From cd6c68b5a9ae27001d68d1d6a9e457a19221d514 Mon Sep 17 00:00:00 2001
From: Hadley Wickham <h.wickham@gmail.com>
Date: Tue, 7 Feb 2023 15:48:02 -0600
Subject: [PATCH] Minimise iteration

---
 databases.qmd |  1 -
 iteration.qmd | 27 ++++-----------------------
 2 files changed, 4 insertions(+), 24 deletions(-)

diff --git a/databases.qmd b/databases.qmd
index fd475b8..5084a08 100644
--- a/databases.qmd
+++ b/databases.qmd
@@ -132,7 +132,6 @@ dbWriteTable(con, "diamonds", ggplot2::diamonds)
 
 If you're using duckdb in a real project, we highly recommend learning about `duckdb_read_csv()` and `duckdb_register_arrow()`.
 These give you powerful and performant ways to quickly load data directly into duckdb, without having to first load it into R.
-
 We'll also show off a useful technique for loading multiple files into a database in @sec-save-database.
 
 ## DBI basics
diff --git a/iteration.qmd b/iteration.qmd
index 8bf303f..de3d086 100644
--- a/iteration.qmd
+++ b/iteration.qmd
@@ -108,21 +108,6 @@ Note grouping columns (`grp` here) are not included in `across()`, because they'
 -   `where(is.POSIXct)` selects all date-time columns.
 -   `where(is.logical)` selects all logical columns.
 
-```{r}
-df_types <- tibble(
-  x1 = 1:3,
-  x2 = runif(3),
-  y1 = sample(letters, 3),
-  y2 = c("banana", "apple", "egg")
-)
-
-df_types |> 
-  summarize(across(where(is.numeric), mean))
-
-df_types |> 
-  summarize(across(where(is.character), str_flatten))
-```
-
 Just like other selectors, you can combine these with Boolean algebra.
 For example, `!where(is.numeric)` selects all non-numeric columns, and `starts_with("a") & where(is.logical)` selects all logical columns whose name starts with "a".
 
@@ -288,12 +273,10 @@ It's clear that `across()` can help to create multiple logical columns, but then
 So dplyr provides two variants of `across()` called `if_any()` and `if_all()`:
 
 ```{r}
-df_miss |> filter(is.na(a) | is.na(b) | is.na(c) | is.na(d))
-# same as:
+# same as df_miss |> filter(is.na(a) | is.na(b) | is.na(c) | is.na(d))
 df_miss |> filter(if_any(a:d, is.na))
 
-df_miss |> filter(is.na(a) & is.na(b) & is.na(c) & is.na(d))
-# same as:
+# same as df_miss |> filter(is.na(a) & is.na(b) & is.na(c) & is.na(d))
 df_miss |> filter(if_all(a:d, is.na))
 ```
 
@@ -332,11 +315,11 @@ summarize_means <- function(df, summary_vars = where(is.numeric)) {
     )
 }
 diamonds |> 
-  group_by(clarity) |> 
+  group_by(cut) |> 
   summarize_means()
 
 diamonds |> 
-  group_by(clarity) |> 
+  group_by(cut) |> 
   summarize_means(c(carat, x:z))
 ```
 
@@ -650,7 +633,6 @@ In more complicated cases, there might be other variables stored in the director
 In that case, use `set_names()` (without any arguments) to record the full path, and then use `tidyr::separate_wider_delim()` and friends to turn them into useful columns.
 
 ```{r}
-# NOTE: this chapter also depends on dev tidyr (in addition to dev purrr and dev dplyr)
 paths |> 
   set_names() |> 
   map(readxl::read_excel) |> 
@@ -763,7 +745,6 @@ df_types <- function(df) {
 }
 
 df_types(starwars)
-df_types(nycflights13::flights)
 ```
 
 You can then apply this function to all of the files, and maybe do some pivoting to make it easier to see where the differences are.