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 (Well at least they're rather out of date, for loops haven't been slow for many years).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.
 

strings.Rmd

@@ -815,7 +815,7 @@ There are a few other functions in base R that accept regular expressions:
 
 stringr is built on top of the __stringi__ package. stringr is useful when you're learning because it exposes a minimal set of functions, that have been carefully picked to handle the most common string manipulation functions. stringi on the other hand is designed to be comprehensive. It contains almost every function you might ever need. stringi has `r length(ls(getNamespace("stringi")))` functions to stringr's `r length(ls("package:stringr"))`.
 
-So if you find yourself struggling to do something that doesn't seem natural in stringr, it's worth taking a look at stringi. The use of the two packages is very similar because stringr was designed to mimic stringi's interface. The main difference is the prefix: `str_` vs `stri_`.
+So if you find yourself struggling to do something that doesn't seem natural in stringr, it's worth taking a look at stringi. The use of the two packages is very similar because stringr was designed to mimic stringi's interface. The main difference is the prefix: `str_` vs. `stri_`.
 
 ### Encoding
 

transform.Rmd

@@ -681,7 +681,7 @@ ggplot(delays, aes(n, delay)) +
   geom_point()
 ```
 
-Not suprisingly, there is much more variation in the average delay when there are few flights. The shape of this plot is very characteristic: whenever you plot a mean (or many other summaries) vs number of observations, you'll see that the variation decreases as the sample size increases.
+Not suprisingly, there is much more variation in the average delay when there are few flights. The shape of this plot is very characteristic: whenever you plot a mean (or many other summaries) vs. number of observations, you'll see that the variation decreases as the sample size increases.
 
 When looking at this sort of plot, it's often useful to filter out the groups with the smallest numbers of observations, so you can see more of the pattern and less of the extreme variation in the smallest groups. This is what the following code does, and also shows you a handy pattern for integrating ggplot2 into dplyr flows. It's a bit painful that you have to switch from `%>%` to `+`, but once you get the hang of it, it's quite convenient.
 

visualize.Rmd

@@ -493,7 +493,7 @@ You can learn which stat a geom uses, as well as what variables it computes by v
 
 Stats are the most subtle part of plotting because you do not see them in action. `ggplot2` applies the transformation and stores the results behind the scenes. You only see the finished plot. Moreover, `ggplot2` applies stats automatically, with a very intuitive set of defaults. As a result, you rarely need to adjust a geom's stat. However, you can do three things with a geom's stat if you wish to.
 
-First, you can change the stat that the geom uses with the geom's stat argument. In the code below, I change the stat of `geom_bar()` from count (the default) to identity. This let's me map the height of the bars to the raw values of a $y$ variable. 
+First, you can change the stat that the geom uses with the geom's stat argument. In the code below, I change the stat of `geom_bar()` from count (the default) to identity. This lets me map the height of the bars to the raw values of a $y$ variable. 
 
 ```{r}
 demo <- data.frame(
@@ -507,7 +507,7 @@ ggplot(data = demo) +
 demo
 ```
 
-I provide a list of the stats that are availalbe to use in ggplot2 at the end of this section. Be careful when you change a geom's stat. Many combinations of geoms and stats will create incompatible results. In practice, you will almost always use a geom's default stat.
+I provide a list of the stats that are available to use in ggplot2 at the end of this section. Be careful when you change a geom's stat. Many combinations of geoms and stats will create incompatible results. In practice, you will almost always use a geom's default stat.
 
 Second, you can give some stats arguments by passing the arguments to your geom function. In the code below, I pass a width argument to the count stat, which controls the widths of the bars. `width = 1` will make the bars wide enough to touch each other.
 

wrangle.Rmd

@@ -4,7 +4,7 @@
 
 With data, the relationships between values matter as much as the values themselves. Tidy data encodes those relationships.
 
-Throughout this book we work with "tibbles" instead of the traditional data frame. Tibbles _are_ data frames but they encode some patterns that make modern usage of R better. Unfortunately R is an old language, and things that made sense 10 or 20 years a go are no longer as valid. It's difficult to change base R without breaking existing code, so most innovation occurs in packages, providing new functions that you should use instead of the old ones.
+Throughout this book we work with "tibbles" instead of the traditional data frame. Tibbles _are_ data frames but they encode some patterns that make modern usage of R better. Unfortunately R is an old language, and things that made sense 10 or 20 years ago are no longer as valid. It's difficult to change base R without breaking existing code, so most innovation occurs in packages, providing new functions that you should use instead of the old ones.
 
 ```{r}
 library(tibble)
@@ -38,7 +38,7 @@ frame_data(
 )
 ```
 
-## Tibbles vs data frames
+## Tibbles vs. data frames
 
 There are two main differences in the usage of a data frame vs a tibble: printing, and subsetting.