The visual display of quantitative information free ebook

Scientific visualization. How seeing turns into showing, how empirical observations turn into explanation and evidence. How to produce and consume evidence presentations. The definitive reference book with real-world solutions you won't find anywhere else The Big Book of Dashboards presents a comprehensive reference for those tasked with building or overseeing the development of business dashboards.

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Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it! Now, two science professors give us the tools to dismantle misinformation and think clearly in a world of fake news and bad data. Our media environment has become hyperpartisan. Science is conducted by press release.

Startup culture elevates bullshit to high art. In Calling Bullshit, Professors Carl Bergstrom and Jevin West give us a set of powerful tools to cut through the most intimidating data. Are the numbers or results too good or too dramatic to be true?

Is the claim comparing like with like? Is it confirming your personal bias? Drawing on a deep well of expertise in statistics and computational biology, Bergstrom and West exuberantly unpack examples of selection bias and muddled data visualization, distinguish between correlation and causation, and examine the susceptibility of science to modern bullshit.

We have always needed people who call bullshit when necessary, whether within a circle of friends, a community of scholars, or the citizenry of a nation. Now that bullshit has evolved, we need to relearn the art of skepticism. Written for statisticians, computer scientists, geographers, research and applied scientists, and others interested in visualizing data, this book presents a unique foundation for producing almost every quantitative graphic found in scientific journals, newspapers, statistical packages, and data visualization systems.

It was designed for a distributed computing environment, with special attention given to conserving computer code and system resources. Both of these data visualisers have some overlap - both are, after all, incredibly capable of transmitting complex data and information in striking displays - but it is Tufte's love of simplicity and worship of data that renders him a phenomenal graphical visualisation composer.

In "The Visual Display of Quantitative Information" he sets out to present some of his more famous inventions for the graphical display of information, namely the range-frame and the dot-dash-plot. Before presenting these, he sets out several guidelines which are key when producing graphics for the visualisation of any form of data and that hold truthful to this day.

A must-read for anyone looking to make their data visualisations clear, clean and impactful. Mar 08, Max rated it liked it. Light read, though sometimes the book judged me for having used histograms to display about 5 datapoints. Among the bad are artsy graphics, that for example use the height of three-dimensional objects like oi Light read, though sometimes the book judged me for having used histograms to display about 5 datapoints.

Among the bad are artsy graphics, that for example use the height of three-dimensional objects like oil barrels to display the oil price development. But with the height the volume changes disproportionally. Among the really bad are pie charts. I can very well imagine that there exists more concise online guides, though. Sep 22, Coop Williams rated it really liked it. Entertaining and illustrative. Tufte shows strong examples of both elegant and ghastly designs, taking several opportunities to improve the latter with surgical erasure.

These examples form the basis for a set of now-canonized principles. The only part I really disagreed with was the beginning of chapter 6, wherein the author proposes revising the box plot design by reducing it to a mere point floating between two lines, with only white space to represent the size of the interquartile range.

It l Entertaining and illustrative. It looks roughly like this: -. I would counter that the "box" aspect of the box plot is crucial to understanding the data at a glance, and that the white space makes interpretation harder on the reader. However, I'm pleased to say that this is the only deviation from sound design in the book. Interesting but I didn't love it. This is a niche book with a cult following - my academic dad for instance, who studied graph theory at one point and cares deeply about data, loves this book.

I see it's one of the top books on Goodreads for "design" in general. I found it quite dated. First published in the early 80s, there are still tons of references to putting pencil and ruler to paper, and the efficiency of one style over another based on reducing the number of pencil-strokes of lab technic Interesting but I didn't love it. I liked the exploration of some very elegant historical graphs, and an exploration of good chart and graph principles - I will take some lasting knowledge away.

Since I bought a copy on Thriftbooks for cheap, I will put in the background for video calls where I want to look smart when talking to a graphic designer or academic. The Visual Display of Quantitative Information is an absolute classic on the creation and use of graphs. Done correctly, a good graph can make complex information instantly comprehensible, reveal relationships and patterns, and both delight and inform.

Done poorly, a bad graph causes eyestrain, confusion, and the deliberate obfuscation of the truth. And in a world where graphs are ordinary, Tufte provides a quick history of how they came to be, and the cognitive leaps required. Tufte rails agains The Visual Display of Quantitative Information is an absolute classic on the creation and use of graphs. Tufte rails against the sins of bad graphics: scaling and axes that lie about trends in the data; the use of unnecessary ink to convey redundant information; visual clutter and bad aesthetics.

He advocates for a kind of elegant minimalism, conveying the most information with a few well-chosen lines of varying weights, and cleverly using edges and white space to mark boundaries, while supporting information with text.

The advice is for a pre-computer graphics era at least in my signed edition , but the aesthetics still hold, even if we aren't drawing graphs with a marker and straight-edge. The problem is that Tufte turned out to be a voice crying in the wilderness. There are the majors flaws, like the use of flashy cluttered "infographics" that combine the worst features of text-heavy articles and data graphics.

But then there is the minor things. I have at my fingertips about a half-dozen data visualizations packages, from Excel boo! And not a single one, by default, does everything that Tufte says. They get close, but the defaults are not quite minimalist enough. And truly great graphs, like Minard's plot of Napoleon's invasion of Russia, with his army vanishing into the snows, still require an artist's touch. Mar 16, Josh Friedlander rated it really liked it Shelves: science-and-mathematics , art-aesthetics-culture.

Most of Tufte's critiques of ugly and dishonest data visualisation have been long internalised, in our age of and "data journalism". Jan 03, Shan Zhong rated it it was amazing. Definitely my favorite printed book. Feb 03, Sasha rated it really liked it. Never was a dude so salty about bad graphs and bad data. Humorous as well as clever.

Before going into the review itself, a comment on a slight oddity of the book which will become important in the review : The copy I read is the 7th printing March of the second edition originally published in ; the first edition was published in At least one chapter has been rewritten or added since the second edition was originally published. Chapter 8 contain Before going into the review itself, a comment on a slight oddity of the book which will become important in the review : The copy I read is the 7th printing March of the second edition originally published in ; the first edition was published in Chapter 8 contains examples and data from and , as well as ending with a data point from !

Generally, one would consider the rewriting of a chapter rather than the correction of typos and other errors to be, if not a new edition, at least worthy of mentioning in the description of the book, but no such mention has been made. Without a copy of an earlier printing of the same edition, I cannot comment on whether other changes are present or absent; this was the only chapter where I noticed data or examples that post-date the original second edition printing.

Filled with examples of both good and bad graphic design, he eloquently argues for rules of design that maximize information and "truth" to avoid misinformation and distraction. Truly a masterwork, it is not without flaws. He has a tendency toward hyperbole, declaring certain graphs to be the "worst ever made" or "best ever made".

He accuses many graphical designers of deliberately lying to the consumer, when the reality in many although certainly not all cases is likely more a case of incompetence or ignorance.

He states rules to follow with a certainty that are sometimes themselves not backed up with data, but rather simply reflect opinion. And finally, some of his principles lack any real discussion or acknowledgement or context, something which stands out in particular with the recognition that most of the examples are rather dated. For example, he has a large discussion focused on the size of printed graphics, a discussion which completely ignores any context in which a graphic might be presented beyond the printed page.

When the second edition was originally printed in , most scientific presentations were still using overheads or physical slides; the widespread use of Powerpoint for presentations did not take off until a few years after the book was published. However, the chapter with this discussion is the one mentioned above that has clearly been updated since ! This enhances the dated feeling of some of the discussion, making one wonder if there is a bit of statistical and graphic Luddite influence to the writing.

However, for for visual presentation in a talk or lecture, Powerpoint is better than many of its competitors. Unfortunately, some of his design discussion simply doesn't translate to non-printed publication, including presentations and graphics to be found on the web, which may have very different design considerations, not the least of which is the potential for consumer interactivity. He has written additional books, some of which I plan on reading, and some of which may get into these issues, although I suspect not.

Despite all of these flaws, I truly believe the book is incredibly well done and its influence since it was originally published cannot be understated. Scattered throughout the book, often although not always recapped at the end of chapters, are "rules" of design that are so striking in their statement, I plan on collecting most of them onto a single piece of paper to hang on the wall by my desk as a visual reminder of what to do and think about when designing my own graphics going forward.

That, more than anything else, illustrates my feelings toward this book. Sep 28, Padraig rated it really liked it Shelves: visualisation. Graphs are capable of conveying a large amount of information very concisely, showing correlations between inputs, but how often do you see graphs in newspapers? Not nearly enough. Shelves: work-related. I'm imaging tufte writing up this rant in a basement with "we're not gonna take it" blaring in the background, every few paragraphs he mumbles something like "this will show them!

Section two is pretty much the kind of five paragraph essay I was required to write in school. It's not very often someone makes an argument that hard. I'm all amped up to create lots o I'm imaging tufte writing up this rant in a basement with "we're not gonna take it" blaring in the background, every few paragraphs he mumbles something like "this will show them! I'm all amped up to create lots of info graphics now. Nov 05, Lindig rated it it was amazing Shelves: non-fiction.

I discovered Tufte when I was collecting movable books and this showed up in my bookstore with a pop-up pyramid in it. I found out later that he had self-published this title because no printer or publisher he approached wanted to do the pop-up and he was determined to have it. It's a wonderful explication of the ways in which to analyze data and figure out how to present it in clean, efficient ways that slide the information into waiting minds. And anybody who enjoys this book will lik I discovered Tufte when I was collecting movable books and this showed up in my bookstore with a pop-up pyramid in it.

And anybody who enjoys this book will like the site flowingdata. Apr 09, Erik rated it it was ok. Interesting subject matter but incredibly pompous author. Jul 06, Steve rated it liked it. I went to a Tufte course and four of his publications were given out as part of the course fee. This is the first one he published on this subject, and the first I've read. Overall, if you've never made a statistical graphic, this covers some of the basics but it feels a bit dated as well. Read this book if you're looking for some history on the subject of plotting data, and plenty of opinions from the well-respected author.

I'm no stranger to making statistical graphics, it's a task that comes I went to a Tufte course and four of his publications were given out as part of the course fee. I'm no stranger to making statistical graphics, it's a task that comes up when writing research publications, at work, and sometimes in my home projects.

I was hoping to find some novel ways to think about plotting data, or at least some clean guidelines beyond those I already knew e.

Since I had no formal training in the subject just exposure to many examples and some tips from random blog posts over the years I figured I had a lot to learn. With that perspective, the material in this book was a bit of a let down. I have to imagine that when it was written, Tufte dropped some wisdom on chart designers, but it has since percolated out into the mainstream, both embedded in tools and anecdotal advice given to plotters. Tufte has a justifiably dim view on how computer drawing tools were used at the time to make charts, but I think he unrealistically kept the focus on manual production of plots in the second edition.

As a point of reference, I have never published in a venue that allowed hand-drawn graphics to be included in a manuscript, and I think that's pretty much a universal standard these days. He also highlights a few plot designs that were pretty interesting and unused in when this first came out, but have not caught on at all in the fields that I know of in the 35 years since.

Time to revisit their utility, maybe? One major omission in this book was the role of bias in chart preparation. Some data visualizations are pretty straight forward and have very little bias injected into the display think scatterplots, with simple points that let you pull out their patterns using "visual analytics".

Now consider his proposed " rugplot ", which is basically a series of 2D scatterplots arranged next to each other such that 1 dimension is shared between adjacent plots just look at the pic near "Data Ink Minimization " in the link. There is a huge hidden bias here: the chart preparer can select any pair of the N dimensions to show next to each other.

Different choices will lead to different stories being told, but with no way for the viewer to really consider alternative hypotheses than the one presented by the designer. But what if you wanted to pick other pairs of dimensions to look through?

You can't. Since there are so many possible combinations to choose from, you would be right to doubt that the designer chose the "best" or even a "fair" representation of the data. This is one specific example of bias in data visualization, but given that this problem crops up in sufficiently complex multi-dimensional data source such as those championed by Tufte, I would have thought it would have been a topic of discussion in the text. Well, this review got away from me a bit. The book is a fun thing to flip through, it's easy reading, and just feels great to hold.

More non-fiction books should be like this! One of his updated books might be more fulfilling to a student of modern statistical graphics, but this one certainly frames the history and advances through the s quite well, if that's what you're looking for. Feb 15, Vishal Katariya rated it it was amazing Shelves: non-fiction , essentials. What an experience. You may find it strange that I look upon this book almost reverentially, for it is merely an exposition of what constitutes good graphic design for data visualization.

However, this is no ordinary exposition: Edward Tufte is unquestionably one of the masters at the forefront in this task, and he does a thorough job of describing some heuristics and "laws" for what make good graphs, plots, maps and so on.

Given his mastery of the subject, I allowed for his sometimes brazen rul What an experience. Given his mastery of the subject, I allowed for his sometimes brazen rules, which almost seem like commandments. Make all your charts wider than tall, for example.

I don't think that's a rule that can be followed universally. Anyway, I suggest you read it if you can get access to it. There is much to learn in case you need to make graphs for your personal or professional use, and there's also the joy of witnessing someone unquestionably good at what they do expounding on their knowledge and expertise.

Shelves: statistics. The main point was: a good graph is one that gives to the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space.

Fascinating books, with fascinating charts -- both graphically and information-wise. This should be mandatory reading for everyone who will ever draw a graph in their life. It is simply amazing. The teachings bordered on my intuition for graph design, so I'll give it a 4. Jan 03, Justin rated it it was amazing.

The Visual display of quantitative information , Graphics Press. The visual display of quantitative information, by Edward R. Tufte Publish date unknown, Graphics Press. The visual display of quantitative information. Publish date unknown, Graphics Press. Excellence in statistical graphics consists of complex ideas communicated with clarity, precision, and efficiency. Classifications Dewey Edition Description The classic book on statistical graphics, charts, tables.

Edition Notes Includes index. Classifications Dewey Decimal Class T83 Community Reviews 0 Feedback? Loading Related Books. Tufte Publish date unknown, Graphics Press in English. Publish date unknown, Graphics Press in English. April 13, Edited by ImportBot. December 13,