159 [250, 140, 140, 200, 170, ... ], 7008 nils
13 :effect string 6 [nil, "None", "None", "None", "None", ... ], 5718 nils
14 :sky string 4 {"No Cloud"=>7113, "Some Cloud"=>5254, "Overcast"=>3356, nil=>3579}
15 :species string 241 ["UNKNOWN BIRD - MEDIUM", "UNKNOWN BIRD - MEDIUM", "UNKNOWN BIRD - SMALL", "UNKNOWN BIRD - SMALL", "UNKNOWN BIRD - SMALL", ... ]
16 :birds_seen string 4 {nil=>14538, "2-10"=>3775, "11-100"=>988, ":-10"=>1}
17 :birds_struck string 6 ["1", "2-10", "1", "1", "1", ... ], 39 nilsRdatasets: openintro: cards, Deck of cards
RedAmber::DataFrame : 52 x 4 Vectors
Vectors : 4 strings
# key type level data_preview
1 :value string 13 ["2", "3", "4", "5", "6", ... ]
2 :color string 2 {"red"=>26, "black"=>26}
3 :suit string 4 {"Heart"=>13, "Diamond"=>13, "Spade"=>13, "Club"=>13}
4 :face string 2 {"FALSE"=>36, "TRUE"=>16}Rdatasets: openintro: cpu, CPU's Released between 2010 and 2020.
RedAmber::DataFrame : 875 x 12 Vectors
Vectors : 7 numeric, 4 strings, 1 temporal
# key type level data_preview
1 :company string 2 {"Intel"=>571, "AMD"=>304}
2 :name string 859 ["Pentium E5500", "Pentium E6700", "Pentium E5700", "Celeron E3500", "Pentium E6800", ... ]
3 :codename string 102 ["Wolfdale", "Wolfdale", "Wolfdale", "Wolfdale", "Wolfdale", ... ]
4 :cores uint8 24 [2, 2, 2, 2, 2, ... ]
5 :threads uint16 27 [2, 2, 2, 2, 2, ... ]
6 :base_clock double 59 [2.8, 3.2, 3.0, 2.7, 3.333, ... ]
7 :boost_clock double 54 [nil, nil, nil, nil, nil, ... ], 318 nils
8 :socket string 60 ["775", "775", "775", "775", "775", ... ]
9 :process uint8 9 [45, 45, 45, 45, 45, ... ]
10 :l3_cache uint16 30 [nil, nil, nil, nil, nil, ... ], 185 nils
11 :tdp uint16 77 [65, 65, 65, 65, 65, ... ]
12 :released date64 194 [#<DateTime: 2010-04-18T09:00:00+09:00 ((2455305j,0s,0n),+32400s,2299161j)>, #<DateTime: 2010-05-30T09:00:00+09:00 ((2455347j,0s,0n),+32400s,2299161j)>, ... ]Rdatasets: openintro: epa2012, Vehicle info from the EPA for 2012
RedAmber::DataFrame : 1129 x 28 Vectors
Vectors : 8 numeric, 19 strings, 1 temporal
# key type level data_preview
1 :model_yr uint16 1 {2012=>1129}
2 :mfr_name string 34 ["aston martin", "aston martin", "aston martin", "aston martin", "aston martin", ... ]
3 :division string 46 ["Aston Martin Lagonda Ltd", "Aston Martin Lagonda Ltd", "Aston Martin Lagonda Ltd", "Aston Martin Lagonda Ltd", "Aston Martin Lagonda Ltd", ... ]
4 :carline string 672 ["V12 Vantage", "V8 Vantage", "V8 Vantage", "V8 Vantage", "V8 Vantage S", ... ]
5 :mfr_code string 33 ["ASX", "ASX", "ASX", "ASX", "ASX", ... ]
6 :model_type_index uint16 416 [8, 2, 11, 1, 3, ... ]
7 :engine_displacement double 47 [5.9, 4.7, 4.7, 4.7, 4.7, ... ]
8 :no_cylinders uint8 9 [12, 8, 8, 8, 8, ... ], 7 nils
9 :transmission_speed string 22 ["Manual(M6)", "Auto(AM6)", "Auto(AM7)", "Manual(M6)", "Auto(AM7)", ... ]
10 :city_mpg uint8 39 [11, 14, 14, 13, 14, ... ]
11 :hwy_mpg uint8 39 [17, 20, 21, 19, 21, ... ]
12 :comb_mpg uint8 40 [13, 16, 16, 15, 16, ... ]
13 :guzzler string 2 {"Y"=>84, "N"=>1045}
14 :air_aspir_method string 3 {nil=>857, "TC"=>244, "SC"=>28}
15 :air_aspir_method_desc string 4 {"Naturally Aspirated"=>850, "Turbocharged"=>244, "Supercharged"=>28, nil=>7}
16 :transmission string 7 ["M", "AM", "AM", "M", "AM", ... ]
17 :transmission_desc string 7 ["Manual", "Automated Manual", "Automated Manual", "Manual", "Automated Manual", ... ]
18 :no_gears uint8 6 [6, 6, 7, 6, 7, ... ]
19 :trans_lockup string 2 {"N"=>309, "Y"=>820}
20 :trans_creeper_gear string 1 {"N"=>1129}
... 8 more Vectors ...Rdatasets: openintro: epa2021, Vehicle info from the EPA for 2021
RedAmber::DataFrame : 1108 x 28 Vectors
Vectors : 8 numeric, 19 strings, 1 temporal
# key type level data_preview
1 :model_yr uint16 1 {2021=>1108}
2 :mfr_name string 22 ["Honda", "aston martin", "aston martin", "Volkswagen Group of", "Volkswagen Group of", ... ]
3 :division string 41 ["Acura", "Aston Martin Lagonda Ltd", "Aston Martin Lagonda Ltd", "Audi", "Audi", ... ]
4 :carline string 788 ["NSX", "Vantage Manual", "Vantage V8", "R8", "R8 2WD", ... ]
5 :mfr_code string 22 ["HNX", "ASX", "ASX", "VGA", "VGA", ... ]
6 :model_type_index uint16 502 [39, 5, 4, 5, 7, ... ]
7 :engine_displacement double 39 [3.5, 4.0, 4.0, 5.2, 5.2, ... ]
8 :no_cylinders uint8 8 [6, 8, 8, 10, 10, ... ]
9 :transmission_speed string 25 ["Auto(AM-S9)", "Manual(M7)", "Auto(S8)", "Auto(AM-S7)", "Auto(AM-S7)", ... ]
10 :city_mpg uint8 43 [21, 14, 18, 13, 14, ... ]
11 :hwy_mpg uint8 44 [22, 21, 24, 20, 23, ... ]
12 :comb_mpg uint8 45 [21, 17, 20, 16, 17, ... ]
13 :guzzler string 2 {"N"=>1044, "Y"=>64}
14 :air_aspir_method string 5 {"TC"=>628, nil=>439, "SC"=>27, "OT"=>8, "TS"=>6}
15 :air_aspir_method_desc string 5 {"Turbocharged"=>628, "Naturally Aspirated"=>439, "Supercharged"=>27, "Other"=>8, "Turbocharged+Supercharged"=>6}
16 :transmission string 7 ["AMS", "M", "SA", "AMS", "AMS", ... ]
17 :transmission_desc string 7 ["Automated Manual- Selectable (e.g. Automated Manual with paddles)", "Manual", "Semi-Automatic", "Automated Manual- Selectable (e.g. Automated Manual with paddles)", "Automated Manual- Selectable (e.g. Automated Manual with paddles)", ... ]
18 :no_gears uint8 7 [9, 7, 8, 7, 7, ... ]
19 :trans_lockup string 2 {"Y"=>879, "N"=>229}
20 :trans_creeper_gear string 2 {"N"=>1107, "Y"=>1}
... 8 more Vectors ...Rdatasets: openintro: kobe_basket, Kobe Bryant basketball performance
RedAmber::DataFrame : 133 x 6 Vectors
Vectors : 1 numeric, 5 strings
# key type level data_preview
1 :vs string 1 {"ORL"=>133}
2 :game uint8 5 {1=>34, 2=>21, 4=>30, 5=>23, 3=>25}
3 :quarter string 5 {"1"=>36, "2"=>25, "3"=>34, "4"=>31, "1OT"=>7}
4 :time string 116 ["9:47", "9:07", "8:11", "7:41", "7:03", ... ]
5 :description string 80 ["Kobe Bryant makes 4-foot two point shot", "Kobe Bryant misses jumper", "Kobe Bryant misses 7-foot jumper", "Kobe Bryant makes 16-foot jumper (Derek Fisher assists)", "Kobe Bryant makes driving layup", ... ]
6 :shot string 2 {"H"=>58, "M"=>75}Rdatasets: openintro: labor_market_discrimination, Are Emily and Greg More Employable Than Lakisha and Jamal?
RedAmber::DataFrame : 4870 x 63 Vectors
Vectors : 55 numeric, 8 strings
# key type level data_preview
1 :education uint8 5 {4=>3504, 3=>1006, 1=>40, 2=>274, 0=>46}
2 :n_jobs uint8 7 [2, 3, 1, 4, 3, ... ]
3 :years_exp uint8 26 [6, 6, 6, 6, 22, ... ]
4 :honors uint8 2 {0=>4613, 1=>257}
5 :volunteer uint8 2 {0=>2866, 1=>2004}
6 :military uint8 2 {0=>4397, 1=>473}
7 :emp_holes uint8 2 {1=>2182, 0=>2688}
8 :occup_specific uint16 61 [17, 316, 19, 313, 313, ... ]
9 :occup_broad uint8 5 {1=>1770, 6=>1248, 5=>450, 4=>1241, 3=>161}
10 :work_in_school uint8 2 {0=>2145, 1=>2725}
11 :email uint8 2 {0=>2536, 1=>2334}
12 :computer_skills uint8 2 {1=>3996, 0=>874}
13 :special_skills uint8 2 {0=>3269, 1=>1601}
14 :first_name string 36 ["Allison", "Kristen", "Lakisha", "Latonya", "Carrie", ... ]
15 :sex string 2 {"f"=>3746, "m"=>1124}
16 :race string 2 {"w"=>2435, "b"=>2435}
17 :h uint8 2 {0=>2424, 1=>2446}
18 :l uint8 2 {1=>2424, 0=>2446}
19 :call uint8 2 {0=>4478, 1=>392}
20 :city string 2 {"c"=>2704, "b"=>2166}
... 43 more Vectors ...Rdatasets: openintro: nba_players_19, NBA Players for the 2018-2019 season
RedAmber::DataFrame : 494 x 7 Vectors
Vectors : 1 numeric, 6 strings
# key type level data_preview
1 :first_name string 380 ["Alex", "Jaylen", "Steven", "Bam", "DeVaughn", ... ]
2 :last_name string 413 ["Abrines", "Adams", "Adams", "Adebayo", "Akoon-Purcell", ... ]
3 :team string 30 ["Thunder", "Hawks", "Thunder", "Heat", "Nuggets", ... ]
4 :team_abbr string 30 ["OKC", "ATL", "OKC", "MIA", "DEN", ... ]
5 :position string 7 ["Guard", "Guard", "Center", "Center-Forward", "Guard-Forward", ... ]
6 :number string 56 ["8", "10", "12", "13", "23", ... ]
7 :height uint8 19 [78, 74, 84, 82, 78, ... ]Rdatasets: openintro: playing_cards, Table of Playing Cards in 52-Card Deck
RedAmber::DataFrame : 52 x 3 Vectors
Vectors : 3 strings
# key type level data_preview
1 :number string 13 ["2", "3", "4", "5", "6", ... ]
2 :suit string 4 {"Spade"=>13, "Diamond"=>13, "Club"=>13, "Heart"=>13}
3 :face_card string 2 {"no"=>36, "yes"=>16}Rdatasets: openintro: reddit_finance, Reddit Survey on Financial Independence.
RedAmber::DataFrame : 1998 x 65 Vectors
Vectors : 36 numeric, 29 strings
# key type level data_preview
1 :num_incomes string 5 {"1"=>1155, "2"=>835, "3"=>5, nil=>1, ">3"=>2}
2 :pan_inc_chg string 4 {"Stayed the same"=>1205, "Increased"=>523, "Decreased"=>261, nil=>9}
3 :pan_inc_chg_pct string 12 ["No change", "No change", "1-10%", "41-50%", "1-10%", ... ], 19 nils
4 :pan_exp_chg string 4 {"Decreased"=>1328, "Stayed the same"=>504, "Increased"=>155, nil=>11}
5 :pan_exp_chg_pct string 12 ["11-20%", "1-10%", "1-10%", "11-20%", "11-20%", ... ], 31 nils
6 :pan_fi_chg string 4 {"No change"=>1644, "Increase"=>288, "Decrease"=>40, nil=>26}
7 :pan_ret_date_chg string 4 {"No change"=>1495, "Become sooner"=>376, "Become later"=>104, nil=>23}
8 :pan_financial_impact string 4 {"Neutral"=>800, "Positive"=>1113, "Negative"=>75, nil=>10}
9 :political string 25 [nil, nil, nil, nil, nil, ... ], 290 nils
10 :race_eth string 8 ["White / Caucasian", "White / Caucasian", "White / Caucasian", "White / Caucasian", "Asian or Pacific Islander", ... ], 19 nils
11 :gender string 7 ["Male", "Male", "Male", "Male", "Male", ... ], 8 nils
12 :age string 12 ["24-28", "29-33", "29-33", "24-28", "29-33", ... ], 2 nils
13 :edu string 12 ["Bachelor's Degree", "Bachelor's Degree", "Bachelor's Degree", "Some college, no degree", "Bachelor's Degree", ... ], 5 nils
14 :rel_status string 9 ["Single, never married", "Married", "In a relationship, but not married", "Single, never married", "In a relationship, but not married", ... ], 3 nils
15 :children string 5 {"Do not have children, but intend to"=>866, "Do not have children, and do not intend to"=>613, "Have children"=>457, "N/A"=>50, nil=>12}
16 :country string 54 ["Australia", "Australia", "Australia", "Australia", "Australia", ... ]
17 :fin_indy string 2 {"No"=>1851, "Yes"=>147}
18 :fin_indy_num double 156 [3500000.0, 1000000.0, 1000000.0, 750000.0, 2800000.0, ... ], 220 nils
19 :fin_indy_pct double 244 [12.0, 26.0, 8.0, 27.0, 78.0, ... ], 226 nils
20 :retire_invst_num double 153 [2500000.0, 1250000.0, 1750000.0, 2000000.0, 4800000.0, ... ], 286 nils
... 45 more Vectors ...Rdatasets: openintro: resume, Which resume attributes drive job callbacks?
RedAmber::DataFrame : 4870 x 30 Vectors
Vectors : 20 numeric, 10 strings
# key type level data_preview
1 :job_ad_id uint16 1323 [384, 384, 384, 384, 385, ... ]
2 :job_city string 2 {"Chicago"=>2704, "Boston"=>2166}
3 :job_industry string 6 ["manufacturing", "manufacturing", "manufacturing", "manufacturing", "other_service", ... ]
4 :job_type string 6 ["supervisor", "supervisor", "supervisor", "supervisor", "secretary", ... ]
5 :job_fed_contractor uint8 3 {nil=>1768, 0=>2746, 1=>356}
6 :job_equal_opp_employer uint8 2 {1=>1418, 0=>3452}
7 :job_ownership string 4 {"unknown"=>1992, "nonprofit"=>318, "private"=>2134, "public"=>426}
8 :job_req_any uint8 2 {1=>3834, 0=>1036}
9 :job_req_communication uint8 2 {0=>4262, 1=>608}
10 :job_req_education uint8 2 {0=>4350, 1=>520}
11 :job_req_min_experience string 13 ["5", "5", "5", "5", "some", ... ]
12 :job_req_computer uint8 2 {1=>2129, 0=>2741}
13 :job_req_organization uint8 2 {0=>4516, 1=>354}
14 :job_req_school string 4 {"none_listed"=>4350, "some_college"=>252, "college"=>222, "high_school_grad"=>46}
15 :received_callback uint8 2 {0=>4478, 1=>392}
16 :firstname string 36 ["Allison", "Kristen", "Lakisha", "Latonya", "Carrie", ... ]
17 :race string 2 {"white"=>2435, "black"=>2435}
18 :gender string 2 {"f"=>3746, "m"=>1124}
19 :years_college uint8 5 {4=>3504, 3=>1006, 1=>40, 2=>274, 0=>46}
20 :college_degree uint8 2 {1=>3504, 0=>1366}
... 10 more Vectors ...Rdatasets: openintro: seattlepets, Names of pets in Seattle
RedAmber::DataFrame : 52519 x 7 Vectors
Vectors : 6 strings, 1 temporal
# key type level data_preview
1 :license_issue_date date64 1064 [#<DateTime: 2018-11-16T09:00:00+09:00 ((2458439j,0s,0n),+32400s,2299161j)>, #<DateTime: 2018-11-11T09:00:00+09:00 ((2458434j,0s,0n),+32400s,2299161j)>, ... ]
2 :license_number string 52497 ["8002756", "S124529", "903793", "824666", "S119138", ... ]
3 :animal_name string 13930 ["Wall-E", "Andre", "Mac", "Melb", "Gingersnap", ... ], 483 nils
4 :species string 4 {"Dog"=>35181, "Cat"=>17294, "Goat"=>38, "Pig"=>6}
5 :primary_breed string 336 ["Mixed Breed, Medium (up to 44 lbs fully grown)", "Terrier, Jack Russell", "Retriever, Labrador", "Domestic Shorthair", "Domestic Shorthair", ... ]
6 :secondary_breed string 261 ["Mix", "Dachshund, Standard Wire Haired", nil, nil, "Mix", ... ], 29517 nils
7 :zip_code string 168 ["98108", "98117", "98136", "98117", "98144", ... ], 397 nilsRdatasets: openintro: sowc_child_mortality, SOWC Child Mortality Data.
RedAmber::DataFrame : 195 x 18 Vectors
Vectors : 16 numeric, 2 strings
# key type level data_preview
1 :countries_and_areas string 195 ["Afghanistan", "Albania", "Algeria", "Andorra", "Angola", ... ]
2 :under5_mortality_1990 uint16 106 [179, 41, 50, 11, 223, ... ]
3 :under5_mortality_2000 uint8 102 [129, 26, 40, 6, 206, ... ]
4 :under5_mortality_2018 uint8 73 [62, 9, 23, 3, 77, ... ]
5 :under5_reduction double 72 [4.1, 6.0, 2.9, 4.4, 5.4, ... ]
6 :under5_mortality_2018_male uint8 73 [66, 9, 25, 3, 83, ... ]
7 :under5_mortality_2018_female uint8 70 [59, 8, 22, 3, 71, ... ]
8 :infant_mortality_1990 uint8 89 [121, 35, 42, 9, 132, ... ]
9 :infant_mortality_2018 uint8 64 [48, 8, 20, 3, 52, ... ]
10 :neonatal_mortality_1990 uint8 56 [75, 13, 23, 6, 54, ... ]
11 :neonatal_mortality_2000 uint8 52 [61, 12, 21, 3, 51, ... ]
12 :neonatal_mortality_2018 uint8 41 [37, 7, 15, 1, 28, ... ]
13 :prob_dying_age5to14_1990 uint8 45 [16, 7, 9, 7, 46, ... ]
14 :prob_dying_age5to14_2018 uint8 29 [5, 2, 4, 1, 16, ... ]
15 :under5_deaths_2018 uint16 59 [74, 0, 24, 0, 94, ... ]
16 :neonatal_deaths_2018 uint16 38 [45, 0, 15, 0, 36, ... ]
17 :neonatal_deaths_percent_under5 string 45 ["60", "74", "62", "50", "38", ... ]
18 :age5to14_deaths_2018 uint8 27 [5, 0, 3, 0, 15, ... ]Rdatasets: openintro: sp500_seq, S&P 500 stock data
RedAmber::DataFrame : 2948 x 1 Vector
Vector : 1 string
# key type level data_preview
1 :race string 7 ["1", "1", "1", "1", "1", ... ]Rdatasets: openintro: ssd_speed, SSD read and write speeds
RedAmber::DataFrame : 54 x 7 Vectors
Vectors : 4 numeric, 3 strings
# key type level data_preview
1 :brand string 17 ["Corsair", "Samsung", "Samsung", "Samsung", "Samsung", ... ]
2 :model string 52 ["Force MP600", "840 Evo", "960 Evo", "850 Pro", "970 Pro", ... ]
3 :samples uint32 54 [11526, 16888, 25990, 14690, 21981, ... ]
4 :form_factor string 3 {"m.2"=>32, "2.5"=>21, "mSATA"=>1}
5 :nvme uint8 2 {1=>30, 0=>24}
6 :read uint16 53 [1958, 470, 1798, 476, 2327, ... ]
7 :write uint16 52 [3144, 389, 1562, 418, 2056, ... ]Rdatasets: openintro: textbooks, Textbook data for UCLA Bookstore and Amazon
RedAmber::DataFrame : 73 x 7 Vectors
Vectors : 3 numeric, 4 strings
# key type level data_preview
1 :dept_abbr string 41 ["Am Ind", "Anthro", "Anthro", "Anthro", "Art His", ... ]
2 :course string 66 [" C170", "9", "135T", "191HB", "M102K", ... ]
3 :isbn string 73 ["978-0803272620", "978-0030119194", "978-0300080643", "978-0226206813", "978-0892365999", ... ]
4 :ucla_new double 65 [27.67, 40.59, 31.68, 16.0, 18.95, ... ]
5 :amaz_new double 71 [27.95, 31.14, 32.0, 11.52, 14.21, ... ]
6 :more string 2 {"Y"=>45, "N"=>28}
7 :diff double 71 [-0.28, 9.45, -0.32, 4.48, 4.74, ... ]Rdatasets: openintro: ucla_f18, UCLA courses in Fall 2018
RedAmber::DataFrame : 3950 x 14 Vectors
Vectors : 2 numeric, 12 strings
# key type level data_preview
1 :year uint16 1 {2018=>3950}
2 :term string 1 {"Fall"=>3950}
3 :subject string 173 ["Aerospace Studies", "Aerospace Studies", "Aerospace Studies", "Aerospace Studies", "Aerospace Studies", ... ]
4 :subject_abbr string 156 ["AERO ST", "AERO ST", "AERO ST", "AERO ST", "AERO ST", ... ], 552 nils
5 :course string 3124 ["Leadership Laboratory", "Heritage and Values", "Team and Leadership Fundamentals", "Air Force Leadership Studies", "National Security Affairs/Preparation for Active Duty", ... ]
6 :course_num string 1602 ["A", "1A", "20A", "130A", "140A", ... ]
7 :course_numeric uint16 444 [nil, 1, 20, 130, 140, ... ], 3 nils
8 :seminar string 2 {"FALSE"=>3548, "TRUE"=>402}
9 :ind_study string 2 {"FALSE"=>3407, "TRUE"=>543}
10 :apprenticeship string 2 {"FALSE"=>3864, "TRUE"=>86}
11 :internship string 2 {"FALSE"=>3900, "TRUE"=>50}
12 :honors_contracts string 2 {"FALSE"=>3916, "TRUE"=>34}
13 :laboratory string 2 {"TRUE"=>72, "FALSE"=>3878}
14 :special_topic string 2 {"TRUE"=>1182, "FALSE"=>2768}Rdatasets: openintro: ucla_textbooks_f18, Sample of UCLA course textbooks for Fall 2018
RedAmber::DataFrame : 201 x 20 Vectors
Vectors : 7 numeric, 13 strings
# key type level data_preview
1 :year uint64 2 {2018=>200, 9780133356038=>1}
2 :term string 2 {"Fall"=>200, "199.96"=>1}
3 :subject string 75 ["American Indian Studies", "Anthropology", "Art", "Arts and Architecture", "Asian", ... ]
4 :subject_abbr string 65 ["AM IND", "ANTHRO", nil, "ART&ARC", nil, ... ], 33 nils
5 :course string 190 ["Introduction to American Indian Studies", "Archaeology: Introduction", "New Genres", "Arts Encounters: Exploring Arts Literacy in 21st Century", "Introduction to Buddhism", ... ], 1 nil
6 :course_num string 116 ["M10", "2", "11D", "10", "M60W", ... ], 1 nil
7 :course_numeric uint8 52 [10, 2, 11, 10, 60, ... ], 1 nil
8 :seminar string 2 {"FALSE"=>200, nil=>1}
9 :ind_study string 2 {"FALSE"=>200, nil=>1}
10 :apprenticeship string 2 {"FALSE"=>200, nil=>1}
11 :internship string 2 {"FALSE"=>200, nil=>1}
12 :honors_contracts string 2 {"FALSE"=>200, nil=>1}
13 :laboratory string 2 {"FALSE"=>200, nil=>1}
14 :special_topic string 2 {"FALSE"=>200, nil=>1}
15 :textbook_isbn uint64 80 [9781138477858, 9780307741806, nil, 9780979757549, 9780199861873, ... ], 112 nils
16 :bookstore_new double 75 [47.97, 14.26, nil, 13.5, 49.26, ... ], 112 nils
17 :bookstore_used double 62 [44.97, 10.96, nil, 11.0, 43.26, ... ], 125 nils
18 :amazon_new double 58 [47.45, 13.55, nil, 12.53, 54.95, ... ], 133 nils
19 :amazon_used double 62 [51.2, 7.1, nil, nil, 24.83, ... ], 129 nils
20 :notes string 19 ["", "", nil, "", nil, ... ], 131 nilsRdatasets: openintro: yrbss, Youth Risk Behavior Surveillance System (YRBSS)
RedAmber::DataFrame : 13583 x 13 Vectors
Vectors : 5 numeric, 8 strings
# key type level data_preview
1 :age uint8 8 [14, 14, 15, 15, 15, ... ], 77 nils
2 :gender string 3 {"female"=>6621, "male"=>6950, nil=>12}
3 :grade string 6 ["9", "9", "9", "9", "9", ... ], 79 nils
4 :hispanic string 3 {"not"=>9928, "hispanic"=>3424, nil=>231}
5 :race string 6 ["Black or African American", "Black or African American", "Native Hawaiian or Other Pacific Islander", "Black or African American", "Black or African American", ... ], 2805 nils
6 :height double 35 [nil, nil, 1.73, 1.6, 1.5, ... ], 1004 nils
7 :weight double 240 [nil, nil, 84.37, 55.79, 46.72, ... ], 1004 nils
8 :helmet_12m string 7 ["never", "never", "never", "never", "did not ride", ... ], 311 nils
9 :text_while_driving_30d string 9 ["0", nil, "30", "0", "did not drive", ... ], 918 nils
10 :physically_active_7d uint8 9 [4, 2, 7, 0, 2, ... ], 273 nils
11 :hours_tv_per_school_day string 8 ["5+", "5+", "5+", "2", "3", ... ], 338 nils
12 :strength_training_7d uint8 9 [0, 0, 0, 0, 1, ... ], 1176 nils
13 :school_night_hours_sleep string 8 ["8", "6", "<5", "6", "9", ... ], 1248 nilsRdatasets: openintro: yrbss_samp, Sample of Youth Risk Behavior Surveillance System (YRBSS)
RedAmber::DataFrame : 100 x 13 Vectors
Vectors : 6 numeric, 7 strings
# key type level data_preview
1 :age uint8 6 [16, 17, 17, 15, 18, ... ], 1 nil
2 :gender string 3 {"female"=>51, "male"=>48, nil=>1}
3 :grade uint8 5 {11=>26, 10=>26, 12=>22, 9=>25, nil=>1}
4 :hispanic string 3 {"not"=>77, "hispanic"=>22, nil=>1}
5 :race string 5 {"Black or African American"=>23, "White"=>53, nil=>22, "American Indian or Alaska Native"=>1, "Native Hawaiian or Other Pacific Islander"=>1}
6 :height double 22 [1.5, 1.78, 1.75, 1.68, 1.7, ... ]
7 :weight double 61 [52.62, 74.84, 106.6, 66.68, 80.29, ... ]
8 :helmet_12m string 7 ["never", "rarely", "never", "never", "never", ... ], 1 nil
9 :text_while_driving_30d string 9 ["1-2", "0", "0", "did not drive", "did not drive", ... ], 8 nils
10 :physically_active_7d uint8 8 [0, 7, 7, 3, 0, ... ]
11 :hours_tv_per_school_day string 8 ["4", "1", "2", "2", "2", ... ], 5 nils
12 :strength_training_7d uint8 8 [0, 5, 0, 1, 2, ... ]
13 :school_night_hours_sleep string 8 ["8", "7", "7", "5", "6", ... ], 9 nilsRdatasets: rpart: car90, Automobile Data from 'Consumer Reports' 1990
RedAmber::DataFrame : 111 x 34 Vectors
Vectors : 25 numeric, 9 strings
# key type level data_preview
1 :Country string 11 ["Japan", "Japan", "Germany", "Germany", "Germany", ... ], 6 nils
2 :Disp uint16 51 [112, 163, 141, 121, 152, ... ], 3 nils
3 :Disp2 double 26 [1.8, 2.7, 2.3, 2.0, 2.5, ... ], 3 nils
4 :"Eng.Rev" uint16 60 [2935, 2505, 2775, 2835, 2625, ... ], 38 nils
5 :"Front.Hd" double 10 [3.5, 2.0, 2.5, 4.0, 2.0, ... ]
6 :"Frt.Leg.Room" double 10 [41.5, 41.5, 41.5, 42.0, 42.0, ... ]
7 :"Frt.Shld" double 24 [53.0, 55.5, 56.5, 52.5, 52.0, ... ]
8 :"Gear.Ratio" double 47 [3.26, 2.95, 3.27, 3.25, 3.02, ... ], 38 nils
9 :Gear2 double 60 [3.21, 3.02, 3.25, 3.25, 2.99, ... ], 8 nils
10 :HP uint16 51 [130, 160, 130, 108, 168, ... ], 3 nils
11 :"HP.revs" uint16 25 [6000, 5900, 5500, 5300, 5800, ... ], 3 nils
12 :Height double 27 [47.5, 50.0, 51.5, 50.5, 49.5, ... ]
13 :Length uint8 49 [177, 191, 193, 176, 175, ... ]
14 :Luggage int8 16 [16, 14, 17, 10, 12, ... ]
15 :Mileage uint8 19 [nil, 20, nil, 27, nil, ... ], 58 nils
16 :Model2 string 21 ["", "", "", "", "", ... ]
17 :Price uint16 104 [11950, 24760, 26900, 18900, 24650, ... ], 6 nils
18 :"Rear.Hd" double 14 [1.5, 2.0, 3.0, 1.0, 1.0, ... ]
19 :"Rear.Seating" double 27 [26.5, 28.5, 31.0, 28.0, 25.5, ... ]
20 :RearShld double 32 [52.0, 55.5, 55.0, 52.0, 51.5, ... ]
... 14 more Vectors ...Rdatasets: Stat2Data: Hawks, Measurements on Three Hawk Species
RedAmber::DataFrame : 908 x 19 Vectors
Vectors : 13 numeric, 6 strings
# key type level data_preview
1 :Month uint8 4 {9=>293, 10=>462, 11=>152, 8=>1}
2 :Day uint8 31 [19, 22, 23, 23, 27, ... ]
3 :Year uint16 12 [1992, 1992, 1992, 1992, 1992, ... ]
4 :CaptureTime string 308 ["13:30", "10:30", "12:45", "10:50", "11:15", ... ]
5 :ReleaseTime string 60 ["", " ", " ", " ", " ", ... ]
6 :BandNumber string 907 ["877-76317", "877-76318", "877-76319", "745-49508", "1253-98801", ... ]
7 :Species string 3 {"RT"=>577, "CH"=>70, "SS"=>261}
8 :Age string 2 {"I"=>684, "A"=>224}
9 :Sex string 3 {""=>576, "F"=>174, "M"=>158}
10 :Wing double 185 [385.0, 376.0, 381.0, 265.0, 205.0, ... ], 1 nil
11 :Weight uint16 308 [920, 930, 990, 470, 170, ... ], 10 nils
12 :Culmen double 180 [25.7, nil, 26.7, 18.7, 12.5, ... ], 7 nils
13 :Hallux double 207 [30.1, nil, 31.3, 23.5, 14.3, ... ], 6 nils
14 :Tail uint16 128 [219, 221, 235, 220, 157, ... ]
15 :StandardTail uint16 119 [nil, nil, nil, nil, nil, ... ], 337 nils
16 :Tarsus double 68 [nil, nil, nil, nil, nil, ... ], 833 nils
17 :WingPitFat uint8 5 {nil=>831, 0=>36, 2=>14, 1=>24, 3=>3}
18 :KeelFat double 9 [nil, nil, nil, nil, nil, ... ], 341 nils
19 :Crop double 12 [nil, nil, nil, nil, nil, ... ], 343 nilsRdatasets: stevedata: anes_partytherms, Major Party (Democrat, Republican) Thermometer Index Data (1978-2012)
RedAmber::DataFrame : 33830 x 19 Vectors
Vectors : 18 numeric, 1 string
# key type level data_preview
1 :year uint16 16 [1978, 1978, 1978, 1978, 1978, ... ]
2 :uid uint32 29209 [19780001, 19780002, 19780003, 19780004, 19780005, ... ]
3 :stateabb string 52 ["FL", "IA", "OH", "PA", "PA", ... ]
4 :therm_dem uint8 78 [80, 50, 40, 60, 85, ... ], 2813 nils
5 :therm_gop uint8 76 [50, 50, 60, 60, 60, ... ], 2847 nils
6 :therm_bmp uint8 92 [65, 50, 50, 60, 73, ... ], 2964 nils
7 :mpti uint8 99 [65, 50, 40, 50, 63, ... ], 2964 nils
8 :age uint8 84 [56, 76, 69, 38, 56, ... ]
9 :educat uint8 8 [3, 1, 3, 3, 1, ... ], 342 nils
10 :urbanism uint8 4 {2=>9003, 1=>5718, 3=>7349, nil=>11760}
11 :pid7 uint8 8 [1, 4, 7, 3, 3, ... ], 319 nils
12 :incomeperc uint8 6 [1, 2, 3, 4, 3, ... ], 4372 nils
13 :race4 uint8 5 {1=>24640, 2=>4577, 4=>1056, 3=>3339, nil=>218}
14 :unemployed uint8 3 {1=>1943, 0=>31676, nil=>211}
15 :polint uint8 5 {2=>5787, 3=>9106, 4=>6494, 1=>3282, nil=>9161}
16 :distrust_govt uint8 5 {3=>16971, 2=>8178, 4=>554, 1=>840, nil=>7287}
17 :govt_crooked uint8 4 {2=>10025, 1=>2345, 3=>9388, nil=>12072}
18 :govt_waste uint8 4 {3=>19883, 2=>8796, nil=>4458, 1=>693}
19 :govt_biginterests uint8 3 {1=>20135, 0=>8018, nil=>5677}Rdatasets: tidyr: billboard, Song rankings for Billboard top 100 in the year 2000
RedAmber::DataFrame : 317 x 79 Vectors
Vectors : 65 numeric, 13 strings, 1 temporal
# key type level data_preview
1 :artist string 228 ["2 Pac", "2Ge+her", "3 Doors Down", "3 Doors Down", "504 Boyz", ... ]
2 :track string 316 ["Baby Don't Cry (Keep...", "The Hardest Part Of ...", "Kryptonite", "Loser", "Wobble Wobble", ... ]
3 :"date.entered" date64 68 [#<DateTime: 2000-02-26T09:00:00+09:00 ((2451601j,0s,0n),+32400s,2299161j)>, #<DateTime: 2000-09-02T09:00:00+09:00 ((2451790j,0s,0n),+32400s,2299161j)>, ... ]
4 :wk1 uint8 63 [87, 91, 81, 76, 57, ... ]
5 :wk2 uint8 73 [82, 87, 70, 76, 34, ... ], 5 nils
6 :wk3 uint8 80 [72, 92, 68, 72, 25, ... ], 10 nils
7 :wk4 uint8 89 [77, nil, 67, 69, 17, ... ], 17 nils
8 :wk5 uint8 92 [87, nil, 66, 67, 17, ... ], 25 nils
9 :wk6 uint8 93 [94, nil, 57, 65, 31, ... ], 37 nils
10 :wk7 uint8 91 [99, nil, 54, 55, 36, ... ], 48 nils
11 :wk8 uint8 92 [nil, nil, 53, 59, 49, ... ], 57 nils
12 :wk9 uint8 93 [nil, nil, 51, 62, 53, ... ], 64 nils
13 :wk10 uint8 93 [nil, nil, 51, 61, 57, ... ], 73 nils
14 :wk11 uint8 93 [nil, nil, 51, 61, 64, ... ], 81 nils
15 :wk12 uint8 92 [nil, nil, 51, 59, 70, ... ], 95 nils
16 :wk13 uint8 87 [nil, nil, 47, 61, 75, ... ], 107 nils
17 :wk14 uint8 88 [nil, nil, 44, 66, 76, ... ], 113 nils
18 :wk15 uint8 87 [nil, nil, 38, 72, 78, ... ], 120 nils
19 :wk16 uint8 79 [nil, nil, 28, 76, 85, ... ], 135 nils
20 :wk17 uint8 80 [nil, nil, 22, 75, 92, ... ], 140 nils
... 59 more Vectors ...Rdatasets: validate: nace_rev2, NACE classification code table
RedAmber::DataFrame : 996 x 10 Vectors
Vectors : 2 numeric, 8 strings
# key type level data_preview
1 :Order uint32 996 [398481, 398482, 398483, 398484, 398485, ... ]
2 :Level uint8 4 {1=>21, 2=>88, 3=>272, 4=>615}
3 :Code string 858 ["A", "1", "1.1", "1.11", "1.12", ... ]
4 :Parent string 382 ["", "A", "1", "1.1", "1.1", ... ]
5 :Description string 847 ["AGRICULTURE, FORESTRY AND FISHING", "Crop and animal production, hunting and related service activities", "Growing of non-perennial crops", "Growing of cereals (except rice), leguminous crops and oil seeds", "Growing of rice", ... ]
6 :This_item_includes string 779 ["This section includes the exploitation of vegetal and animal natural resources, comprising the activities of growing of crops, raising and breeding of animals, harvesting of timber and other plants, animals or animal products from a farm or their natural habitats.", "This division includes two basic activities, namely the production of crop products and production of animal products, covering also the forms of organic agriculture, the growing of genetically modified crops and the raising of genetically modified animals. This division includes growing of crops in open fields as well in greenhouses.\n \nGroup 01.5 (Mixed farming) breaks with the usual principles for identifying main activity. It accepts that many agricultural holdings have reasonably balanced crop and animal production, and that it would be arbitrary to classify them in one category or the other.", "This group includes the growing of non-perennial crops, i.e. plants that do not last for more than two growing seasons. Included is the growing of these plants for the purpose of seed production.", "This class includes all forms of growing of cereals, leguminous crops and oil seeds in open fields. The growing of these crops is often combined within agricultural units.\n\nThis class includes:\n- growing of cereals such as:\n . wheat\n . grain maize\n . sorghum\n . barley\n . rye\n . oats\n . millets\n . other cereals n.e.c.\n- growing of leguminous crops such as:\n . beans\n . broad beans\n . chick peas\n . cow peas\n . lentils\n . lupines\n . peas\n . pigeon peas\n . other leguminous crops\n- growing of oil seeds such as:\n . soya beans\n . groundnuts\n . castor bean\n . linseed\n . mustard seed\n . niger seed\n . rapeseed\n . safflower seed\n . sesame seed\n . sunflower seed\n . other oil seeds", "This class includes:\n- growing of rice (including organic farming and the growing of genetically modified rice)", ... ]
7 :This_item_also_includes string 201 ["", "This division also includes service activities incidental to agriculture, as well as hunting, trapping and related activities.", "", "", "", ... ]
8 :Rulings string 134 ["", "", "", "", "", ... ]
9 :This_item_excludes string 486 ["", "Agricultural activities exclude any subsequent processing of the agricultural products (classified under divisions 10 and 11 (Manufacture of food products and beverages) and division 12 (Manufacture of tobacco products)), beyond that needed to prepare them for the primary markets. The preparation of products for the primary markets is included here.\n\nThe division excludes field construction (e.g. agricultural land terracing, drainage, preparing rice paddies etc.) classified in section F (Construction) and buyers and cooperative associations engaged in the marketing of farm products classified in section G. Also excluded is the landscape care and maintenance, which is classified in class 81.30.", "", "This class excludes:\n- growing of rice, see 01.12\n- growing of sweet corn, see 01.13\n- growing of maize for fodder, see 01.19\n- growing of oleaginous fruits, see 01.26", "", ... ]
10 :"Reference_to_ISIC_Rev._4" string 725 ["A", "1", "9", "73", "74", ... ]Rdatasets: validate: samplonomy, Economic data on Samplonia
RedAmber::DataFrame : 1199 x 5 Vectors
Vectors : 1 numeric, 4 strings
# key type level data_preview
1 :region string 10 ["Agria", "Agria", "Agria", "Agria", "Agria", ... ]
2 :freq string 2 {"A"=>239, "Q"=>960}
3 :period string 30 ["2014", "2014", "2014", "2014", "2014Q1", ... ]
4 :measure string 4 {"gdp"=>299, "import"=>300, "export"=>300, "balance"=>300}
5 :value double 477 [600000.0, 210000.0, 222000.0, 12000.0, 60000.0, ... ], 2 nilsRdatasets: vcd: Baseball, Baseball Data
RedAmber::DataFrame : 322 x 25 Vectors
Vectors : 17 numeric, 8 strings
# key type level data_preview
1 :name1 string 174 ["Al", "Alan", "Alan", "Alan", "Alex", ... ]
2 :name2 string 294 ["Newman", "Ashby", "Trammell", "Wiggins", "Trevino", ... ]
3 :atbat86 uint16 247 [185, 315, 574, 239, 202, ... ]
4 :hits86 uint8 144 [37, 81, 159, 60, 53, ... ]
5 :homer86 uint8 36 [1, 7, 21, 0, 4, ... ]
6 :runs86 uint8 96 [23, 24, 107, 30, 31, ... ]
7 :rbi86 uint8 103 [8, 38, 75, 11, 26, ... ]
8 :walks86 uint8 89 [21, 39, 59, 22, 27, ... ]
9 :years uint8 22 [2, 14, 10, 6, 9, ... ]
10 :atbat uint16 314 [214, 3449, 4631, 1941, 1876, ... ]
11 :hits uint16 288 [42, 835, 1300, 510, 467, ... ]
12 :homeruns uint16 146 [1, 69, 90, 4, 15, ... ]
13 :runs uint16 261 [30, 321, 702, 309, 192, ... ]
14 :rbi uint16 262 [9, 414, 504, 103, 186, ... ]
15 :walks uint16 248 [24, 375, 488, 207, 161, ... ]
16 :league86 string 2 {"N"=>147, "A"=>175}
17 :div86 string 2 {"E"=>157, "W"=>165}
18 :team86 string 24 ["Mon", "Hou", "Det", "Bal", "LA", ... ]
19 :posit86 string 25 ["2B", "C", "SS", "2B", "C", ... ]
20 :outs86 uint16 232 [76, 632, 238, 121, 304, ... ]
... 5 more Vectors ...Rdatasets: vcd: Lifeboats, Lifeboats on the Titanic
RedAmber::DataFrame : 18 x 8 Vectors
Vectors : 5 numeric, 2 strings, 1 temporal
# key type level data_preview
1 :launch date64 12 [#<DateTime: 1912-04-15T09:45:00+09:00 ((2419508j,2700s,0n),+32400s,2299161j)>, #<DateTime: 1912-04-15T09:55:00+09:00 ((2419508j,3300s,0n),+32400s,2299161j)>, ... ]
2 :side string 2 {"Port"=>9, "Starboard"=>9}
3 :boat string 18 ["7", "5", "3", "1", "9", ... ]
4 :crew uint8 10 [3, 5, 15, 7, 8, ... ]
5 :men uint8 7 [4, 6, 10, 3, 6, ... ]
6 :women uint8 15 [20, 30, 25, 2, 42, ... ]
7 :total uint8 16 [27, 41, 50, 12, 56, ... ]
8 :cap uint8 3 {65=>14, 40=>2, 47=>2}Rdatasets: vcd: SpaceShuttle, Space Shuttle O-ring Failures
RedAmber::DataFrame : 24 x 6 Vectors
Vectors : 4 numeric, 2 strings
# key type level data_preview
1 :FlightNumber string 24 ["1", "2", "3", "4", "5", ... ]
2 :Temperature uint8 17 [66, 70, 69, 80, 68, ... ]
3 :Pressure uint8 3 {50=>8, 100=>2, 200=>14}
4 :Fail string 3 {"no"=>16, "yes"=>7, nil=>1}
5 :nFailures uint8 4 {0=>16, 1=>5, nil=>1, 2=>2}
6 :Damage uint8 5 {0=>15, 4=>6, nil=>1, 2=>1, 11=>1}