Github user olarayej commented on a diff in the pull request:
https://github.com/apache/spark/pull/9613#discussion_r45537532
--- 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)) {
+ cat(paste0("'", class(object), "': ", cachedCount, " obs. of
",
+ length(names), " variables:\n"))
+ } else {
+ cat(paste0("'", class(object), "': ", length(names), "
variables:\n"))
+ }
+
+ # Whether the ... should be printed at the end of each row
+ ellipsis <- FALSE
+
+ # Add ellipsis (i.e., "...") if there are more rows than shown
+ if (!is.null(cachedCount) && (cachedCount > 6)) {
+ ellipsis <- TRUE
+ }
+
+ if (nrow(dataFrame) > 0) {
--- End diff --
Good point :-). I thought about that before but I realized there are three
issues:
1) Header is different (DataFrame vs data.frame)
2) Number of rows would not match, and in some cases we don't wanna show it.
3) We're still not clear in the mapping between column types of DataFrame
and data.frame
I added a comment on JIRA SPARK-10863 (see link below). If we implemented
corresponding data types in R, we could leverage some part of utils:::str() in
SparkR:::str().
https://issues.apache.org/jira/browse/SPARK-10863
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