My ggplot2 cheat sheet: Search by task. There's a reason ggplot2 is one of the most popular add-on packages for R: It's a powerful, flexible and well-thought-out platform to create data visualizations you can customize to your heart's content. But it also can be a bit overwhelming. While I find the logic of plot layers to be intuitive, some of the syntax can be a bit of a challenge. Unless you do a lot of work in ggplot2, I'm not sure how easy it is to remember that, for example, the simple task of "make my graph title bold" requires the rather wordy theme(plot.title = element_text(face = "bold")). So I've come up with a two-step method that's drop-dead simple -- at least for me -- to do my most common dataviz tasks in ggplot2. Below is a cheat sheet, easily searchable by task, to see just how to do some of favorite and most-used ggplot2 options -- everything from creating basic bar charts and line graphs to customizing colors and automatically adding annotations.
Part 2 will make this even easier. Understanding Linear Regression. Abstract: Although Linear Regression is arguably one of the most popular analytical techniques, I believe it isn’t understood well. Several fundamental assumptions are violated during application. The objective of this note is to provide an overview of the assumptions and possible fixes. Linear regression is arguably one of the most widely used techniques in the data science world.
But, a comprehensive understanding of this technique is not universal and it is at a level that is less than desired. First, a little history, the term regression was first used by Sir Francis Galton, a 19th century polymath. Linear regression is an approach to model a relationship between a dependent variable (y) and one or more independent variables (x). Linearity and Additiveness of relationship between dependent and independent variables.Errors are statistically independentConstant variance of errors (Homoscedasticity)Errors are Normaly distributed Read full article.
Download Microsoft Mathematics 4.0 from Official Microsoft Download Center. Photos du journal - I fucking love science. The Hidden Beauty: Sand Under the Microscope. These microscopic images allow us to see things from a new and unfamiliar perspective. By magnifying 10, 100 or even 300 times, and focusing on the smallest details of the world, we’re able to see in clarity the smallest, most miniscule realms of reality. And who would ever expect that it could be this beautiful? The magnified photography seen here, taken by Dr. Gary Greenberg, is made to demonstrate how even something as insignificant as sand, under the right lenses, can be be perceived in an entirely different way. These close-up photos, which magnify sand up to 300 times, show that each grain of sand is unique in its own way. Dr. These sands are likely from the coasts of Hawaii (Dr. Grains of sand from a beach at the island of Maui, the second-largest of the Hawaiian Islands. 18 grains of sand magnified Sand from Lake Winnebigoshish, located in the middle of Minnesota This crystal, found within the sand from Zushi Beach in Japan, contains what appears to be a sapphire crystal.
Make Your Own Graphene With Just a Blender. Researchers unveiled the process for producing hundreds of liters of a solution containing graphene sheets -- it’s so simple, it can be replicated using household appliances (but to be quite honest, you probably shouldn’t actually try this at home). The ability to make large quantities of the cheap, good-quality carbon sheet that’s only an atom thick will boost its deployment in various commercial applications, from low-cost flexible electronic displays to filler material in plastic bottles. A team led by Jonathan Coleman from Trinity College Dublin transformed flakes of graphite into graphene solutions using commercially available tools: high-shear mixers and kitchen blenders. The shearing force generated by the rapidly rotating tools is intense enough to separate the layers of graphene in the graphite -- but without damaging their two-dimensional structure.
Here’s the recipe, outlined by Nature: The work was published in Nature Materials this week. Photo Gallery. Big Data Science sur Twitter : "Free, On-Demand Data Science Program to Quickly Become a #DataScientist: Free, On-Demand Data Science Program to Quickly Become a Data Scientist. We have re-designed our online, accelerated data science apprenticeship: it is now available to anyone, at no cost, with no restrictions, and does not require any application nor deadlines. Data sets, a cheat sheet to get you started, real-life projects to work on, sample code, and tons of resources, are provided on DSC, and are regularly updated, including very recently. This is an ideal program for professionals with a quantitative background and some industry experience - in a nutshell, for anyone who understands our cheat sheet and can get started using it. This program is for self-learners. Completed projects, submitted to and reviewed by Dr.
Vincent Granville, will be featured, published, and promoted on our network, reaching out to the the largest audience of data science decision makers, peers and hiring managers. Ideal for people who want to change career paths, consultants, people managing data scientists, or students starting an analytic degree. Click here to get started. The cloudiest places on Earth, in one beautiful map. Average cloud cover across Earth, from 2002 to 2015. White areas are cloudy; blue areas are not. Click to enlarge. (NASA Earth Observatory) Since 2002, NASA's Aqua Satellite has been orbiting Earth, taking thousands of high-resolution photos of our planet in order to help scientists better understand our water cycle and weather.
Recently, NASA compiled all this data to produce the gorgeous map above — a map of the average cloud cover everywhere on Earth for the last 13 years. Dark blue areas see very little cloud cover. When you look closely, you can see all sorts of interesting patterns going on. The Sahara, Arabian Desert, and Kalahari are all at roughly 30°N or S. These are the result of Hadley cells: winds patterns that blow dry, high-altitude air masses from the equator to the tropics, where they descend, sucking up moisture and preventing cloud formation. (UK Met Office) On the flip side, the same patterns form a distinct band of cloudiness at the equator. Nature's Pharmacy. In recent years, newspapers have run countless stories of athletes using steroids to enhance their performance on the track or field and to speed recovery from injury. But steroids are not illicit concoctions prepared on the sly in hidden laboratories.
Rather, they are natural compounds, manufactured largely by biochemical companies to treat bona fide medical conditions including arthritis and sex hormone deficiency. You might be surprised to learn that the bases for steroids and many other natural compounds used to help treat human illnesses originate in plants. In the early 20th century, chemists began to find structural similarity among a large group of natural substances found in humans and other mammals, including cholesterol, bile acids, and sex hormones.
Although the medicinal potential of these compounds quickly became obvious, extracting sufficient quantities of these substances from animal tissue and fluids was very expensive.
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