Pairwise group comparisons are often performed. While there are many packages that can perform these analyses, often it is the case that only a subset of comparisons are desired. 'SimplifyStats' performs pairwise comparisons and returns the results in a tidy fashion.

In many analyses, pairwise group comparisons or groupwise descriptive statistics are produced for numerous variables. 'SimplifyStats' is an R package consisting of a set of functions that simplify this process.

The function **group_summarize** accepts a data frame as input and uses the names of user-specified columns of grouping variables to partition the data. For each unique combination of interactions between the grouping variables, univariate descriptive statistics are computed for another set of user-specified columns of numeric variables.

The specific statistics computed are:

- Sample size (N)
- Mean
- Standard deviation (StdDev)
- Standard error (StdErr)
- Minimum value (Min)
- First quartile value (Quartile1)
- Median
- Third quartile value (Quartile3)
- Maximum value (Max)
- Proportion of missing values (PropNA)
- Kurtosis
- Skewness
- Jarque-Bera test P value (Jarque-Bera_p.value)
- Shapiro-Wilk test P value (Shapiro-Wilk_p.value)

These values are returned in an object of class *group_summary*, which holds the results and the input parameters (excluding the input data frame). The results are stored in a list of data frames where each element of the list is named according to the variable for which statistics were computed. Additional parameters, i.e. na.rm = TRUE, can be passed to *group_summarize*.

Like *group_summary*, the function **pairwise_stats** accepts as input a data frame and the names of user-specified columns of grouping variables. Unlike *group_summary*, *pairwise_stats* can accept only one numeric variable for analysis. Using a user-specified function, which must accept as both its first and second argument a vector of values corresponding to each group (i.e. t.test, wilcox.test, ks.test, or a custom function f(a,b)), every combination of group comparisons are made. In some cases the order in which these vectors are passed to the function matters, i.e. when settting *alternative = "greater"* in *t.test*. To account for this possiblity *two_way = TRUE* can be passed to *group_summarize*. This will test all possible pairs of unique grouping variable interactions in forward and reverse order. With this function, all two sample hypothesis tests can be quickly computed.

- This is the first release of the package.
- Updated examples.