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functions.qmd

@@ -403,7 +403,7 @@ Embracing a variable means to wrap it in braces so (e.g.) `var` becomes `{{ var
 Embracing a variable tells dplyr to use the value stored inside the argument, not the argument as the literal variable name.
 One way to remember what's happening is to think of `{{ }}` as looking down a tunnel --- `{{ var }}` will make a dplyr function look inside of `var` rather than looking for a variable called `var`.
 
-So to make grouped_mean`()` work, we need to surround `group_var` and `mean_var` with `{{ }}`:
+So to make `grouped_mean()` work, we need to surround `group_var` and `mean_var` with `{{ }}`:
 
 ```{r}
 grouped_mean <- function(df, group_var, mean_var) {
@@ -819,7 +819,7 @@ Additionally, `function()` should always be followed by squiggly brackets (`{}`)
 This makes it easier to see the hierarchy in your code by skimming the left-hand margin.
 
 ```{r}
-# missing extra two spaces
+# Missing extra two spaces
 density <- function(color, facets, binwidth = 0.1) {
 diamonds |> 
   ggplot(aes(x = carat, y = after_stat(density), color = {{ color }})) +

iteration.qmd

@@ -3,6 +3,7 @@
 ```{r}
 #| results: "asis"
 #| echo: false
+
 source("_common.R")
 status("complete")
 ```
@@ -17,7 +18,7 @@ In most other languages, you'd need to explicitly double each element of `x` usi
 This book has already given you a small but powerful number of tools that perform the same action for multiple "things":
 
 -   `facet_wrap()` and `facet_grid()` draws a plot for each subset.
--   `group_by()` plus `summarize()` computes a summary statistics for each subset.
+-   `group_by()` plus `summarize()` computes summary statistics for each subset.
 -   `unnest_wider()` and `unnest_longer()` create new rows and columns for each element of a list-column.
 
 Now it's time to learn some more general tools, often called **functional programming** tools because they are built around functions that take other functions as inputs.
@@ -283,7 +284,7 @@ df_miss |> filter(if_all(a:d, is.na))
 ### `across()` in functions
 
 `across()` is particularly useful to program with because it allows you to operate on multiple columns.
-For example, [Jacob Scott](https://twitter.com/_wurli/status/1571836746899283969) uses this little helper which wraps a bunch of lubridate function to expand all date columns into year, month, and day columns:
+For example, [Jacob Scott](https://twitter.com/_wurli/status/1571836746899283969) uses this little helper which wraps a bunch of lubridate functions to expand all date columns into year, month, and day columns:
 
 ```{r}
 expand_dates <- function(df) {

strings.qmd

@@ -551,6 +551,7 @@ For example here are two inline CSVs with unusual encodings[^strings-7]:
 
 ```{r}
 #| message: false
+
 x1 <- "text\nEl Ni\xf1o was particularly bad this year"
 read_csv(x1)