typo

Computing-on-the-language.rmd

@@ -27,7 +27,7 @@ Look at the labels on the axes. How did R know that the variable on the x axis w
 
 The goal of this chapter is to help you understand NSE in existing R code, and to show you how to write your own functions that use it. In [capturing expressions](#capturing-expressions) you'll learn how to capture unevaluated expressions using `substitute()`. In [non-standard evaluation](#subset) you'll learn how `subset()` combines `substitute()` with `eval()` to allow you to succinctly to select rows from a data frame. [Scoping issues](#scoping-issues) will teach you about the scoping issues that arise in NSE, and show you how to resolve them.
 
-NSE is great for interactive use, but can be hard to program with. [Calling from another function](#calling-from-another-function) shows why every function that uses NSE should have an escape hatch, a version that uses regular evaluation. Not all functions have an escape hatch, so [substitute](#substitute) shows you how you to can use `substitute()` to modify unevaluated cocde, making it possible to program with any function that uses NSE.
+NSE is great for interactive use, but can be hard to program with. [Calling from another function](#calling-from-another-function) shows why every function that uses NSE should have an escape hatch, a version that uses regular evaluation. Not all functions have an escape hatch, so [substitute](#substitute) shows you how you to can use `substitute()` to modify unevaluated code, making it possible to program with any function that uses NSE.
 
 While powerful, NSE makes code substantially harder to reason about. The chapter concludes with a look at the downsides of NSE in [the downsides](#nse-downsides).