May 2016

Scientific RMarkdown

May 31, 2016 | - r

Recently, in my own little scientific community bubble there was increasing interest in markdown and its use for science. As a big fan of markdown and espacially rmarkdown, I created the following cheat sheet and shared it at a couple of events. Sinc... [Read more...]

heatmaply: interactive heat maps (with R)

May 31, 2016 | Tal Galili

I am pleased to announce heatmaply, my new R package for generating interactive heat maps, based on the plotly R package. tl;dr By running the following 3 lines of code: install.packages("heatmaply") library(heatmaply) heatmaply(mtcars, k_col = 2, k_row = 3) %>% layout(margin = list(l = 130, b = 40)) You will get ...
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Expedia Data Analysis Part 1

May 31, 2016 | datacademy

Expedia Hotel Recommendations Hotel Cluster – Mobile – Package Relationship Channel of Marketing Expedia Hotel Recommendations This dataset can be found at Kaggle. We are given logs of visitors at different Expedia sites and are asked to predict the hotel clusters in the test set. Expedia aims to use customer data to ...
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Principal Components Regression in R: Part 3

May 31, 2016 | Joseph Rickert

by John Mount Ph. D. Data Scientist at Win-Vector LLC In her series on principal components analysis for regression in R, Win-Vector LLC's Dr. Nina Zumel broke the demonstration down into the following pieces: Part 1: the proper preparation of data and use of principal components analysis (particularly for supervised learning ... [Read more...]

Understanding beta binomial regression (using baseball statistics)

May 31, 2016 | David Robinson

Previously in this series: Understanding the beta distribution Understanding empirical Bayes estimation Understanding credible intervals Understanding the Bayesian approach to false discovery rates Understanding Bayesian A/B testing In this series we’ve been using the empirical Bayes method to estimate batting averages of baseball players. Empirical Bayes is useful ... [Read more...]

QGIS, Open Source GIS & R

May 31, 2016 | Kurt Menke

Today’s post is by Kurt Menke, the owner of Bird’s Eye View GIS, a GIS consultancy. Kurt also wrote the book Mastering QGIS. In my latest course (Shapefiles for R Programmers) I briefly introduce people to QGIS. Kurt’s post below gives you a roadmap for learning more.  ...
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On ranger respect.unordered.factors

May 30, 2016 | John Mount

It is often said that “R it its packages.” One package of interest is ranger a fast parallel C++ implementation of random forest machine learning. Ranger is great package and at first glance appears to remove the “only 63 levels allowed for string/categorical variables” limit found in the Fortran randomForest ...
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zoo time series exercises

May 30, 2016 | Siva Sunku

The zoo package consists of the methods for totally ordered indexed observations. It aims at performing calculations containing irregular time series of numeric vectors, matrices & factors. The zoo package interfaces to all other time series packages on CRAN. This makes it easy to pass the time series objects between zoo & ... [Read more...]

satRday Event in Cape Town

May 30, 2016 | Daniel Emaasit

This blog post was first published on EXEGETIC ANALYTICS‘s blog and kindly re-posted on Data Science Africa. We are planning to host one of the three inaugural satRday conferences in Cape Town during 2017. The R Consortium has committed to funding three of these events: one will be in Hungary, ... [Read more...]

Solving Math Puzzles with data.tree

May 29, 2016 | gluc

I got a note from Karim Lahrichi, who even thinks about math when he’s supposed to be drinking beer. The bar puzzle they were trying to solve goes like this: Using all of the numbers 1, 3, 4, 6 exactly once, and any combination of: addition, subtraction, multiplication and division (and parenthesis to ... [Read more...]

Visualizing Bootrapped Stepwise Regression in R using Plotly

May 29, 2016 | Riddhiman

We all have used stepwise regression at some point. Stepwise regression is known to be sensitive to initial inputs. One way to mitigate this sensitivity is to repeatedly run stepwise regression on bootstrap samples. R has a nice package called bootStepAIC() which (from its description) “Implements a Bootstrap procedure to ... [Read more...]
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