Parts are worth the read, but other parts *really* need to be skimmed.
I was expecting, and got, a book full of interesting tidbits about the history oParts are worth the read, but other parts *really* need to be skimmed.
I was expecting, and got, a book full of interesting tidbits about the history of maps and mapmaking, interweaved with a quest to track down a modern-day map thief. There were nice insights into how maps were always used as political and military tools, how they are secured by librarians and produced by cartographers nowadays, how the trade in old maps for decorative purposes has become a bidding war and priced out the actual historians... Lots of good stuff.
But I was not expecting this to be mashed up with the author's personal sob story, journey of self-discovery, and excessive map metaphors. * What do the author's grandfather's business failures and their effect on his mother's psyche have to do with maps and map theft? NOTHING. But we get to hear all about it anyway. * Imagine a book where every friggin' noun that comes up is compared to a map, mapmaker, or famous explorer. OK, no need to imagine. This is that book! Author, you are not just like Fremont or Columbus. We don't need to hear your delusions of grandeur. Step back and let your subjects shine. * Most of all, there is just So! Much! BS! about how "Maybe Bland [the thief] and I were not so different after all," apparently because the thief was obsessed with stealing maps and the author is obsessed with getting the story. Dude, we don't care about your quest to get the backstory. You're a friggin' journalist. That's your job. If you can't get every last juicy detail, that's OK, just admit it and move on. Don't waste pages and pages on how hard it is to do your damn job. * "I heard my voice straining at the words, 'I'm a good guy.' It struck me that I had been trying not to convince Bland but to reassure myself. And I knew that I, too, was beginning to lose my way." NO. You are just acting like an unprofessional interviewer and an unprofessional writer.
But maybe it's all just a misunderstanding: Consider writing a short magazine article, say about maps. You might throw in a cheesy paragraph at the end drawing big conclusions, excessive metaphors, and unfounded comparisons, because it's OK and even fun to do that once in an article. Now consider writing a whole book on the subject. The article went cheesy once, for maybe a maximum of 1/5th of the article. How do you extrapolate the Acceptable Cheese Limit from an article to a book? Do you go cheesy in the book once? Or do you go cheesy for 1/5th of the entire friggin' book? This author clearly, and wrongly, went with the latter. If a good editor had excised all the similes and self-discovery, it would be a really good read....more
Just started, but I love the use of simple graphs in the text. Also, the mild statistical-term shoutouts (beta distribution, etc) are nice for those oJust started, but I love the use of simple graphs in the text. Also, the mild statistical-term shoutouts (beta distribution, etc) are nice for those of us in the know, while seemingly easy to skim/skip by the laymen.
The introduction is a bit over-the-top about how much he promises *not* to be over-the-top about Big Data... But there are lots of good lines too.
p.17: "Sex appeal isn't something commonly quantified like this, so let me put it in a more familiar context: translate this plot to IQ, and you have a world where the women think 58 percent of men are brain damaged." p.21: "WEIRD research: white, educated, industrialized, rich, and democratic." I hadn't heard this term before (for psych studies carried out on convenience sample of college students) but it makes sense. Ch.3: great points about how kids today are writing MUCH more than a generation ago, despite the complaints that the internet is dumbing us down. He also mentions that people who write "u" and "i" instead of "you" and "I" on Twitter tend to already do that in other forms of writing -- people are consistent across mediums in that sense. p.69: The idiosyncratic copy-and-pasted message is convincing. I would not have guessed this is a copy-and-paste job. More power to ya. p.79: The idea of "assimilation" or "dispersion" in a social network graph (due to Backstrom & Kleinberg) is interesting, but the author seems to overcomplicate it. Maybe I misunderstood, but as far as I can tell, your marriage is "assimilated" unless (1) you have no friends in common with your spouse, or (2) ALL your friends are common -- both of you only have one group of friends. It seems clear that those are both unhealthy social situations, not just for your marriage but also otherwise. So why's this a big new insight?
[I stopped taking notes around here as I finished reading, then misplaced the book for a while, so I can't remember what most of the other highlights were.]
p.164ish: The "most typical" and "most antithetical" word lists by gender+race are both fascinating and kind of sad. It's a shame that, as far as I can see, the only few science words ("feynman" and "xkcd") are on the most-antithetical-for-black-men list. But it is funny that the most-antithetical-for-asians lists are full of misspellings. p.244: [Nice explanation of statistical precision.] "Ironically, with research like this, precision is often less appropriate than a generalization. That's why I often round findings to the nearest 5 or 10 and the words 'roughly' and 'approximately' and 'about' appear frequently in these pages. When you see in some article that '89.6 percent' of people do x, the real finding is that 'many' or 'nearly all' or 'roughly 90 percent' of them do it, it's just that the writer probably thought the decimals sounded cooler and more authoritative. The next time a scientist runs the numbers, perhaps the outcome will be 85.2 percent. The next time, maybe it's 93.4. Look out at the churning ocean and ask yourself exactly which whitecap is 'sea level.' It's a pointless exercise at best. At worst, it's a misleading one." p.245: [Funny and true.] "Data sets move through the research community like yeti---I have a bunch of interesting stuff but I can't say from where; I heard someone at Temple has tons of Amazon reviews; I think L has a scrape of Facebook." p.247: Parsons code! I remember, over a decade ago, having the brilliant idea to write a computer program that would listen to a song on the radio, or to your humming, and help you identify the song. You could even link the user to Amazon or somewhere to sell them a recording and make some money off this program. It seemed like a great project for my Signals & Systems engineering class. But I couldn't immediately think of a simple way to do it, and ended up choosing a different project, and never looked into this further. So apparently the Parsons code is an old, well-known idea, and it's how Shazam does it. I wish I'd had a bit more follow-through and tried this out then!
Overall a fun, quick read, with nice insight into how Google and Facebook and their pals deal with our data and what can be learned from it....more
p.2: "In fact, scientists and engineers become dependent on graphical representations so that, in their absence, they fail to accomplish tasks, interrp.2: "In fact, scientists and engineers become dependent on graphical representations so that, in their absence, they fail to accomplish tasks, interrupt meetings in order to fetch some representation, or at least use gestures to reproduce transient facsimile in the air."...more
There's so much overlap in all these recent data visualization books (Alberto Cairo, Stephen Few, Stephen Kosslyn, etc.) that I have trouble rememberiThere's so much overlap in all these recent data visualization books (Alberto Cairo, Stephen Few, Stephen Kosslyn, etc.) that I have trouble remembering exactly what's distinct about this one vs the others. There are also a few confusing typos that change the meaning of his (otherwise good) examples.
But it's a worthwhile quick read if datavis is an area of interest to you. And I do like the examples, such as the use of histograms as colorbars on a choropleth on p.275. Chapter 5 provides particularly nice advice on fonts/typography, annotations, data transformations, etc. Chapter 6 includes a handy chart relating questions (e.g., "What ___ is the best and worst?") to corresponding statistical concepts (max and min) and possible visuals (bar chart). I also like Yau's suggestion on p.264 to consider relatability ("...let readers see the data as it pertains to them... When you note the overall trend, you most likely looked at your own age and gender to find the corresponding probability...").
I don't think I could recommend this to an R novice (Appendix A, "A Brief Introduction to R," does seem reasonable for a beginner but I'm not sure aboI don't think I could recommend this to an R novice (Appendix A, "A Brief Introduction to R," does seem reasonable for a beginner but I'm not sure about the rest)... but it's a well-explained and thorough resource for someone with solid R experience already. It helped me solidify some concepts about R graphics and pointed out features of which I hadn't been aware. I'll be coming back to this when I need a refresher on plotting regions, layout, data symbols, etc. The "par" cheatsheets on p.51 and 53 are great. And if you want to extend R using grid graphics, this is the place to start....more
I appreciate the author's work on this book: there are plenty of useful examples and helpful reference tables. But I see room for major improvements tI appreciate the author's work on this book: there are plenty of useful examples and helpful reference tables. But I see room for major improvements too.
This is VERY much just a cookbook: example after example, with little to no discussion of how R works in general. A few key concepts would be really helpful: scripts vs the console, functions and objects, vectors vs data frames vs matrices, square bracket array notation, etc. Some of these things come up often in many "recipes," but the explanations are either interspersed throughout the text or just omitted. So when you want to tweak a more-complicated recipe to plot your own data, you're likely to be confused about key concepts. I'd love to see an appendix with this info, or at least some suggested links! Paul Murrell's R Graphics has a great example of such an appendix.
Also, some of the graphics examples could be much better. Many of R's built-in examples aren't great either! But I would expect more from a book that's explicitly meant to be an example-based intro for beginners. Consider the heatmap on p.25: what is this even trying to convey? Why is there no legend?
I find it hard to read when they omit spaces around the assignment operator, such as in "x<-3" ... It's assigning 3 to x, but it looks like it's checking whether x is less than -3.
Anyhow, this book is a good start, but I hope there's an improved second edition soon.
PS -- be aware that this book is in black-and-white, so it can't convey some of the color-based graphics well....more