ARROW-11704: [R] Wire up dplyr::mutate() for datasets

r/NEWS.md

@@ -21,7 +21,7 @@
 
 ## dplyr methods
 
-* `dplyr::mutate()` on Arrow `Table` and `RecordBatch` is now supported in Arrow for many applications. Where not yet supported, the implementation falls back to pulling data into an R `data.frame` first.
+* `dplyr::mutate()` is now supported in Arrow for many applications. For queries on `Table` and `RecordBatch` that are not yet supported in Arrow, the implementation falls back to pulling data into an R `data.frame` first, as in the previous release. For queries on `Dataset`, it raises an error if the feature is not implemented.
 * String functions `nchar()`, `tolower()`, and `toupper()`, along with their `stringr` spellings `str_length()`, `str_to_lower()`, and `str_to_upper()`, are supported in Arrow `dplyr` calls. `str_trim()` is also supported.
 
 ## Other improvements

r/R/dataset-scan.R

@@ -157,13 +157,19 @@ ScannerBuilder <- R6Class("ScannerBuilder", inherit = ArrowObject,
   public = list(
     Project = function(cols) {
       # cols is either a character vector or a named list of Expressions
-      if (!is.character(cols)) {
-        # We don't yet support mutate() on datasets, so this is just a list
-        # of FieldRefs, and we need to back out the field names
-        cols <- get_field_names(cols)
+      if (is.character(cols)) {
+        dataset___ScannerBuilder__ProjectNames(self, cols)
+      } else {
+        # If we have expressions, but they all turn out to be field_refs,
+        # we can still call the simple method
+        field_names <- get_field_names(cols)
+        if (all(nzchar(field_names))) {
+          dataset___ScannerBuilder__ProjectNames(self, field_names)
+        } else {
+          # Else, we are projecting/mutating
+          dataset___ScannerBuilder__ProjectExprs(self, cols, names(cols))
+        }
       }
-      assert_is(cols, "character")
-      dataset___ScannerBuilder__Project(self, cols)
       self
     },
     Filter = function(expr) {

r/R/dplyr.R

@@ -506,9 +506,6 @@ mutate.arrow_dplyr_query <- function(.data,
   }
 
   .data <- arrow_dplyr_query(.data)
-  if (query_on_dataset(.data)) {
-    not_implemented_for_dataset("mutate()")
-  }
 
   .keep <- match.arg(.keep)
   .before <- enquo(.before)
@@ -529,6 +526,7 @@ mutate.arrow_dplyr_query <- function(.data,
   # Deparse and take the first element in case they're long expressions
   names(exprs)[unnamed] <- map_chr(exprs[unnamed], as_label)
 
+  is_dataset <- query_on_dataset(.data)
   mask <- arrow_mask(.data)
   results <- list()
   for (i in seq_along(exprs)) {
@@ -539,6 +537,15 @@ mutate.arrow_dplyr_query <- function(.data,
     if (inherits(results[[new_var]], "try-error")) {
       msg <- paste('Expression', as_label(exprs[[i]]), 'not supported in Arrow')
       return(abandon_ship(call, .data, msg))
+    } else if (is_dataset &&
+               !inherits(results[[new_var]], "Expression") &&
+               !is.null(results[[new_var]])) {
+      # We need some wrapping to handle literal values
+      if (length(results[[new_var]]) != 1) {
+        msg <- paste0('In ', new_var, " = ", as_label(exprs[[i]]), ", only values of size one are recycled")
+        return(abandon_ship(call, .data, msg))
+      }
+      results[[new_var]] <- Expression$scalar(results[[new_var]])
     }
     # Put it in the data mask too
     mask[[new_var]] <- mask$.data[[new_var]] <- results[[new_var]]
@@ -583,7 +590,7 @@ abandon_ship <- function(call, .data, msg = NULL) {
       # Default message: function not implemented
       not_implemented_for_dataset(paste0(dplyr_fun_name, "()"))
     } else {
-      stop(msg, call. = FALSE)
+      stop(msg, "\nCall collect() first to pull data into R.", call. = FALSE)
     }
   }
 

r/src/dataset.cpp

@@ -308,11 +308,24 @@ std::shared_ptr<ds::PartitioningFactory> dataset___HivePartitioning__MakeFactory
 // ScannerBuilder, Scanner
 
 // [[arrow::export]]
-void dataset___ScannerBuilder__Project(const std::shared_ptr<ds::ScannerBuilder>& sb,
-                                       const std::vector<std::string>& cols) {
+void dataset___ScannerBuilder__ProjectNames(const std::shared_ptr<ds::ScannerBuilder>& sb,
+                                            const std::vector<std::string>& cols) {
   StopIfNotOk(sb->Project(cols));
 }
 
+// [[arrow::export]]
+void dataset___ScannerBuilder__ProjectExprs(
+    const std::shared_ptr<ds::ScannerBuilder>& sb,
+    const std::vector<std::shared_ptr<ds::Expression>>& exprs,
+    const std::vector<std::string>& names) {
+  // We have shared_ptrs of expressions but need the Expressions
+  std::vector<ds::Expression> expressions;
+  for (auto expr : exprs) {
+    expressions.push_back(*expr);
+  }
+  StopIfNotOk(sb->Project(expressions, names));
+}
+
 // [[arrow::export]]
 void dataset___ScannerBuilder__Filter(const std::shared_ptr<ds::ScannerBuilder>& sb,
                                       const std::shared_ptr<ds::Expression>& expr) {

r/src/expression.cpp

@@ -50,8 +50,11 @@ std::shared_ptr<ds::Expression> dataset___expr__field_ref(std::string name) {
 // [[arrow::export]]
 std::string dataset___expr__get_field_ref_name(
     const std::shared_ptr<ds::Expression>& ref) {
-  auto refname = ref->field_ref()->name();
-  return *refname;
+  auto field_ref = ref->field_ref();
+  if (field_ref == nullptr) {
+    return "";
+  }
+  return *field_ref->name();
 }
 
 // [[arrow::export]]

r/tests/testthat/test-dataset.R

@@ -717,6 +717,96 @@ test_that("filter() with expressions", {
   )
 })
 
+test_that("mutate()", {
+  ds <- open_dataset(dataset_dir, partitioning = schema(part = uint8()))
+  mutated <- ds %>%
+    select(chr, dbl, int) %>%
+    filter(dbl * 2 > 14 & dbl - 50 < 3L) %>%
+    mutate(twice = int * 2)
+  expect_output(
+    print(mutated),
+"FileSystemDataset (query)
+chr: string
+dbl: double
+int: int32
+twice: expr
+
+* Filter: ((multiply_checked(dbl, 2) > 14) and (subtract_checked(dbl, 50) < 3))
+See $.data for the source Arrow object",
+    fixed = TRUE
+  )
+  expect_equivalent(
+    mutated %>%
+      collect() %>%
+      arrange(dbl),
+    rbind(
+      df1[8:10, c("chr", "dbl", "int")],
+      df2[1:2, c("chr", "dbl", "int")]
+    ) %>%
+      mutate(
+        twice = int * 2
+      )
+  )
+})
+
+test_that("transmute()", {
+  ds <- open_dataset(dataset_dir, partitioning = schema(part = uint8()))
+  mutated <-
+  expect_equivalent(
+    ds %>%
+      select(chr, dbl, int) %>%
+      filter(dbl * 2 > 14 & dbl - 50 < 3L) %>%
+      transmute(twice = int * 2) %>%
+      collect() %>%
+      arrange(twice),
+    rbind(
+      df1[8:10, "int", drop = FALSE],
+      df2[1:2, "int", drop = FALSE]
+    ) %>%
+      transmute(
+        twice = int * 2
+      )
+  )
+})
+
+test_that("mutate() features not yet implemented", {
+  expect_error(
+    ds %>%
+      group_by(int) %>%
+      mutate(avg = mean(int)),
+    "mutate() on grouped data not supported in Arrow\nCall collect() first to pull data into R.",
+    fixed = TRUE
+  )
+})
+
+
+test_that("mutate() with scalar (length 1) literal inputs", {
+  expect_equal(
+    ds %>%
+      mutate(the_answer = 42) %>%
+      collect() %>%
+      pull(the_answer),
+    rep(42, nrow(ds))
+  )
+
+  expect_error(
+    ds %>% mutate(the_answer = c(42, 42)),
+    "In the_answer = c(42, 42), only values of size one are recycled\nCall collect() first to pull data into R.",
+    fixed = TRUE
+  )
+})
+
+test_that("mutate() with NULL inputs", {
+  expect_equal(
+    ds %>%
+      mutate(int = NULL) %>%
+      collect(),
+    ds %>%
+      select(-int) %>%
+      collect()
+  )
+})
+
 test_that("filter scalar validation doesn't crash (ARROW-7772)", {
   expect_error(
     ds %>%
@@ -832,7 +922,6 @@ test_that("dplyr method not implemented messages", {
     expect_error(x, "is not currently implemented for Arrow Datasets")
   }
   expect_not_implemented(ds %>% arrange(int))
-  expect_not_implemented(ds %>% mutate(int = int + 2))
   expect_not_implemented(ds %>% filter(int == 1) %>% summarize(n()))
 })
 
@@ -1137,7 +1226,7 @@ test_that("Dataset writing: no partitioning", {
 test_that("Dataset writing: partition on null", {
   skip_on_os("windows") # https://issues.apache.org/jira/browse/ARROW-9651
   ds <- open_dataset(hive_dir)
-  
+
   dst_dir <- tempfile()
   partitioning = hive_partition(lgl = boolean())
   write_dataset(ds, dst_dir, partitioning = partitioning)