I’ve been seeing a lot of hot takes on if one should do data science in R or in Python. I’ll comment generally on the topic, and then add my own myopic gear-head micro benchmark. I’ll jump in: If learning the language is the big step: then ... [Read more...]

We have had some trouble with some articles being damaged or hard to access in the Win Vector blog. I (John Mount) do want to apologize for that. In particular the graphs are missing for Dr. Nina Zumel’s wonderful y-aware Pricipal Components regression series. The complete R .md and .... [Read more...]

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Part of the deal of having a package up on CRAN is: at any time one may be sent an automated email like the following. Dear maintainer, Please see the problems shown on URL. Please correct before TODAY+14DAYS to safely retain your package on CRAN. The CRAN Team If ... [Read more...]

Introduction Professor Sourav Chatterjee recently published a new coefficient of correlation called XICOR (refs: JASA, R package, Arxiv, Hacker News, and a Python package (different author)). The basic formula (in the tie-free case) is: Take X and Y as n-vectors of observations of random variable. Compute the ranks r(i) ... [Read more...]

We have an exciting new article to share: Don’t Feel Guilty About Selecting Variables. If you are at all interested in the probabilistic justification of important data science techniques, such as variable selection or pruning, this should be an informative and fun read. “Data Science” is often criticized with ... [Read more...]

A kind reader recently shared the following comment on the Practical Data Science with R 2nd Edition live-site. Thanks for the chapter on data frames and data.tables. It has helped me overcome an obstacle freeing me from a lot of warnings telling me my data table was not a ... [Read more...]

Data science is often a case of brining the tools to the problems and data, instead of insisting on bringing the problems and data to the tools. To support cross-language data science we have been working on cross-language tools, documentation, and training. For example: vtreat data preparation package for supervised ... [Read more...]

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Thank you very much Why R? for being awesome hosts. We are really pleased with how your virtual MeetUp went. For those who missed it here is a link. [Read more...]

Thank you very much Why R? for being awesome hosts. We are really pleased with how your virtual MeetUp went. For those who missed it here is a link. [Read more...]

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R is a powerful data science language because, like Matlab, numpy, and Pandas, it exposes vectorized operations. That is, a user can perform operations on hundreds (or even billions) of cells by merely specifying the operation on the column or vector of values. Of course, sometimes it takes a while ... [Read more...]

wrapr 2.0.0 is now up on CRAN. This means the := variant of unpack[] is now easy to install. Please give it a try! [Read more...]

I would like to re-share vtreat (R version, Python version) a data preparation documentation for machine learning tasks. vtreat is a system for preparing messy real world data for predictive modeling tasks (classification, regression, and so on). In particular it is very good at re-coding high-cardinality string-valued (or categorical) variables ... [Read more...]

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For all our remote learners, we are sharing a free coupon code for our R video course Introduction to Data Science. The code is ITDS2020, and can be used at this URL https://www.udemy.com/course/introduction-to-data-science/?couponCode=ITDS2020 . Pleas... [Read more...]

Here is a small quote from Practical Data Science with R Chapter 1. It is often too much to ask for the data scientist to become a domain expert. However, in all cases the data scientist must develop strong domain empathy to help define and solve the ... [Read more...]

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