Descriptive statistics consist on presenting the distribution of series for a sample in tables (frequency table for one series, contingency tables for two series), ploting this distribution and computing some statistics that summarise it. descstat provides a complete toolbox to perform this tasks. It has been writen using some packages of the tidyverse (especially dplyr, tidyr and purrr) and its usage follow the tidyverse conventions, especially the selection of series using their unquoted names and the use of the pipe operator and of tibbles.

The bin class:

In a frequency (or contingency table), continuous numerical series are presented as bins. Moreover, for some surveys, the individual values are not known, but only the fact that these values belongs to a bin. Therefore, it is crucial to be able to work easily with bins, ie:

• creating bins from numerical values, which is performed by the base::cut function which turns a numerical series to a bin,
• coercing bins to numerical values, eg getting from the [10,20) bin the lower bound (10), the upper bound (20), the center (15) or whatever other value of the bin,
• reducing the number of bins by merging some of them (for example [0,10), [10, 20), [20,30), [30,Inf) to [0,20), [20,Inf)

these latter two tasks are performed using the new bin class provided by this package and the accompanying as_numeric function for the coercion to numeric and the cut method for bins merging. Especially, coercing bins to their center values is the basis of the computation of descripting statistics for bins.

Frequency and contingency tables:

The freq_table and cont_table are based on the dplyr::count function but offer a much richer interface and performs easily usual operations which are tedious to obtain with dplyr::count or base::table functions. This includes:

• adding a total,
• for frequency tables, computing other kind of frequencies than the counts, for example relative frequencies, percentage, cummulative frequencies, etc.,
• for contingency tables, computing easily the joint, marginal and conditional distributions,
• printing easily the contingency table as a double entry table.

Plotting the distribution:

A pre_plot function is provided to put the tibble in form in order to use classic plots for univariate or bivariate distributions. This includes histogram, frequency plot, pie chart, cummulative plot and Lorenz curve. The final plot can then be obtained using some geoms of ggplot2.

Descriptive statistics:

A full set of statistical functions (of central tendency, dispersion, shape, concentration and covariation) are provided and can be applied directly on objects of class freq_table or cont_table. Some of them are methods of generics defined by the base or stats package, some other are defined as methods for generics function provided by the descstat function when the corresponding R function is not generic. For example,

• mean is generic, so that we wrote a mean.freq_table method to compute directly the mean of a series from a frequency table.

• var is not generic, so that we provide the variance generic and a method for freq_table objects.