diff --git a/communication.qmd b/communication.qmd
index 5720e3b..40da620 100644
--- a/communication.qmd
+++ b/communication.qmd
@@ -744,6 +744,15 @@ For example, if we extract two classes of cars and plot them separately, it's di
 ```{r}
 #| layout-ncol: 2
 #| fig-width: 4
+#| fig-alt: |
+#|   On the left, a scatterplot of highway mileage vs. displacement of SUVs.
+#|   On the right, a scatterplot of the same variables for compact cars.
+#|   Points are colored by drive type for both plots. Among SUVs more of 
+#|   the cars are 4-wheel drive and the others are rear-wheel drive, while 
+#|   among compact cars more of the cars are front-wheel drive and the others 
+#|   are 4-wheel drive. SUV plot shows a clear negative relationship 
+#|   between higway mileage and displacement while in the compact cars plot 
+#|   the relationship is much flatter.
 
 suv <- mpg |> filter(class == "suv")
 compact <- mpg |> filter(class == "compact")
@@ -762,6 +771,16 @@ One way to overcome this problem is to share scales across multiple plots, train
 ```{r}
 #| layout-ncol: 2
 #| fig-width: 4
+#| fig-alt: |
+#|   On the left, a scatterplot of highway mileage vs. displacement of SUVs.
+#|   On the right, a scatterplot of the same variables for compact cars.
+#|   Points are colored by drive type for both plots. Both plots are plotted 
+#|   on the same scale for highway mileage, displacement, and drive type, 
+#|   resulting in the legend showing all three types (front, rear, and 4-wheel 
+#|   drive) for both plots even though there are no front-wheel drive SUVs and 
+#|   no rear-wheel drive compact cars. Since the x and y scales are the same, 
+#|   and go well beyond minimum or maximum highway mileage and displacement, 
+#|   the points do not take up the entire plotting area.
 
 x_scale <- scale_x_continuous(limits = range(mpg$displ))
 y_scale <- scale_y_continuous(limits = range(mpg$hwy))
@@ -829,6 +848,9 @@ Finally, you can customize the non-data elements of your plot with a theme:
 
 ```{r}
 #| message: false
+#| fig-alt: |
+#|   Scatterplot of highway mileage vs. displacement of cars, colored by class 
+#|   of car. The plot background is white, with gray grid lines.
 
 ggplot(mpg, aes(x = displ, y = hwy)) +
   geom_point(aes(color = class)) +
diff --git a/functions.qmd b/functions.qmd
index 1dfc9cd..df68593 100644
--- a/functions.qmd
+++ b/functions.qmd
@@ -647,6 +647,10 @@ Wouldn't it be nice if you could wrap this up into a histogram function?
 This is easy as pie once you know that `aes()` is a data-masking function and you need to embrace:
 
 ```{r}
+#| fig-alt: |
+#|   A histogram of carats of diamonds, ranging from 0 to 5, showing a unimodal, 
+#|   right-skewed distribution with a peak between 0 to 1 carats.
+
 histogram <- function(df, var, binwidth = NULL) {
   df |> 
     ggplot(aes(x = {{ var }})) + 
@@ -672,6 +676,11 @@ It's straightforward to add more variables to the mix.
 For example, maybe you want an easy way to eyeball whether or not a dataset is linear by overlaying a smooth line and a straight line:
 
 ```{r}
+#| fig-alt: |
+#|   Scatterplot of height vs. mass of StarWars characters showing a positive 
+#|   relationship. A smooth curve of the relationship is plotted in red, and 
+#|   the best fit line is ploted in blue.
+
 # https://twitter.com/tyler_js_smith/status/1574377116988104704
 linearity_check <- function(df, x, y) {
   df |>
@@ -689,6 +698,10 @@ starwars |>
 Or maybe you want an alternative to colored scatterplots for very large datasets where overplotting is a problem:
 
 ```{r}
+#| fig-alt: |
+#|   Hex plot of price vs. carat of diamonds showing a positive relationship. 
+#|   There are more diamonds that are less than 2 carats than more than 2 carats.
+
 # https://twitter.com/ppaxisa/status/1574398423175921665
 hex_plot <- function(df, x, y, z, bins = 20, fun = "mean") {
   df |> 
@@ -764,6 +777,10 @@ This works similarly to `str_glue()`, so any value wrapped in `{ }` will be inse
 But it also understands `{{ }}`, which automatically inserts the appropriate variable name:
 
 ```{r}
+#| fig-alt: |
+#|   Histogram of carats of diamonds, ranging from 0 to 5. The distribution is 
+#|   unimodal and right skewed with a peak between 0 to 1 carats.
+
 histogram <- function(df, var, binwidth) {
   label <- rlang::englue("A histogram of {{var}} with binwidth {binwidth}")
   
diff --git a/iteration.qmd b/iteration.qmd
index d264cf3..0e1283a 100644
--- a/iteration.qmd
+++ b/iteration.qmd
@@ -948,6 +948,11 @@ We can take the same basic approach to create many plots.
 Let's first make a function that draws the plot we want:
 
 ```{r}
+#| fig-alt: |
+#|   Histogram of carats of diamonds from the by_clarity dataset, ranging from 
+#|   0 to 5 carats. The distribution is unimodal and right skewed with a peak 
+#|   around 1 carat.
+
 carat_histogram <- function(df) {
   ggplot(df, aes(x = carat)) + geom_histogram(binwidth = 0.1)  
 }
diff --git a/joins.qmd b/joins.qmd
index bed44bc..180a918 100644
--- a/joins.qmd
+++ b/joins.qmd
@@ -754,10 +754,15 @@ Inequality joins use `<`, `<=`, `>=`, or `>` to restrict the set of possible mat
 #| label: fig-join-lt
 #| echo: false
 #| out-width: ~
-#| fig-cap: >
+#| fig-cap: |
 #|   An inequality join where `x` is joined to `y` on rows where the key 
 #|   of `x` is less than the key of `y`. This makes a triangular
 #|   shape in the top-left corner.
+#| fig-alt: |
+#|   A diagram depicting an inequality join where a data frame x is joined by 
+#|   a data frame y where the key of x is less than the key of y, resulting 
+#|   in a triangular shape in the top-left corner.
+
 knitr::include_graphics("diagrams/join/lt.png", dpi = 270)
 ```
 
diff --git a/quarto.qmd b/quarto.qmd
index bc2cdfa..b4d982b 100644
--- a/quarto.qmd
+++ b/quarto.qmd
@@ -469,6 +469,10 @@ plot <- ggplot(mpg, aes(x = displ, y = hwy)) + geom_point()
 #| echo: false
 #| fig-width: 4
 #| out-width: "50%"
+#| fig-alt: |
+#|   Scatterplot of highway mileage vs. displacement of cars, where the points 
+#|   are normally sized and the axis text and labels are in similar font size 
+#|   to the surrounding text.
 
 plot
 ```
@@ -477,6 +481,10 @@ plot
 #| echo: false
 #| fig-width: 6
 #| out-width: "50%"
+#| fig-alt: |
+#|   Scatterplot of highway mileage vs. displacement of cars, where the points 
+#|   are smaller than in the previous plot and the axis text and labels are 
+#|   smallter than the surrounding text.
 
 plot
 ```
@@ -485,6 +493,10 @@ plot
 #| echo: false
 #| fig-width: 8
 #| out-width: "50%"
+#| fig-alt: |
+#|   Scatterplot of highway mileage vs. displacement of cars, where the points 
+#|   are even smaller than in the previous plot and the axis text and labels are 
+#|   even smallter than the surrounding text.
 
 plot
 ```
diff --git a/strings.qmd b/strings.qmd
index 9d83c58..f2b9e5b 100644
--- a/strings.qmd
+++ b/strings.qmd
@@ -38,7 +38,11 @@ This is particularly useful if you use RStudio because typing `str_` will trigge
 
 ```{r}
 #| echo: false
-#| fig-alt: "`str_c` typed into the RStudio console with the autocomplete tooltip shown on top, which lists functions beginning with `str_c`. The funtion signature and beginning of the man page for the highlighted function from the autocomplete list are shown in a panel to its right."
+#| fig-alt: |
+#|   str_c typed into the RStudio console with the autocomplete tooltip shown 
+#|   on top, which lists functions beginning with str_c. The funtion signature 
+#|   and beginning of the man page for the highlighted function from the 
+#|   autocomplete list are shown in a panel to its right.
 
 knitr::include_graphics("screenshots/stringr-autocomplete.png")
 ```