Earlier this month, I, along with John Nash, Spencer Graves, and Ludovic Vannoorenberghe, organized a session at useR!2017 focused on discovering, learning about, and evaluating R packages. You can check out the recording of the session. There are more than 11,000 packages on CRAN, and R users must approach this abundance of packages... Read more
xray: The R Package to Have X Ray Vision on Your Datasets
This package lets you analyze the variables of a dataset, to evaluate how the data is shaped. Consider this the first step when you have your data for modeling, you can use this package to analyze all variables and check if there is anything weird worth transforming or even... Read more
Anomaly Detection in R
Introduction Inspired by this Netflix post, I decided to write a post based on this topic using R. There are several nice packages to achieve this goal, the one we´re going to review is AnomalyDetection. Download full –and tiny– R code of this post here. Normal Vs. Abnormal The definition for abnormal,... Read more
Word Vectors with Tidy Data Principles
Last week I saw Chris Moody’s post on the Stitch Fix blog about calculating word vectors from a corpus of text using word counts and matrix factorization, and I was so excited! This blog post illustrates how to implement that approach to find word vector representations in R using tidy data... Read more
rquery: Fast Data Manipulation in R
Win-Vector LLC recently announced the rquery R package, an operator based query generator. In this note I want to share some exciting and favorable initial rquery benchmark timings. Note we have now (1-16-2018) re-run this benchmark with a faster, better tuned, version of the data.table solution (same package, just better use of it). Let’s take a look at... Read more
R, as I’ve pointed out before, has a package discovery problem. There’s a new package, colorblindr, which lets you see the impact of various sorts of colour-blindness on a colour palette, a very useful thing for designing good graphics. When it’s mentioned on Twitter, you see lots of people glad... Read more
Making a machine learning model usually takes a lot of crying, pain, feature engineering, suffering, training, debugging, validation, desperation, testing and a little bit of agony due to the infinite pain. After all that, we deploy the model and use it to make predictions for future data. We can run our little devil on a batch... Read more
SIR model with deSolve & ggplot2
This is my first post ever and in 2017!  Since I am recent graduate and currently un-employed, my hope is to upload some interesting material on using R on a weekly basis. Making this an informative and motivational blog to share my interests and mini-projects in R. Now on to this... Read more
Group-By Modeling in R Made Easy
There are several aspects of the R language that make it hard to learn, and repeating a model for groups in a data set used to be one of them. Here I briefly describe R’s built-in approach, show a much easier one, then refer you to a new approach described... Read more
On Machine Learning and Programming Languages
This article was co-written by Mike Innes (Julia Computing), David Barber (UCL), Tim Besard (UGent), James Bradbury (Salesforce Research), Valentin Churavy (MIT), Simon Danisch (MIT), Alan Edelman (MIT), Stefan Karpinski (Julia Computing), Jon Malmaud (MIT), Jarrett Revels (MIT), Viral Shah (Julia Computing), Pontus Stenetorp (UCL) and Deniz Yuret (Koç... Read more