diff --git a/base-R.qmd b/base-R.qmd
index f2caedb..c4a93b3 100644
--- a/base-R.qmd
+++ b/base-R.qmd
@@ -10,7 +10,7 @@ status("polishing")
 To finish off the programming section, we're going to give you a quick tour of the most important base R functions that we don't otherwise discuss in the book.
 These tools are particularly useful as you do more programming and will help you read code that you'll encounter in the wild.
 
-This is a good place to remind you that the tidyverse is not only way to solve data science problems.
+This is a good place to remind you that the tidyverse is not the only way to solve data science problems.
 We teach the tidyverse in this book because tidyverse packages share a common design philosophy, which increases the consistency across functions, making each new function or package a little easier to learn and use.
 It's not possible to use the tidyverse without using base R, so we've actually already taught you a **lot** of base R functions: from `library()` to load packages, to `sum()` and `mean()` for numeric summaries, to the factor, date, and POSIXct data types, and of course all the basic operators like `+`, `-`, `/`, `*`, `|`, `&`, and `!`.
 What we haven't focused on so far is base R workflows, so we will highlight a few of those in this chapter.
@@ -92,7 +92,7 @@ There are five main types of things that you can subset a vector with, i.e. that
 
 5.  **Nothing**.
     The final type of subsetting is nothing, `x[]`, which returns the complete `x`.
-    This is not useful for subsetting vectors, but as well see shortly it is useful when subsetting 2d structures like tibbles.
+    This is not useful for subsetting vectors, but as we'll see shortly it is useful when subsetting 2d structures like tibbles.
 
 ### Subsetting data frames