Update logicals.qmd (#1166)

logicals.qmd

@@ -11,7 +11,7 @@ status("polishing")
 
 In this chapter, you'll learn tools for working with logical vectors.
 Logical vectors are the simplest type of vector because each element can only be one of three possible values: `TRUE`, `FALSE`, and `NA`.
-It's relatively rare to find logical vectors in your raw data, but you'll create and manipulate in the course of almost every analysis.
+It's relatively rare to find logical vectors in your raw data, but you'll create and manipulate them in the course of almost every analysis.
 
 We'll begin by discussing the most common way of creating logical vectors: with numeric comparisons.
 Then you'll learn about how you can use Boolean algebra to combine different logical vectors, as well as some useful summaries.
@@ -39,7 +39,7 @@ x * 2
 ```
 
 This makes it easier to explain individual functions at the cost of making it harder to see how it might apply to your data problems.
-Just remember that any manipulation we do to a free-floating vector, you can do to a variable inside data frame with `mutate()` and friends.
+Just remember that any manipulation we do to a free-floating vector, you can do to a variable inside a data frame with `mutate()` and friends.
 
 ```{r}
 df <- tibble(x)
@@ -102,7 +102,7 @@ x == c(1, 2)
 
 What's going on?
 Computers store numbers with a fixed number of decimal places so there's no way to exactly represent 1/49 or `sqrt(2)` and subsequent computations will be very slightly off.
-We can see the exact values by calling `print()` with the the `digits`[^logicals-1] argument:
+We can see the exact values by calling `print()` with the `digits`[^logicals-1] argument:
 
 [^logicals-1]: R normally calls print for you (i.e. `x` is a shortcut for `print(x)`), but calling it explicitly is useful if you want to provide other arguments.
 
@@ -148,7 +148,7 @@ x == y
 # We don't know!
 ```
 
-So if you want to find all flights with `dep_time` is missing, the following code doesn't work because `dep_time == NA` will yield a `NA` for every single row, and `filter()` automatically drops missing values:
+So if you want to find all flights where `dep_time` is missing, the following code doesn't work because `dep_time == NA` will yield `NA` for every single row, and `filter()` automatically drops missing values:
 
 ```{r}
 flights |> 
@@ -197,7 +197,7 @@ We'll come back to cover missing values in more depth in @sec-missing-values.
 ## Boolean algebra
 
 Once you have multiple logical vectors, you can combine them together using Boolean algebra.
-In R, `&` is "and", `|` is "or", and `!` is "not", and `xor()` is exclusive or[^logicals-2].
+In R, `&` is "and", `|` is "or", `!` is "not", and `xor()` is exclusive or[^logicals-2].
 @fig-bool-ops shows the complete set of Boolean operations and how they work.
 
 [^logicals-2]: That is, `xor(x, y)` is true if x is true, or y is true, but not both.
@@ -225,7 +225,7 @@ knitr::include_graphics("diagrams/transform.png", dpi = 270)
 As well as `&` and `|`, R also has `&&` and `||`.
 Don't use them in dplyr functions!
 These are called short-circuiting operators and only ever return a single `TRUE` or `FALSE`.
-They're important for programming, not data science
+They're important for programming, not data science.
 
 ### Missing values {#sec-na-boolean}
 
@@ -249,7 +249,7 @@ Similar reasoning applies with `NA & FALSE`.
 ### Order of operations
 
 Note that the order of operations doesn't work like English.
-Take the following code finds all flights that departed in November or December:
+Take the following code that finds all flights that departed in November or December:
 
 ```{r}
 #| eval: false
@@ -258,7 +258,7 @@ flights |>
    filter(month == 11 | month == 12)
 ```
 
-You might be tempted to write it like you'd say in English: "find all flights that departed in November or December":
+You might be tempted to write it like you'd say in English: "Find all flights that departed in November or December.":
 
 ```{r}
 flights |> 
@@ -267,9 +267,9 @@ flights |>
 
 This code doesn't error but it also doesn't seem to have worked.
 What's going on?
-Here R first evaluates `month == 11` creating a logical vector, which we call `nov`.
+Here, R first evaluates `month == 11` creating a logical vector, which we call `nov`.
 It computes `nov | 12`.
-When you use a number with a logical operator it converts everything apart from 0 to TRUE, so this is equivalent to `nov | TRUE` which will always be `TRUE`, so every row will be selected:
+When you use a number with a logical operator it converts everything apart from 0 to `TRUE`, so this is equivalent to `nov | TRUE` which will always be `TRUE`, so every row will be selected:
 
 ```{r}
 flights |> 
@@ -317,7 +317,7 @@ flights |>
 
 1.  Find all flights where `arr_delay` is missing but `dep_delay` is not. Find all flights where neither `arr_time` nor `sched_arr_time` are missing, but `arr_delay` is.
 2.  How many flights have a missing `dep_time`? What other variables are missing in these rows? What might these rows represent?
-3.  Assuming that a missing `dep_time` implies that a flight is cancelled, look at the number of cancelled flights per day. Is there a pattern? Is there a connection between the proportion of cancelled flights and average delay of non-cancelled flights?
+3.  Assuming that a missing `dep_time` implies that a flight is cancelled, look at the number of cancelled flights per day. Is there a pattern? Is there a connection between the proportion of cancelled flights and the average delay of non-cancelled flights?
 
 ## Summaries {#sec-logical-summaries}
 
@@ -350,7 +350,7 @@ That leads us to the numeric summaries.
 
 When you use a logical vector in a numeric context, `TRUE` becomes 1 and `FALSE` becomes 0.
 This makes `sum()` and `mean()` very useful with logical vectors because `sum(x)` will give the number of `TRUE`s and `mean(x)` the proportion of `TRUE`s.
-That lets us see the distribution of delays across the days of the year as shown in @fig-prop-delayed-dist.
+That lets us see the distribution of delays across the days of the year as shown in @fig-prop-delayed-dist
 
 ```{r}
 #| label: fig-prop-delayed-dist
@@ -369,7 +369,7 @@ flights |>
   geom_histogram(binwidth = 0.05)
 ```
 
-Or we could ask how many flights left before 5am, which are often flights that were delayed from the previous day:
+Or we could ask: "How many flights left before 5am?", which are often flights that were delayed from the previous day:
 
 ```{r}
 flights |> 
@@ -387,7 +387,7 @@ There's one final use for logical vectors in summaries: you can use a logical ve
 This makes use of the base `[` (pronounced subset) operator, which you'll learn more about in @sec-subset-many.
 
 Imagine we wanted to look at the average delay just for flights that were actually delayed.
-One way to do so would be to first filter the flights:
+One way to do so would be to first filter the flights and then calculate the average delay:
 
 ```{r}
 flights |> 
@@ -424,7 +424,7 @@ Also note the difference in the group size: in the first chunk `n()` gives the n
 ### Exercises
 
 1.  What will `sum(is.na(x))` tell you? How about `mean(is.na(x))`?
-2.  What does `prod()` return when applied to a logical vector? What logical summary function is it equivalent to? What does `min()` return applied to a logical vector? What logical summary function is it equivalent to? Read the documentation and perform a few experiments.
+2.  What does `prod()` return when applied to a logical vector? What logical summary function is it equivalent to? What does `min()` return when applied to a logical vector? What logical summary function is it equivalent to? Read the documentation and perform a few experiments.
 
 ## Conditional transformations
 
@@ -433,7 +433,7 @@ There are two important tools for this: `if_else()` and `case_when()`.
 
 ### `if_else()`
 
-If you want to use one value when a condition is true and another value when it's `FALSE`, you can use `dplyr::if_else()`[^logicals-4].
+If you want to use one value when a condition is `TRUE` and another value when it's `FALSE`, you can use `dplyr::if_else()`[^logicals-4].
 You'll always use the first three argument of `if_else()`. The first argument, `condition`, is a logical vector, the second, `true`, gives the output when the condition is true, and the third, `false`, gives the output if the condition is false.
 
 [^logicals-4]: dplyr's `if_else()` is very similar to base R's `ifelse()`.
@@ -468,7 +468,7 @@ y1 <- c(3, NA, 4, 6)
 if_else(is.na(x1), y1, x1)
 ```
 
-You might have noticed a small infelicity in our labeling: zero is neither positive nor negative.
+You might have noticed a small infelicity in our labeling example above: zero is neither positive nor negative.
 We could resolve this by adding an additional `if_else()`:
 
 ```{r}
@@ -576,8 +576,8 @@ We don't expect you to memorize these rules, but they should become second natur
 The definition of a logical vector is simple because each value must be either `TRUE`, `FALSE`, or `NA`.
 But logical vectors provide a huge amount of power.
 In this chapter, you learned how to create logical vectors with `>`, `<`, `<=`, `=>`, `==`, `!=`, and `is.na()`, how to combine them with `!`, `&`, and `|`, and how to summarize them with `any()`, `all()`, `sum()`, and `mean()`.
-You also learned the powerful `if_else()` and `case_when()` that allow you to return values depending on the value of a logical vector.
+You also learned the powerful `if_else()` and `case_when()` functions that allow you to return values depending on the value of a logical vector.
 
-We'll see logical vectors again and in the following chapters.
+We'll see logical vectors again and again in the following chapters.
 For example in @sec-strings you'll learn about `str_detect(x, pattern)` which returns a logical vector that's `TRUE` for the elements of `x` that match the `pattern`, and in @sec-dates-and-times you'll create logical vectors from the comparison of dates and times.
 But for now, we're going to move onto the next most important type of vector: numeric vectors.