misty (version 0.7.0)

descript: Descriptive Statistics

Description

This function computes summary statistics for one or more than one variable, optionally by a grouping and/or split variable.

Usage

descript(..., data = NULL,
        print = c("all", "default", "n", "nNA", "pNA", "m", "se.m", "var", "sd",
                  "min", "p25", "med", "p75", "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)

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

Arguments

...

a numeric vector, matrix or data frame with numeric variables, i.e., factors and character variables are excluded from ... before conducting the analysis. Alternatively, an expression indicating the variable names in data e.g., descript(x1, x2, x3, data = dat). Note that the operators ., +, -, ~, :, ::, and ! can also be used to select variables, see 'Details' in the df.subset function.

data

a data frame when specifying one or more variables in the argument .... Note that the argument is NULL when specifying a numeric vector, matrix, or data frame for the argument ....

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), m (arithmetic mean), se.m (standard error of the arithmetic mean), var (variance), sd (standard deviation), med (median),min (minimum), p25 (25th percentile, first quartile), p75 (75th percentile, third quartile), max (maximum), range (range), iqr (interquartile range), skew (skewness), and kurt (excess kurtosis). The default setting is print = ("n", "nNA", "pNA", "m", "sd", "min", "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 ..., 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.

Author

Takuya Yanagida

References

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

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(mpg, cyl, hp, data = mtcars)

# Alternative specification using the 'data' argument
descript(mtcars[, c("mpg", "cyl", "hp")])

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

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

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

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

# Alternative specification
descript(mtcars[, c("mpg", "cyl", "hp")], group = mtcars$vs)

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

# Alternative specification
descript(mtcars[, c("mpg", "cyl", "hp")], split = mtcars$am)

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

# Alternative specification
descript(mtcars[, c("mpg", "cyl", "hp")], group = mtcars$vs, split = mtcars$am)

if (FALSE) {
#----------------------------------------------------------------------------
# Write Output

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

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