Github user shivaram commented on a diff in the pull request:
https://github.com/apache/spark/pull/9613#discussion_r45793856
--- Diff: R/pkg/R/DataFrame.R ---
@@ -2199,3 +2199,97 @@ setMethod("coltypes",
rTypes
})
+
+#' Display the structure of a DataFrame, including column names, column
types, as well as a
+#' a small sample of rows.
+#' @name str
+#' @title Compactly display the structure of a dataset
+#' @rdname str
+#' @family DataFrame functions
+#' @param object a DataFrame
+#' @examples \dontrun{
+#' # Create a DataFrame from the Iris dataset
+#' irisDF <- createDataFrame(sqlContext, iris)
+#'
+#' # Show the structure of the DataFrame
+#' str(irisDF)
+#' }
+setMethod("str",
+ signature(object = "DataFrame"),
+ function(object) {
+
+ # TODO: These could be made global parameters, though in R
it's not the case
+ MAX_CHAR_PER_ROW <- 120
+ MAX_COLS <- 100
+
+ # Get the column names and types of the DataFrame
+ names <- names(object)
+ types <- coltypes(object)
+
+ # Get the number of rows.
+ # TODO: Ideally, this should be cached
+ cachedCount <- nrow(object)
+
+ # Get the first elements of the dataset. Limit number of
columns accordingly
+ dataFrame <- if (ncol(object) > MAX_COLS) {
+ head(object[, c(1:MAX_COLS)])
+ } else {
+ head(object)
+ }
+
+ # The number of observations will be displayed only if the
number
+ # of rows of the dataset has already been cached.
+ if (!is.null(cachedCount)) {
--- End diff --
Can we add this logic at that point then ? It seems to be unnecessarily
complicating the code here.
cc @rxin @davies Does the Scala layer cache the number of rows somewhere
after a query evaluation ? In general it looks like it'll be good to know if an
operation will be expensive or cheap at some high level.
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