docs: update roxygen

- Add comments to examples
- Explicit namespacing for `stats::filter()` and `dplyr::filter()` - to avoid conflict warning messages upon package load
- Make explicit the method used

R/p_test.R

@@ -16,9 +16,13 @@
 #' @param paired Specify whether the dataset is paired or not. Defaults to TRUE.
 #'
 #' @import dplyr
-#' @import stats
+#'
+#' @details
+#' This function is a wrapper around `wilcox.test()` from {stats}.
 #'
 #' @examples
+#' # Simulate a binary variable X
+#' # Returns a single p-value
 #' sq_data %>%
 #'   mutate(X = ifelse(Email_hours > 6, 1, 0)) %>%
 #'   p_test(outcome = "X", behavior = "External_network_size")
@@ -40,5 +44,6 @@ p_test <- function(data,
   neg <- train %>% filter(outcome == 0, na.rm=TRUE) %>% select(behavior)
 
   s <- stats::wilcox.test(unlist(pos), unlist(neg), paired = paired)
+
   return(s$p.value)
 }

man/p_test.Rd

@@ -4,7 +4,7 @@
 \alias{p_test}
 \title{Calculate the p-value of the null hypothesis that two outcomes are from the same dataset}
 \usage{
-p_test(data, outcome, behavior)
+p_test(data, outcome, behavior, paired = FALSE)
 }
 \arguments{
 \item{data}{A Person Query dataset in the form of a data frame.}
@@ -13,8 +13,20 @@ p_test(data, outcome, behavior)
 the values 1 or 0. Used to group the two distributions.}
 
 \item{behavior}{A character vector specifying the column to be used as the behavior to test.}
+
+\item{paired}{Specify whether the dataset is paired or not. Defaults to TRUE.}
 }
 \description{
 Specify an outcome variable and return p-test outputs.
 All numeric variables in the dataset are used as predictor variables.
 }
+\details{
+This function is a wrapper around \code{wilcox.test()} from {stats}.
+}
+\examples{
+# Simulate a binary variable X
+# Returns a single p-value
+sq_data \%>\%
+  mutate(X = ifelse(Email_hours > 6, 1, 0)) \%>\%
+  p_test(outcome = "X", behavior = "External_network_size")
+}