From a41c532ace08c0d56432f28e6e3df4480fa5fd48 Mon Sep 17 00:00:00 2001
From: Nic Crane <thisisnic@gmail.com>
Date: Thu, 19 Feb 2026 09:21:40 +0000
Subject: [PATCH] GH-35806: [R] Improve error message for null type inference
 with sparse CSV data

When a CSV column contains only missing values in the first block of data,
Arrow infers the type as null. If a non-null value appears later, the
conversion fails with an unhelpful error suggesting `skip = 1`.

This change adds a specific check for "conversion error to null" and
provides a more helpful message explaining the cause (type inference
from sparse data) and the solution (specify column types explicitly).

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
---
 r/R/util.R                          | 15 +++++++++++++++
 r/tests/testthat/test-dataset-csv.R | 18 ++++++++++++++++++
 2 files changed, 33 insertions(+)

diff --git a/r/R/util.R b/r/R/util.R
index c63e1ee5459..acbd39e2037 100644
--- a/r/R/util.R
+++ b/r/R/util.R
@@ -196,6 +196,21 @@ repeat_value_as_array <- function(object, n) {
 }
 
 handle_csv_read_error <- function(msg, call, schema) {
+  # Handle null type inference issue with sparse data
+  if (grepl("conversion error to null", msg)) {
+    msg <- c(
+      msg,
+      i = paste(
+        "Column type was inferred as null because the first block of data",
+        "(default 1MB, set via `block_size` in read options) contained only",
+        "missing values. Try specifying the column types explicitly using the",
+        "`col_types` or `schema` argument."
+      )
+    )
+    abort(msg, call = call)
+  }
+
+  # Handle schema + header row issue
   if (grepl("conversion error", msg) && inherits(schema, "Schema")) {
     msg <- c(
       msg,
diff --git a/r/tests/testthat/test-dataset-csv.R b/r/tests/testthat/test-dataset-csv.R
index 749d1672ac5..145a376da97 100644
--- a/r/tests/testthat/test-dataset-csv.R
+++ b/r/tests/testthat/test-dataset-csv.R
@@ -711,3 +711,21 @@ test_that("open_dataset() with `decimal_point` argument", {
     tibble(x = 1.2, y = "c")
   )
 })
+
+
+test_that("more informative error when column inferred as null due to sparse data (GH-35806)", {
+  tf <- tempfile()
+  on.exit(unlink(tf))
+
+  # Create a CSV where the second column has NAs in the first rows
+  # but a value later - this causes Arrow to infer null type
+  writeLines(c("x,y", paste0(1:100, ",")), tf)
+  write("101,foo", tf, append = TRUE)
+
+  # Use small block_size to force type inference from only the first rows
+  expect_error(
+    open_dataset(tf, format = "csv", read_options = csv_read_options(block_size = 100L)) |>
+      collect(),
+    "inferred as null"
+  )
+})