Posted by Peter Solymos

Run Shiny Apps Locally

You might be eager to deploy your Shiny app to a remote server. But the simplest way to run a Shiny app is to run it locally. You only need the shiny R package installed, and you can run the app in your browser. In this post you'll see a few ways of how to organize your files to be served … Continue reading →

The Best Resources for Learning Shiny App Development

The Hosting Data Apps website is dedicated to help you learn about your hosting options. As outlined in the opening post, data app development related content is kept to a minimum. In this post we list resources that provide accessible introduction to Shiny app development.What is Shiny againShiny … Continue reading →

The Anatomy of a Shiny Application

Shiny lets you quickly build web applications using the R programming language. In this post I will walk you through how a Shiny application is structured. The goal is simply to have the most basic and dependency free app that we can deploy. Let's get going!What is R and ShinyR is a free … Continue reading →

Introducing the bSims R package for simulating bird point counts

The bSims R package is a highly scientific and utterly addictive bird point count simulator. Highly scientific, because it implements a spatially explicit mechanistic simulation that is based on statistical models widely used in bird point count analysis (i.e. removal models, distance … Continue reading →

Fitting removal models with the detect R package

In a paper recently published in the Condor, titled Evaluating time-removal models for estimating availability of boreal birds during point-count surveys: sample size requirements and model complexity, we assessed different ways of controlling for point-count duration in bird counts using data … Continue reading →

Fitting removal models with the detect R package

In a paper recently published in the Condor, titled Evaluating time-removal models for estimating availability of boreal birds during point-count surveys: sample size requirements and model complexity, we assessed different ways of controlling for point-count duration in bird counts using data … Continue reading →

Shiny slider examples with the intrval R package

The intrval R package is lightweight (~11K), standalone (apart from importing from graphics, has exactly 0 non-base dependency), and it has a very narrow scope: it implements relational operators for intervals — very well aligned with the tiny manifesto. In this post we will explore the use of … Continue reading →

Shiny slider examples with the intrval R package

The intrval R package is lightweight (~11K), standalone (apart from importing from graphics, has exactly 0 non-base dependency), and it has a very narrow scope: it implements relational operators for intervals — very well aligned with the tiny manifesto. In this post we will explore the use of … Continue reading →

Phylogeny and species traits predict bird detectability

It all started with this paper in Methods in Ecol. Evol. where we looked at detectability of many species. So we wanted to use life history traits to validate our results. But we had to cut the manuscript, and there was this leftover with some neat patterns, but without much focus. It took a few … Continue reading →

PVA: Publication Viability Analysis, round 3

A friend and colleague of mine, Péter Batáry has circulated news from Nature magazine about the EU freezing innovation funds to Bulgaria. The article had a figure about publication trends for Bulgaria, compared with Romania and Hungary. As I have blogged about such trends in ecology before (here … Continue reading →

The progress bar just got a lot cheaper

The pbapply R package that adds progress bar to vectorized functions has been know to accumulate overhead when calling parallel::mclapply with forking (see this post for more background on the issue). Strangely enough, a GitHub issue held the key to the solution that I am going to outline below. … Continue reading →

What is new in the intrval R package?

An update (v 0.1-1) of the intrval package was recently published on CRAN. The package simplifies interval related logical operations (read more about the motivation in this post). So what is new in this version? Some of the inconsistencies in the 1st CRAN release have been cleaned up, and I have … Continue reading →

Relational operators for intervals with the intrval R package

I recently posted a piece about how to write and document special functions in R. I meant that as a prelude for the topic I am writing about in this post. Let me start at the beginning. The other day Dirk Eddelbuettel tweeted about the new release of the data.table package (v1.9.8). There were new … Continue reading →

How to write and document %special% functions in R

I spend a considerable time of my working hours with data processing where I often use the %in% R function as x %in% y. Whenever I need the negation of that, I used to write !(x %in% y). Not much of a hassle, but still, wouldn’t it be nicer to have x %notin% y instead? So I decided to code it for … Continue reading →

Progress bar overhead comparisons

As a testament to my obsession with progress bars in R, here is a quick investigation about the overhead cost of drawing a progress bar during computations in R. I compared several approaches including my pbapply and Hadley Wickham’s plyr. Let’s compare the good old lapply function from base … Continue reading →