6. Integration and Refactoring
6.1 Transpose
> Mtcars MPG cyl disp hp Drat wt Qsec VS AM gear Carbmazda RX4 21 6 160 110 3.9 2.6 0 1 4 4Mazda RX4 Wag 6 3.9 2.9 0 1 4 4Datsun 710 23 4 108 9 3 3.8 2.3 1 1 4 1Hornet 4 Drive 6 258 3.1 3.2 1 0 3 1Hornet sportabout 19 8 175 3.1 3.4 0 0 3 2Valiant 6 225 1Duster 2.8 3.5 1 0 3 360 8 245 3.2 3.6 0 0 3 4Merc 240D 4 147 3.7 3.2 1 0 4 2Merc 4 141 3.9 3.1 1 0 4 2MERC 280 19 6 168 123 3.9 3.4 18 1 0 4 4MERC 280C 6 168 123 3.9 3.4 1 0 4 4Merc 450SE 16 8 276 180 3.1 4.1 17 0 0 3 3Merc 450SL 8 276 3.1 3.7 0 0 3 3Merc 450SLC 15 8 276 180 3.1 3.8 18 0) 0 33Cadillac Fleetwood 8 472 205 2.9 5.2 0 0 3 4Lincoln Continental 10 8 460 215 3.0 5.4 18 0 0 3 4Chrysler Imperial 8 3.2 5.3 0 0 3 4Fiat 128 32 4 79 66 4.1 2.2 1 1 4 1Honda Civic 4 4.9 1.6 1 1 4 2Toyota Corolla 34 4 71 6 5 4.2 1.8 1 1 4 1Toyota Corona 4 1Dodge 3.7 2.5 1 0 3 Challenger 16 8 318 2.8 3.5 0 0 3 2AMC Javelin 8 304 2Camaro 3.1 3.4 0 0 3 Z28 8 245 3.7 3.8 0 0 3 4Pontiac Firebird 8 2Fiat 175 3.1 3.8 0 0 3 X1-9 4 4.1 1.9 1 1 4 1Porsche 914-2 26 4 120 91 4.4 2.1 17 0 1 5 2Lotus Europa 4 113 3.8 1.5 1 1 5 2Ford Pantera L 16 8 351 264 4.2 3.2 14 0 1 5 4Ferrari DIno 6 145 175 3.6 2.8 0 1 5 6Maserati Bora 15 8 301 335 3.5 3.6 15 0 1 5 8Volvo 142E 4 121 109 4.1 2.8 1 1 4 2> > > Cars <-mtcars[1:5, 1:4]> cars MPG cyl disp Hpmazda RX4 6 110Mazda RX4 Wag 6 110Datsun 710 23 4 108 93Hornet 4 Drive 6 258 110Hornet sportabout 8 175> t (Cars) Mazda RX4 Mazda RX4 Wag Dat Sun 710 Hornet 4 Drive Hornet Sportaboutmpg 19cyl 6 6 4 6 8disp 160 160 108 258 360HP 175>
6.2 Consolidating data
> Options (digits=3) > Attach (mtcars) > AggData <-Aggregate (Mtcars, By=list (cyl,gear), Fun=mean, Na.rm=true) > AggData group.1 group.2 MPG cyl disp hp drat wt qsec vs am Gear CARB1 4 3 21.5 4 3.70 2.46 20.0 1.0 0.00 3 1.002 6 3 19.8 6 242 108 2.92 3.34 19.8 1.0 0.00 3 1.003 8 3 15.1 8 358 194 3.12 4.10 17.1 0.0 0.00 3 3.084 4 4 26.9 4 103 4.11 2.38 19.6 1.0 0.75 4 1.505 6 4 19.8 6 164 by 3.91 3.09 17.7 0.5 0.50
4 4.006 4 5 28.2 4 108 102 4.10 1.83 16.8 0.5 1.00 5 2.007 6 5 19.7 6 145 1 3.62 2.77 15.5 0.0 1.00 5 6.008 8 5 15.4 8 326 300 3.88 3.37 14.6 0.0 1.00
6.3 Reshape Bag
> ID <-C (1,1,2,2) > Time <-C (1,2,1,2) > X1 <-C (5,3,6,2) > X2 <-C (6,5,1,4) > MyData <-data.f Rame (ID, time, X1, X2) > MyData ID time X1 X21 1 1 5 1 2 3 / 2 1 6 2 2 2 4
6.3.1 Fusion
> Library (Reshape) > MD <-Melt (MyData, id= (c ("id", "Time")) > MD ID time variable value1 1 1< C20/>x1 1 2 X1 2 1 X1 2 2 X1 25 1 1 X2 1 2 X2 2 1 X2 2 2 X2
6.3.2 Re-casting
> Library (reshape) > MD <-Melt (MyData, id=c ("id", "Time")) > MD ID Time Variable value1 1 1 x1 1 2 x1 2 1 x1 2 2 x1 25 1 1 X2 1 2 x2 2 1 x2 2 2 x2 4> cast (MD, id~variable, mean) ID x 1 x21 1 4 5.52 2 4 2.5> cast (MD, time~variable, mean) time X1 x21 1 5.5 3.52 2 2.5 4.5> cast (MD, Id~ti Me, mean) ID 1 1 5.5 2 3.5 3> cast (MD, id+time~variable) ID time x1 x21 1 1 5 62 1 2 3 53 2 1 6 2 2 2 4> cast (MD, id+variable~time) ID variable 1 1 x1 5 1 x2 6 2 x1 6 2 4 2 x2 1 4> cast (MD, id+variable~time) ID variable 1 1 x1 5 + 1 x2 6 2 X1 6 24 2 X2 1 4> CAST (MD, id~variable+time) ID x1_1 x1_2 x2_1 x2_21 1 5 3 6 2 6 2 1 4>
R language Combat-Advanced data Management (4)