guideR (version 0.4.0)

proportion: Compute proportions

Description

proportion() lets you quickly count observations (like dplyr::count()) and compute relative proportions. Proportions are computed separately by group (see examples).

Usage

proportion(data, ...)
# S3 method for data.frame
proportion(
  data,
  ...,
  .by = NULL,
  .na.rm = FALSE,
  .weight = NULL,
  .scale = 100,
  .sort = FALSE,
  .drop = FALSE,
  .drop_na_by = FALSE,
  .conf.int = FALSE,
  .conf.level = 0.95,
  .options = list(correct = TRUE)
)
# S3 method for survey.design
proportion(
  data,
  ...,
  .by = NULL,
  .na.rm = FALSE,
  .scale = 100,
  .sort = FALSE,
  .drop_na_by = FALSE,
  .conf.int = FALSE,
  .conf.level = 0.95,
  .options = NULL
)
# S3 method for default
proportion(
  data,
  ...,
  .na.rm = FALSE,
  .scale = 100,
  .sort = FALSE,
  .drop = FALSE,
  .conf.int = FALSE,
  .conf.level = 0.95,
  .options = list(correct = TRUE)
)

Value

A tibble.

A tibble with one row per group.

Arguments

data

A vector, a data frame, data frame extension (e.g. a tibble), or a survey design object.

...

<data-masking> Variable(s) for those computing proportions.

.by

<tidy-select> Optional additional variables to group by (in addition to those eventually previously declared using dplyr::group_by()).

.na.rm

Should NA values be removed (from variables declared in ...)?

.weight

<data-masking> Frequency weights. Can be NULL or a variable.

.scale

A scaling factor applied to proportion. Use 1 for keeping proportions unchanged.

.sort

If TRUE, will show the highest proportions at the top.

.drop

If TRUE, will remove empty groups from the output.

.drop_na_by

If TRUE, will remove any NA values observed in the .by variables (or variables defined with dplyr::group_by()).

.conf.int

If TRUE, will estimate confidence intervals.

.conf.level

Confidence level for the returned confidence intervals.

.options

Additional arguments passed to stats::prop.test() or srvyr::survey_prop().

Examples

# using a vector
titanic$Class |> proportion()

# univariable table
titanic |> proportion(Class)
titanic |> proportion(Class, .sort = TRUE)
titanic |> proportion(Class, .conf.int = TRUE)
titanic |> proportion(Class, .conf.int = TRUE, .scale = 1)

# bivariable table
titanic |> proportion(Class, Survived) # proportions of the total
titanic |> proportion(Survived, .by = Class) # row proportions
titanic |> # equivalent syntax
  dplyr::group_by(Class) |>
  proportion(Survived)

# combining 3 variables or more
titanic |> proportion(Class, Sex, Survived)
titanic |> proportion(Sex, Survived, .by = Class)
titanic |> proportion(Survived, .by = c(Class, Sex))

# missing values
dna <- titanic
dna$Survived[c(1:20, 500:530)] <- NA
dna |> proportion(Survived)
dna |> proportion(Survived, .na.rm = TRUE)

# \donttest{
## SURVEY DATA ------------------------------------------------------

ds <- srvyr::as_survey(titanic)

# univariable table
ds |> proportion(Class)
ds |> proportion(Class, .sort = TRUE)
ds |> proportion(Class, .conf.int = TRUE)
ds |> proportion(Class, .conf.int = TRUE, .scale = 1)

# bivariable table
ds |> proportion(Class, Survived) # proportions of the total
ds |> proportion(Survived, .by = Class) # row proportions
ds |> dplyr::group_by(Class) |> proportion(Survived)

# combining 3 variables or more
ds |> proportion(Class, Sex, Survived)
ds |> proportion(Sex, Survived, .by = Class)
ds |> proportion(Survived, .by = c(Class, Sex))

# missing values
dsna <- srvyr::as_survey(dna)
dsna |> proportion(Survived)
dsna |> proportion(Survived, .na.rm = TRUE)
# }