Data visualisation has become very vogue in the digital humanities community. Although there have been a scattering of brave practitioners over the past few years, only very recently has this interdisciplinary area started to feature prominently at DH conferences as a mainstream practise worthy of consideration.
For the last few months I have been looking for an opportunity (i.e. a bit of time) to delve into R and Processing, specifically with an eye towards taking some existing visualisations I am working on to a new level. 
The first book of interest is R in a Nutshell by James Adler recently published by O’Reilly.
R is a language and an environment to support data analytics and visualisation. Its approachable, extensible and open source. One of the advantages of R over other comers is the number of rather polished interpreters available for it and some of the great examples floating about that have been constructed in R. Hence my interest. I come to this interest from a digital humanities background and wondered whether the language could be of use for working with my own data coming from farm diaries exploring the cycle of seasonal farm activities.
I had an opportunity to peruse this volume and put it through it’s paces. I come to R as a neophyte having not attempted any custom data visualisation development aside from using commercial tools such as SAS, SPSS and Tableau or web services to create handcrafted visualisation for refinement in illustrator or Photoshop.
R in a Nutshell, as a book, is targeted at a user much like me. It offers a variety of approaches ranging from material for absolute beginners to experienced R users who would like to broaden their use of the environment. It allows for pick and choose areas of interest deigned to provide a bespoke learning course.
What do you need R for? Do you need to create customised statistical maniuplation of large datasets not typically accomplished using SPSS or SAS on your desktop? R may be the tool for you. This book is superb introduction to the tool, but it also serves the function of handy desktop companion for exploring further.
Starting from a detailed overview of the various ways in which R can be deployed on different operating systems and addressed as either a gui-based application to command line operation. Adler makes copious use of great examples of code to play with yourself. The structure of the book offers diverse patterns of approach to cater to people of all levels of experience with R. It is well organised and has the useful ability to locate information when it is of immediate applicability. If there is a downside, it is the amount of time it takes to play through examples to really determine whether R is the tool for you. I certainly can’t fault the book for this especially as it offers such a well structured approach to learning R. However, I can see where a digital humanities scholar who hadn’t ever considered doing any bit of code and waded into R right off the bat may simply be discouraged by a systematic process of introduction rather than a cookbook approach.
This is where we make a clever shift to our other book of interest: Getting Started with Processing by Casey Reas and Ben Fry. As you might suspect this book takes a very different and possibly more useful to a neophyte approach – moreover, I really enjo
yed this book. In a word it is superb. It approaches learning Processing from simple beginnings and truly embraces the idea of learning by doing. It is full of exercises and short snippets of discussion that simply ‘get you Processing’. As an added benefit and a superb way to do things, the latest downloadable builds of Processing include all the examples in this book accessible through the Examples menu. So as you work along you can edit to your hearts content and if you want to start with code and just simply tweak, you have it at your finger tips. If your style of learning is otherwise and you want to manually work through the examples, do that and if at the end things don’t quite work out you have the working code readily available. This works very well.
The book is organised into a few simple and general chapters. As you dig down into the recipes within the chapters you are presented with the breadth of the language. This approach wouldn’t work if things were formal and too structured. However, this book is very informal in tone and approach and you playfully follow through, not really conscious that you are following a rigid path.
Even the typography of the book adds to the fun. There are oversized headers and very nicely visual word snippets of the ‘experience’ of playing with Processing. It adds a wonderfully lyrical aspect to the book and really engages so as to keep you reading and playing along with the authors. The book mixes visuals and textuals very well. The decision to use ‘sketchy’ visuals as though they were hand-drawn keeps with this informal and engaged approach. Works for me.
Processing is very powerful and I feel that after working through this book I can see some very exciting projects that I can consider for my own work. I feel armed with just enough information to be dangerous and look forward to playing further. For this I will fall back more on Processing (published a few years back) the more definitive guide to the wealth of options available in the language. This is where R in a Nutshell offer the bonus of being a fine tutorial, but also a very useful reference tool. These are books about rather different programming tools that offer very different perspectives on accomplishing similar things. They provided me with a great intro to both tools. many books promise to be logical in their structure and to build to the more complex. I found that R in a Nutshell adopted a more reasoned approach to this. By that I mean the author sought to provided a variety of structured paths through the material depending on level of expertise or desired outcome/usage of the language. These were well considered. I apprroached this from the perspective of the complete neophyte (not a difficult role to fill – nor that far-fetched). Getting Started with Processing was more fun.
I should note that both of these books were provided in electronic form for review and I am grateful to Mary Rotman at O’Reilly for making this possible.