Github user NarineK commented on a diff in the pull request:
https://github.com/apache/spark/pull/12989#discussion_r62619916
--- Diff: R/pkg/inst/tests/testthat/test_sparkSQL.R ---
@@ -2070,6 +2072,16 @@ test_that("dapply() on a DataFrame", {
rownames(expected) <- NULL
expect_identical(expected, result)
+ result <- dapplyCollect(
+ df,
+ function(x) {
+ y <- x[x$a > 1, ]
+ y <- cbind(y, y$a + 1L)
--- End diff --
Well, let's assume I use dapply to train linear model. The model object
contains a list of different attributes for example coefficients, residuals ,
... etc.
model <- lm(Species ~ ., iris).
dapply wraps my model in a data.frame which is fine.
Now, as next I'd like to make predictions using dapply and the model which
I've previously trained.
This means, that I probably need something like this:
```
df1 <- dapply(
df,
model,
function (x, model) {
predict (model, x, ... )
}
)
```
Can we do something similar ?
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