A set of methods to generate random samples from data frames and data simulations. For data frames, individual rows are sampled. For vectors, elements are sampled.
Usage
sample(x, n, replace = FALSE, ...)
# S3 method for default
sample(
x,
n = length(x),
replace = FALSE,
prob = NULL,
.by = NULL,
groups = .by,
orig.ids = FALSE,
...
)
resample(..., replace = TRUE)Arguments
- x
The object from which to sample
- n
Size of the sample.
- replace
Logical flag: whether to sample with replacement. (default:
FALSE)- prob
Probabilities to use for sampling, one for each element of
x- .by
Variables to use to define groups for sampling, as in
{dplyr}. The sample size applies to each group.- groups
Variable indicating blocks to sample within
- orig.ids
Logical. If
TRUE, append a column named "orig.ids" with the row from the originalxthat the same came from.- ...
Arguments to pass along to specific sample methods.
Details
These are based in spirit on the sample functions in the {mosaic} package,
but are redefined here to 1) avoid a dependency on {mosaic} and 2) bring the arguments in
line with the .by = features of {dplyr}.
Examples
sample(sim_03, n=5) # run a simulation
#> # A tibble: 5 × 3
#> g x y
#> <dbl> <dbl> <dbl>
#> 1 0.552 1.17 -0.242
#> 2 -0.675 -0.788 0.753
#> 3 0.214 1.13 -1.25
#> 4 0.311 0.0875 0.0741
#> 5 1.17 1.70 0.981
sample(Clock_auction, n = 3) # from a data frame
#> # A tibble: 3 × 3
#> age bidders price
#> <dbl> <dbl> <dbl>
#> 1 194 5 1356
#> 2 182 11 1979
#> 3 175 8 1545
sample(1:6, n = 6) # sample from a vector
#> [1] 2 5 6 4 1 3