Embedding a Shiny app inside my blog
Embedding shiny apps inside an html page [Read more...]
May 2018
I’m not following you II: Twitter data and R
My activity on twitter revolves around four accounts. Practice note:@clathrin: science, cell biology, sometimes music and cycling/running@roylelab: lab news and announcements, part of @Warwick_CMCB@quantixed: programming, data, nerdery.Opinions my own etc — Steve Royle (@clathrin) May 4, 2018 I try to segregate what happens on each account, and ...
The DRY Principle and Knowing When to Make a Package
Don’t Repeat Yourself (DRY)
Probably everyone who has done some kind of programming has heard of the
“Don’t Repeat
Yourself” (DRY)
principle. In a nutshell, it’s about reducing code redundancy for the
purpose of reducing error and enhancing readability.
Undoubtedly the most common manifestation of the DRY ...
Regression with Interaction Terms – How Centering Predictors influences Main Effects
Centering predictors in a regression model with only main effects has no influence on the main effects. In contrast, in a regression model including interaction terms centering predictors does have an influence on the main effects. After getting confused by this, I read this nice paper by Afshartous & Preston (2011) on ... [Read more...]
How to Plot With Ggiraph: Exercises
INTRODUCTION The ggiraph is an htmlwidget and a ggplot2 extension. It allows ggplot graphics to be animated. Animation is made with ggplot geometries that can understand three arguments: Tooltip: a column of data-sets that contain tooltips to be displayed when the mouse is over elements. Onclick: a column of data-sets ... [Read more...]
A Different Way To Think About Drawdown — Geometric Calmar Ratio
This post will discuss the idea of the geometric Calmar ratio — a way to modify the Calmar ratio to … Continue reading → [Read more...]
tidyposterior slides
tidyposterior is an R package for comparing models based on their resampling statistics. There are a few case studies on the webpage to illustrate the process.
I gave a talk at th... [Read more...]
Do you really need a multilevel model? A preview of powerlmm 0.4.0
In this post I will show some of the new simulation features that will be available in powerlmm 0.4.0. You can already install the dev version from GitHub.
# GitHub
devtools::install_github("rpsychologist/powerlmm")
The revamped simulation functions offer 3 major new features:
Compare multiple model formulas, including OLS models (no random ... [Read more...]
greybox package for R
I am delighted to announce a new package on CRAN. It is called “greybox”. I know, what my American friends will say, as soon as they see the name – they will claim that there is a typo, and that it should be “a” instead of “e”. But in fact no ...
Survey books, courses and tools by @ellis2013nz
Surveys everywhere!
Surveys are everywhere, and thinking about them in different ways has dominated my work so far this year. What’s right and wrong with them, how to improve them, do we need them, can we replace this one or that one with administrative data, why do the media (... [Read more...]
Survey books, courses and tools by @ellis2013nz
Surveys everywhere!
Surveys are everywhere, and thinking about them in different ways has dominated my work so far this year. What’s right and wrong with them, how to improve them, do we need them, can we replace this one or that one with administrative data, why do the media (... [Read more...]
About Risks and Side-Effects… Consult your Purrr-Macist
Capture errors, warnings and messages, but keep your list operations going In a recent post about text mining, I discussed some solutions to webscraping the contents of our STATWORX blog using the purrr-package. However, while preparing the next the episode of my series on text mining, I remembered a little ...
MLE with General Optimization Functions in R
In my previous post (https://statcompute.wordpress.com/2018/02/25/mle-in-r/), it is shown how to estimate the MLE based on the log likelihood function with the general-purpose optimization algorithm, e.g. optim(), and that the optimizer is more flexible and efficient than wrappers in statistical packages. A benchmark comparison are given ... [Read more...]
Seventeen Minutes From Tweet To Package
Earlier today, @noamross posted to Twitter: #rstats #lazyweb What's the R/httr/curl equivalent of curl -F "[email protected]" https://t.co/abbugLz9ZW — Noam Ross (@noamross) May 3, 2018 The answer was a 1:1 “file upload” curl to httr translation: but I wanted to do more than that since Noam took 20 ... [Read more...]
Make Your Own AFL Graph
“The greatest value of a picture is when it forces us to notice what we never expected to see.” - J. W. Tukey (1977)
To learn a new skill I think there needs to be 2 main drivers
An interest in a topic
easy to get to tangible wins to encourage deeper ...
FitzRoy
I don’t know if anyone knows this, but I love sports.
A key barrier to entry for the growth of the AFL analytics community is simply data access which prevents not only people having a go at writing, but it also prevents current media having reproduc... [Read more...]
Bayesian Inference with Backfitting MCMC
Previous posts in this series on MCMC samplers for Bayesian inference (in order of publication): Bayesian Simple Linear Regression with Gibbs Sampling in R Blocked Gibbs Sampling in R for Bayesian Multiple Linear Regression Metropolis-in-Gibbs Sampling... [Read more...]
Qualitative Data Science: Using RQDA to analyse interviews
How can R be used to conduct qualitative data science? An example analyses interviews with customer advocacy groups and regulators of water utilities using the RQDA package.
The post Qualitative Data Science: Using RQDA to analyse interviews appeared first on The Lucid Manager.
Qualitative Data Science: Using RQDA to analyse interviews
How can R be used to conduct qualitative data science? An example analyses interviews with customer advocacy groups and regulators of water utilities using the RQDA package. Continue reading →
The post Qualitative Data Science: Using RQDA to analyse interviews appeared first on The Devil is in the Data.
Regression Modeling for Time Series
Query Google Trends
Explore and Decompose the Series
Model the Linear Relationship
Accounting for Autocorrelation
Summary
A little over a month ago Rob Hyndman finished the 2nd edition of his open source book Forecasting: Principles and Practice. Take a look, it’s a fantastic introduction and companion to applied time ...
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