Google Trends is a useful way to compare changes in popularity of certain search terms over time, and Google Trends data can be used as a proxy for all sorts of difficult-to-measure quantities like economic activity and disease propagation. If you'd like to use Google Trends data in your own ... [Read more...]

A new package has hit the CRAN shelves this week. While knitr is one of the most useful R packages in existence, ezknitr is a simple extension to it that adds flexibility in several ways. One common source of frustration with knitr is that ... [Read more...]

A new package has hit the CRAN shelves this week. While knitr is one of the most useful R packages in existence, ezknitr is a simple extension to it that adds flexibility in several ways. One common source of frustration with knitr is that it assumes the directory where the ... [Read more...]

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The github page for the forecast package currently shows the following information Note the downloads figure: 264K/month. I know the package is popular, but that seems crazy. Also, the downloads figure on github only counts the downloads from the RStudio mirror, and ignores downloads from the other 125 mirrors around ... [Read more...]

PDF/Github repo My thesis analyses chilean university students performance in different courses. These students belong to different major programs imparted at Universidad de Chile: Accounting, Business Administration, Economics and Information Systems... [Read more...]

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XGBoost is the flavour of the moment for serious competitors on kaggle. It was developed by Tianqi Chen and provides a particularly efficient implementation of the Gradient Boosting algorithm. Although there is a CLI implementation of XGBoost you'll probably be more interested in using it from either R or Python. ... [Read more...]

Part 2: Preparing for text analysis Cigarette Smoking Man: But I’ve come today not to ask, but to offer. To offer you the truths that you so desperately sought. This is part 2 of a natural language processing analysis of X-Files episode summaries. In part 1 I explained how to get the ... [Read more...]

obsessively-detailed instructions to analyze publicly-available survey data with free tools - the r language, the survey package, and (for big data) sqlsurvey + monetdb.governments spend billions of dollars each year surveying their populations.  ... [Read more...]

Today I am happy to announce that my new online course, Mapmaking in R with Choroplethr, is now available for preorder. The course is designed to teach you everything you need to start your first mapping project in R using choroplethr. The course will teach you how to: Conduct exploratory ... [Read more...]

By Doug Ashton – Data Scientist, UK Just like you I like to try out all the latest tech. If there’s a new feature in Shiny then I’ll download the latest version without thinking. I’ve currently got 4 versions of … Continue reading → [Read more...]

One of the techniques that has caused the most excitement in the machine learning community is boosting, which in essence is a process of iteratively refining, e.g. by reweighting, of estimated regression and classification functions (though it has primarily been applied to the latter), in order to improve predictive ... [Read more...]

Part 1 demonstrates how to grab the location data of the cars (and other status, like fuel, cleaness etc.) at a given time from Car2go API. However, in order to show how did the car moved over an extended period, we need to do a timed loop to grab the ... [Read more...]

Poisson and Negative Binomial regressions are two popular approaches to model frequency measures in the operational loss and can be implemented in Python with the statsmodels package as below: Although Quasi-Poisson regressions is not currently supported by the statsmodels package, we are still able to estimate the model with the ... [Read more...]

The latest update of my book includes a chapter on lists. This chapter explores the list data type and how …Continue reading → [Read more...]

Nicole has been having a lot of fun the last few days creating her own Shiny apps. We work in the same space, and let’s just say her enthusiasm is very contagious. While […] [Read more...]

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I think that over the past months, I have been saying non-correct things about classification with categorical covariates. Because I never took time to look at it carefuly. Consider some simulated dataset, with a logistic regression, __ n=1e3 __ set.seed(1) __ X1=runif(n) __ q=quantile(X1,(0:26)/26) __ q[1]=0 __ X2=cut(X1,... [Read more...]