guideR (version 0.1.0)

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, ..., .by = NULL)
# S3 method for data.frame
proportion(
  data,
  ...,
  .by = NULL,
  .na.rm = FALSE,
  .weight = NULL,
  .scale = 100,
  .sort = FALSE,
  .drop = 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,
  .conf.int = FALSE,
  .conf.level = 0.95,
  .options = NULL
)

Value

A tibble.

A tibble with one row per group.

Arguments

data

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.

.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

# 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)
# }