collapse provides the following functions to efficiently summarize and examine data:

• qsu, shorthand for quick-summary, is an extremely fast summary command inspired by the (xt)summarize command in the STATA statistical software. It computes a set of 7 statistics (nobs, mean, sd, min, max, skewness and kurtosis) using a numerically stable one-pass method. Statistics can be computed weighted, by groups, and also within-and between entities (for multilevel / panel data).

• qtab, shorthand for quick-table, is a faster and more versatile alternative to table. Notably, it also supports tabulations with frequency weights, as well as computing a statistic over combinations of variables. 'qtab's inherit the 'table' class, allowing for seamless application of 'table' methods.

• descr computes a concise and detailed description of a data frame, including (sorted) frequency tables for categorical variables and various statistics and quantiles for numeric variables. It is inspired by Hmisc::describe, but about 10x faster.

• pwcor, pwcov and pwnobs compute (weighted) pairwise correlations, covariances and observation counts on matrices and data frames. Pairwise correlations and covariances can be computed together with observation counts and p-values. The elaborate print method displays all of these statistics in a single correlation table.

• varying very efficiently checks for the presence of any variation in data (optionally) within groups (such as panel-identifiers). A variable is variant if it has at least 2 distinct non-missing data points.

## Table of Functions

 Function / S3 Generic Methods Description qsu default, matrix, data.frame, grouped_df, pseries, pdata.frame, sf Fast (grouped, weighted, panel-decomposed) summary statistics qtab No methods, for data frames or vectors Fast (weighted) cross tabulation descr default, grouped_df (default method handles most objects) Detailed statistical description of data frame pwcor No methods, for matrices or data frames Pairwise (weighted) correlations pwcov No methods, for matrices or data frames Pairwise (weighted) covariances pwnobs No methods, for matrices or data frames Pairwise observation counts varying default, matrix, data.frame, pseries, pdata.frame, grouped_df Fast variation check