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These functions extend the functionality of dplyr::sample_n() and dplyr::slice_sample() by allowing for repeated sampling of data. This operation is especially helpful while creating sampling distributions—see the examples below!

Usage

rep_sample_n(tbl, size, replace = FALSE, reps = 1, prob = NULL)

rep_slice_sample(
  .data,
  n = NULL,
  prop = NULL,
  replace = FALSE,
  weight_by = NULL,
  reps = 1
)

Arguments

tbl, .data

Data frame of population from which to sample.

size, n, prop

size and n refer to the sample size of each sample. The size argument to rep_sample_n() is required, while in rep_slice_sample() sample size defaults to 1 if not specified. prop, an argument to rep_slice_sample(), refers to the proportion of rows to sample in each sample, and is rounded down in the case that prop * nrow(.data) is not an integer. When using rep_slice_sample(), please only supply one of n or prop.

replace

Should samples be taken with replacement?

reps

Number of samples to take.

prob, weight_by

A vector of sampling weights for each of the rows in .data—must have length equal to nrow(.data).

Value

A tibble of size reps * n rows corresponding to repssamples of size n from .data, grouped by replicate.

Details

rep_sample_n() and rep_slice_sample() are designed to behave similar to their dplyr counterparts. As such, they have at least the following differences:

  • In case replace = FALSE having size bigger than number of data rows in rep_sample_n() will give an error. In rep_slice_sample() having such n or prop > 1 will give warning and output sample size will be set to number of rows in data.

Note that the dplyr::sample_n() function has been superseded by dplyr::slice_sample().

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
library(ggplot2)
library(tibble)

# take 1000 samples of size n = 50, without replacement
slices <- gss %>%
  rep_slice_sample(n = 50, reps = 1000)

slices
#> # A tibble: 50,000 × 12
#> # Groups:   replicate [1,000]
#>    replicate  year   age sex    college  partyid hompop hours income class
#>        <int> <dbl> <dbl> <fct>  <fct>    <fct>    <dbl> <dbl> <ord>  <fct>
#>  1         1  1990    48 female no degr… dem          2    32 $2500… work…
#>  2         1  1990    36 female no degr… ind          4    56 $2500… midd…
#>  3         1  2018    51 female no degr… ind          1    40 $2000… work…
#>  4         1  1998    45 male   degree   rep          1    50 $2500… midd…
#>  5         1  1980    48 female no degr… dem          2    40 $6000… work…
#>  6         1  2016    61 female degree   ind          2    55 $2500… work…
#>  7         1  1985    51 female no degr… dem          4    28 $2500… midd…
#>  8         1  1988    42 female no degr… ind          8    38 $2500… midd…
#>  9         1  1996    28 female no degr… rep          3    30 $2500… work…
#> 10         1  1987    28 male   no degr… rep          3    40 $1500… work…
#> # … with 49,990 more rows, and 2 more variables: finrela <fct>,
#> #   weight <dbl>

# compute the proportion of respondents with a college
# degree in each replicate
p_hats <- slices %>%
  group_by(replicate) %>%
  summarize(prop_college = mean(college == "degree"))

# plot sampling distribution
ggplot(p_hats, aes(x = prop_college)) +
  geom_density() +
  labs(
    x = "p_hat", y = "Number of samples",
    title = "Sampling distribution of p_hat"
  )


# sampling with probability weights. Note probabilities are automatically
# renormalized to sum to 1
df <- tibble(
  id = 1:5,
  letter = factor(c("a", "b", "c", "d", "e"))
)

rep_slice_sample(df, n = 2, reps = 5, weight_by = c(.5, .4, .3, .2, .1))
#> # A tibble: 10 × 3
#> # Groups:   replicate [5]
#>    replicate    id letter
#>        <int> <int> <fct> 
#>  1         1     3 c     
#>  2         1     2 b     
#>  3         2     2 b     
#>  4         2     1 a     
#>  5         3     1 a     
#>  6         3     4 d     
#>  7         4     4 d     
#>  8         4     3 c     
#>  9         5     3 c     
#> 10         5     1 a