I'm looking for some references on creating effective graphs/data visualizations.
I've found a bunch of books that show how to create data visualizations using certain tools (like R/ggplot vs python/pandas) but that's not really what I'm looking for. I'm looking for a reference that explains different types of charts with respect to stats/math. I want more theory than process.
I want to know the different types of charts and how to use them. Anything helps!
I think that the work of William Cleveland is going to be closer to what you want that that of Tufte. Cleveland wrote two books:
The first book, in particular, may be what you want. Here is a publisher's description:
Visualizing Data is about visualization tools that provide deep insight into the structure of data. There are graphical tools such as coplots, multiway dot plots, and the equal count algorithm. There are fitting tools such as loess and bisquare that fit equations, nonparametric curves, and nonparametric surfaces to data. But the book is much more than just a compendium of useful tools. It conveys a strategy for data analysis that stresses the use of visualization to thoroughly study the structure of data and to check the validity of statistical models fitted to data. The result of the tools and the strategy is a vast increase in what you can learn from your data. The book demonstrates this by reanalyzing many data sets from the scientific literature, revealing missed effects and inappropriate models fitted to data.
An even more theoretical book is The Grammar of Graphics by Leland Wilkinson. The description:
This book was written for statisticians, computer scientists, geographers, researchers, and others interested in visualizing data. It presents a unique foundation for producing almost every quantitative graphic found in scientific journals, newspapers, statistical packages, and data visualization systems. While the tangible results of this work have been several visualization software libraries, this book focuses on the deep structures involved in producing quantitative graphics from data. What are the rules that underlie the production of pie charts, bar charts, scatterplots, function plots, maps, mosaics, and radar charts? Those less interested in the theoretical and mathematical foundations can still get a sense of the richness and structure of the system by examining the numerous and often unique color graphics it can produce. The second edition is almost twice the size of the original, with six new chapters and substantial revision. Much of the added material makes this book suitable for survey courses in visualization and statistical graphics.
This book is very theoretical.
ggplot2 in R. But it's also a great book to skip and skim.
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Look at the series of books written by Ed Tufte. They are discussed by wikipedia in the article https://en.wikipedia.org/wiki/Edward_Tufte.
The Visual Display of Quantitative Information. 1983; Second Edition 2001. Cheshire, CT: Graphics Press
Envisioning information. 1990. Cheshire, CT: Graphics Press
Visual Explanations: Images and Quantities, Evidence and Narrative Graphics Press. 1997. Cheshire, CT: Graphics Press
Beautiful Evidence. 2006. Cheshire, CT: Graphics Press
Seeing with Fresh Eyes. 2020. Cheshire, CT: Graphics Press.
At the risk of being crucified, I would advise against Tufte, Wilkinson, Cleveland etc. and all other classics if you're just starting out.
The reason is the following objective laid out by you (emphasis added):
I'm looking for some references on creating effective graphs/data visualizations.
So even though you don't explicitly want language dependent books/tutorials, you want your knowledge to be applied rather than an abstract theoretical exercise over coffee. Starting with what I call the classics is like reading Shakespeare because you want your language to be more eloquent. The discussions in the books are excellent for laying the foundations to understand effective data visualization; but considering the technological advancements up to today - the books aren't much help in developing the applied bent of mind (Grammar of Graphics- Wilkinson being the slight exception because of the relevance to ggplot2 but in that case I would advise reading works of Hadley Wickham, the package author instead).
Some good resources you could look at are FlowingData (Nathan Yau), Perceptual Edge (Stephen Few) and Storytelling with Data (Cole Knaflic) and the books by the blog authors. The reason being as follows:
It's a pity Aaron Koblin hasn't published any books about his unique take on large data visualizations.
I do not discount how useful Tufte, Cleveland and Wilkinson's work is, but after toiling through a few of them and still only being marginally better at modern data visualization tools, Stephen Few's "Show me the Numbers" was like a light switch went on.
It depends strongly on the language you prefer. As I am not using Python for data visualisation frequently I can only recommend you books relating to data visualisation in R. After writing this post I reread your question and Nr. 1, Nr. 2 and maybe Nr. 4 might be the most theoretical. Though Nr. 6 also explaines you theoretical aspects it is specialised on visualising unsupervised machine learning techniques.
The author Paul Murrell has a significant part in developing the graphics of the R language. He developed the "Grammar of Graphics" concept which is the concept underlying the ggplot2 library. The book is rather advanced although you do not need a lot of preknowledge necesarrily and pretty theoretical. It is the best book for people who genuinely want to understand the concepts of data visualisation in R, but I do not recommend it for beginners.
Is a must for interactive data visualisation. Various JavaScript libraries are translated into and adapted to R. You can include most Widgets in RShiny, Markdown (rendered as HTML) or in the console). My favorite HTML Widgets are
datatable (written by Yuhui Xe from RStudio who also wrote the knitR and the bookdown package. Prolific for showing tables))
This book is rather beginner friendly. Its examples are primarily shown in ggplot2. When I started learning advanced data visualisation techniques in R I primarily used this one and the official ggplot2 website.
Is the best starting point to learn ggplot2, but it can appear overwhelmingly if you are not willing to be passionate and if you don't have a lot of time. ggplot2 is awesome, but it can have a steep learning curve, e.g. you cannot write the "+" at the beginning of the line. All theoretical concepts are also explained.
Shiny is the most used R-library for building up apps with R. It can be substituted by BI tools like Tableau or Qlickview. shinyjs is a great extension of shiny which combines shiny with javascript, but you can also include HTML, CSS and JavaScript on your own.
This book comes from the same authors as the Guide to beautiful graphics (nr.3). It is a specialized book for visualising unsupervised machine learning techniques and particularly clustering.
In case you just start visualising and I overwhelmed you a little bit.
Several answers here ignore the request in the question "I'm looking for a reference that explains different types of charts with respect to stats/math. I want more theory than process." In particular, books by Few, Knaflic and Yau are often weak (and even sometimes quite incorrect) in linking their discussion to statistical principles.
Antony Unwin's Graphical Data Analysis with R see publisher's site has much more on the logic of statistical graphics than many of the books mentioned in other answers. Its use of R as a vehicle need not be a disadvantage to people who mostly or wholly use some other software. I am one such person but I have found the discussion rich and challenging. Even I when disagree with the author on some details, it is worth working out why. This is a 2015 book: while I suspect that the R details may be a little out-of-date, otherwise it wears very well and bears repeated consultation and reflection.