Github user titicaca commented on a diff in the pull request:
https://github.com/apache/spark/pull/16689#discussion_r98612545
--- Diff: R/pkg/R/DataFrame.R ---
@@ -1138,6 +1138,11 @@ setMethod("collect",
if (!is.null(PRIMITIVE_TYPES[[colType]]) && colType !=
"binary") {
vec <- do.call(c, col)
stopifnot(class(vec) != "list")
+ class(vec) <-
+ if (colType == "timestamp")
+ c("POSIXct", "POSIXt")
+ else
+ PRIMITIVE_TYPES[[colType]]
--- End diff --
Currently all tests are passed, except for the two modified tests with NA
types as discussed before. The followings are the all type convertions from
SparkDataframe to R data.frame, which have been tested in the existing tests in
test_sparkSQL.R.
```
PRIMITIVE_TYPES <- as.environment(list(
"tinyint" = "integer",
"smallint" = "integer",
"int" = "integer",
"bigint" = "numeric",
"float" = "numeric",
"double" = "numeric",
"decimal" = "numeric",
"string" = "character",
"binary" = "raw",
"boolean" = "logical",
"timestamp" = "POSIXct",
"date" = "Date",
# following types are not SQL types returned by dtypes(). They are listed
here for usage
# by checkType() in schema.R.
# TODO: refactor checkType() in schema.R.
"byte" = "integer",
"integer" = "integer"
))
```
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