stratified: Take a Stratified Sample From a Dataset
In splitstackshape: Stack and Reshape Datasets After Splitting Concatenated Values

Description Usage Arguments Value Note Author(s) See Also Examples

The stratified function samples from a data.table in which one or more columns can be used as a "stratification" or "grouping" variable. The result is a new data.table with the specified number of samples from each group.

1 2	stratified(indt, group, size, select = NULL, replace = FALSE, keep.rownames = FALSE, bothSets = FALSE, ...)

`indt`	The input `data.table`.
`group`	The column or columns that should be used to create the groups. Can be a character vector of column names (recommended) or a numeric vector of column positions. Generally, if you are using more than one variable to create your "strata", you should list them in the order of slowest varying to quickest varying. This can be a vector of names or column indexes.
`size`	The desired sample size. If `size` is a value between `0` and `1` expressed as a decimal, size is set to be proportional to the number of observations per group. If `size` is a single positive integer, it will be assumed that you want the same number of samples from each group. If `size` is a named vector, the function will check to see whether the length of the vector matches the number of groups and that the names match the group names.
`select`	A named list containing levels from the `"group"` variables in which you are interested. The list names must be present as variable names for the input dataset.
`replace`	Logical. Should sampling be with replacement? Defaults to `FALSE`.
`keep.rownames`	Logical. If the input is a `data.frame` with `rownames`, `as.data.table` would normally drop the rownames. If `TRUE`, the rownames would be retained in a column named `rn`. Defaults to `FALSE`.
`bothSets`	Logical. Should both the sampled and non-sampled sets be returned as a `list`? Defaults to `FALSE`.
`...`	Optional arguments to `base::sample()`.

If bothSets = TRUE, a list of two data.tables; otherwise, a data.table.

Slightly different sizes than requested: Because of how computers deal with floating-point arithmetic, and because R uses a "round to even" approach, the size per strata that results when specifying a proportionate sample may be one sample higher or lower per strata than you might have expected.

Ananda Mahto

sampling::strata() from the "strata" package; dplyr::sample_n() and dplyr::sample_frac() from "dplyr".

# Generate a sample data.frame to play with
set.seed(1)
DF <- data.frame(
  ID = 1:100,
  A = sample(c("AA", "BB", "CC", "DD", "EE"), 100, replace = TRUE),
  B = rnorm(100), C = abs(round(rnorm(100), digits=1)),
  D = sample(c("CA", "NY", "TX"), 100, replace = TRUE),
  E = sample(c("M", "F"), 100, replace = TRUE))

# Take a 10% sample from all -A- groups in DF
stratified(DF, "A", .1)

# Take a 10% sample from only "AA" and "BB" groups from -A- in DF
stratified(DF, "A", .1, select = list(A = c("AA", "BB")))

# Take 5 samples from all -D- groups in DF, specified by column number
stratified(DF, group = 5, size = 5)

# Use a two-column strata: -E- and -D-
stratified(DF, c("E", "D"), size = .15)

# Use a two-column strata (-E- and -D-) but only use cases where -E- == "M"
stratified(DF, c("E", "D"), .15, select = list(E = "M"))

## As above, but where -E- == "M" and -D- == "CA" or "TX"
stratified(DF, c("E", "D"), .15, select = list(E = "M", D = c("CA", "TX")))

# Use a three-column strata: -E-, -D-, and -A-
stratified(DF, c("E", "D", "A"), size = 2)

## Not run: 
# The following will produce errors
stratified(DF, "D", c(5, 3))
stratified(DF, "D", c(5, 3, 2))

## End(Not run)

# Sizes using a named vector
stratified(DF, "D", c(CA = 5, NY = 3, TX = 2))

# Works with multiple groups as well
stratified(DF, c("D", "E"), 
           c("NY F" = 2, "NY M" = 3, "TX F" = 1, "TX M" = 1,
             "CA F" = 5, "CA M" = 1))