> example(select) select> #data.frame select> where(mtcars, cyl>4 & mpg > 15) mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 select> #pipe select> as.data.frame(where(input(mtcars), cyl > 4 & mpg > 15)) mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 select> # select two columns select> as.data.frame(transmute(input(mtcars), cyl, mpg)) cyl mpg 1 6 21.0 2 6 21.0 3 4 22.8 4 6 21.4 5 8 18.7 6 6 18.1 7 8 14.3 8 4 24.4 9 4 22.8 10 6 19.2 11 6 17.8 12 8 16.4 13 8 17.3 14 8 15.2 15 8 10.4 16 8 10.4 17 8 14.7 18 4 32.4 19 4 30.4 20 4 33.9 21 4 21.5 22 8 15.5 23 8 15.2 24 8 13.3 25 8 19.2 26 4 27.3 27 4 26.0 28 4 30.4 29 8 15.8 30 6 19.7 31 8 15.0 32 4 21.4 select> # create additional column select> as.data.frame(transmute(input(mtcars), ratio = cyl/mpg, .cbind = TRUE)) mpg cyl disp hp drat wt qsec vs am gear Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 carb ratio Mazda RX4 4 0.2857143 Mazda RX4 Wag 4 0.2857143 Datsun 710 1 0.1754386 Hornet 4 Drive 1 0.2803738 Hornet Sportabout 2 0.4278075 Valiant 1 0.3314917 Duster 360 4 0.5594406 Merc 240D 2 0.1639344 Merc 230 2 0.1754386 Merc 280 4 0.3125000 Merc 280C 4 0.3370787 Merc 450SE 3 0.4878049 Merc 450SL 3 0.4624277 Merc 450SLC 3 0.5263158 Cadillac Fleetwood 4 0.7692308 Lincoln Continental 4 0.7692308 Chrysler Imperial 4 0.5442177 Fiat 128 1 0.1234568 Honda Civic 2 0.1315789 Toyota Corolla 1 0.1179941 Toyota Corona 1 0.1860465 Dodge Challenger 2 0.5161290 AMC Javelin 2 0.5263158 Camaro Z28 4 0.6015038 Pontiac Firebird 2 0.4166667 Fiat X1-9 1 0.1465201 Porsche 914-2 2 0.1538462 Lotus Europa 2 0.1315789 Ford Pantera L 4 0.5063291 Ferrari Dino 6 0.3045685 Maserati Bora 8 0.5333333 Volvo 142E 2 0.1869159 select> # summaries select> as.data.frame(transmute(input(mtcars), mean(cyl), mean(mpg))) mean.cyl. mean.mpg. 1 6.1875 20.09062 select> # summaries by groups select> as.data.frame(transmute(group(input(mtcars), cyl), mean(mpg))) cyl mean.mpg. 1 6 19.74286 1.1 4 26.66364 1.2 8 15.10000
Thursday, 9 October 2014
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