diff --git a/EDA.Rmd b/EDA.Rmd index 3c65e37..0c4b625 100644 --- a/EDA.Rmd +++ b/EDA.Rmd @@ -143,7 +143,7 @@ ggplot(data = smaller, mapping = aes(x = carat, colour = cut)) + geom_freqpoly(binwidth = 0.1) ``` -There are a few challenges with this type of plot, which we will come back to in visualizing[ a categorical and a continuous variable](#cat-cont). +There are a few challenges with this type of plot, which we will come back to in visualizing[a categorical and a continuous variable](#cat-cont). Now that you can visualize variation, what should you look for in your plots? And what type of follow-up questions should you ask? @@ -681,9 +681,8 @@ diamonds |> ## Learning more -If you want to learn more about the mechanics of ggplot2, I'd highly recommend grabbing a copy of the ggplot2 book: . -It's been recently updated, so it includes dplyr and tidyr code, and has much more space to explore all the facets of visualization. -Unfortunately the book isn't generally available for free, but if you have a connection to a university you can probably get an electronic version for free through SpringerLink. +If you want to learn more about the mechanics of ggplot2, I'd highly recommend reading the [ggplot2 book](https://ggplot2-book.org). +It's been recently updated and has much more space to explore all the facets of visualization. Another useful resource is the [*R Graphics Cookbook*](https://amzn.com/1449316956) by Winston Chang. Much of the contents are available online at .