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)

Arguments

tbl

tbl of data.

size

For sample_n(), the number of rows to select. For sample_frac(), the fraction of rows to select. If tbl is grouped, size applies to each group.

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 vignette("programming") for an introduction to these concepts.

.env

This variable is deprecated and no longer has any effect. To evaluate weight in a particular context, you can now unquote a quosure.

Examples

by_cyl <- mtcars %>% group_by(cyl) # Sample fixed number per group sample_n(mtcars, 10)
#> 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 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
sample_n(mtcars, 50, replace = TRUE)
#> mpg cyl disp hp drat wt qsec vs am gear carb #> 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 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 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 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 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 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 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.1 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.2 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.1 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.1 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 1 #> Toyota Corona.3 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Merc 450SLC.2 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Cadillac Fleetwood.1 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Valiant.1 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 1 #> Fiat X1-9.1 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Duster 360.1 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> Hornet 4 Drive.1 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Lincoln Continental.2 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Merc 240D.1 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
sample_n(mtcars, 10, weight = mpg)
#> mpg cyl disp hp drat wt qsec vs am gear carb #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> 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 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
sample_n(by_cyl, 3)
#> # A tibble: 9 x 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 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 3 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> 4 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 5 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> 6 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 7 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> 8 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> 9 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
sample_n(by_cyl, 10, replace = TRUE)
#> # A tibble: 30 x 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 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> 3 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 4 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> 5 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> 6 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> 7 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 8 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 9 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> 10 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> # ... with 20 more rows
sample_n(by_cyl, 3, weight = mpg / mean(mpg))
#> # A tibble: 9 x 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 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> 3 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> 4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 5 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> 6 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 7 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> 8 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> 9 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
# 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 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 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 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> 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 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 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 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 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 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 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 #> Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
sample_frac(mtcars, 0.1)
#> mpg cyl disp hp drat wt qsec vs am gear carb #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
sample_frac(mtcars, 1.5, replace = TRUE)
#> mpg cyl disp hp drat wt qsec vs am gear carb #> 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 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Merc 280C 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 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Hornet Sportabout 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.1 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.1 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.1 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.2 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Hornet Sportabout.2 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Hornet Sportabout.3 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> Merc 450SL.3 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> 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 #> Hornet 4 Drive.1 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Hornet Sportabout.4 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.5 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 2 #> AMC Javelin.1 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Ferrari Dino.1 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Lotus Europa.1 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Merc 450SLC.1 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Fiat 128.2 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> Mazda RX4 Wag.1 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Merc 240D.2 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
sample_frac(mtcars, 0.1, weight = 1 / mpg)
#> mpg cyl disp hp drat wt qsec vs am gear carb #> Volvo 142E 21.4 4 121.0 109 4.11 2.78 18.6 1 1 4 2 #> Merc 240D 24.4 4 146.7 62 3.69 3.19 20.0 1 0 4 2 #> Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3
sample_frac(by_cyl, 0.2)
#> # A tibble: 6 x 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 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> 2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> 3 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 4 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> 5 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> 6 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
sample_frac(by_cyl, 1, replace = TRUE)
#> # A tibble: 32 x 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 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> 2 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> 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 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 6 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 #> 7 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> 8 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> 9 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> 10 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> # ... with 22 more rows