descript: Descriptive Statistics

In misty: Miscellaneous Functions 'T. Yanagida'

Descriptive Statistics

Description

This function computes summary statistics for one or more than one variable, optionally by a grouping and/or split variable. By default, the function prints the number of observations (n), number of missing values (nNA), percentage of missing values (%NA), number of unique elements after omitting missing values (nUQ), arithmetic mean (M), standard deviation (SD), minimum (Min), percentage of observations at the minimum (%Min), maximum (Max), percentage of observations at the maximum (%Max), skewness (Skew), and kurtosis (Kurt).

Usage

descript(data, ...,
         print = c("all", "default", "n", "nNA", "pNA", "nUQ", "m", "se.m",
                   "var", "sd", "min", "p.min", "p25", "med", "p75", "max", "p.max",
                   "range", "iqr", "skew", "kurt"),
         group = NULL, split = NULL, sample = FALSE, sort.var = FALSE,
         na.omit = FALSE, digits = 2, as.na = NULL, write = NULL, append = TRUE,
         check = TRUE, output = TRUE)

Arguments

data	a numeric vector or data frame with numeric variables, i.e., factors and character variables are excluded from data before conducting the analysis.
...	an expression indicating the variable names in data, e.g., descript(dat, x1, x2, x3). Note that the operators +, -, ~, :, ::, and ! can also be used to select variables, see 'Details' in the df.subset function.
print	a character vector indicating which statistical measures to be printed on the console, i.e., n (number of observations), nNA (number of missing values), pNA (percentage of missing values), nUQ (number of unique elements after omitting missing values), m (arithmetic mean), se.m (standard error of the arithmetic mean), var (variance), sd (standard deviation), med (median),min (minimum), p.min (percentage of observations at the minimum), p25 (25th percentile, first quartile), p75 (75th percentile, third quartile), max (maximum), p.max (percentage of observations at the maximum)range (range), iqr (interquartile range), skew (skewness), and kurt (excess kurtosis). The default setting is print = c("n", "nNA", "pNA", "nUQ", "m", "sd", "min", "pmin", "max", "p.max", "skew", "kurt").
group	a numeric vector, character vector or factor as grouping variable. Alternatively, a character string indicating the variable name of the grouping variable in data can be specified.
split	a numeric vector, character vector or factor as split variable. Alternatively, a character string indicating the variable name of the split variable in data can be specified.
sample	logical: if TRUE (default), the univariate sample skewness or kurtosis is computed, while the population skewness or kurtosis is computed when sample = FALSE.
sort.var	logical: if TRUE, output table is sorted by variables when specifying group.
na.omit	logical: if TRUE, incomplete cases are removed before conducting the analysis (i.e., listwise deletion).
digits	an integer value indicating the number of decimal places to be used.
as.na	a numeric vector indicating user-defined missing values, i.e. these values are converted to NA before conducting the analysis. Note that as.na() function is only applied to data, but not to group or split.
write	a character string naming a file for writing the output into either a text file with file extension ".txt" (e.g., "Output.txt") or Excel file with file extension ".xlsx" (e.g., "Output.xlsx"). If the file name does not contain any file extension, an Excel file will be written.
append	logical: if TRUE (default), output will be appended to an existing text file with extension .txt specified in write, if FALSE existing text file will be overwritten.
check	logical: if TRUE (default), argument specification is checked.
output	logical: if TRUE (default), output is shown on the console.

Details

Floor and Ceiling Effects

This function computes the percentage of observations at both the minimum and maximum to evaluate floor and ceiling effects in continuous variables. Historically, floor or ceiling effects are considered to be present if more than 15% of observations are at the lowest or highest possible score (McHorney & Tarlov, 1995; Terwee et al., 2007). Muthen (2023, see video at 7:58) noted the rule of thumb that linear models should be avoided when the floor or ceiling effect of the outcome variable exceeds 25%.

Value

Returns an object of class misty.object, which is a list with following entries:

call	function call
type	type of analysis
data	list with the input specified in data, group, and split
args	specification of function arguments
result	result table

Author(s)

Takuya Yanagida [e-mail]

References

McHorney, C. A., & Tarlov, A. R. (1995). Individual-patient monitoring in clinical practice: are available health status surveys adequate?. Quality of Life Research, 4(4), 293-307. https://doi.org/10.1007/BF01593882

Muthen, B. (2023, Feb. 28). Mplus Web Talk No. 6 - Using Mplus To Do Dynamic Structural Equation Modeling: Segment 3, Descriptive Analyses [Video]. YouTube. https://www.statmodel.com/Webtalk6.shtml

Rasch, D., Kubinger, K. D., & Yanagida, T. (2011). Statistics in psychology - Using R and SPSS. John Wiley & Sons.

Terwee, C. B., Bot, S. D., de Boer, M. R., van der Windt, D. A., Knol, D. L., Dekker, J., Bouter, L. M., & de Vet, H. C. (2007). Quality criteria were proposed for measurement properties of health status questionnaires. Journal of Clinical Epidemiology, 60(1), 34-42. https://doi.org/10.1016/j.jclinepi.2006.03.012

See Also

ci.mean, ci.mean.diff, ci.median, ci.prop, ci.prop.diff, ci.var, ci.sd, freq, crosstab, multilevel.descript, na.descript.

Examples

#----------------------------------------------------------------------------
# Descriptive statistics

# Example 1a: Descriptive statistics for 'mpg', 'cyl', and 'hp'
descript(mtcars, mpg, cyl, hp)

# Alternative specification without using the '...' argument
descript(mtcars[, c("mpg", "cyl", "hp")])

# Example 1b: Print all available statistical measures
descript(mtcars, mpg, cyl, hp, print = "all")

# Example 1c: Print default statistical measures plus median
descript(mtcars, mpg, cyl, hp, print = c("default", "med"))

#----------------------------------------------------------------------------
# Grouping and Split Variable

# Example 2a: Grouping variable
descript(mtcars, mpg, cyl, hp, group = "vs")

# Alternative specification without using the '...' argument
descript(mtcars[, c("mpg", "cyl", "hp")], group = mtcars$vs)

# Another alternative specification without using the '...' argument
descript(mtcars[, c("mpg", "cyl", "hp", "vs")], group = "vs")

# Example 2b: Split variable
descript(mtcars, mpg, cyl, hp, split = "am")

# Alternative specification without using the '...' argument
descript(mtcars[, c("mpg", "cyl", "hp")], split = mtcars$am)

# Another alternative specification without using the '...' argument
descript(mtcars[, c("mpg", "cyl", "hp", "am")], split = "am")

# Example 2c: Grouping and split variable
descript(mtcars, mpg, cyl, hp, group = "vs", split = "am")

# Alternative specification without using the '...' argument
descript(mtcars[, c("mpg", "cyl", "hp")], group = mtcars$vs, split = mtcars$am)

# Another alternative specification without using the '...' argument
descript(mtcars[, c("mpg", "cyl", "hp", "vs", "am")], group = "vs", split = "am")

## Not run: 
#----------------------------------------------------------------------------
# Write Output

# Example 3a: Text file
descript(mtcars, write = "Descript_Text.txt")

# Example 3b: Excel file
descript(mtcars, write = "Descript_Excel.xlsx")

## End(Not run)

misty documentation built on Sept. 9, 2025, 5:23 p.m.