From e119132cb478a104163a6c0de59327e849fec9b7 Mon Sep 17 00:00:00 2001
From: alberto-agudo <91462184+alberto-agudo@users.noreply.github.com>
Date: Mon, 20 Mar 2023 22:11:16 +0100
Subject: [PATCH] Eliminating repeated word (#1380)

---
 arrow.qmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/arrow.qmd b/arrow.qmd
index 0226ca9..1cff1c1 100644
--- a/arrow.qmd
+++ b/arrow.qmd
@@ -19,7 +19,7 @@ But CSV files aren't very efficient: you have to do quite a lot of work to read
 In this chapter, you'll learn about a powerful alternative: the [parquet format](https://parquet.apache.org/), an open standards-based format widely used by big data systems.
 
 We'll pair parquet files with [Apache Arrow](https://arrow.apache.org), a multi-language toolbox designed for efficient analysis and transport of large datasets.
-We'll use Apache Arrow via the the [arrow package](https://arrow.apache.org/docs/r/), which provides a dplyr backend allowing you to analyze larger-than-memory datasets using familiar dplyr syntax.
+We'll use Apache Arrow via the [arrow package](https://arrow.apache.org/docs/r/), which provides a dplyr backend allowing you to analyze larger-than-memory datasets using familiar dplyr syntax.
 As an additional benefit, arrow is extremely fast: you'll see some examples later in the chapter.
 
 Both arrow and dbplyr provide dplyr backends, so you might wonder when to use each.