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slice_sample( )
In this document, I will introduce the slice_sample( ) function and show what it’s for.
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.3 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ ggplot2 3.4.3 ✔ tibble 3.2.1
✔ lubridate 1.9.2 ✔ tidyr 1.3.0
✔ purrr 1.0.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
What is it for?
Slice_sample( ) from the dplyr package randomly selects a row from a dataset.
mpg cyl disp hp drat wt qsec vs am gear carb
Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2
There are various useful arguments in the slice_sample( ) function. We can add n to select a set number of rows.
mpg cyl disp hp drat wt qsec vs am gear carb
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
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
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
We can also use prop if we want to sample a proportion of samples available in the data set.
slice_sample (mtcars, prop= 0.25 )
mpg cyl disp hp drat wt qsec vs am gear carb
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 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
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 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
The default has it where replacement is false.
slice_sample (mtcars, n= 6 ,replace= TRUE )
mpg cyl disp hp drat wt qsec vs am gear carb
Fiat 128...1 32.4 4 78.7 66 4.08 2.20 19.47 1 1 4 1
Merc 230 22.8 4 140.8 95 3.92 3.15 22.90 1 0 4 2
Duster 360 14.3 8 360.0 245 3.21 3.57 15.84 0 0 3 4
Camaro Z28 13.3 8 350.0 245 3.73 3.84 15.41 0 0 3 4
Datsun 710 22.8 4 108.0 93 3.85 2.32 18.61 1 1 4 1
Fiat 128...6 32.4 4 78.7 66 4.08 2.20 19.47 1 1 4 1
Lastly, we can add weight_by to add sampling weights to any non-negative vectors.
slice_sample (mtcars, n= 5 , weight_by = wt)
mpg cyl disp hp drat wt qsec vs am gear carb
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 280C 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
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
Is it helpful?
This function is definitely helpful. In larger data set, we can get a smaller random sample fairly easily.