Information on Data Sets in Packages
datasets.RdThe data function is used both to load data sets from packages,
and give a display of the names and titles of data sets in one or more packages,
however it does not return a result that can be easily used to get additional
information about the nature of data sets in packages.
The datasets() function is designed to produce a more useful summary
display of data sets in one or more packages. It extracts the class
and dimension information (dim or codelength) of each item,
and formats these to provide additional descriptors.
Usage
datasets(package,
allClass=FALSE,
incPackage=length(package) > 1,
maxTitle=NULL)Arguments
- package
a character vector giving the package(s) to look in
- allClass
a logical variable. Include all classes of the item (
TRUE) or just the last class (FALSE)?- incPackage
include the package name in result?
- maxTitle
maximum length of data set Title
Details
The requested packages must be installed, and are silently loaded in order
to extract class and size information.
Value
A data.frame whose rows correspond to data sets found in package.
The columns (for a single package) are:
- Item
data set name, a character variable
- class
class, the object class of the data set, typically one of
"data.frame","table","array"...- dim
an abbreviation of the dimensions of the data set, in a form like
"36x3"for a data.frame or matrix with 36 rows and 3 columns.- Title
data set title
Note
In Rmd documents, `datasets("package") |> knitr::kable()` can be used to create a more pleasing display.
Examples
datasets("vcdExtra")
#> Item class dim
#> 1 Abortion table 2x2x2
#> 2 Accident data.frame 80x5
#> 3 AirCrash data.frame 439x5
#> 4 Alligator data.frame 80x5
#> 5 Asbestos array 5x4
#> 6 Bartlett table 2x2x2
#> 7 Burt data.frame 27x3
#> 8 Caesar table 3x2x2x2
#> 9 Cancer table 2x2x2
#> 10 Cormorants data.frame 343x8
#> 11 CyclingDeaths data.frame 208x2
#> 12 DaytonSurvey data.frame 32x6
#> 13 Depends table 15
#> 14 Detergent table 2x2x2x3
#> 15 Donner data.frame 90x5
#> 16 Draft1970 data.frame 366x3
#> 17 Draft1970table table 12x3
#> 18 Dyke table 2x2x2x2x2
#> 19 Fungicide array 2x2x2x2
#> 20 GSS data.frame 6x3
#> 21 Geissler data.frame 90x4
#> 22 Gilby table 6x4
#> 23 Glass data.frame 25x3
#> 24 HairEyePlace array 4x5x2
#> 25 Hauser79 data.frame 25x3
#> 26 Heart table 2x2x3
#> 27 Heckman table 2x2x2x2x2
#> 28 HospVisits table 3x3
#> 29 HouseTasks table 13x4
#> 30 Hoyt table 4x3x7x2
#> 31 ICU data.frame 200x22
#> 32 JobSat table 4x4
#> 33 Mammograms array 4x4
#> 34 Mental data.frame 24x3
#> 35 Mice data.frame 30x4
#> 36 Mobility table 5x5
#> 37 PhdPubs data.frame 915x6
#> 38 ShakeWords data.frame 100x2
#> 39 TV array 5x11x3
#> 40 Titanicp data.frame 1309x6
#> 41 Toxaemia data.frame 60x5
#> 42 Vietnam data.frame 40x4
#> 43 Vote1980 data.frame 28x4
#> 44 WorkerSat data.frame 8x4
#> 45 Yamaguchi87 data.frame 75x4
#> Title
#> 1 Abortion Opinion Data
#> 2 Traffic Accident Victims in France in 1958
#> 3 Air Crash Data
#> 4 Alligator Food Choice
#> 5 Effect of Exposure to Asbestos
#> 6 Bartlett Data on Plum Root Cuttings
#> 7 Burt (1950) Data on Hair, Eyes, Head and Stature
#> 8 Risk Factors for Infection in Caesarian Births
#> 9 Survival of Breast Cancer Patients
#> 10 Advertising Behavior by Males Cormorants
#> 11 London Cycling Deaths
#> 12 Dayton Student Survey on Substance Use
#> 13 Dependencies of R Packages
#> 14 Detergent preference data
#> 15 Survival in the Donner Party
#> 16 USA 1970 Draft Lottery Data
#> 17 USA 1970 Draft Lottery Table
#> 18 Sources of Knowledge of Cancer
#> 19 Carcinogenic Effects of a Fungicide
#> 20 General Social Survey- Sex and Party affiliation
#> 21 Geissler's Data on the Human Sex Ratio
#> 22 Clothing and Intelligence Rating of Children
#> 23 British Social Mobility from Glass(1954)
#> 24 Hair Color and Eye Color in Caithness and Aberdeen
#> 25 Hauser (1979) Data on Social Mobility
#> 26 Sex, Occupation and Heart Disease
#> 27 Labour Force Participation of Married Women 1967-1971
#> 28 Hospital Visits Data
#> 29 Household Tasks Performed by Husbands and Wives
#> 30 Minnesota High School Graduates
#> 31 ICU data set
#> 32 Cross-classification of job satisfaction by income
#> 33 Mammogram Ratings
#> 34 Mental Impairment and Parents SES
#> 35 Mice Depletion Data
#> 36 Social Mobility data
#> 37 Publications of PhD Candidates
#> 38 Shakespeare's Word Type Frequencies
#> 39 TV Viewing Data
#> 40 Passengers on the Titanic
#> 41 Toxaemia Symptoms in Pregnancy
#> 42 Student Opinion about the Vietnam War
#> 43 Race and Politics in the 1980 Presidential Vote
#> 44 Worker Satisfaction Data
#> 45 Occupational Mobility in Three Countries
# datasets(c("vcd", "vcdExtra"))
datasets("datasets", maxTitle=50)
#> Item class dim
#> 1 AirPassengers ts 144
#> 2 BJsales ts 150
#> 3 BJsales.lead (BJsales) ts 150
#> 4 BOD data.frame 6x2
#> 5 CO2 data.frame 84x5
#> 6 ChickWeight data.frame 578x4
#> 7 DNase data.frame 176x3
#> 8 EuStockMarkets matrix 1860x4
#> 9 Formaldehyde data.frame 6x2
#> 10 HairEyeColor table 4x4x2
#> 11 Harman23.cor list 3
#> 12 Harman74.cor list 3
#> 13 Indometh data.frame 66x3
#> 14 InsectSprays data.frame 72x2
#> 15 JohnsonJohnson ts 84
#> 16 LakeHuron ts 98
#> 17 LifeCycleSavings data.frame 50x5
#> 18 Loblolly data.frame 84x3
#> 19 Nile ts 100
#> 20 Orange data.frame 35x3
#> 21 OrchardSprays data.frame 64x4
#> 22 PlantGrowth data.frame 30x2
#> 23 Puromycin data.frame 23x3
#> 24 Seatbelts ts 192x8
#> 25 Theoph data.frame 132x5
#> 26 Titanic table 4x2x2x2
#> 27 ToothGrowth data.frame 60x3
#> 28 UCBAdmissions table 2x2x6
#> 29 UKDriverDeaths ts 192
#> 30 UKgas ts 108
#> 31 USAccDeaths ts 72
#> 32 USArrests data.frame 50x4
#> 33 USJudgeRatings data.frame 43x12
#> 34 USPersonalExpenditure array 5x5
#> 35 UScitiesD dist 45
#> 36 VADeaths array 5x4
#> 37 WWWusage ts 100
#> 38 WorldPhones array 7x7
#> 39 ability.cov list 3
#> 40 airmiles ts 24
#> 41 airquality data.frame 153x6
#> 42 anscombe data.frame 11x8
#> 43 attenu data.frame 182x5
#> 44 attitude data.frame 30x7
#> 45 austres ts 89
#> 46 beaver1 (beavers) data.frame 114x4
#> 47 beaver2 (beavers) data.frame 100x4
#> 48 cars data.frame 50x2
#> 49 chickwts data.frame 71x2
#> 50 co2 ts 468
#> 51 crimtab table 42x22
#> 52 discoveries ts 100
#> 53 esoph data.frame 88x5
#> 54 euro numeric 11
#> 55 euro.cross (euro) array 11x11
#> 56 eurodist dist 210
#> 57 faithful data.frame 272x2
#> 58 fdeaths (UKLungDeaths) ts 72
#> 59 freeny data.frame 39x5
#> 60 freeny.x (freeny) array 39x4
#> 61 freeny.y (freeny) ts 39
#> 62 infert data.frame 248x8
#> 63 iris data.frame 150x5
#> 64 iris3 array 50x4x3
#> 65 islands numeric 48
#> 66 ldeaths (UKLungDeaths) ts 72
#> 67 lh ts 48
#> 68 longley data.frame 16x7
#> 69 lynx ts 114
#> 70 mdeaths (UKLungDeaths) ts 72
#> 71 morley data.frame 100x3
#> 72 mtcars data.frame 32x11
#> 73 nhtemp ts 60
#> 74 nottem ts 240
#> 75 npk data.frame 24x5
#> 76 occupationalStatus table 8x8
#> 77 precip numeric 70
#> 78 presidents ts 120
#> 79 pressure data.frame 19x2
#> 80 quakes data.frame 1000x5
#> 81 randu data.frame 400x3
#> 82 rivers numeric 141
#> 83 rock data.frame 48x4
#> 84 sleep data.frame 20x3
#> 85 stack.loss (stackloss) numeric 21
#> 86 stack.x (stackloss) array 21x3
#> 87 stackloss data.frame 21x4
#> 88 state.abb (state) character 50
#> 89 state.area (state) numeric 50
#> 90 state.center (state) list 2
#> 91 state.division (state) factor 50
#> 92 state.name (state) character 50
#> 93 state.region (state) factor 50
#> 94 state.x77 (state) array 50x8
#> 95 sunspot.month ts 3177
#> 96 sunspot.year ts 289
#> 97 sunspots ts 2820
#> 98 swiss data.frame 47x6
#> 99 treering ts 7980
#> 100 trees data.frame 31x3
#> 101 uspop ts 19
#> 102 volcano array 87x61
#> 103 warpbreaks data.frame 54x3
#> 104 women data.frame 15x2
#> Title
#> 1 Monthly Airline Passenger Numbers 1949-1960
#> 2 Sales Data with Leading Indicator
#> 3 Sales Data with Leading Indicator
#> 4 Biochemical Oxygen Demand
#> 5 Carbon Dioxide Uptake in Grass Plants
#> 6 Weight versus age of chicks on different diets
#> 7 Elisa assay of DNase
#> 8 Daily Closing Prices of Major European Stock Indic
#> 9 Determination of Formaldehyde
#> 10 Hair and Eye Color of Statistics Students
#> 11 Harman Example 2.3
#> 12 Harman Example 7.4
#> 13 Pharmacokinetics of Indomethacin
#> 14 Effectiveness of Insect Sprays
#> 15 Quarterly Earnings per Johnson & Johnson Share
#> 16 Level of Lake Huron 1875-1972
#> 17 Intercountry Life-Cycle Savings Data
#> 18 Growth of Loblolly pine trees
#> 19 Flow of the River Nile
#> 20 Growth of Orange Trees
#> 21 Potency of Orchard Sprays
#> 22 Results from an Experiment on Plant Growth
#> 23 Reaction Velocity of an Enzymatic Reaction
#> 24 Road Casualties in Great Britain 1969-84
#> 25 Pharmacokinetics of Theophylline
#> 26 Survival of passengers on the Titanic
#> 27 The Effect of Vitamin C on Tooth Growth in Guinea
#> 28 Student Admissions at UC Berkeley
#> 29 Road Casualties in Great Britain 1969-84
#> 30 UK Quarterly Gas Consumption
#> 31 Accidental Deaths in the US 1973-1978
#> 32 Violent Crime Rates by US State
#> 33 Lawyers' Ratings of State Judges in the US Superio
#> 34 Personal Expenditure Data
#> 35 Distances Between European Cities and Between US C
#> 36 Death Rates in Virginia (1940)
#> 37 Internet Usage per Minute
#> 38 The World's Telephones
#> 39 Ability and Intelligence Tests
#> 40 Passenger Miles on Commercial US Airlines, 1937-19
#> 41 New York Air Quality Measurements
#> 42 Anscombe's Quartet of 'Identical' Simple Linear Re
#> 43 The Joyner-Boore Attenuation Data
#> 44 The Chatterjee-Price Attitude Data
#> 45 Quarterly Time Series of the Number of Australian
#> 46 Body Temperature Series of Two Beavers
#> 47 Body Temperature Series of Two Beavers
#> 48 Speed and Stopping Distances of Cars
#> 49 Chicken Weights by Feed Type
#> 50 Mauna Loa Atmospheric CO2 Concentration
#> 51 Student's 3000 Criminals Data
#> 52 Yearly Numbers of Important Discoveries
#> 53 Smoking, Alcohol and (O)esophageal Cancer
#> 54 Conversion Rates of Euro Currencies
#> 55 Conversion Rates of Euro Currencies
#> 56 Distances Between European Cities and Between US C
#> 57 Old Faithful Geyser Data
#> 58 Monthly Deaths from Lung Diseases in the UK
#> 59 Freeny's Revenue Data
#> 60 Freeny's Revenue Data
#> 61 Freeny's Revenue Data
#> 62 Infertility after Spontaneous and Induced Abortion
#> 63 Edgar Anderson's Iris Data
#> 64 Edgar Anderson's Iris Data
#> 65 Areas of the World's Major Landmasses
#> 66 Monthly Deaths from Lung Diseases in the UK
#> 67 Luteinizing Hormone in Blood Samples
#> 68 Longley's Economic Regression Data
#> 69 Annual Canadian Lynx trappings 1821-1934
#> 70 Monthly Deaths from Lung Diseases in the UK
#> 71 Michelson Speed of Light Data
#> 72 Motor Trend Car Road Tests
#> 73 Average Yearly Temperatures in New Haven
#> 74 Average Monthly Temperatures at Nottingham, 1920-1
#> 75 Classical N, P, K Factorial Experiment
#> 76 Occupational Status of Fathers and their Sons
#> 77 Annual Precipitation in US Cities
#> 78 Quarterly Approval Ratings of US Presidents
#> 79 Vapor Pressure of Mercury as a Function of Tempera
#> 80 Locations of Earthquakes off Fiji
#> 81 Random Numbers from Congruential Generator RANDU
#> 82 Lengths of Major North American Rivers
#> 83 Measurements on Petroleum Rock Samples
#> 84 Student's Sleep Data
#> 85 Brownlee's Stack Loss Plant Data
#> 86 Brownlee's Stack Loss Plant Data
#> 87 Brownlee's Stack Loss Plant Data
#> 88 US State Facts and Figures
#> 89 US State Facts and Figures
#> 90 US State Facts and Figures
#> 91 US State Facts and Figures
#> 92 US State Facts and Figures
#> 93 US State Facts and Figures
#> 94 US State Facts and Figures
#> 95 Monthly Sunspot Data, from 1749 to "Present"
#> 96 Yearly Sunspot Data, 1700-1988
#> 97 Monthly Sunspot Numbers, 1749-1983
#> 98 Swiss Fertility and Socioeconomic Indicators (1888
#> 99 Yearly Treering Data, -6000-1979
#> 100 Diameter, Height and Volume for Black Cherry Trees
#> 101 Populations Recorded by the US Census
#> 102 Topographic Information on Auckland's Maunga Whau
#> 103 The Number of Breaks in Yarn during Weaving
#> 104 Average Heights and Weights for American Women
# just list dataset names in a package
datasets("vcdExtra")[,"Item"]
#> [1] "Abortion" "Accident" "AirCrash" "Alligator"
#> [5] "Asbestos" "Bartlett" "Burt" "Caesar"
#> [9] "Cancer" "Cormorants" "CyclingDeaths" "DaytonSurvey"
#> [13] "Depends" "Detergent" "Donner" "Draft1970"
#> [17] "Draft1970table" "Dyke" "Fungicide" "GSS"
#> [21] "Geissler" "Gilby" "Glass" "HairEyePlace"
#> [25] "Hauser79" "Heart" "Heckman" "HospVisits"
#> [29] "HouseTasks" "Hoyt" "ICU" "JobSat"
#> [33] "Mammograms" "Mental" "Mice" "Mobility"
#> [37] "PhdPubs" "ShakeWords" "TV" "Titanicp"
#> [41] "Toxaemia" "Vietnam" "Vote1980" "WorkerSat"
#> [45] "Yamaguchi87"
datasets("vcd")[,"Item"]
#> [1] "Arthritis" "Baseball" "BrokenMarriage" "Bundesliga"
#> [5] "Bundestag2005" "Butterfly" "CoalMiners" "DanishWelfare"
#> [9] "Employment" "Federalist" "Hitters" "HorseKicks"
#> [13] "Hospital" "JobSatisfaction" "JointSports" "Lifeboats"
#> [17] "MSPatients" "NonResponse" "OvaryCancer" "PreSex"
#> [21] "Punishment" "RepVict" "Rochdale" "Saxony"
#> [25] "SexualFun" "SpaceShuttle" "Suicide" "Trucks"
#> [29] "UKSoccer" "VisualAcuity" "VonBort" "WeldonDice"
#> [33] "WomenQueue"