Untitled. Data is beautiful, data is a story. Packages. Using R to quickly create an interactive online map using the leafletR package. The Molecular Ecologist. First off, thanks to Tim and Jeremy for the invitation to write a guest post here on using R to make maps!
As a brief introduction, my name is Kim Gilbert, and I am a Ph.D. student at the University of British Columbia working with Mike Whitlock. I am broadly interested in population genetics and population structure, and am currently studying local adaptation in a tree species. If you want to know more, check out my website, where I also have this tutorial as a .pdf presentation. Okay, onward with R! And apologies in advance that the R code provided will not show color coded text (a limitation of wordpress), but I decided that it is more useful to leave as text in order to allow copying and pasting rather than insert screenshots that might look nicer but require retyping on your part.
In the field of molecular ecology we see many, many maps. Packages. Data Science - Community. Web Technologies and Services. Data Sets. Index of /ml/machine-learning-databases. Publishable Stuff. DataRemixed. VizWiz - Data Visualization Done Right. Visual Business Intelligence. For data sensemakers and others who are concerned with the integrity of data sensemaking and its outcomes, the most important book published in 2016 was Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy, by Cathy O’Neil.
This book is much more than a clever title. It is a clarion call of imminent necessity. Data can be used in harmful ways. This fact has become magnified to an extreme in the so-called realm of Big Data, fueled by an indiscriminate trust in information technologies, a reliance on fallacious correlations, and an effort to gain efficiencies no matter the cost in human suffering. VC blog.
Posted: November 26th, 2014 | Author: Manuel Lima | Filed under: Uncategorized | No Comments » As some attentive users of Visual Complexity might have noticed, the number of projects featured on the website has slowly come to a halt, with the perpetual grand total of 777 being a grieving reminder of inactivity for well over a year.
Today, If you go the the main page and look at the top right corner, you will see an invigorating new message: “Indexing 782 projects”. Of course I didn’t want to write this blog post to announce that five new projects have been added to the database. Information aesthetics - Data Visualization & Information Design. Principal Component Analysis step by step. In this article I want to explain how a Principal Component Analysis (PCA) works by implementing it in Python step by step.
A Quick-R Companion. R for Public Health. DiffusePrioR. Blog. An introductory comparison of using the two languages.
Background R was made especially for data analysis and graphics. SQL was made especially for databases. They are allies. Error Statistics Philosophy. Machine Learning (Theory) Data Mining: Text Mining, Visualization and Social Media. The Turing Test for artificial intelligence is a reasonably well understood idea: if, through a written form of communication, a machine can convince a human that it too is a human, then it passes the test.
The elegance of this approach (which I believe is its primary attraction) is that it avoids any troublesome definition of intelligence and appeals to an innate ability in humans to detect entities which are not 'one of us'. This form of AI is the one that is generally presented in entertainment (films, novels, etc.). However, to an engineer, there are some problems with this as the accepted popular idea of artificial intelligence. I believe that software engineering can be evaluated in a simple measure of productivity. Hilarymason.com. Big Data, Plainly Spoken (aka 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. Typically, business leaders consider a test to have failed when the analysis fails to support their hypothesis. "We ran all these tests varying the color of the buttons, and nothing significant ever surfaced, and it was all a waste of time! " Bad outcome isn't the primary reason for A/B test failure. 1. 2. Statisfaction. Statisfaction. Doing Bayesian Data Analysis. Three-Toed Sloth. "Learning Spatio-Temporal Dynamics" : Boasting about my student's just-completed doctoral dissertation. Over 2500 words extolling new statistical methods, plus mathematical symbols and ugly computer graphics, without any actual mathematical content, or even enough detail to let others in the field judge the results.
On Monday, my student Georg M. Goerg , last seen here leading Skynet to declare war on humanity at his dissertation proposal , defeated the snake — that is, defended his thesis: Blog. Old tails: a crude power law fit on ebook sales We use R to take a very brief look at the distribution of e-book sales on Amazon.com.
Read more… You don’t need to understand pointers to program using R Practical Data Science with R: Release date announced It took a little longer than we’d hoped, but we did it! If you haven’t yet, order it now! (softbound 416 pages, black and white; includes access to color PDF, ePub and Kindle when available) Can a classifier that never says “yes” be useful? Many data science projects and presentations are needlessly derailed by not having set shared business relevant quantitative expectations early on (for some advice see Setting expectations in data science projects). Access to Statistics. An Academic Blogging Experiment. Bayesianbiologist.