Simplifies Pairwise Statistical Analyses.

SimplifyStats

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.

Functions by category

Groupwise descriptive statistics

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.

Pairwise hypothesis testing

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.