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Junk Charts

Junk Charts
This post is part 2 of an appreciation of the chart project by Google Newslab, advised by Alberto Cairo, on the gender and racial diversity of the newsroom. Part 1 can be read here. In the previous discussion, I left out the following scatter bubble plot. This plot is available in two versions, one for gender and one for race. The key question being asked is whether the leadership in the newsroom is more or less diverse than the rest of the staff. The story appears to be a happy one: in many newsrooms, the leadership roughly reflects the staff in terms of gender distribution (even though both parts of the whole compare disfavorably to the gender ratio in the neighborhoods, as we saw in the previous post.) Unfortunately, there are a few execution problems with this scatter plot. First, take a look at the vertical axis labels on the right side. I find this decision confounding. The horizontal axis? Here is the same chart with improved axis labels: Re-labeling serves up a new issue. Related:  Data Visualizations

Best of the visualization web At the end of each month I pull together a collection of links to some of the most relevant, interesting or thought-provoking web content I've come across during the previous month. Here's the latest collection from January 2018. Visualisations & Infographics Includes static and interactive visualisation examples, infographics and galleries/collections of relevant imagery. SRF | 'Roger Federer: 20 Years, 20 Titles' Mapping Police Violence | '2017 Police Violence Report... collected data on over 1,100 killings by police in 2017.' SCMP | '2017: the safest skies record' SCMP | ... and here's a photo of the print version Economics | 'All the president’s tweets' Pixel Mixer | 'Anatomy of a Viz - The Level is in the Details' Mike Vizneros | 'A Chamber Divided: What We Can Learn By Using BioFabric Charts' Taylor Baldwin | 'Audiofabric' Guardian | 'Bussed out: How America moves its homeless' Twitter | 'A climate change cross stitch' Guardian | 'How the NHS winter beds crisis is hitting patient care'

Raphaël—JavaScript Library Visual Numbers Rule Your World Two years ago, Wired breathlessly extolled the virtues of A/B testing (link). A lot of Web companies are in the forefront of running hundreds or thousands of tests daily. The reality is that most A/B tests fail. A/B tests fail for many reasons. Bad outcome isn't the primary reason for A/B test failure. 1. 2. 3. These issues are often ignored or dismissed. The Facebook Data Science team just launched an open platform for running online experiments, called PlanOut. The rest of this post gets into some technical, sausage-factory stuff, so be warned. Bad design is when the experiment is set up in such a way that it does not provide data to answer the research question. Let's say you want to test changing the text on your registration button from "Sign up Now" to "Join Free". The problem is noise in your data. The examples of failed designs are endless. Worse than bad or no design is bad execution of a good design. Start with the experimental unit. - integrated logging - quality-control charts

The Functional Art Amazon Kindle Keyboard Shortcuts A list of Keyboard Shortcuts for the Amazon Kindle. [edit] Keyboard Shortcuts Note on syntax: Keys separated by "+" should be held. Keys separated by "-" should be pressed and released. [edit] Global Keys Alt+G refresh screen (anti-ghosting) Alt+Shift+R reboot Kindle Alt+Shift+. restart GUI and show your Serial Number with Bar code Alt+Shift+G screenshot An SD card is required to store screenshots; they are saved in .GIF format in the card's root. [edit] Home Page Alt+Shift+M Minesweeper game (and GoMoku in Kindle3 by pressing G in Minesweeper) Alt+Z rescan picture directories Alt+T show time (does not work in Kindle 3 but you can hit Menu button to see time) Number Keys flip to entered book list page (e.g. entering 1-0 will take you to page 10) Alt+Home Open Amazon Store [edit] Reader Alt+B toggle bookmark Alt+T spell out time (does not work in Kindle 3 but you can hit Menu button to see time) Alt+0 (zero) enable/disable slideshow (note that this will also work with text. Alt+6 ?

Gamestorming 40 Essential Tools and Resources to Visualize Data One of the most frequent questions I get is, "What software do you use to visualize data?" A lot of people are excited to play with their data, but don't know how to go about doing it or even start. Here are the tools I use or have used and resources that I own or found helpful for data visualization – starting with organizing the data, to graphs and charts, and lastly, animation and interaction. Organizing the Data by sleepy sparrow Data are hardly ever in the format that you need them to be in. PHP was the first scripting language I learned that was well-suited for the Web, so I'm pretty comfortable with it. Python Most computer science types - at least the ones I've worked with - scoff at PHP and opt for Python mostly because Python code is often better structured (as a requirement) and has cooler server-side functions. MySQL When I have a lot of data - like on the magnitude of the tends to hundreds of thousands - I use PHP or Python to stick it in a MySQL database. Ah, good old R.

Visual Business Intelligence We typically think of quantitative scales as linear, with equal quantities from one labeled value to the next. For example, a quantitative scale ranging from 0 to 1000 might be subdivided into equal intervals of 100 each. Linear scales seem natural to us. If we took a car trip of 1000 miles, we might imagine that distance as subdivided into ten 100 mile segments. It isn’t likely that we would imagine it subdivided into four logarithmic segments consisting of 1, 9, 90, and 900 mile intervals. Logarithms and their scales are quite useful in mathematics and at times in data analysis, but they are only useful for presenting data on those relatively rare cases when addressing an audience that consists of those who have been trained to think in logarithms. For my own analytical purposes, I use logarithmic scales primarily for a single task: to compare rates of change. I decided to write this blog piece when I ran across the following graph in Steven Pinker’s new book Enlightenment Now:

Web Design by Designer, Speaker and Writer Brian Hoff How to be a data journalist | News Data journalism is huge. I don't mean 'huge' as in fashionable - although it has become that in recent months - but 'huge' as in 'incomprehensibly enormous'. It represents the convergence of a number of fields which are significant in their own right - from investigative research and statistics to design and programming. The reality is that almost no one is doing all of that, but there are enough different parts of the puzzle for people to easily get involved in, and go from there. 1. 'Finding data' can involve anything from having expert knowledge and contacts to being able to use computer assisted reporting skills or, for some, specific technical skills such as MySQL or Python to gather the data for you. 2. Interrogating data well means you need to have a good understanding of jargon and the wider context within which data sits, plus statistics - a familiarity with spreadsheets can help save a lot of time. 3. 4. Tools such as ManyEyes for visualisation, and Yahoo! How to begin?

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