From dc49c3872c3530ac08716fd4f4c235b01266a4ce Mon Sep 17 00:00:00 2001
From: Tanner Stauss <44127135+tmstauss@users.noreply.github.com>
Date: Sun, 21 Feb 2021 12:19:32 -0700
Subject: [PATCH] Typo (#1670)

---
 Perf-measure.Rmd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Perf-measure.Rmd b/Perf-measure.Rmd
index c0733f76b..558236d42 100644
--- a/Perf-measure.Rmd
+++ b/Perf-measure.Rmd
@@ -204,7 +204,7 @@ There are some other limitations to profiling:
  
 A __microbenchmark__ is a measurement of the performance of a very small piece of code, something that might take milliseconds (ms), microseconds (µs), or nanoseconds (ns) to run. Microbenchmarks are useful for comparing small snippets of code for specific tasks. Be very wary of generalising the results of microbenchmarks to real code: the observed differences in microbenchmarks will typically be dominated by higher-order effects in real code; a deep understanding of subatomic physics is not very helpful when baking.
 
-A great tool for microbenchmarking in R is the bench package [@bench]. The bench package uses a a high precision timer, making it possible to compare operations that only take a tiny amount of time. For example, the following code compares the speed of two approaches to computing a square root.
+A great tool for microbenchmarking in R is the bench package [@bench]. The bench package uses a high precision timer, making it possible to compare operations that only take a tiny amount of time. For example, the following code compares the speed of two approaches to computing a square root.
 
 ```{r bench-sqrt}
 x <- runif(100)