Fix broken links due to new base R chapter

base-R.qmd

@@ -1,4 +1,4 @@
-# A field guide to base R
+# A field guide to base R {#sec-base-r}
 
 ```{r}
 #| results: "asis"
@@ -31,7 +31,7 @@ To finish off, we'll briefly discuss two important plotting functions.
 library(tidyverse)
 ```
 
-## Selecting multiple elements with `[`
+## Selecting multiple elements with `[` {#sec-subset-many}
 
 `[` is used to extract sub-components from vectors and data frames, and is called like `x[i]` or `x[i, j]`.
 In this section, we'll introduce you to the power of `[`, first showing you how you can use it with vectors, then how the same principles extend in a straightforward way to two-dimensional (2d) structures like data frames.
@@ -210,7 +210,7 @@ This function was the inspiration for much of dplyr's syntax.
 2.  Why is `x[-which(x > 0)]` not the same as `x[x <= 0]`?
     Read the documentation for `which()` and do some experiments to figure it out.
 
-## Selecting a single element `$` and `[[`
+## Selecting a single element `$` and `[[` {#sec-subset-one}
 
 `[`, which selects many elements, is paired with `[[` and `$`, which extract a single element.
 In this section, we'll show you how to use `[[` and `$` to pull columns out of a data frames, discuss a couple more differences between `data.frames` and tibbles, and emphasize some important differences between `[` and `[[` when used with lists.

logicals.qmd

@@ -384,7 +384,7 @@ flights |>
 ### Logical subsetting
 
 There's one final use for logical vectors in summaries: you can use a logical vector to filter a single variable to a subset of interest.
-This makes use of the base `[` (pronounced subset) operator, which you'll learn more about in @sec-vector-subsetting.
+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:

numbers.qmd

@@ -691,7 +691,7 @@ Finally, don't forget what you learned in @sec-sample-size: whenever creating nu
 ### Positions
 
 There's one final type of summary that's useful for numeric vectors, but also works with every other type of value: extracting a value at specific position.
-You can do this with the base R `[` function, but we're not going to cover it until @sec-vector-subsetting, because it's a very powerful and general function.
+You can do this with the base R `[` function, but we're not going to cover it in detail until @sec-subset-many, because it's a very powerful and general function.
 For now we'll introduce three specialized functions that you can use to extract values at a specified position: `first(x)`, `last(x)`, and `nth(x, n)`.
 
 For example, we can find the first and last departure for each day:

program.qmd

@@ -41,19 +41,20 @@ If you spend a little time rewriting your code while the ideas are fresh, you ca
 But this doesn't mean you should rewrite every function: you need to balance what you need to achieve now with saving time in the long run.
 (But the more you rewrite your functions the more likely your first attempt will be clear.)
 
-In the following three chapters, you'll learn skills that will allow you to both tackle new programs and to solve existing problems with greater clarity and ease:
+In the following three chapters, you'll learn skills to improve your programming skills:
 
 1.  Copy-and-paste is a powerful tool, but you should avoid doing it more than twice.
     Repeating yourself in code is dangerous because it can easily lead to errors and inconsistencies.
     Instead, in @sec-functions, you'll learn how to write **functions** which let you extract out repeated code so that it can be easily reused.
 
-2.  As you start to write more powerful functions, you'll need a solid grounding in R's **data structures**, provided by vectors, which we discuss in @sec-vectors.
-    You must master the four common atomic vectors, the three important S3 classes built on top of them, and understand the mysteries of the list and data frame.
-
-3.  Functions extract out repeated code, but you often need to repeat the same actions on different inputs.
+2.  Functions extract out repeated code, but you often need to repeat the same actions on different inputs.
     You need tools for **iteration** that let you do similar things again and again.
     These tools include for loops and functional programming, which you'll learn about in @sec-iteration.
 
+3.  As you read more code written by others, you'll see more code that doesn't use the tidyverse.
+    In @sec-base-r, you'll learn some of the most important base R functions that you'll see in the wild.
+    These functions tend to be designed to use individual vectors, rather than data frames, often making them a good fit for your programming needs.
+
 ## Learning more
 
 The goal of these chapters is to teach you the minimum about programming that you need to practice data science.

rectangling.qmd

@@ -87,7 +87,7 @@ str(x5)
 ```
 
 As lists get even larger and more complex, `str()` eventually starts to fail, and you'll need to switch to `View()`[^rectangling-1].
-@fig-view-collapsed shows the result of calling `View(x4)`. The viewer starts by showing just the top level of the list, but you can interactively expand any of the components to see more, as in @fig-view-expand-1. RStudio will also show you the code you need to access that element, as in @fig-view-expand-2. We'll come back to how this code works in @sec-lists.
+@fig-view-collapsed shows the result of calling `View(x4)`. The viewer starts by showing just the top level of the list, but you can interactively expand any of the components to see more, as in @fig-view-expand-1. RStudio will also show you the code you need to access that element, as in @fig-view-expand-2. We'll come back to how this code works in @sec-subset-one.
 
 [^rectangling-1]: This is an RStudio feature.
 

workflow-pipes.qmd

@@ -115,7 +115,7 @@ But they're still good to know about even if you've never used `%>%` because you
 -   The `|>` placeholder is deliberately simple and can't replicate many features of the `%>%` placeholder: you can't pass it to multiple arguments, and it doesn't have any special behavior when the placeholder is used inside another function.
     For example, `df %>% split(.$var)` is equivalent to `split(df, df$var)` and `df %>% {split(.$x, .$y)}` is equivalent to `split(df$x, df$y)`.
 
-    With `%>%` you can use `.` on the left-hand side of operators like `$`, `[[`, `[` (which you'll learn about in @sec-vectors), so you can extract a single column from a data frame with (e.g.) `mtcars %>% .$cyl`.
+    With `%>%` you can use `.` on the left-hand side of operators like `$`, `[[`, `[` (which you'll learn about in @sec-subset-many), so you can extract a single column from a data frame with (e.g.) `mtcars %>% .$cyl`.
     A future version of R may add similar support for `|>` and `_`.
     For the special case of extracting a column out of a data frame, you can also use `dplyr::pull()`: