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Fix/data-visualize (#1390) 

* remove old reference to now-deleted right plot, and fix typos

* I was wrong. This is not a typo. Recover it.
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data-visualize.qmd
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@ -701,10 +701,9 @@ Note the terminology we have used here:
We can use stacked bar plots to visualize the relationship between two categorical variables.
For example, the following two stacked bar plots both display the relationship between `island` and `species`, or specifically, visualizing the distribution of `species` within each island.
The first plot shows the frequencies of each species of penguins on each island and the plot on the right shows the relative frequencies (proportions) of each species within each island (despite the incorrectly labeled y-axis that says "count").
The first plot shows the frequencies of each species of penguins on each island.
The plot of frequencies show that there are equal numbers of Adelies on each island.
But we don't have a good sense of the percentage balance within each island.
In the proportions plot, we've lost our notion of total penguins, but we've gained the advantage of "breakdown by island".
```{r}
#| fig-alt: >
@ -927,7 +926,7 @@ We started with the basic idea that underpins ggplot2: a visualization is a mapp
You then learned about increasing the complexity and improving the presentation of your plots layer-by-layer.
You also learned about commonly used plots for visualizing the distribution of a single variable as well as for visualizing relationships between two or more variables, by levering additional aesthetic mappings and/or splitting your plot into small multiples using faceting.
We'll use visualizations again and again through out this book, introducing new techniques as we need them as well as do a deeper dive into creating visualizations with ggplot2 in @sec-layers through @sec-exploratory-data-analysis.
We'll use visualizations again and again throughout this book, introducing new techniques as we need them as well as do a deeper dive into creating visualizations with ggplot2 in @sec-layers through @sec-communication.
With the basics of visualization under your belt, in the next chapter we're going to switch gears a little and give you some practical workflow advice.
We intersperse workflow advice with data science tools throughout this part of the book because it'll help you stay organized as you write increasing amounts of R code.