Describe a distribution — describe_distribution • datawizard

This function describes a distribution by a set of indices (e.g., measures of centrality, dispersion, range, skewness, kurtosis).

Usage

describe_distribution(x, ...)

# S3 method for class '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 class 'factor'
describe_distribution(x, dispersion = TRUE, range = TRUE, verbose = TRUE, ...)

# S3 method for class '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,
  ...
)

Arguments

x

A numeric vector, a character vector, a data frame, or a list. See Details.

...

Additional arguments to be passed to or from methods.

centrality

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".

dispersion

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.

iqr

Logical, if TRUE, the interquartile range is calculated (based on stats::IQR(), using type = 6).

range

Return the range (min and max).

quartiles

Return the first and third quartiles (25th and 75pth percentiles).

ci

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).

iterations

The number of bootstrap replicates for computing confidence intervals. Only applies when ci is not NULL.

threshold

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.

verbose

Toggle warnings and messages.

select

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"), a character vector of variable names (e.g., c("col1", "col2", "col3")), or a character vector of variable names including ranges specified via : (e.g., c("col1:col3", "col5")),
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".

exclude

See select, however, column names matched by the pattern from exclude will be excluded instead of selected. If NULL (the default), excludes no columns.

include_factors

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.

ignore_case

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.

regex

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.

Value

A data frame with columns that describe the properties of the variables.

Details

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.

Note

There is also a plot()-method implemented in the see-package.

Examples

describe_distribution(rnorm(100))
#>  Mean |   SD |  IQR |         Range | Skewness | Kurtosis |   n | n_Missing
#> ---------------------------------------------------------------------------
#> -0.03 | 1.09 | 1.69 | [-3.31, 2.71] |    -0.19 |     0.11 | 100 |         0

data(iris)
describe_distribution(iris)
#> Variable     | Mean |   SD |  IQR |        Range | Skewness | Kurtosis |   n | n_Missing
#> ----------------------------------------------------------------------------------------
#> Sepal.Length | 5.84 | 0.83 | 1.30 | [4.30, 7.90] |     0.31 |    -0.55 | 150 |         0
#> Sepal.Width  | 3.06 | 0.44 | 0.52 | [2.00, 4.40] |     0.32 |     0.23 | 150 |         0
#> Petal.Length | 3.76 | 1.77 | 3.52 | [1.00, 6.90] |    -0.27 |    -1.40 | 150 |         0
#> Petal.Width  | 1.20 | 0.76 | 1.50 | [0.10, 2.50] |    -0.10 |    -1.34 | 150 |         0
describe_distribution(iris, include_factors = TRUE, quartiles = TRUE)
#> Variable     | Mean |   SD |  IQR |               Range |  Quartiles | Skewness
#> -------------------------------------------------------------------------------
#> Sepal.Length | 5.84 | 0.83 | 1.30 |          [4.3, 7.9] | 5.10, 6.40 |     0.31
#> Sepal.Width  | 3.06 | 0.44 | 0.52 |            [2, 4.4] | 2.80, 3.30 |     0.32
#> Petal.Length | 3.76 | 1.77 | 3.52 |            [1, 6.9] | 1.60, 5.10 |    -0.27
#> Petal.Width  | 1.20 | 0.76 | 1.50 |          [0.1, 2.5] | 0.30, 1.80 |    -0.10
#> Species      |      |      |      | [setosa, virginica] |            |     0.00
#> 
#> Variable     | Kurtosis |   n | n_Missing
#> -----------------------------------------
#> Sepal.Length |    -0.55 | 150 |         0
#> Sepal.Width  |     0.23 | 150 |         0
#> Petal.Length |    -1.40 | 150 |         0
#> Petal.Width  |    -1.34 | 150 |         0
#> Species      |    -1.51 | 150 |         0
describe_distribution(list(mtcars$mpg, mtcars$cyl))
#> Variable   |  Mean |   SD |  IQR |          Range | Skewness | Kurtosis |  n | n_Missing
#> ----------------------------------------------------------------------------------------
#> mtcars$mpg | 20.09 | 6.03 | 7.53 | [10.40, 33.90] |     0.67 |    -0.02 | 32 |         0
#> mtcars$cyl |  6.19 | 1.79 | 4.00 |   [4.00, 8.00] |    -0.19 |    -1.76 | 32 |         0