This is a wrapper around sample.int() to make it easy to
select random rows from a table. It currently only works for local
tbls.
sample_n(tbl, size, replace = FALSE, weight = NULL, .env = NULL) sample_frac(tbl, size = 1, replace = FALSE, weight = NULL, .env = NULL)
| tbl | tbl of data. |
|---|---|
| size | For |
| replace | Sample with or without replacement? |
| weight | Sampling weights. This must evaluate to a vector of non-negative numbers the same length as the input. Weights are automatically standardised to sum to 1. This argument is automatically quoted and later
evaluated in the context of the data
frame. It supports unquoting. See
|
| .env | This variable is deprecated and no longer has any
effect. To evaluate |
#> mpg cyl disp hp drat wt qsec vs am gear carb #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1sample_n(mtcars, 50, replace = TRUE)#> mpg cyl disp hp drat wt qsec vs am gear carb #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Mazda RX4.1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Merc 450SL.1 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Toyota Corona.1 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Merc 280C.1 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Merc 280C.2 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Chrysler Imperial.1 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Porsche 914-2.1 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Merc 450SE.1 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Merc 230.1 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Cadillac Fleetwood.1 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental.1 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Ford Pantera L.1 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Lotus Europa.1 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> Lotus Europa.2 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Toyota Corona.2 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Merc 280C.3 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Toyota Corona.3 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Dodge Challenger.1 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> Merc 450SL.2 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Lotus Europa.3 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 #> Merc 450SLC.1 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Volvo 142E.1 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 #> Merc 280C.4 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Ford Pantera L.2 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1sample_n(mtcars, 10, weight = mpg)#> mpg cyl disp hp drat wt qsec vs am gear carb #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2sample_n(by_cyl, 3)#> # A tibble: 9 × 11 #> # Groups: cyl [3] #> mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 2 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> 3 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 4 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 5 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> 7 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> 8 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 9 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4sample_n(by_cyl, 10, replace = TRUE)#> # A tibble: 30 × 11 #> # Groups: cyl [3] #> mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> 2 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 5 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> 6 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> 7 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 8 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 9 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 10 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> # ... with 20 more rowssample_n(by_cyl, 3, weight = mpg / mean(mpg))#> # A tibble: 9 × 11 #> # Groups: cyl [3] #> mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 2 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 #> 3 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> 4 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 5 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> 6 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> 7 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> 8 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> 9 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2# Sample fixed fraction per group # Default is to sample all data = randomly resample rows sample_frac(mtcars)#> mpg cyl disp hp drat wt qsec vs am gear carb #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2sample_frac(mtcars, 0.1)#> mpg cyl disp hp drat wt qsec vs am gear carb #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1sample_frac(mtcars, 1.5, replace = TRUE)#> mpg cyl disp hp drat wt qsec vs am gear carb #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> Fiat X1-9.1 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Ford Pantera L.1 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Merc 280C.1 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Merc 450SL.1 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Hornet Sportabout.1 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Ford Pantera L.2 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> Toyota Corona.1 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Merc 450SL.2 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Hornet Sportabout.2 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Fiat 128.1 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Merc 240D.1 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Datsun 710.1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Merc 450SL.3 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Hornet Sportabout.3 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Hornet Sportabout.4 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Merc 450SL.4 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Lotus Europa.1 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Hornet 4 Drive.1 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Hornet 4 Drive.2 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout.5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Porsche 914-2.1 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Hornet Sportabout.6 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2sample_frac(mtcars, 0.1, weight = 1 / mpg)#> mpg cyl disp hp drat wt qsec vs am gear carb #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3sample_frac(by_cyl, 0.2)#> # A tibble: 6 × 11 #> # Groups: cyl [3] #> mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> 2 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 3 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> 4 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> 5 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> 6 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2sample_frac(by_cyl, 1, replace = TRUE)#> # A tibble: 32 × 11 #> # Groups: cyl [3] #> mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> 3 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> 4 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> 5 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> 6 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> 7 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> 8 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 9 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 10 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> # ... with 22 more rows