R - start here to learn R 106

Christmas day is soon upon us, so here's a greeting made with R: Each frame is a Voronoi Tesselation: about 1,000 points are chosen across the plane, which each... Read more »

Data preparation and cleaning are some of the most important steps of predictive analytic and data science tasks. They are laborious, where most of the errors are made, your last line of defense against a wild data, and hold the biggest opportunities for outcome improvement. No matter how much time you spend on then, they … Continue... Read more »

Bayesian Inference is a way of combining information from data with things we think we already know. For example, if we wanted to get an estimate of the mean... Read more »

Descriptive Analytics is the examination of data or content, usually manually performed, to answer the question “What happened?”. In order to be able to solve this set of exercises...Read more »

Presenting ‘Googly’, a cool Shiny app that I developed over the last couple of days. This interactive Shiny app was on my mind for quite some time, and I... Read more »

Data on the shadow economy? I’m reading Kenneth Rogoff’s The Curse of Cash. It was one of Bloomberg’s Best Books of 2016 and the Financial Times’ Best Economics Books... Read more »

# create data x <- c(8,7,6,7,6,5,6,5,4,5,4,3,4,3,2,3,2,1,0.5,0.1) dat1 <- data.frame(x1 = 1:length(x), x2 = x) dat2 <- data.frame(x1 = 1:length(x), x2 = -x) dat1$xvar <- dat2$xvar <- NA dat1$yvar <-... Read more »

The R package samplesize4surveys contains functions that allow to calculate sample sizes for estimating proportions, means, difference of proportions and even difference of two means. It also permits the... Read more »

The book I’ve been working on these pasts months (you can read about it here, and read it for free here) is now available on Leanpub! You can grab... Read more »