1 files changed, 91 insertions, 0 deletions
diff --git a/src/arrow/r/R/dplyr-filter.R b/src/arrow/r/R/dplyr-filter.R
new file mode 100644
index 000000000..3c8c08ea5
--- /dev/null
+++ b/src/arrow/r/R/dplyr-filter.R
@@ -0,0 +1,91 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+
+# The following S3 methods are registered on load if dplyr is present
+
+filter.arrow_dplyr_query <- function(.data, ..., .preserve = FALSE) {
+  # TODO something with the .preserve argument
+  filts <- quos(...)
+  if (length(filts) == 0) {
+    # Nothing to do
+    return(.data)
+  }
+
+  .data <- as_adq(.data)
+  # tidy-eval the filter expressions inside an Arrow data_mask
+  filters <- lapply(filts, arrow_eval, arrow_mask(.data))
+  bad_filters <- map_lgl(filters, ~ inherits(., "try-error"))
+  if (any(bad_filters)) {
+    # This is similar to abandon_ship() except that the filter eval is
+    # vectorized, and we apply filters that _did_ work before abandoning ship
+    # with the rest
+    expr_labs <- map_chr(filts[bad_filters], format_expr)
+    if (query_on_dataset(.data)) {
+      # Abort. We don't want to auto-collect if this is a Dataset because that
+      # could blow up, too big.
+      stop(
+        "Filter expression not supported for Arrow Datasets: ",
+        oxford_paste(expr_labs, quote = FALSE),
+        "\nCall collect() first to pull data into R.",
+        call. = FALSE
+      )
+    } else {
+      arrow_errors <- map2_chr(
+        filters[bad_filters], expr_labs,
+        handle_arrow_not_supported
+      )
+      if (length(arrow_errors) == 1) {
+        msg <- paste0(arrow_errors, "; ")
+      } else {
+        msg <- paste0("* ", arrow_errors, "\n", collapse = "")
+      }
+      warning(
+        msg, "pulling data into R",
+        immediate. = TRUE,
+        call. = FALSE
+      )
+      # Set any valid filters first, then collect and then apply the invalid ones in R
+      .data <- set_filters(.data, filters[!bad_filters])
+      return(dplyr::filter(dplyr::collect(.data), !!!filts[bad_filters]))
+    }
+  }
+
+  set_filters(.data, filters)
+}
+filter.Dataset <- filter.ArrowTabular <- filter.arrow_dplyr_query
+
+set_filters <- function(.data, expressions) {
+  if (length(expressions)) {
+    if (is_list_of(expressions, "Expression")) {
+      # expressions is a list of Expressions. AND them together and set them on .data
+      new_filter <- Reduce("&", expressions)
+    } else if (inherits(expressions, "Expression")) {
+      new_filter <- expressions
+    } else {
+      stop("filter expressions must be either an expression or a list of expressions", call. = FALSE)
+    }
+
+    if (isTRUE(.data$filtered_rows)) {
+      # TRUE is default (i.e. no filter yet), so we don't need to & with it
+      .data$filtered_rows <- new_filter
+    } else {
+      .data$filtered_rows <- .data$filtered_rows & new_filter
+    }
+  }
+  .data
+}