Skip to contents

Summary Utility (su)

su.Rd

Computes a compact table of summary statistics for each variable in a vector, matrix, or data frame. The following metrics are returned per variable: number of observations (Obs), missing values (NAs), mean, standard deviation (StDev), interquartile range (IQR), minimum (Min), user-specified quantiles (probs), and maximum (Max).

Usage

su(
  x,
  mat.var.in.col = TRUE,
  digits = 4,
  probs = c(0.1, 0.25, 0.5, 0.75, 0.9),
  print = FALSE
)

Arguments

x

a numeric vector, matrix, or data frame. For matrices, variables are assumed to be in columns; set mat.var.in.col = FALSE to treat rows as variables.

mat.var.in.col

logical. If TRUE (default), a matrix is interpreted as variables in columns. If FALSE, the matrix is transposed so that rows are treated as variables.

digits

integer. Number of digits to use when printing (only affects printed output when print = TRUE). Default is 4.

probs

numeric vector of probabilities in \([0, 1]\) for which quantiles are computed. Default is c(0.1, 0.25, 0.5, 0.75, 0.9).

print

logical. If TRUE, prints the transposed summary table using the specified number of digits. Default is FALSE.

Value

A matrix (coercible to data.frame) where each row corresponds to a variable and columns contain the summary statistics: Obs, NAs, Mean, StDev, IQR, Min, the requested probs quantiles (named), and Max. The returned object is given class "snreg" for compatibility with package-specific print/summarization methods.

Details

Compact Summary Statistics for Vectors, Matrices, and Data Frames

Input handling:

  • If x is a matrix with a single row or column, it is treated like a vector. Column or row names are used (if available). Otherwise, a default name is created.

  • If x is a matrix with multiple variables, variables are taken as columns. Use mat.var.in.col = FALSE to transpose and treat rows as variables.

  • If x is a vector, its deparsed symbol name is used as the variable name.

  • If x is a data frame, each column is summarized.

Missing values are excluded in all summary computations.

Examples

if (FALSE) { # \dontrun{
  # Vector
  set.seed(1)
  v <- rnorm(100)
  su(v, print = TRUE)

  # Matrix: variables in columns
  M <- cbind(x = rnorm(50), y = runif(50))
  su(M)

  # Matrix: variables in rows
  Mr <- rbind(x = rnorm(50), y = runif(50))
  su(Mr, mat.var.in.col = FALSE)

  # Data frame
  DF <- data.frame(a = rnorm(30), b = rexp(30), c = rbinom(30, 1, 0.3))
  out <- su(DF)
  head(out)
} # }