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!
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
)
tbl, .data  Data frame of population from which to sample. 

size, n, 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

A tibble of size reps * n
rows corresponding to reps
samples of size n
from .data
, grouped by replicate
.
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function has been superseded by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()
.
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 finrela
#> <int> <dbl> <dbl> <fct> <fct> <fct> <dbl> <dbl> <ord> <fct> <fct>
#> 1 1 1998 27 fema… no deg… rep 3 40 $1500… work… average
#> 2 1 2012 26 fema… no deg… ind 10 20 $2500… work… below …
#> 3 1 1996 54 male no deg… ind 4 30 $1500… work… below …
#> 4 1 1993 39 fema… no deg… ind 6 37 $2000… work… average
#> 5 1 2010 49 male degree ind 1 40 $2500… midd… average
#> 6 1 1991 27 fema… degree rep 1 50 $2500… midd… average
#> 7 1 1985 38 fema… no deg… dem 4 38 $8000… lowe… far be…
#> 8 1 2000 37 male no deg… dem 3 50 $2500… work… average
#> 9 1 2000 22 male no deg… ind 2 15 $4000… midd… average
#> 10 1 1998 45 male degree rep 1 50 $2500… midd… average
#> # … with 49,990 more rows, and 1 more variable: 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 1 a
#> 2 1 2 b
#> 3 2 1 a
#> 4 2 4 d
#> 5 3 1 a
#> 6 3 3 c
#> 7 4 2 b
#> 8 4 1 a
#> 9 5 1 a
#> 10 5 5 e