Monthly Archives: May 2018

simpler SQL with dplyr

May 31, 2018
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simpler SQL with dplyr

comparing dplyr with SQL nested queries - Following on from my last post, where I demonstrated R to some first time R users, I want to do a wee comparison of dplyr V SQL, so that folks, particularly those in the NHS who might be R curious, can see just what the fuss is about. To do so...

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Geocomputation with R: workshop at eRum

May 31, 2018
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Geocomputation with R: workshop at eRum

Geocomputation with R: brief history, vector, raster, mapping, R-GIS bridges

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Classification from scratch, logistic with kernels 3/8

May 31, 2018
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Classification from scratch, logistic with kernels 3/8

Third post of our series on classification from scratch, following the previous post introducing smoothing techniques, with (b)-splines. Consider here kernel based techniques. Note that here, we do not use the “logistic” model… it is purely non-parametric. kernel based estimated, from scratch I like kernels because they are somehow very intuitive. With GLMs, the goal is to estimate ....

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New round of R Consortium grants announced

May 31, 2018
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The R Consortium has just announced its latest round of project grants. After reviewing the proposals submitted by the R community, the Infrastructure Steering Committee has elected to fund the following projects for the Spring 2018 season: Further updates to the DBI package, to provide a consistent interface between R and databases Updating the infrastructure in R for building...

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Three ways of visualizing a graph on a map

May 31, 2018
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Three ways of visualizing a graph on a map

When visualizing a network with nodes that refer to a geographic place, it is often useful to put these nodes … Read More →

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Defining Marketing with the Rvest and Tidytext Packages

May 30, 2018
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Defining Marketing with the Rvest and Tidytext Packages

I am preparing to facilitate another session of the marketing course for the La Trobe University MBA. The first lecture delves into the definition of marketing. Like most other social phenomena, marketing is tough to define. Definitions of social constructs... Continue Reading → The post Defining Marketing with the Rvest and Tidytext Packages appeared first on The Devil is in...

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Exploring R Packages with cranly

May 30, 2018
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Exploring R Packages with cranly

In a previous post, I showed a very simple example of using the R function tools::CRAN_package_db() to analyze information about CRAN packages. CRAN_package_db() extracts the metadata CRAN stores on all of its 12,000 plus packages and arranges it into a “database”, actually a complicated data frame in which some columns have vectors or lists as entries. It’s simple to run...

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Harry Potter and rankings with comperank

May 30, 2018
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Ranking Harry Potter books with comperank package. Prologue Package comperank is on CRAN now. It offers consistent implementations of several ranking and rating methods. Originally, it was intended to be my first CRAN package when I started to build it 13 months ago. Back then I was very curious to learn...

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Algorithmic Trading: Using Quantopian’s Zipline Python Library In R And Backtest Optimizations By Grid Search And Parallel Processing

Algorithmic Trading: Using Quantopian’s Zipline Python Library In R And Backtest Optimizations By Grid Search And Parallel Processing

We are ready to demo our new new experimental package for Algorithmic Trading, flyingfox, which uses reticulate to to bring Quantopian’s open source algorithmic trading Python library, Zipline, to R. The flyingfox library is part of our NEW Business Science Labs innovation lab, which is dedicated to bringing experimental packages to our followers early on so they can test...

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Quick guide for converting from JAGS or BUGS to NIMBLE

May 30, 2018
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Quick guide for converting from JAGS or BUGS to NIMBLE

Converting to NIMBLE from JAGS, OpenBUGS or WinBUGS Converting to NIMBLE from JAGS, OpenBUGS or WinBUGS NIMBLE is a hierarchical modeling package that uses nearly the same modeling language as the popular MCMC packages WinBUGS, OpenBUGS and JAGS. NIMBLE makes the modeling language extensible — you can add distributions and functions — and also allows

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