Github user sun-rui commented on a diff in the pull request:
https://github.com/apache/spark/pull/9654#discussion_r45290804
--- Diff: R/pkg/R/DataFrame.R ---
@@ -293,6 +294,121 @@ setMethod("names<-",
}
})
+#' @rdname columns
+#' @name colnames
+setMethod("colnames",
+ signature(x = "DataFrame"),
+ function(x, do.NULL = TRUE, prefix = "col") {
+ columns(x)
+ })
+
+#' @rdname columns
+#' @name colnames<-
+setMethod("colnames<-",
+ signature(x = "DataFrame", value = "character"),
+ function(x, value) {
+ sdf <- callJMethod(x@sdf, "toDF", as.list(value))
+ dataFrame(sdf)
+ })
+
+#' coltypes
+#'
+#' Get column types of a DataFrame
+#'
+#' @name coltypes
+#' @param x (DataFrame)
+#' @return value (character) A character vector with the column types of
the given DataFrame
+#' @rdname coltypes
+#' @family DataFrame functions
+#' @export
+#' @examples
+#'\dontrun{
+#' irisDF <- createDataFrame(sqlContext, iris)
+#' coltypes(irisDF)
+#'}
+setMethod("coltypes",
+ signature(x = "DataFrame"),
+ function(x) {
+ # Get the data types of the DataFrame by invoking dtypes()
function
+ types <- sapply(dtypes(x), function(x) {x[[2]]})
+
+ # Map Spark data types into R's data types using DATA_TYPES
environment
+ rTypes <- sapply(types, USE.NAMES=F, FUN=function(x) {
+ # Check for primitive types
+ type <- PRIMITIVE_TYPES[[x]]
+
+ if (is.null(type)) {
+ # Check for complex types
+ for (t in names(COMPLEX_TYPES)) {
+ if (substring(x, 1, nchar(t)) == t) {
+ type <- COMPLEX_TYPES[[t]]
+ break
+ }
+ }
+
+ if (is.null(type)) {
+ stop(paste("Unsupported data type: ", x))
+ }
+ }
+ type
+ })
+
+ # Find which types don't have mapping to R
+ naIndices <- which(is.na(rTypes))
+
+ # Assign the original scala data types to the unmatched ones
+ rTypes[naIndices] <- types[naIndices]
+
+ rTypes
+ })
+
+#' coltypes
+#'
+#' Set the column types of a DataFrame.
+#'
+#' @name coltypes<-
--- End diff --
I mean make the style consistent with others. for example,
@param x (DataFrame) -> @param x A DataFrame.
---
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