Data Exploration Mtcars using R

load data mtcars data(mtcars)

structure of mtcars str(mtcars)

## 'data.frame':    32 obs. of  11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...

dimension of dataset dim(mtcars)

## [1] 32 11

get names of each variables or columns names(mtcars)

##  [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear"
## [11] "carb"

select 5 row of mtcars mtcars[1:5,]

##                    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

select 5 col of mtcars mtcars[,1:5]

##                      mpg cyl  disp  hp drat
## Mazda RX4 21.0 6 160.0 110 3.90
## Mazda RX4 Wag 21.0 6 160.0 110 3.90
## Datsun 710 22.8 4 108.0 93 3.85
## Hornet 4 Drive 21.4 6 258.0 110 3.08
## Hornet Sportabout 18.7 8 360.0 175 3.15
## Valiant 18.1 6 225.0 105 2.76
## Duster 360 14.3 8 360.0 245 3.21
## Merc 240D 24.4 4 146.7 62 3.69
## Merc 230 22.8 4 140.8 95 3.92
## Merc 280 19.2 6 167.6 123 3.92
## Merc 280C 17.8 6 167.6 123 3.92
## Merc 450SE 16.4 8 275.8 180 3.07
## Merc 450SL 17.3 8 275.8 180 3.07
## Merc 450SLC 15.2 8 275.8 180 3.07
## Cadillac Fleetwood 10.4 8 472.0 205 2.93
## Lincoln Continental 10.4 8 460.0 215 3.00
## Chrysler Imperial 14.7 8 440.0 230 3.23
## Fiat 128 32.4 4 78.7 66 4.08
## Honda Civic 30.4 4 75.7 52 4.93
## Toyota Corolla 33.9 4 71.1 65 4.22
## Toyota Corona 21.5 4 120.1 97 3.70
## Dodge Challenger 15.5 8 318.0 150 2.76
## AMC Javelin 15.2 8 304.0 150 3.15
## Camaro Z28 13.3 8 350.0 245 3.73
## Pontiac Firebird 19.2 8 400.0 175 3.08
## Fiat X1-9 27.3 4 79.0 66 4.08
## Porsche 914-2 26.0 4 120.3 91 4.43
## Lotus Europa 30.4 4 95.1 113 3.77
## Ford Pantera L 15.8 8 351.0 264 4.22
## Ferrari Dino 19.7 6 145.0 175 3.62
## Maserati Bora 15.0 8 301.0 335 3.54
## Volvo 142E 21.4 4 121.0 109 4.11

select head of mtcars dataset 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

select end of mtcars dataset tail(mtcars)

##                 mpg cyl  disp  hp drat    wt qsec vs am gear carb
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.7 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.5 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.5 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.6 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.6 1 1 4 2

summaries the dataset summary(mtcars)

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

quantiles of dataset quantile(mtcars$wt)

##      0%     25%     50%     75%    100% 
## 1.51300 2.58125 3.32500 3.61000 5.42400

select quantiles by percent quantile(mtcars$wt, c(.2, .4, .8))

##   20%   40%   80% 
## 2.349 3.158 3.770

variance of weight var(mtcars$wt)

## [1] 0.957379

get histogram of hp hist(mtcars$hp)

Image for post
Image for post

get plot density of mtcars plot(density(mtcars$wt))

Image for post
Image for post

get table of group by gear table(mtcars$gear)

## 
## 3 4 5
## 15 12 5

get pie chart of gear pie(table(mtcars$gear))

Image for post
Image for post

get barplot of gear barplot(table(mtcars$gear))

Image for post
Image for post

get covariance between mpg and gear cov(mtcars$mpg, mtcars$gear)

## [1] 2.135685

get covariance all variables cov(mtcars[,1:11])

##              mpg         cyl        disp          hp         drat
## mpg 36.324103 -9.1723790 -633.09721 -320.732056 2.19506351
## cyl -9.172379 3.1895161 199.66028 101.931452 -0.66836694
## disp -633.097208 199.6602823 15360.79983 6721.158669 -47.06401915
## hp -320.732056 101.9314516 6721.15867 4700.866935 -16.45110887
## drat 2.195064 -0.6683669 -47.06402 -16.451109 0.28588135
## wt -5.116685 1.3673710 107.68420 44.192661 -0.37272073
## qsec 4.509149 -1.8868548 -96.05168 -86.770081 0.08714073
## vs 2.017137 -0.7298387 -44.37762 -24.987903 0.11864919
## am 1.803931 -0.4657258 -36.56401 -8.320565 0.19015121
## gear 2.135685 -0.6491935 -50.80262 -6.358871 0.27598790
## carb -5.363105 1.5201613 79.06875 83.036290 -0.07840726
## wt qsec vs am gear
## mpg -5.1166847 4.50914919 2.01713710 1.80393145 2.1356855
## cyl 1.3673710 -1.88685484 -0.72983871 -0.46572581 -0.6491935
## disp 107.6842040 -96.05168145 -44.37762097 -36.56401210 -50.8026210
## hp 44.1926613 -86.77008065 -24.98790323 -8.32056452 -6.3588710
## drat -0.3727207 0.08714073 0.11864919 0.19015121 0.2759879
## wt 0.9573790 -0.30548161 -0.27366129 -0.33810484 -0.4210806
## qsec -0.3054816 3.19316613 0.67056452 -0.20495968 -0.2804032
## vs -0.2736613 0.67056452 0.25403226 0.04233871 0.0766129
## am -0.3381048 -0.20495968 0.04233871 0.24899194 0.2923387
## gear -0.4210806 -0.28040323 0.07661290 0.29233871 0.5443548
## carb 0.6757903 -1.89411290 -0.46370968 0.04637097 0.3266129
## carb
## mpg -5.36310484
## cyl 1.52016129
## disp 79.06875000
## hp 83.03629032
## drat -0.07840726
## wt 0.67579032
## qsec -1.89411290
## vs -0.46370968
## am 0.04637097
## gear 0.32661290
## carb 2.60887097

get correlation betwen mpg and gear cor(mtcars$mpg, mtcars$gear)

## [1] 0.4802848

get correlation all variables cor(mtcars[,1:11])

##             mpg        cyl       disp         hp        drat         wt
## mpg 1.0000000 -0.8521620 -0.8475514 -0.7761684 0.68117191 -0.8676594
## cyl -0.8521620 1.0000000 0.9020329 0.8324475 -0.69993811 0.7824958
## disp -0.8475514 0.9020329 1.0000000 0.7909486 -0.71021393 0.8879799
## hp -0.7761684 0.8324475 0.7909486 1.0000000 -0.44875912 0.6587479
## drat 0.6811719 -0.6999381 -0.7102139 -0.4487591 1.00000000 -0.7124406
## wt -0.8676594 0.7824958 0.8879799 0.6587479 -0.71244065 1.0000000
## qsec 0.4186840 -0.5912421 -0.4336979 -0.7082234 0.09120476 -0.1747159
## vs 0.6640389 -0.8108118 -0.7104159 -0.7230967 0.44027846 -0.5549157
## am 0.5998324 -0.5226070 -0.5912270 -0.2432043 0.71271113 -0.6924953
## gear 0.4802848 -0.4926866 -0.5555692 -0.1257043 0.69961013 -0.5832870
## carb -0.5509251 0.5269883 0.3949769 0.7498125 -0.09078980 0.4276059
## qsec vs am gear carb
## mpg 0.41868403 0.6640389 0.59983243 0.4802848 -0.55092507
## cyl -0.59124207 -0.8108118 -0.52260705 -0.4926866 0.52698829
## disp -0.43369788 -0.7104159 -0.59122704 -0.5555692 0.39497686
## hp -0.70822339 -0.7230967 -0.24320426 -0.1257043 0.74981247
## drat 0.09120476 0.4402785 0.71271113 0.6996101 -0.09078980
## wt -0.17471588 -0.5549157 -0.69249526 -0.5832870 0.42760594
## qsec 1.00000000 0.7445354 -0.22986086 -0.2126822 -0.65624923
## vs 0.74453544 1.0000000 0.16834512 0.2060233 -0.56960714
## am -0.22986086 0.1683451 1.00000000 0.7940588 0.05753435
## gear -0.21268223 0.2060233 0.79405876 1.0000000 0.27407284
## carb -0.65624923 -0.5696071 0.05753435 0.2740728 1.00000000

get aggregate mpg to gear aggregate(mpg ~ gear, summary, data = mtcars)

##   gear mpg.Min. mpg.1st Qu. mpg.Median mpg.Mean mpg.3rd Qu. mpg.Max.
## 1 3 10.40 14.50 15.50 16.11 18.40 21.50
## 2 4 17.80 21.00 22.80 24.53 28.08 33.90
## 3 5 15.00 15.80 19.70 21.38 26.00 30.40

get boxplot chart mpg to gear boxplot(mpg ~ gear, data = mtcars)

Image for post
Image for post

Written by

Data Scientist | NLP and Speech Recognition Researcher | Indonesian

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store