19.1. Standard Data Sets¶

R ships with a set of built-in datasets.

Viewing the list of datasets:

> data()

Loading a dataset:

> data(Orange)

Description of the dataset:

> ?Orange

Lets assign a generic name to this dataset:

> ds <- Orange

Seeing the first few rows of the dataset:

> head(ds)
  Tree  age circumference
  1  118            30
  1  484            58
  1  664            87
  1 1004           115
  1 1231           120
  1 1372           142

Seeing the last few rows:

> tail(ds)
   Tree  age circumference
  5  484            49
  5  664            81
  5 1004           125
  5 1231           142
  5 1372           174
  5 1582           177

Number of records:

> nrow(ds)
[1] 35

Sampling some random rows from the dataset:

> rows <- sample(nrow(ds), 10)
> rows <- sort(rows)
> ds[rows,]
   Tree  age circumference
   1  484            58
   1  664            87
  2  664           111
  2 1372           203
  2 1582           203
  4  118            32
  4  484            62
  4 1372           209
  5  118            30
  5  664            81

dplyr library provides similar functionality to sample records:

> dplyr::sample_n(ds, 10)
   Tree  age circumference
  4  664           112
  3 1582           140
  3 1004           108
   1  484            58
   1 1231           120
  5  484            49
  3 1231           115
  5 1231           142
  3  118            30
  4 1004           167

Finding the column names (variable names) in the dataset:

> names(ds)
[1] "Tree"          "age"           "circumference"

Computing averages:

> mean(ds$age)
[1] 922.1429
> mean(ds$circumference)
[1] 115.8571

Computing variances:

> var(ds$age)
[1] 241930.7
> var(ds$circumference)
[1] 3304.891

Summary of the dataset:

> summary(ds)
 Tree       age         circumference
 3:7   Min.   : 118.0   Min.   : 30.0
 1:7   1st Qu.: 484.0   1st Qu.: 65.5
 5:7   Median :1004.0   Median :115.0
 2:7   Mean   : 922.1   Mean   :115.9
 4:7   3rd Qu.:1372.0   3rd Qu.:161.5
       Max.   :1582.0   Max.   :214.0

Finding class of each variable in the dataset:

> sapply(Orange, class)
$Tree
[1] "ordered" "factor"

$age
[1] "numeric"

$circumference
[1] "numeric"

This works better for other datasets like iris, mtcars:

> sapply(iris, class)
Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species
   "numeric"    "numeric"    "numeric"    "numeric"     "factor"

> sapply(mtcars, class)
      mpg       cyl      disp        hp      drat        wt      qsec        vs        am
"numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
     gear      carb
"numeric" "numeric"

19.1.1. mtcars Data Set¶

Loading:

> data("mtcars")

Basic info:

> nrow(mtcars)
[1] 32
> ncol(mtcars)
[1] 11
> head(mtcars)
                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Summary:

> summary(mtcars)
      mpg             cyl             disp             hp             drat             wt
 Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0   Min.   :2.760   Min.   :1.513
 1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5   1st Qu.:3.080   1st Qu.:2.581
 Median :19.20   Median :6.000   Median :196.3   Median :123.0   Median :3.695   Median :3.325
 Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7   Mean   :3.597   Mean   :3.217
 3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0   3rd Qu.:3.920   3rd Qu.:3.610
 Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0   Max.   :4.930   Max.   :5.424
      qsec             vs               am              gear            carb
 Min.   :14.50   Min.   :0.0000   Min.   :0.0000   Min.   :3.000   Min.   :1.000
 1st Qu.:16.89   1st Qu.:0.0000   1st Qu.:0.0000   1st Qu.:3.000   1st Qu.:2.000
 Median :17.71   Median :0.0000   Median :0.0000   Median :4.000   Median :2.000
 Mean   :17.85   Mean   :0.4375   Mean   :0.4062   Mean   :3.688   Mean   :2.812
 3rd Qu.:18.90   3rd Qu.:1.0000   3rd Qu.:1.0000   3rd Qu.:4.000   3rd Qu.:4.000
 Max.   :22.90   Max.   :1.0000   Max.   :1.0000   Max.   :5.000   Max.   :8.000

Scaling the variables:

> mtcars.scaled <- scale(mtcars)

Computing the covariance matrix:

> mtcars.scaled.cov <- cov(mtcars.scaled)

Identifying variable pairs with significant covariance:

> abs(mtcars.scaled.cov) > 0.8
       mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
mpg   TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
cyl   TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
disp  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
hp   FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
drat FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
wt    TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE
qsec FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE
vs   FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE
am   FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE
gear FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE
carb FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE

Relationship between miles per gallon and displacement:

> ggplot(mtcars) + geom_point(mapping=aes(x=mpg, y=disp))

19.1.2. iris Data Set¶

Loading:

> data("iris")

Basic info:

> nrow(iris)
[1] 150
> ncol(iris)
[1] 5
> head(iris)
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

Summary:

> summary(iris)
  Sepal.Length    Sepal.Width     Petal.Length    Petal.Width          Species
 Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100   setosa    :50
 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300   versicolor:50
 Median :5.800   Median :3.000   Median :4.350   Median :1.300   virginica :50
 Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199
 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800
 Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500

Unique species:

> table(iris$Species)

    setosa versicolor  virginica
        50         50         50

19.1.3. ToothGrowth Data Set¶

Loading:

> data("ToothGrowth")

Basic info:

> nrow(ToothGrowth)
[1] 60
> ncol(ToothGrowth)
[1] 3
> head(ToothGrowth)
   len supp dose
1  4.2   VC  0.5
2 11.5   VC  0.5
3  7.3   VC  0.5
4  5.8   VC  0.5
5  6.4   VC  0.5
6 10.0   VC  0.5

Summary:

> summary(ToothGrowth)
      len        supp         dose
 Min.   : 4.20   OJ:30   Min.   :0.500
 1st Qu.:13.07   VC:30   1st Qu.:0.500
 Median :19.25           Median :1.000
 Mean   :18.81           Mean   :1.167
 3rd Qu.:25.27           3rd Qu.:2.000
 Max.   :33.90           Max.   :2.000

19.1.4. PlantGrowth Data Set¶

Loading:

> data("PlantGrowth")

Basic info:

> nrow(PlantGrowth)
[1] 30
> ncol(PlantGrowth)
[1] 2
> head(PlantGrowth)
  weight group
1   4.17  ctrl
2   5.58  ctrl
3   5.18  ctrl
4   6.11  ctrl
5   4.50  ctrl
6   4.61  ctrl

Summary:

> summary(PlantGrowth)
     weight       group
 Min.   :3.590   ctrl:10
 1st Qu.:4.550   trt1:10
 Median :5.155   trt2:10
 Mean   :5.073
 3rd Qu.:5.530
 Max.   :6.310

19.1.5. USArrests Data Set¶

Loading:

> data('USArrests')

Basic info:

> nrow(USArrests)
[1] 50
> ncol(USArrests)
[1] 4
> head(USArrests)
           Murder Assault UrbanPop Rape
Alabama      13.2     236       58 21.2
Alaska       10.0     263       48 44.5
Arizona       8.1     294       80 31.0
Arkansas      8.8     190       50 19.5
California    9.0     276       91 40.6
Colorado      7.9     204       78 38.7

Summary:

> summary(USArrests)
     Murder          Assault         UrbanPop          Rape
 Min.   : 0.800   Min.   : 45.0   Min.   :32.00   Min.   : 7.30
 1st Qu.: 4.075   1st Qu.:109.0   1st Qu.:54.50   1st Qu.:15.07
 Median : 7.250   Median :159.0   Median :66.00   Median :20.10
 Mean   : 7.788   Mean   :170.8   Mean   :65.54   Mean   :21.23
 3rd Qu.:11.250   3rd Qu.:249.0   3rd Qu.:77.75   3rd Qu.:26.18
 Max.   :17.400   Max.   :337.0   Max.   :91.00   Max.   :46.00

19.1.6. Titanic dataset¶

It is a 4-dimensional array resulting from cross-tabulating 2201 observations on 4 variables.

The 4 dimensions are:

Class: 1st, 2nd, 3rd, Crew
Sex: Male Female
Age: Child Adult
Survived: No, Yes

> dim(Titanic)
[1] 4 2 2 2

19.1.7. Datasets in the datasets Package¶

Dataset	Description
AirPassengers	Monthly Airline Passenger Numbers 1949-1960
BJsales	Sales Data with Leading Indicator
BJsales.lead (BJsales)	Sales Data with Leading Indicator
BOD	Biochemical Oxygen Demand
CO2	Carbon Dioxide Uptake in Grass Plants
ChickWeight	Weight versus age of chicks on different diets
DNase	Elisa assay of DNase
EuStockMarkets	Daily Closing Prices of Major European Stock Indices, 1991-1998
Formaldehyde	Determination of Formaldehyde
HairEyeColor	Hair and Eye Color of Statistics Students
Harman23.cor	Harman Example 2.3
Harman74.cor	Harman Example 7.4
Indometh	Pharmacokinetics of Indomethacin
InsectSprays	Effectiveness of Insect Sprays
JohnsonJohnson	Quarterly Earnings per Johnson & Johnson Share
LakeHuron	Level of Lake Huron 1875-1972
LifeCycleSavings	Intercountry Life-Cycle Savings Data
Loblolly	Growth of Loblolly pine trees
Nile	Flow of the River Nile
Orange	Growth of Orange Trees
OrchardSprays	Potency of Orchard Sprays
PlantGrowth	Results from an Experiment on Plant Growth
Puromycin	Reaction Velocity of an Enzymatic Reaction
Seatbelts	Road Casualties in Great Britain 1969-84
Theoph	Pharmacokinetics of Theophylline
Titanic	Survival of passengers on the Titanic
ToothGrowth	The Effect of Vitamin C on Tooth Growth in Guinea Pigs
UCBAdmissions	Student Admissions at UC Berkeley
UKDriverDeaths	Road Casualties in Great Britain 1969-84
UKgas	UK Quarterly Gas Consumption
USAccDeaths	Accidental Deaths in the US 1973-1978
USArrests	Violent Crime Rates by US State
USJudgeRatings	Lawyers’ Ratings of State Judges in the US Superior Court
USPersonalExpenditure	Personal Expenditure Data
UScitiesD	Distances Between European Cities and Between US Cities
VADeaths	Death Rates in Virginia (1940)
WWWusage	Internet Usage per Minute
WorldPhones	The World’s Telephones
ability.cov	Ability and Intelligence Tests
airmiles	Passenger Miles on Commercial US Airlines, 1937-1960
airquality	New York Air Quality Measurements
anscombe	Anscombe’s Quartet of ‘Identical’ Simple Linear Regressions
attenu	The Joyner-Boore Attenuation Data
attitude	The Chatterjee-Price Attitude Data
austres	Quarterly Time Series of the Number of Australian Residents
beaver1 (beavers)	Body Temperature Series of Two Beavers
beaver2 (beavers)	Body Temperature Series of Two Beavers
cars	Speed and Stopping Distances of Cars
chickwts	Chicken Weights by Feed Type
co2	Mauna Loa Atmospheric CO2 Concentration
crimtab	Student’s 3000 Criminals Data
discoveries	Yearly Numbers of Important Discoveries
esoph	Smoking, Alcohol and (O)esophageal Cancer
euro	Conversion Rates of Euro Currencies
euro.cross (euro)	Conversion Rates of Euro Currencies
eurodist	Distances Between European Cities and Between US Cities
faithful	Old Faithful Geyser Data
fdeaths (UKLungDeaths)	Monthly Deaths from Lung Diseases in the UK
freeny	Freeny’s Revenue Data
freeny.x (freeny)	Freeny’s Revenue Data
freeny.y (freeny)	Freeny’s Revenue Data
infert	Infertility after Spontaneous and Induced Abortion
iris	Edgar Anderson’s Iris Data
iris3	Edgar Anderson’s Iris Data
islands	Areas of the World’s Major Landmasses
ldeaths (UKLungDeaths)	Monthly Deaths from Lung Diseases in the UK
lh	Luteinizing Hormone in Blood Samples
longley	Longley’s Economic Regression Data
lynx	Annual Canadian Lynx trappings 1821-1934
mdeaths (UKLungDeaths)	Monthly Deaths from Lung Diseases in the UK
morley	Michelson Speed of Light Data
mtcars	Motor Trend Car Road Tests
nhtemp	Average Yearly Temperatures in New Haven
nottem	Average Monthly Temperatures at Nottingham, 1920-1939
npk	Classical N, P, K Factorial Experiment
occupationalStatus	Occupational Status of Fathers and their Sons
precip	Annual Precipitation in US Cities
presidents	Quarterly Approval Ratings of US Presidents
pressure	Vapor Pressure of Mercury as a Function of Temperature
quakes	Locations of Earthquakes off Fiji
randu	Random Numbers from Congruential Generator RANDU
rivers	Lengths of Major North American Rivers
rock	Measurements on Petroleum Rock Samples
sleep	Student’s Sleep Data
stack.loss (stackloss)	Brownlee’s Stack Loss Plant Data
stack.x (stackloss)	Brownlee’s Stack Loss Plant Data
stackloss	Brownlee’s Stack Loss Plant Data
state.abb (state)	US State Facts and Figures
state.area (state)	US State Facts and Figures
state.center (state)	US State Facts and Figures
state.division (state)	US State Facts and Figures
state.name (state)	US State Facts and Figures
state.region (state)	US State Facts and Figures
state.x77 (state)	US State Facts and Figures
sunspot.month	Monthly Sunspot Data, from 1749 to “Present”
sunspot.year	Yearly Sunspot Data, 1700-1988
sunspots	Monthly Sunspot Numbers, 1749-1983
swiss	Swiss Fertility and Socioeconomic Indicators (1888) Data
treering	Yearly Treering Data, -6000-1979
trees	Girth, Height and Volume for Black Cherry Trees
uspop	Populations Recorded by the US Census
volcano	Topographic Information on Auckland’s Maunga Whau Volcano
warpbreaks	The Number of Breaks in Yarn during Weaving
women	Average Heights and Weights for American Women

19.1.8. US states facts and figures¶

Names of states:

> datasets::state.name
 [1] "Alabama"        "Alaska"         "Arizona"        "Arkansas"       "California"     "Colorado"
 [7] "Connecticut"    "Delaware"       "Florida"        "Georgia"        "Hawaii"         "Idaho"
[13] "Illinois"       "Indiana"        "Iowa"           "Kansas"         "Kentucky"       "Louisiana"
[19] "Maine"          "Maryland"       "Massachusetts"  "Michigan"       "Minnesota"      "Mississippi"
[25] "Missouri"       "Montana"        "Nebraska"       "Nevada"         "New Hampshire"  "New Jersey"
[31] "New Mexico"     "New York"       "North Carolina" "North Dakota"   "Ohio"           "Oklahoma"
[37] "Oregon"         "Pennsylvania"   "Rhode Island"   "South Carolina" "South Dakota"   "Tennessee"
[43] "Texas"          "Utah"           "Vermont"        "Virginia"       "Washington"     "West Virginia"
[49] "Wisconsin"      "Wyoming"

Abbreviations of states:

> datasets::state.abb
 [1] "AL" "AK" "AZ" "AR" "CA" "CO" "CT" "DE" "FL" "GA" "HI" "ID" "IL" "IN" "IA" "KS" "KY" "LA" "ME" "MD" "MA"
[22] "MI" "MN" "MS" "MO" "MT" "NE" "NV" "NH" "NJ" "NM" "NY" "NC" "ND" "OH" "OK" "OR" "PA" "RI" "SC" "SD" "TN"
[43] "TX" "UT" "VT" "VA" "WA" "WV" "WI" "WY"

Longitudes and latitudes:

> datasets::state.center
$x
 [1]  -86.7509 -127.2500 -111.6250  -92.2992 -119.7730 -105.5130  -72.3573  -74.9841  -81.6850  -83.3736
[11] -126.2500 -113.9300  -89.3776  -86.0808  -93.3714  -98.1156  -84.7674  -92.2724  -68.9801  -76.6459
[21]  -71.5800  -84.6870  -94.6043  -89.8065  -92.5137 -109.3200  -99.5898 -116.8510  -71.3924  -74.2336
[31] -105.9420  -75.1449  -78.4686 -100.0990  -82.5963  -97.1239 -120.0680  -77.4500  -71.1244  -80.5056
[41]  -99.7238  -86.4560  -98.7857 -111.3300  -72.5450  -78.2005 -119.7460  -80.6665  -89.9941 -107.2560

$y
 [1] 32.5901 49.2500 34.2192 34.7336 36.5341 38.6777 41.5928 38.6777 27.8744 32.3329 31.7500 43.5648 40.0495
[14] 40.0495 41.9358 38.4204 37.3915 30.6181 45.6226 39.2778 42.3645 43.1361 46.3943 32.6758 38.3347 46.8230
[27] 41.3356 39.1063 43.3934 39.9637 34.4764 43.1361 35.4195 47.2517 40.2210 35.5053 43.9078 40.9069 41.5928
[40] 33.6190 44.3365 35.6767 31.3897 39.1063 44.2508 37.5630 47.4231 38.4204 44.5937 43.0504

Divisions:

> datasets::state.division
 [1] East South Central Pacific            Mountain           West South Central Pacific
 [6] Mountain           New England        South Atlantic     South Atlantic     South Atlantic
[11] Pacific            Mountain           East North Central East North Central West North Central
[16] West North Central East South Central West South Central New England        South Atlantic
[21] New England        East North Central West North Central East South Central West North Central
[26] Mountain           West North Central Mountain           New England        Middle Atlantic
[31] Mountain           Middle Atlantic    South Atlantic     West North Central East North Central
[36] West South Central Pacific            Middle Atlantic    New England        South Atlantic
[41] West North Central East South Central West South Central Mountain           New England
[46] South Atlantic     Pacific            South Atlantic     East North Central Mountain
9 Levels: New England Middle Atlantic South Atlantic East South Central ... Pacific
> table(datasets::state.division)

       New England    Middle Atlantic     South Atlantic East South Central West South Central
                 6                  3                  8                  4                  4
East North Central West North Central           Mountain            Pacific
                 5                  7                  8                  5

Area in square miles:

> datasets::state.area
 [1]  51609 589757 113909  53104 158693 104247   5009   2057  58560  58876   6450  83557  56400  36291  56290
[16]  82264  40395  48523  33215  10577   8257  58216  84068  47716  69686 147138  77227 110540   9304   7836
[31] 121666  49576  52586  70665  41222  69919  96981  45333   1214  31055  77047  42244 267339  84916   9609
[46]  40815  68192  24181  56154  97914

Regions:

> datasets::state.region
 [1] South         West          West          South         West          West          Northeast
 [8] South         South         South         West          West          North Central North Central
[15] North Central North Central South         South         Northeast     South         Northeast
[22] North Central North Central South         North Central West          North Central West
[29] Northeast     Northeast     West          Northeast     South         North Central North Central
[36] South         West          Northeast     Northeast     South         North Central South
[43] South         West          Northeast     South         West          South         North Central
[50] West
Levels: Northeast South North Central West
> table(datasets::state.region)

    Northeast         South North Central          West
            9            16            12            13

Several statistics for the states:

> head(datasets::state.x77)
           Population Income Illiteracy Life Exp Murder HS Grad Frost   Area
Alabama          3615   3624        2.1    69.05   15.1    41.3    20  50708
Alaska            365   6315        1.5    69.31   11.3    66.7   152 566432
Arizona          2212   4530        1.8    70.55    7.8    58.1    15 113417
Arkansas         2110   3378        1.9    70.66   10.1    39.9    65  51945
California      21198   5114        1.1    71.71   10.3    62.6    20 156361
Colorado         2541   4884        0.7    72.06    6.8    63.9   166 103766

> summary(datasets::state.x77)
   Population        Income       Illiteracy       Life Exp         Murder          HS Grad
 Min.   :  365   Min.   :3098   Min.   :0.500   Min.   :67.96   Min.   : 1.400   Min.   :37.80
 1st Qu.: 1080   1st Qu.:3993   1st Qu.:0.625   1st Qu.:70.12   1st Qu.: 4.350   1st Qu.:48.05
 Median : 2838   Median :4519   Median :0.950   Median :70.67   Median : 6.850   Median :53.25
 Mean   : 4246   Mean   :4436   Mean   :1.170   Mean   :70.88   Mean   : 7.378   Mean   :53.11
 3rd Qu.: 4968   3rd Qu.:4814   3rd Qu.:1.575   3rd Qu.:71.89   3rd Qu.:10.675   3rd Qu.:59.15
 Max.   :21198   Max.   :6315   Max.   :2.800   Max.   :73.60   Max.   :15.100   Max.   :67.30
     Frost             Area
 Min.   :  0.00   Min.   :  1049
 1st Qu.: 66.25   1st Qu.: 36985
 Median :114.50   Median : 54277
 Mean   :104.46   Mean   : 70736
 3rd Qu.:139.75   3rd Qu.: 81163
 Max.   :188.00   Max.   :566432

19.1. Standard Data Sets¶

19.1.1. mtcars Data Set¶

19.1.2. iris Data Set¶

19.1.3. ToothGrowth Data Set¶

19.1.4. PlantGrowth Data Set¶

19.1.5. USArrests Data Set¶

19.1.6. Titanic dataset¶

19.1.7. Datasets in the datasets Package¶

19.1.8. US states facts and figures¶

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