Current state of the matter The reason for bringing this here is that I witnessed an interesting exchange some time ago, regarding one article and their use of post-hoc power, pinpointed by @ADAlthousePhD: Hi @scottlemaire, I would like to issue a public expression of concern about a paper published recently in the Journal of Surgical Research, on which you are the editor in chief. — Andrew Althouse (@ADAlthousePhD) April 30, 2019 .

An updated tutorial to set up a blogdown blog Install Blogdown Install Hugo ./From RStudio ./From other sources Git & GitHub repos RStudio and blogdown setup Customising paths and styles Modify config.toml file Custom css Custom javascript MathJax Site build with blogdown Deploying the site Updating the local git repository Pushing to GitHub Add R code A new post in blogdown Deploying the site with the new post This .

J Espasandin, O Lado, C Díaz, A Bouzas, I Guler, A Baluja. These days, between the 19th and 21st of February, has taken place the learning activity titled “An Introduction to the Joint Modeling of Longitudinal and Survival Data, with Applications in R” organized by the Interdisciplinary Group of Biostatistics (ICBUSC), directed by Professor Carmen Cadarso-Suárez, from the University of Santiago de Compostela. The international nature of this scientific activity has been marked by the presence of researchers from different European countries such as Germany, Portugal, Holland, Greece or Turkey.

Introduction The last two days arrived at my twitter feed some discussions on how bad are the following sentences at the beginning of your R script/notebook, sparked by @JennyBryan’s slides at the IASC-ARS/NZSA Conference: setwd() and rm(list = ls()) The only two things that make @JennyBryan 😤😠🤯. Instead use projects + here::here() #rstats pic.twitter.com/GwxnHePL4n — Hadley Wickham (@hadleywickham) December 11, 2017 Blog post elaborating on my advice to avoid setwd() and rm(list = ls()) at top of an #rstats script.

Introduction There are several ways to mine tables and other content from a pdf, using R. After a lot of trial & error, here’s how I managed to extract global exam results from an international, massive, yearly examination, the EDAIC. This is my first use case of “pdf mining” with R, and also a fairly simple one. However, more complex and very fine examples of this can be found elsewhere, using both pdftools and tabulizer packages.

Background A hospital may be defined as a place, or building, or set of facilities where patients go to receive the care they need. In ancient times, a hospital was not much more than a hotel for the poor, where they could stay and be cared for. Some centuries passed, and now we call hospitals to extremely complex structures, from both physical and functional perspectives.

The blogdown R package Finally, -after 24h of failed attempts-, I could get my favourite Hugo theme up and running with R Studio and Blogdown. After exploring some alternatives, like Shirin’s (with Jekyll), and Amber Thomas advice (which involved Git skills beyond my basic abilities), I was able to install Yihui’s hugo-lithium-theme in a new repository. However, I wanted to explore other blog templates, hosted in GiHub, like:

Wordclouds are one of the most visually straightforward, compelling ways of displaying text info in a graph. Of course, we have a lot of web pages (and even apps) that, given an input text, will plot you some nice tagclouds. However, when you need reproducible results, or getting done complex tasks -like combined wordclouds from several files-, a programming environment may be the best option. In R, there are (as always), several alternatives to get this done, such as tagcloud and wordcloud.

One of the things I most like from R + Shiny is that it enables me to serve the power and flexibility of R in small “chunks” to cover different needs, allowing people not used to R to benefit from it. However, what I like most is that’s really fun and easy to program those utilities for a person without any specific programming background. Here’s a small hack done in R/Shiny: it covered an urgent need for a study involving patient randomisation to two branches of treatment, in what is commonly known as a clinical trial.

My PC just passed away. My good old one since 2009… so I decided to buy a desktop computer running OSX 10.10 (although I cannot exclude the possibility of partitioning the disk and installing also Linux…). For now, I missed some apps, which I installed with Fink (a popular debian-based distro ported to Mac). One of the programs I was fiddling around with was BayesX: “Bayesian Inference in Structured Additive Regression Models”, authored by great people at Muenchen University, (specially Nadja and Thomas, whom I’m glad to know personally).