Merge pull request #59 from bgreenwell/patch-7

Fixed typos and made minor grammar changes

functions.Rmd

@@ -15,7 +15,7 @@ One of the best ways to improve your reach as a data scientist is to write funct
 1.  You can give a function an evocative name that makes your code easier to 
     understand.
 
-Writing good functions is a lifetime journey. Even after using R for many years we still learn new techniques and better ways of approaching old problems. The goal of this chapter is not master every esoteric detail of functions but to get you started with some pragmatic advice that you can start using right away.
+Writing good functions is a lifetime journey. Even after using R for many years we still learn new techniques and better ways of approaching old problems. The goal of this chapter is not to master every esoteric detail of functions but to get you started with some pragmatic advice that you can start using right away.
 
 As well as practical advice for writing functions, this chapter also gives you some suggestions for how to style your code. Good coding style is like using correct punctuation. You can manage without it, but it sure makes things easier to read. As with styles of punctuation, there are many possible variations. Here we present the style we use in our code, but the most important thing is to be consistent.
 
@@ -41,7 +41,7 @@ df$d <- (df$d - min(df$d, na.rm = TRUE)) /
   (max(df$d, na.rm = TRUE) - min(df$d, na.rm = TRUE))
 ```
 
-You might be able to puzzle out that this rescales each column to have a range from 0 to 1. But did you spot the mistake? I made an error when copying-and-pasting the code for `df$b`, and I forgot to change an `a` to a `b`. Extracting repeated code out into a function is a good idea because prevents you from making this type of mistake.
+You might be able to puzzle out that this rescales each column to have a range from 0 to 1. But did you spot the mistake? I made an error when copying-and-pasting the code for `df$b`: I forgot to change an `a` to a `b`. Extracting repeated code out into a function is a good idea because it prevents you from making this type of mistake.
 
 To write a function you need to first analyse the code. How many inputs does it have?
 
@@ -50,7 +50,7 @@ To write a function you need to first analyse the code. How many inputs does it
   (max(df$a, na.rm = TRUE) - min(df$a, na.rm = TRUE))
 ```
 
-This code only has one input: `df$a`. (You might wonder if that `TRUE` is also an input: you can explore why it's not in the exercise below). To make the single input more clear, it's a good idea to rewrite the code using a temporary variables with a general name. Here this function only takes one vector input, so I'll call it `x`:
+This code only has one input: `df$a`. (You might wonder if that `TRUE` is also an input: you can explore why it's not in the exercise below). To make the single input more clear, it's a good idea to rewrite the code using a temporary variables with a general name. Here this function only takes one vector of input, so I'll call it `x`:
 
 ```{r}
 x <- 1:10
@@ -86,7 +86,7 @@ There are three key steps to making a function:
 
 Note the process that I followed here: I only made the function after I'd figured out how to make it work with a simple input. It's much easier to start with working code and turn it into a function as opposed to creating a function and then trying to make it work.
 
-Now we have `rescale01()` we can use that to simplify our original example:
+Now that we have `rescale01()` we can use that to simplify our original example:
 
 ```{r}
 df$a <- rescale01(df$a)
@@ -95,7 +95,7 @@ df$c <- rescale01(df$c)
 df$d <- rescale01(df$d)
 ```
 
-Compared to our original code, this is easier to understand and we've eliminated one class of class of copy-and-paste errors. There's still quite a bit of duplication since we're doing the same thing to multiple columns. You'll learn how to eliminate that duplication in the next chapter, Iteration.
+Compared to our original code, this is easier to understand and we've eliminated one class of copy-and-paste errors. There's still quite a bit of duplication since we're doing the same thing to multiple columns. You'll learn how to eliminate that duplication in the next chapter, Iteration.
 
 ### Practice