Merge branch 'master' of github.com:hadley/r4ds

DESCRIPTION

@@ -35,6 +35,7 @@ Imports:
 Remotes:
   gaborcsardi/rcorpora,
   garrettgman/DSR,
+  hadley/modelr,
   hadley/purrr,
   hadley/stringr,
   hadley/ggplot2,

iteration.Rmd

@@ -19,9 +19,9 @@ In [functions], we talked about how important it is to reduce duplication in you
 
 One part of reducing duplication is writing functions. Functions allow you to identify repeated patterns of code and extract them out into indepdent pieces that you can reuse and easily update as code changes. Iteration helps you when you need to do the same thing to multiple inputs: repeating the same operation on different columns, or on different datasets. (Generally, you won't need to use explicit iteration to deal with different subsets of your data: in most cases the implicit iteration in dplyr will take care of that problem for you.)
 
-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.
+In this chapter you'll learn about two important iteration paradigms: imperative programming and functional programming, and the machinery 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 (Well at least they're rather out of date, for loops haven't been slow for many years).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)
@@ -261,7 +261,7 @@ for (i in seq_along(means)) {
 str(output)
 ```
 
-But this type of is not very efficient because in each iteration, R has to copy all the data from the previous iterations. In technical terms you get "quadratic" ($O(n^2)$) behaviour which means that a loop with three times as many elements would take nine times ($3^2$) as long to run.
+But this is not very efficient because in each iteration, R has to copy all the data from the previous iterations. In technical terms you get "quadratic" ($O(n^2)$) behaviour which means that a loop with three times as many elements would take nine times ($3^2$) as long to run.
 
 A better solution to save the results in a list, and then combine into a single vector after the loop is done:
 
@@ -375,7 +375,7 @@ I mention while loops briefly, because I hardly ever use them. They're most ofte
     }
     ```
 
-## For loops vs functionals
+## For loops vs. functionals
 
 For loops are not as important in R as they are in other languages because R is a functional programming language. This means that it's possible to wrap up for loops in a function, and call that function instead of using the for loop directly.