Create smooth animations in R with the tweenr package

There are several tools available in R for creating animations (movies) from statistical graphics. The animation package by Yihui Xie will create an animated GIF or video file, using a series of R charts you generate as the frames. And the gganimate package by David Robinson is an extension to ggplot2 that will create a movie from charts created using the ggplot2 syntax: in much the same way that you can create multiple panels using faceting, you can instead create an animation with multiple frames. But from a storytelling perspective, such animations can sometimes seem rather disjointed. For example, here’s the example (from the gganimate documentation) of crating an animated bubble chart from the gapminder data. (NOTE: to use the gganimate package, you will need to install ImageMagick. On Windows, be sure to select the “install legacy utilities” option during install:…
Original Post: Create smooth animations in R with the tweenr package

Create smooth animations in R with the tweenr package

There are several tools available in R for creating animations (movies) from statistical graphics. The animation package by Yihui Xie will create an animated GIF or video file, using a series of R charts you generate as the frames. And the gganimate package by David Robinson is an extension to ggplot2 that will create a movie from charts created using the ggplot2 syntax: in much the same way that you can create multiple panels using faceting, you can instead create an animation with multiple frames. But from a storytelling perspective, such animations can sometimes seem rather disjointed. For example, here’s the example (from the gganimate documentation) of crating an animated bubble chart from the gapminder data. (NOTE: to use the gganimate package, you will need to install ImageMagick. On Windows, be sure to select the “install legacy utilities” option during install:…
Original Post: Create smooth animations in R with the tweenr package

RStudio Connect 1.5.0 – Introducing Tags!

We’re excited to announce a powerful new ability to organize content in RStudio Connect: version 1.5.0. Tags allow publishers to arrange what they’ve published and enable users to find and discover the content most relevant to them. The release also includes a newly designed (and customizable!) landing page and multiple important security enhancements. Tagging Content with a Custom Tag Schema Tags can be used to manage and organize servers that have hundreds or even thousands of pieces of content published to them. Administrators can define a custom tag schema tailored to their organization. Publishers can then organize their content using tags, allowing all users to find the content they want by navigating through the tag schema. See more details in the video below: New Landing Page The default landing page has been given a fresh look. Even better, administrators can…
Original Post: RStudio Connect 1.5.0 – Introducing Tags!

Ridge Regression and the Lasso

In my last post Which linear model is best? I wrote about usingstepwise selection as a method for selecting linear models, which turnsout to have some issues (see this article, and Wikipedia).This post will be about two methods that slightly modify ordinary leastsquares (OLS) regression – ridge regression and the lasso. Ridge regression and the lasso are closely related, but only the Lassohas the ability to select predictors. Like OLS, ridge attempts tominimize residual sum of squares of predictors in a given model.However, ridge regression includes an additional ‘shrinkage’ term – thesquare of the coefficient estimate – which shrinks the estimate of thecoefficients towards zero. The impact of this term is controlled byanother term, lambda (determined seperately). Two interestingimplications of this design are the facts that when λ = 0 the OLScoefficients are returned and when λ = ∞, coefficients…
Original Post: Ridge Regression and the Lasso

Take The Next Step in Your Data Science Career

[unable to retrieve full-text content]The Saint Mary’s College Master of Science in Data Science program will prepare you to enter into the data analysis process at any stage, from the initial formulation of the question, to visualizing data, to interpreting the results and drawing conclusions.
Original Post: Take The Next Step in Your Data Science Career

Natural Language Generation overview – is NLG is worth a thousand pictures ?

[unable to retrieve full-text content]NLG tools automate the analysis and enhance traditional BI platforms by explaining in plain English the significance of visualizations and findings – here is an overview of the market.
Original Post: Natural Language Generation overview – is NLG is worth a thousand pictures ?

The first ever AI survey for Insurance: Get the low-down on how AI will impact you

[unable to retrieve full-text content]Check the new “AI, Analytics and GDPR Survey 2017”, where Insurance Nexus quizzed 250 of the brightest minds in insurance, and learn the latest trends in analytics, AI and GDPR to help you adjust your strategy.
Original Post: The first ever AI survey for Insurance: Get the low-down on how AI will impact you

R⁶ — Idiomatic (for the People)

NOTE: I’ll do my best to ensure the next post will have nothing to do with Twitter, and this post might not completely meet my R⁶ criteria. A single, altruistic, nigh exuberant R tweet about slurping up a directory of CSVs devolved quickly — at least in my opinion, and partly (sadly) with my aid — into a thread that ultimately strayed from a crucial point: idiomatic is in the eye of the beholder. I’m not linking to the twitter thread, but there are enough folks with sufficient Klout scores on it (is Klout even still a thing?) that you can easily find it if you feel so compelled. I’ll take a page out of the U.S. High School “write an essay” playbook and start with a definition of idiomatic: using, containing, or denoting expressions that are natural to a…
Original Post: R⁶ — Idiomatic (for the People)

Only Hours Left to Get 50% off DataCamp

This week, DataCamp is celebrating 1 million learners on its platform and is offering 50% off for unlimited access to the full curriculum. Join the learners who have completed over 40 million exercises on DataCamp and learn R or Python for data science interactively at your own pace with over 75 courses and 17 tracks. DataCamp instructors are all experts and thought-leaders in data science such as Matt Dowle (data.table), Hadley Wickham (R-Studio), Max Kuhn (caret), Garrett Grolemund (R-Studio), Jeffrey Ryan (xts & quantmod) and more. Get 50% off for full DataCamp access Below are some of the tracks and courses available. You can choose a career track, which is a deep dive into a subject that covers all the skills needed to get the job you want, or a skill track, which focuses on mastering a specific subject. Here…
Original Post: Only Hours Left to Get 50% off DataCamp