Merge pull request #50 from radugrosu/patch-8

Update robust-code.Rmd

robust-code.Rmd

@@ -4,7 +4,7 @@ library(magrittr)
 
 # Robust code
 
-(This is an advanced topic. You shouldn't worry too much about it when you first start writing functions. Instead you should focus on getting a function that works right for the easiest 80% of the problem. Then in time, you'll learn how to get to 99% with minimal extra effort. The defaults in this book should steer you in the right direction: we avoid teaching you functions with major suprises.)
+(This is an advanced topic. You shouldn't worry too much about it when you first start writing functions. Instead you should focus on getting a function that works right for the easiest 80% of the problem. Then in time, you'll learn how to get to 99% with minimal extra effort. The defaults in this book should steer you in the right direction: we avoid teaching you functions with major surprises.)
 
 In this section you'll learn an important principle that lends itself to reliable and readable code: favour code that can be understood with a minimum of context. On one extreme, take this code:
 
@@ -18,7 +18,7 @@ What does it do? You can glean only a little from the context: `foo()` is a func
 df2 <- arrange(df, qux)
 ```
 
-It's now much easier to see what's going on! Function and variable names are important because they tell you about (or at least jog your memory of) what the code does. That helps you understand code in isolation, even if you don't completely understand all the details. Unfortunately naming things is hard, and its hard to give concrete advice apart from giving objects short but evocative names.  As autocomplete in RStudio has gotten better, I've tended to use longer names that are more descriptive. Short names are faster to type, but you write code relatively infrequently compared to the number of times that you read it.
+It's now much easier to see what's going on! Function and variable names are important because they tell you about (or at least jog your memory of) what the code does. That helps you understand code in isolation, even if you don't completely understand all the details. Unfortunately naming things is hard, and it's hard to give concrete advice apart from giving objects short but evocative names.  As autocomplete in RStudio has gotten better, I've tended to use longer names that are more descriptive. Short names are faster to type, but you write code relatively infrequently compared to the number of times that you read it.
 
 The idea of minimising the context needed to understand your code goes beyond just good naming. You also want to favour functions with predictable behaviour and few surprises. If a function does radically different things when its inputs differ slightly, you'll need to carefully read the surrounding context in order to predict what it will do. The goal of this section is to educate you about the most common ways R functions can be surprising and to provide you with unsurprising alternatives.
 
@@ -67,7 +67,7 @@ df$x
 
 ### Unpredictable types
 
-One of the most frustrating for programming is they way `[` returns a vector if the result has a single column, and returns a data frame otherwise. In other words, if you see code like `df[x, ]` you can't predict what it will return without knowing the value of `x`. This can trip you up in surprising ways. For example, imagine you've written this function to return the last row of a data frame:
+One of the aspects most frustrating for programming is that `[` returns a vector if the result has a single column, and returns a data frame otherwise. In other words, if you see code like `df[x, ]` you can't predict what it will return without knowing the value of `x`. This can trip you up in surprising ways. For example, imagine you've written this function to return the last row of a data frame:
 
 ```{r}
 last_row <- function(df) {
@@ -116,7 +116,7 @@ df[3:4] %>% sapply(class) %>% str()
 
 In the next chapter, you'll learn about the purrr package which provides a variety of alternatives. In this case, you could use `map_chr()` which always returns a character vector: if it can't, it will throw an error. Another option is the base `vapply()` function which takes a third argument indicating what the output should look like.
 
-This doesn't make `sapply()` bad and `vapply()` and `map_chr()` good. `sapply()` is nice because you can use it interactively without having to think about what `f` will return. 95% of the time it will do the right thing, and if it doesn't you can quickly fix it. `map_chr()` is more important when your programming because a clear error message is more valuable when an operation is buried deep inside a tree of function calls. At this point its worth thinking more about 
+This doesn't make `sapply()` bad and `vapply()` and `map_chr()` good. `sapply()` is nice because you can use it interactively without having to think about what `f` will return. 95% of the time it will do the right thing, and if it doesn't you can quickly fix it. `map_chr()` is more important when you're programming because a clear error message is more valuable when an operation is buried deep inside a tree of function calls. At this point it's worth thinking more about 
 
 ### Non-standard evaluation
 
@@ -184,7 +184,7 @@ big_x <- function(df, threshold) {
 }
 ```
 
-Because dplyr currently has no way to force a name to be interpreted as either a local or parent variable, as I've only just realised that's really you should avoid NSE. In a future version you should be able to do:
+Because dplyr currently has no way to force a name to be interpreted as either a local or parent variable, as I've only just realised, that's really why you should avoid NSE. In a future version you should be able to do:
 
 ```{r}
 big_x <- function(df, threshold) {
@@ -212,7 +212,7 @@ Functions are easiest to reason about if they have two properties:
 
 The first property is particularly important. If a function has hidden additional inputs, it's very difficult to even know where the important context is!
 
-The biggest breaker of this rule in base R are functions that create data frames. Most of these functions have a `stringsAsFactors` argument that defaults to `getOption("stringsAsFactors")`. This means that a global option affects the operation of a very large number of functions, and you need to be aware that depending on an external state a function might produce either a character vector or a factor. In this book, we steer you away from that problem by recommnding functions like `readr::read_csv()` and `dplyr::data_frame()` that don't rely on this option. But be aware of it!  Generally if a function is affected by a global option, you should avoid setting it.
+The biggest breakers of this rule in base R are functions that create data frames. Most of these functions have a `stringsAsFactors` argument that defaults to `getOption("stringsAsFactors")`. This means that a global option affects the operation of a very large number of functions, and you need to be aware that, depending on an external state, a function might produce either a character vector or a factor. In this book, we steer you away from that problem by recommending functions like `readr::read_csv()` and `dplyr::data_frame()` that don't rely on this option. But be aware of it!  Generally if a function is affected by a global option, you should avoid setting it.
 
 Only use `options()` to control side-effects of a function. The value of an option should never affect the return value of a function. There are only three violations of this rule in base R: `stringsAsFactors`, `encoding`, `na.action`. For example, base R lets you control the number of digits printed in default displays with (e.g.) `options(digits = 3)`. This is a good use of an option because it's something that people frequently want control over, but doesn't affect the computation of a result, just its display. Follow this principle with your own use of options.