Minor grammatical corrections.

iteration.Rmd

@@ -21,7 +21,7 @@ One part of reducing duplication is writing functions. Functions allow you to id
 
 In this chapter you'll learn about two important iteration paradigms: imperative programming and functional programming, and the machinary each provides. On the imperative side you have things like for loops and while loops, which are a great place to start because they make iteration very explicit, so it's obvious what's happening. However, for loops are quite verbose, and include quite a bit of book-keeping code, that is duplicated for every for loop. Functional programming (FP) offers tools to extract out this duplicated code, so each common for loop pattern gets its own function. Once you master the vocabulary of FP, you can solve many common iteration problems with less code, more ease, and fewer errors.
 
-Some people will tell you to avoid for loops because they are slow. They're wrong! (Well at least they're rather out of date, for loops haven't been slow for many years). The chief benefits of using FP functions like `lapply()` or `purrr::map()` is that they are more expressive and make code both easier to write and easier to read.
+Some people will tell you to avoid for loops because they are slow. They're wrong! (Well at least they're rather out of date, as for loops haven't been slow for many years). The chief benefits of using FP functions like `lapply()` or `purrr::map()` is that they are more expressive and make code both easier to write and easier to read.
 
 In later chapters you'll learn how to apply these iterating ideas when modelling. You can often use multiple simple models to help understand a complex dataset, or you might have multiple models because you're bootstrapping or cross-validating. The techniques you'll learn in this chapter will be invaluable.
 
@@ -248,7 +248,7 @@ for (i in seq_along(x)) {
 
 ### Unknown output length
 
-Sometimes you might know now how long the output will be. For example, imagine you want to simulate some random vectors of random lengths. You might be tempted to solve this problem by progressively growing the vector:
+Sometimes you might not know how long the output will be. For example, imagine you want to simulate some random vectors of random lengths. You might be tempted to solve this problem by progressively growing the vector:
 
 ```{r}
 means <- c(0, 1, 2)