This function describes a distribution by a set of indices (e.g., measures of centrality, dispersion, range, skewness, kurtosis).
describe_distribution(x, ...)# S3 method for numeric
describe_distribution(
x,
centrality = "mean",
dispersion = TRUE,
iqr = TRUE,
range = TRUE,
quartiles = FALSE,
ci = NULL,
iterations = 100,
threshold = 0.1,
verbose = TRUE,
...
)
# S3 method for factor
describe_distribution(x, dispersion = TRUE, range = TRUE, verbose = TRUE, ...)
# S3 method for data.frame
describe_distribution(
x,
select = NULL,
exclude = NULL,
centrality = "mean",
dispersion = TRUE,
iqr = TRUE,
range = TRUE,
quartiles = FALSE,
include_factors = FALSE,
ci = NULL,
iterations = 100,
threshold = 0.1,
ignore_case = FALSE,
regex = FALSE,
verbose = TRUE,
...
)
A data frame with columns that describe the properties of the variables.
A numeric vector, a character vector, a data frame, or a list. See
Details.
Additional arguments to be passed to or from methods.
The point-estimates (centrality indices) to compute. Character
(vector) or list with one or more of these options: "median", "mean", "MAP"
(see map_estimate()), "trimmed" (which is just mean(x, trim = threshold)),
"mode" or "all".
Logical, if TRUE, computes indices of dispersion related
to the estimate(s) (SD and MAD for mean and median, respectively).
Dispersion is not available for "MAP" or "mode" centrality indices.
Logical, if TRUE, the interquartile range is calculated
(based on stats::IQR(), using type = 6).
Return the range (min and max).
Return the first and third quartiles (25th and 75pth percentiles).
Confidence Interval (CI) level. Default is NULL, i.e. no
confidence intervals are computed. If not NULL, confidence intervals
are based on bootstrap replicates (see iterations). If
centrality = "all", the bootstrapped confidence interval refers to
the first centrality index (which is typically the median).
The number of bootstrap replicates for computing confidence
intervals. Only applies when ci is not NULL.
For centrality = "trimmed" (i.e. trimmed mean), indicates
the fraction (0 to 0.5) of observations to be trimmed from each end of the
vector before the mean is computed.
Toggle warnings and messages.
Variables that will be included when performing the required tasks. Can be either
a variable specified as a literal variable name (e.g., column_name),
a string with the variable name (e.g., "column_name"), or a character
vector of variable names (e.g., c("col1", "col2", "col3")),
a formula with variable names (e.g., ~column_1 + column_2),
a vector of positive integers, giving the positions counting from the left
(e.g. 1 or c(1, 3, 5)),
a vector of negative integers, giving the positions counting from the
right (e.g., -1 or -1:-3),
one of the following select-helpers: starts_with(), ends_with(),
contains(), a range using : or regex(""). starts_with(),
ends_with(), and contains() accept several patterns, e.g
starts_with("Sep", "Petal").
or a function testing for logical conditions, e.g. is.numeric() (or
is.numeric), or any user-defined function that selects the variables
for which the function returns TRUE (like: foo <- function(x) mean(x) > 3),
ranges specified via literal variable names, select-helpers (except
regex()) and (user-defined) functions can be negated, i.e. return
non-matching elements, when prefixed with a -, e.g. -ends_with(""),
-is.numeric or -(Sepal.Width:Petal.Length). Note: Negation means
that matches are excluded, and thus, the exclude argument can be
used alternatively. For instance, select=-ends_with("Length") (with
-) is equivalent to exclude=ends_with("Length") (no -). In case
negation should not work as expected, use the exclude argument instead.
If NULL, selects all columns. Patterns that found no matches are silently
ignored, e.g. extract_column_names(iris, select = c("Species", "Test"))
will just return "Species".
See select, however, column names matched by the pattern
from exclude will be excluded instead of selected. If NULL (the default),
excludes no columns.
Logical, if TRUE, factors are included in the
output, however, only columns for range (first and last factor levels) as
well as n and missing will contain information.
Logical, if TRUE and when one of the select-helpers or
a regular expression is used in select, ignores lower/upper case in the
search pattern when matching against variable names.
Logical, if TRUE, the search pattern from select will be
treated as regular expression. When regex = TRUE, select must be a
character string (or a variable containing a character string) and is not
allowed to be one of the supported select-helpers or a character vector
of length > 1. regex = TRUE is comparable to using one of the two
select-helpers, select = contains("") or select = regex(""), however,
since the select-helpers may not work when called from inside other
functions (see 'Details'), this argument may be used as workaround.
If x is a data frame, only numeric variables are kept and will be
displayed in the summary.
If x is a list, the behavior is different whether x is a stored list. If
x is stored (for example, describe_distribution(mylist) where mylist
was created before), artificial variable names are used in the summary
(Var_1, Var_2, etc.). If x is an unstored list (for example,
describe_distribution(list(mtcars$mpg))), then "mtcars$mpg" is used as
variable name.
if (FALSE) { # require("bayestestR", quietly = TRUE)
describe_distribution(rnorm(100))
data(iris)
describe_distribution(iris)
describe_distribution(iris, include_factors = TRUE, quartiles = TRUE)
describe_distribution(list(mtcars$mpg, mtcars$cyl))
}
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