Classical or Robust Descriptive Statistics

This function calculates various descriptive statistics (robust and non-robust) for a specified variable or all variables in a given data frame or tibble.

Usage

summary_stats(x, var = NULL, digits = 2, robust = FALSE, drop.na = TRUE)

Arguments

x

A data frame or tibble.

var

A character vector of names (or a numeric vector of column positions) specifying the variable(s) for which to calculate the summary statistics. If left as NULL (the default), summary statistics will be calculated for all numeric variables in the data frame/tibble.

digits

An integer specifying the number of significant digits to display after the decimal point in the output.

robust

A logical value indicating whether to compute robust descriptive statistics. If FALSE (the default), computes the classical descriptive statistics for describing the distribution of a univariate variable.

drop.na

A logical value indicating whether to remove missing values (NA) from the calculations. If TRUE (the default), missing values will be removed. If FALSE, the statistics of variables containing missing values are NA.

Value

A tibble with one row per variable. The classical statistics are the mean, mode, median, IQR, standard deviation, variance, coefficient of variation (cv, in \ the median, MAD (scaled to be consistent at the normal distribution), Qn and Sn estimators, medcouple, left/right medcouples, biweight location, scale and midvariance, robust coefficient of variation (rcv = MAD / median, in \ Robust statistics that cannot be computed (e.g. for a constant variable) are NA.

Author

Christian L. Goueguel

Examples

data(forageLIBS)
# mineral contents (%) of the forage samples
summary_stats(forageLIBS[c("Ca", "Mg", "P", "K", "S")])
#> # A tibble: 5 × 14
#>   variable  mean  mode median   IQR    sd variance    cv    min   max range
#>   <chr>    <dbl> <dbl>  <dbl> <dbl> <dbl>    <dbl> <dbl>  <dbl> <dbl> <dbl>
#> 1 Ca        0.65  0.5    0.63  0.26  0.18     0.03  27.8 0.173  1.45  1.28 
#> 2 Mg        0.21  0.14   0.2   0.07  0.05     0     25.3 0.0527 0.365 0.312
#> 3 P         0.26  0.21   0.26  0.08  0.06     0     24.3 0.0751 0.522 0.447
#> 4 K         2.05  2.07   2.01  0.66  0.53     0.29  26.0 0.497  4.04  3.54 
#> 5 S         0.2   0.19   0.2   0.06  0.04     0     21.6 0.12   0.39  0.27 
#> # ℹ 3 more variables: skewness <dbl>, kurtosis <dbl>, count <int>
summary_stats(forageLIBS, var = c("Ca", "K"), robust = TRUE)
#> # A tibble: 2 × 13
#>   variable median   mad    Qn    Sn medcouple   LMC   RMC biloc biscale bivar
#>   <chr>     <dbl> <dbl> <dbl> <dbl>     <dbl> <dbl> <dbl> <dbl>   <dbl> <dbl>
#> 1 Ca         0.63  0.18  0.18  0.18      0.14  0.08  0.18  0.64    0.18  0.03
#> 2 K          2.01  0.51  0.51  0.49      0.06  0.29  0.17  2.02    0.53  0.28
#> # ℹ 2 more variables: rcv <dbl>, count <int>