For continuous variables, it is a t-test or ANOVA (depending on the number of levels of the group). Logical if set to TRUE then the appropriate parametric bivariate tests of significance are performed (if `test = TRUE`). A message is printed when the variances of the continuous variables being tested do not meet the assumption of Homogeneity of Variance (using Breusch-Pagan Test of Heteroskedasticity) and, therefore, the argument `var.equal = FALSE` is used in the test. Logical if set to TRUE then the appropriate bivariate tests of significance are performed if splitby has more than 1 level. How to calculate percentages for factor variables when splitby != NULL: if FALSE calculates percentages by variable within groups if TRUE calculates percentages across groups for one level of the factor variable. Whether a total (not stratified with the splitby or group_by()) should also be reported in the table secondĪ vector or list of quoted continuous variables for which the FUN2 should be applied row_wise The function to be applied to summarize the numeric data default is to report the means and standard deviations FUN2Ī secondary function to be applied to summarize the numeric data default is to report the medians and 25% and 75% quartiles total The categorical variable to stratify (in formula form splitby = ~gender) or quoted splitby = "gender" instead, dplyr::group_by(.) can be used within a pipe (this is the default when the data object is a grouped data frame from dplyr::group_by(.)). Finally, any empty rows (where the row is NA for each variable selected) will be removed for an accurate n count. As it is currently, it CANNOT handle both indices and unquoted names simultaneously. If indices, it needs to be a single vector (e.g. Variables in the data set that are to be summarized unquoted names separated by commas (e.g.
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