Selects a random sample using a specified method and sample size. Selection can also optionally be stratified and/or include a measure of size (mos) if a PPS method is used.
Usage
select_sample(
frame,
method,
n,
outall = FALSE,
strata = NULL,
mos = NULL,
sort_vars = NULL,
sort_method = NULL
)Arguments
- frame
A data frame, data.table, or tibble from which to draw the sample. No default.
- method
The desired sampling method. Valid options are "srs", "sys_eq", "sys_pps", and "chromy_pps". No default.
- n
The sample size to draw from the frame. If strata is NULL, must be a positive integer. If strata is not NULL, must be a data.frame, tibble, or data.table with columns for each stratification variable as the same type and variable names as the frame plus a column with the sample size (
sample_size) which is a positive integer. No default.- outall
A logical value indicating whether to return the entire frame with a selection indicator or just the sample. Default is FALSE.
- strata
A vector of characters with variable names of strata. Default is NULL.
- mos
A character string defining the variable name on the frame for the measure of size. If not NULL, must have method = c("sys_pps", "chromy_pps"). If NULL, must have method=c("srs", "sys"). Default is NULL.
- sort_vars
A vector of characters indicating the variables that should be used to sort the frame. If not NULL, cannot have method = "srs". Default is NULL.
- sort_method
A character string defining the method to implicitly sort the frame. Valid options are "serpentine" and "nest". Must coincide with sort_var; i.e., both must be NULL or both must be not NULL. Default is NULL.
Value
A tidytable object containing the entire frame with a selection indicator or just the sample, dependent on the value of outall. Selection probability and sampling weight are also included. May include various summary messages to the console when applicable for certain sampling methods.
Examples
# SRS of 100 US counties, using geographic region as strata
# n is a data frame containing the strata values and corresponding desired sample size
# Sample size column must be titled 'sample_size'
n_df_srs <- data.frame(
Region = as.factor(c("Northeast", "Midwest", "South", "West")),
sample_size = c(25, 25, 25, 25)
)
county_2023 |>
select_sample(method = "srs", n = n_df_srs, strata = "Region")
#> Stratum: Region = South
#> --Frame size: 1422
#> --Sample size: 25
#> Stratum: Region = West
#> --Frame size: 449
#> --Sample size: 25
#> Stratum: Region = Northeast
#> --Frame size: 218
#> --Sample size: 25
#> Stratum: Region = Midwest
#> --Frame size: 1055
#> --Sample size: 25
#> # A tidytable: 100 × 27
#> Region GEOID Name State Division Pop_Tot Pop_Pct_White_NH Pop_Pct_Black_NH
#> <fct> <chr> <chr> <chr> <fct> <dbl> <dbl> <dbl>
#> 1 South 05043 Drew C… AR West So… 17143 64.9 26.7
#> 2 South 05097 Montgo… AR West So… 8571 89.7 0.840
#> 3 South 12011 Browar… FL South A… 1946127 31.9 27.6
#> 4 South 12109 St. Jo… FL South A… 292243 78.6 4.93
#> 5 South 13071 Colqui… GA South A… 45907 54.3 22.8
#> 6 South 13093 Dooly … GA South A… 11026 40.9 47.7
#> 7 South 13127 Glynn … GA South A… 84987 62.1 24.5
#> 8 South 13259 Stewar… GA South A… 4978 24.6 59.7
#> 9 South 13273 Terrel… GA South A… 8941 36.4 59.6
#> 10 South 21131 Leslie… KY East So… 10261 96.8 0.526
#> # ℹ 90 more rows
#> # ℹ 19 more variables: Pop_Pct_AIAN_NH <dbl>, Pop_Pct_Asian_NH <dbl>,
#> # Pop_Pct_NHPI_NH <dbl>, Pop_Pct_Other_NH <dbl>, Pop_Pct_Hispanic <dbl>,
#> # HU_Tot <dbl>, HU_Pct_Occupied <dbl>, HU_Pct_Vacant <dbl>,
#> # Pop_Pct_0004 <dbl>, Pop_Pct_0509 <dbl>, Pop_Pct_1014 <dbl>,
#> # Pop_Pct_2544 <dbl>, Pop_Pct_4564 <dbl>, Pop_Pct_6574 <dbl>,
#> # Pop_Pct_75plus <dbl>, Pop_Pct_1517 <dbl>, Pop_Pct_1824 <dbl>, …
# Systematic sample of 250 US universities. Each unit has an equal probability of being selected
# Includes a nested sort of enrollment total within sector
# Returns all obs from original data frame with a selection indicator column
sample_sys_eq <- ipeds |>
select_sample(
method = "sys_eq", n = 250, outall = TRUE,
sort_vars = c("SECTOR", "ENRTOT"), sort_method = "nest"
)
#> Frame size: 5914
#> Sample size: 250
#> Sampling interval (k): 23.656
#> Random start (r): 10.85918
# For samples taken with outall = TRUE, the sample size can be verified by summing
# the SelectionIndicator column.
sample_sys_eq
#> # A tidytable: 5,914 × 19
#> UNITID INSTNM STABBR FIPS OBEREG ICLEVEL SECTOR LOCALE DEGGRANT HLOFFER
#> <dbl> <chr> <chr> <dbl> <fct> <fct> <fct> <fct> <fct> <fct>
#> 1 180203 Aaniiih Na… MT 30 Rocky… Four o… Publi… Rural… Degree-… Bachel…
#> 2 491297 University… WI 55 Great… Four o… Publi… Rural… Degree-… Bachel…
#> 3 200086 Nueta Hida… ND 38 Plain… Four o… Publi… Rural… Degree-… Bachel…
#> 4 219408 Sisseton W… SD 46 Plain… Four o… Publi… Rural… Degree-… Bachel…
#> 5 260372 Lac Courte… WI 55 Great… Four o… Publi… Rural… Degree-… Master…
#> 6 214607 Pennsylvan… PA 42 Mid E… Four o… Publi… Subur… Degree-… Master…
#> 7 366340 Stone Chil… MT 30 Rocky… Four o… Publi… Rural… Degree-… Bachel…
#> 8 200466 Sitting Bu… ND 38 Plain… Four o… Publi… Rural… Degree-… Master…
#> 9 434584 Ilisagvik … AK 2 Far W… Four o… Publi… Rural… Degree-… Bachel…
#> 10 243188 University… PR 72 Other… Four o… Publi… Rural… Degree-… Bachel…
#> # ℹ 5,904 more rows
#> # ℹ 9 more variables: ENRTOT <dbl>, EFUG <dbl>, EFUG1ST <dbl>, EFUGFT <dbl>,
#> # EFGRAD <dbl>, EFGRADFT <dbl>, SelectionIndicator <lgl>,
#> # SelectionProbability <dbl>, SamplingWeight <dbl>
sum(sample_sys_eq$SelectionIndicator)
#> [1] 250
# Systematic PPS sample of 250 US universities. Each unit's probability of selection
# is proportional to its size measure.
# Using enrollment total as MOS
# Includes a nested sort of enrollment total within sector
sample_sys_pps <- ipeds |>
select_sample(
method = "sys_pps", n = 250, mos = "ENRTOT",
sort_vars = c("SECTOR", "ENRTOT"), sort_method = "nest"
)
#> Frame size: 5914
#> Sample size: 250
#> Sampling interval (k): 78812
#> Random start (r): 10272.68
# For pps samples, it is possible for a single sampling unit to be selected multiple times
# due to a large mos value. This is especially true as desired sample size increases. The
# result is the final sample may not meet the desired sample size. To verify the pps sample,
# the NumberHits column can be summed and should total to the desired sample size.
sample_sys_pps
#> # A tidytable: 247 × 19
#> UNITID INSTNM STABBR FIPS OBEREG ICLEVEL SECTOR LOCALE DEGGRANT HLOFFER
#> <dbl> <chr> <chr> <dbl> <fct> <fct> <fct> <fct> <fct> <fct>
#> 1 214795 Pennsylvan… PA 42 Mid E… Four o… Publi… Rural… Degree-… Bachel…
#> 2 187596 Navajo Tec… NM 35 South… Four o… Publi… Rural… Degree-… Doctor…
#> 3 228501 Sul Ross S… TX 48 South… Four o… Publi… Town:… Degree-… Master…
#> 4 204705 Ohio State… OH 39 Great… Four o… Publi… Subur… Degree-… Bachel…
#> 5 163338 University… MD 24 Mid E… Four o… Publi… Town:… Degree-… Doctor…
#> 6 487010 The Univer… TN 47 South… Four o… Publi… City:… Degree-… Doctor…
#> 7 219259 Northern S… SD 46 Plain… Four o… Publi… Town:… Degree-… Master…
#> 8 218645 University… SC 45 South… Four o… Publi… Subur… Degree-… Master…
#> 9 207397 Oklahoma S… OK 40 South… Four o… Publi… City:… Degree-… Bachel…
#> 10 155025 Emporia St… KS 20 Plain… Four o… Publi… Town:… Degree-… Doctor…
#> # ℹ 237 more rows
#> # ℹ 9 more variables: ENRTOT <dbl>, EFUG <dbl>, EFUG1ST <dbl>, EFUGFT <dbl>,
#> # EFGRAD <dbl>, EFGRADFT <dbl>, SamplingWeight <dbl>, NumberHits <int>,
#> # ExpectedHits <dbl>
sum(sample_sys_pps$NumberHits)
#> [1] 250
# Sequential aka Chromy's method PPS sample of 500 PUMAs, using geographic region as strata
# Includes a serpentine sort of geographic division then state
# Using population total as MOS, each unit's probability of selection is proportional to its
# size measure.
# Note that there may be a discrepancy between the desired and final sample sizes. The final
# sample size can be verified by totaling NumberHits.
n_df_chr <- data.frame(
Region = as.factor(c("Northeast", "Midwest", "South", "West")),
sample_size = c(125, 125, 125, 125)
)
puma_2023 |>
select_sample(
method = "chromy_pps", n = n_df_chr, strata = "Region", mos = "Pop_Tot",
sort_vars = c("Division", "State"), sort_method = "serpentine"
)
#> Stratum: Region = South
#> --Frame size: 952
#> --Sample size: 125
#> Stratum: Region = West
#> --Frame size: 581
#> --Sample size: 125
#> Stratum: Region = Northeast
#> --Frame size: 423
#> --Sample size: 125
#> Stratum: Region = Midwest
#> --Frame size: 506
#> --Sample size: 125
#> # A tidytable: 500 × 28
#> Region GEOID Name State Division Pop_Tot Pop_Pct_White_NH Pop_Pct_Black_NH
#> <fct> <chr> <chr> <chr> <fct> <dbl> <dbl> <dbl>
#> 1 South 1000105 Sout… DE South A… 107205 59.0 23.3
#> 2 South 1200902 Brev… FL South A… 124268 82.9 2.04
#> 3 South 1201106 Brow… FL South A… 186422 29.4 28.1
#> 4 South 1201113 Brow… FL South A… 125752 26.9 22.6
#> 5 South 1201500 Char… FL South A… 195083 82.4 5.10
#> 6 South 1203104 Duva… FL South A… 120322 45.6 28.3
#> 7 South 1203107 Duva… FL South A… 131852 64.7 9.97
#> 8 South 1205704 Hill… FL South A… 136079 54.3 6.31
#> 9 South 1205707 Hill… FL South A… 152043 38.3 18.3
#> 10 South 1206902 Lake… FL South A… 136281 73.1 9.06
#> # ℹ 490 more rows
#> # ℹ 20 more variables: Pop_Pct_AIAN_NH <dbl>, Pop_Pct_Asian_NH <dbl>,
#> # Pop_Pct_NHPI_NH <dbl>, Pop_Pct_Other_NH <dbl>, Pop_Pct_Hispanic <dbl>,
#> # HU_Tot <dbl>, HU_Pct_Occupied <dbl>, HU_Pct_Vacant <dbl>,
#> # Pop_Pct_0004 <dbl>, Pop_Pct_0509 <dbl>, Pop_Pct_1014 <dbl>,
#> # Pop_Pct_2544 <dbl>, Pop_Pct_4564 <dbl>, Pop_Pct_6574 <dbl>,
#> # Pop_Pct_75plus <dbl>, Pop_Pct_1517 <dbl>, Pop_Pct_1824 <dbl>, …