Thursday 9 October 2014

select

> 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

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