Tag: visualization

Feed: R-bloggers. Author: Kamal Kumar. Categories Basic Statistics Tags Data Visualisation Linear Regression R Programming Hope you liked the Part 1 ,Part 2 and Part 3 of this Series. In this Part 4, we will go through the tools used during the Improve phase of Six Sigma DMAIC cycle. The most representative tool used during the Improve Phase is DOE-Design of experiments. Proper use of DOE can lead to process improvement, but a bad design of experiment can lead to undesired results – inefficiency, higher costs. What is experiment An experiment is a test or series of tests in which ... Read More

Feed: R-bloggers. Author: pmur002. This report describes several R packages that allow HTML content to be rendered as part of an R plot. The core package is called ‘layoutEngine’, but that package requires a “backend” package to perform HTML layout calculations. Three example backends are demonstrated: ‘layoutEngineCSSBox’, ‘layoutEnginePhantomJS’, and ‘layoutEngineDOM’. We also introduce two new font packages, ‘gyre’ and ‘courier’. Paul Murrell Download Related To leave a comment for the author, please follow the link and comment on their blog: R – Stat Tech. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: Data science, ... Read More

Feed: R-bloggers. Author: Achim Zeileis. In a new paper in Monthly Weather Review, minimum CRPS and maximum likelihood estimation are compared for fitting heteroscedastic (or nonhomogenous) regression models under different response distributions. Minimum CRPS is more robust to distributional misspecification while maximum likelihood is slightly more efficient under correct specification. An R implementation is available in the crch package. Citation Manuel Gebetsberger, Jakob W. Messner, Georg J. Mayr, Achim Zeileis (2018). “Estimation Methods for Nonhomogeneous Regression Models: Minimum Continuous Ranked Probability Score versus Maximum Likelihood.” Monthly Weather Review. 146(12), 4323-4338. doi:10.1175/MWR-D-17-0364.1 Abstract Nonhomogeneous regression models are widely used to statistically ... Read More

Feed: R-bloggers. Author: John Mount. In our last note we used wrapr::qe() to help quote expressions. In this note we will discuss quoting and code-capturing interfaces (interfaces that capture user source code) a bit more. My position on code-capturing interfaces (or non-standard-evaluation/NSE) is: if poorly handled, they can be a large interface price/risk to pay for the minor convenience of eliding a few quotation marks. Computation is meant to be over values. In fact one can think of computation as transforming code plus initial values to resulting values. Our wrapr R package has the sensible design principle: NSE can be ... Read More

Feed: R-bloggers. Author: R on YIHAN WU. The knitr and rmarkdown packages are used in conjunction with pandoc to convert R code and figures to a variety of formats including PDF, and word. Here, I’m exploring how to convert HTML back to markdown format. This post came about when I was searching how to convert XML to markdown, which I still haven’t found an easy way to do. Pandoc is not the only way to convert HTML to markdown (see turndown, html2text) Pandoc is packaged within RStudio and on Windows, the executables are located within Program Files/RStudio/bin/pandoc. The rmarkdown package ... Read More

Feed: R-bloggers. Author: Learning Machines. Have you ever thought about why the definition of a function in R is different from many other programming languages? The part that causes the biggest difficulties (especially for beginners of R) is that you state the name of the function at the beginning and use the assignment operator – as if functions were like any other data type, like vectors, matrices or data frames… Congratulations! You just encountered one of the big ideas of functional programming: functions are indeed like any other data type, they are not special – or in programming lingo, functions ... Read More

Feed: R-bloggers. Author: Thinking inside the box. Our linl package for writing LaTeX letter with (R)markdown had a fairly minor release today, following up on the previous release well over a year ago. This version just contains one change which Mark van der Loo provided a few months ago with a clean PR. As another user was just bitten the same issue when using an included letterhead – which was fixed but unreleased – we decided it was time for a release. So there it is. linl makes it easy to write letters in markdown, with some extra bells and ... Read More

Feed: R-bloggers. Author: Brian Lee Yung Rowe. I will be pushing a new version of futile.logger (version 1.5) to CRAN in January. This version introduces a number of enhancements and fixes some bugs. It will also contain at least one breaking change. I am making the release process public, since the package is now used in a number of other packages. If you use futile.logger, this is your opportunity to influence the direction of the package and prepare for changes. Please use the github issue tracker for discussion. There are two main themes for enhancements: 1) integration with the signal ... Read More

Feed: R-bloggers. Author: Tinniam V Ganesh. The second edition of my book ‘Deep Learning from first principles:Second Edition- In vectorized Python, R and Octave’, is now available on Amazon, in both paperback ($14.99)  and kindle ($9.99/Rs449/-)  versions. Since this book is almost 70% code, all functions, and code snippets have been formatted to use the fixed-width font ‘Lucida Console’. In addition line numbers have been added to all code snippets. This makes the code more organized and much more readable. I have also fixed typos in the book The book includes the following chapters Table of Contents Preface 4 Introduction 6 ... Read More

Feed: R-bloggers. Author: Angel Sevilla Camins' Blog. Summary Are you a data scientist who only wants to focus on modelling and coding and not on setting up a GPU cluster? Then, this blog might be interesting for you. We developed an automated pipeline using gitlab and Kubernetes that is able to run code in two GPU environments, GCP and bare-metal; no need to worry about drivers, Kubernetes cluster creation or deletion. The only thing that you should do is to push your code and it runs in a GPU!Source code for both the custom Docker images and the Kubernetes objects ... Read More