Github user sun-rui commented on a diff in the pull request:

    https://github.com/apache/spark/pull/9218#discussion_r43717935
  
    --- Diff: R/pkg/R/DataFrame.R ---
    @@ -276,6 +276,75 @@ setMethod("names<-",
                 }
               })
     
    +#' @rdname columns
    +#' @name colnames
    +setMethod("colnames",
    +          signature(x = "DataFrame"),
    +          function(x) {
    +            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)
    +          })
    +
    +rToScalaTypes <- new.env()
    +rToScalaTypes[["integer"]]   <- "integer" # in R, integer is 32bit
    +rToScalaTypes[["numeric"]]   <- "double"  # in R, numeric == double which 
is 64bit
    +rToScalaTypes[["double"]]    <- "double"
    +rToScalaTypes[["character"]] <- "string"
    +rToScalaTypes[["logical"]]   <- "boolean"
    +
    +#' coltypes
    +#'
    +#' Set the column types of a DataFrame.
    +#'
    +#' @name coltypes
    +#' @param x (DataFrame)
    +#' @return value (character) A character vector with the target column 
types for the given
    +#'    DataFrame. Column types can be one of integer, numeric/double, 
character, logical, or NA
    +#'    to keep that column as-is.
    +#' @rdname coltypes
    +#' @aliases coltypes
    +#' @export
    +#' @examples
    +#'\dontrun{
    +#' sc <- sparkR.init()
    +#' sqlContext <- sparkRSQL.init(sc)
    +#' path <- "path/to/file.json"
    +#' df <- jsonFile(sqlContext, path)
    +#' coltypes(df) <- c("character", "integer")
    +#' coltypes(df) <- c(NA, "numeric")
    +#'}
    +setMethod("coltypes<-",
    +          signature(x = "DataFrame", value = "character"),
    +          function(x, value) {
    +            cols <- columns(x)
    +            ncols <- length(cols)
    +            if (length(value) == 0 || length(value) != ncols) {
    --- End diff --
    
    Since SQLContext.emptyDataFrame allows DataFrames with 0 column, we'd 
better not exclude the possibility.
    
    Yes, your code checks length == 0, for suppose the following call:
    ```
        coltypes(df) <- character()
    ```
    Your code will throw error message:
    "Length of type vector should match the number of columns for DataFrame"
    But I think this case can be silently ignored.


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