I've been reading Tukey's book "Exploratory Data Analysis". Being written in 1977, the book emphasizes paper/pencil methods. Is there a more 'modern' successor which takes into account that we can now instantaneosly plot large data sets?
The closest thing is Cleveland's Visualizing Data. It's about Exploratory Data Analysis, it's about computer-generated visualizations, it's profound, it's a classic.
Well, its not an exact replica, but I found tons of useful plotting advice (and R code) in Gelman and Hill's Data Analysis using Regression and Multilevel/Hierarchical Models
In addition, his blog is often full of useful graphics advice.
Interactive Graphics for Data Analysis: Principles and Examples is one I like; the book description says it "discusses exploratory data analysis (EDA) and how interactive graphical methods can help gain insights as well as generate new questions and hypotheses from datasets."
Hadley Wickham's ggplot2 book is interesting because it teaches both the Grammar of Graphics and how to use the ggplot2 software.
Ronald Pearson's Exploring Data in Engineering, the Sciences, and Medicine is worth mentioning here. Its main target readership seems to be scientists not afraid of a little mathematics who wish they knew more statistics. That is quite a large group, and one well represented here. It's a little quirky and offbeat, but it covers a lot of ground and it includes much sensible advice. It's not Tukey revisited in the sense that it offers many new ideas, but it can be rewarding to study, even when you think it is a little wrong-headed.
This book seems to have attracted very little notice, quite possibly because it is very expensive, not obviously suitable as a course text, and as yet only available in hardback. But it is intelligent and readable and free of the garbage of modern introductory textbooks (pages and pages of elementary exercises, silly icons, gratuitous photos of happy young people, fussy layout with boxes, whatever, etc.).
This has two chapters publicly available on the web that describe the process of data analysis, and handling missing values. There's a new book coming out by Antony Unwin soon.
Claus Wilke's 2019 book "Fundamentals of Data Visualization" is another possible "modern successor." The book's preprint is still freely available online.
Like Tukey's EDA, Wilke's book is focused on exploring your data using graphs while keeping in mind the things that matter to statisticians: thinking in terms of distributions, thinking about precision & uncertainty in our estimates, thinking about bias-variance tradeoffs when smoothing a trend or choosing a histogram bin size, and so on.
Wilke assumes you'll be making your graphs on the computer and provides the code for all his graphs (mostly in R's
ggplot2) on GitHub. But the book itself is written in a software-agnostic way: the text is about best practices, not about how to implement them in a specific software tool. There's a brief chapter on choosing the right viz software tool for your needs.
He also concisely introduces concepts like Wilkinson's Grammar of Graphics; recommends best practices in line with folks like Cleveland and Tufte; and discusses how to make effective graphics for communication, not just exploration. Wilke's book does not break new ground on these fronts (unlike the Tukey or Cleveland books mentioned in other answers), but rather does a great job of distilling it and putting it all in one place, illustrated with good/bad/ugly examples using real datasets. It's become my go-to book for introducing data visualization to statisticians.
Another couple of good books to read are Beautiful Visualization and Beautiful Data. These are edited books, there are amazingly good examples of exploring data with plots, and some absolutely appalling chapters.
Another book that has some good examples of using ggplot2 is a new one by Winston Chang
I think of Understanding robust and exploratory analysis by Hoaglin, Mosteller and Tukey an the companion volume on Exploring data tables and shapes as the technical follow-up to EDA. I also see data analysis and regression, a second course in statistics by Mosteller and Tukey as follow-up to EDA. The various Cleveland books mentioned above are treasures.