hardhat (version 1.0.0)

weighted_table: Weighted table

Description

weighted_table() computes a weighted contingency table based on factors provided in ... and a double vector of weights provided in weights. It can be seen as a weighted extension to base::table() and an alternative to stats::xtabs().

weighted_table() always uses the exact set of levels returned by levels() when constructing the table. This results in the following properties:

• Missing values found in the factors are never included in the table unless there is an explicit NA factor level. If needed, this can be added to a factor with base::addNA() or forcats::fct_expand(x, NA).

• Levels found in the factors that aren't actually used in the underlying data are included in the table with a value of 0. If needed, you can drop unused factor levels by re-running your factor through factor(), or by calling forcats::fct_drop().

See the examples section for more information about these properties.

Usage

weighted_table(..., weights, na_remove = FALSE)

Arguments

...

Factors of equal length to use in the weighted table. If the ... are named, those names will propagate onto the "dimnames names" of the resulting table. At least one factor must be provided.

weights

A double vector of weights used to fill the cells of the weighted table. This must be the same length as the factors provided in ....

na_remove

A single TRUE or FALSE for handling whether or not missing values in weights should be removed when summing up the weights.

Value

The weighted table as an array of double values.

Details

The result of weighted_table() does not have a "table" class attached to it. It is only a double array. This is because "table" objects are defined as containing integer counts, but weighted tables can utilize fractional weights.

Examples

# NOT RUN {
x <- factor(c("x", "y", "z", "x", "x", "y"))
y <- factor(c("a", "b", "a", "a", "b", "b"))
w <- c(1.5, 2, 1.1, .5, 3, 2)

weighted_table(x = x, y = y, weights = w)

# ---------------------------------------------------------------------------
# If `weights` contains missing values, then missing values will be
# propagated into the weighted table
x <- factor(c("x", "y", "y"))
y <- factor(c("a", "b", "b"))
w <- c(1, NA, 3)

weighted_table(x = x, y = y, weights = w)

# You can remove the missing values while summing up the weights with
# `na_remove = TRUE`
weighted_table(x = x, y = y, weights = w, na_remove = TRUE)

# ---------------------------------------------------------------------------
# If there are missing values in the factors, those typically don't show
# up in the weighted table
x <- factor(c("x", NA, "y", "x"))
y <- factor(c("a", "b", "a", NA))
w <- 1:4

weighted_table(x = x, y = y, weights = w)

# This is because the missing values aren't considered explicit levels
levels(x)

# You can force them to show up in the table by using `addNA()` ahead of time
# (or `forcats::fct_expand(x, NA)`)
x <- addNA(x, ifany = TRUE)
y <- addNA(y, ifany = TRUE)
levels(x)

weighted_table(x = x, y = y, weights = w)

# ---------------------------------------------------------------------------
# If there are levels in your factors that aren't actually used in the
# underlying data, then they will still show up in the table with a `0` value
x <- factor(c("x", "y", "x"), levels = c("x", "y", "z"))
y <- factor(c("a", "b", "a"), levels = c("a", "b", "c"))
w <- 1:3

weighted_table(x = x, y = y, weights = w)

# If you want to drop these empty factor levels from the result, you can
# rerun `factor()` ahead of time to drop them (or `forcats::fct_drop()`)
x <- factor(x)
y <- factor(y)
levels(x)

weighted_table(x = x, y = y, weights = w)
# }