deshanxiao commented on code in PR #37549:
URL: https://github.com/apache/spark/pull/37549#discussion_r947573399
##########
R/pkg/R/SQLContext.R:
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@@ -492,6 +492,37 @@ read.text <- function(path, ...) {
dataFrame(sdf)
}
+#' Create a SparkDataFrame from a csv file.
+#'
+#' Loads a Parquet file, returning the result as a SparkDataFrame.
+#'
+#' @param path Path of file to read. A vector of multiple paths is allowed.
+#' @param ... additional external data source specific named properties.
+#' You can find the csv-specific options for reading csv files in
+# nolint start
+#'
\url{https://spark.apache.org/docs/latest/sql-data-sources-csv.html#data-source-option}{Data
Source Option} in the version you use.
+# nolint end
+#' @return SparkDataFrame
+#' @rdname read.spark.csv
+#' @examples
+#'\dontrun{
+#' sparkR.session()
+#' path <- "path/to/file.csv"
+#' df <- read.spark.csv(path)
+#' }
+#' @name read.spark.csv
+#' @note read.spark.csv since 3.3.0
+read.spark.csv <- function(path, ...) {
Review Comment:
Yes, We can read csv file by following code:
`df <- read.df("examples/src/main/resources/people.csv", "csv", sep = ";",
inferSchema = TRUE, header = TRUE)
`
However, considering that other formats have corresponding advanced
functions(read.text() etc.), it is necessary to add a high-level api here.
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