Create print.one_skim_df, document reassign_skim_attrs and modify rec…

…oncile_skimrs in response to changes in 4.2 and R CMD CHK issues.

NAMESPACE

@@ -21,6 +21,7 @@ S3method(knit_print,skim_df)
 S3method(knit_print,skim_list)
 S3method(knit_print,summary_skim_df)
 S3method(mutate,skim_df)
+S3method(print,one_skim_df)
 S3method(print,skim_df)
 S3method(print,skim_list)
 S3method(print,summary_skim_df)

R/reshape.R

@@ -79,7 +79,7 @@ reconcile_skimmers <- function(data, groups, base) {
   ))
   matched_cols <- dplyr::intersect(all_columns, with_base_columns)
   extra_cols <- dplyr::setdiff(all_columns, with_base_columns)
-  if (length(extra_cols) > 0) {
+  if (length(extra_cols) > 0 && "skim_type" %in% names(data)) {
     grouped <- dplyr::group_by(data, .data$skim_type)
     complete_by_type <- dplyr::summarize_at(
       grouped,

R/skim_obj.R

@@ -257,9 +257,12 @@ strip_skim_attrs <- function(object) {
   attributes(object) <- stripped
   object
 }
-
+#' @param object a skim object
+#' @param skim_df name of new skim object
+#' @param ... additional options
+#' 
 #' Pass attributes from a `skimr` object to a new object.
-#' @noRd
+#' @keywords internal
 reassign_skim_attrs <- function(object, skim_df, ...) {
   defaults <- list(
     class = c("skim_df", "tbl_df", "tbl", "data.frame"),

R/skim_print.R

@@ -61,6 +61,27 @@ print.skim_df <- function(x,
   }
 }
 
+#' @export
+print.one_skim_df <- function(x,
+                          include_summary = TRUE,
+                          n = Inf,
+                          width = Inf,
+                          summary_rule_width = getOption(
+                            "skimr_summary_rule_width",
+                            default = 40
+                          ),
+                          ...) {
+  if (is_skim_df(x) && nrow(x) > 0) {
+    if (include_summary) {
+      print(summary(x), .summary_rule_width = summary_rule_width, ...)
+    }
+
+    print(x, width = width, n = n, ...)
+  } else {
+    NextMethod("print")
+  }
+}
+
 
 # Methods for correctly formatting a a `one_skim_df`. We leverage the
 # customiztion options in `pillar` for this. It divides the results into: a