Monthly Archives: January 2013

Using SPARQL Query Libraries to Generate Simple Linked Data API Wrappers

January 31, 2013
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Using SPARQL Query Libraries to Generate Simple Linked Data API Wrappers

A handful of open Linked Data have appeared through my feeds in the last couple of days, including (via RBloggers) SPARQL with R in less than 5 minutes, which shows how to query US data.gov Linked Data and then Leigh Dodds’ Brief Review of the Land Registry Linked Data. I was going to post a

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Sorting Numeric Vectors in C++ and R

January 31, 2013
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Sorting Numeric Vectors in C++ and R

Consider the problem to sort all elements of the given vector in ascending order. We can simply use the function std::sort from the C++ STL. #include <Rcpp.h> using namespace Rcpp;// ] NumericVector stl_sort(NumericVector x) { NumericVector y = clone(x); std::sort(y.begin(), y.end()); return y; } library(rbenchmark) set.seed(123) z <- rnorm(100000) x <- rnorm(100)# check that stl_sort is the same as sort stopifnot(all.equal(stl_sort(x), sort(x)))#...

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Using Boost via the new BH package

January 31, 2013
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Using Boost via the new BH package

Earlier today the new BH package arrived on CRAN. Over the years, Jay Emerson, Michael Kane and I had numerous discussions about a basic Boost infrastructure package providing Boost headers for other CRAN packages. JJ and Romain chipped in as well, and Jay finally took the lead by first creating a repo on...

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Sorting Numeric Vectors in C++ and R

January 31, 2013
By
Sorting Numeric Vectors in C++ and R

Consider the problem to sort all elements of the given vector in ascending order. We can simply use the function std::sort from the C++ STL. #include <Rcpp.h> using namespace Rcpp;// ] NumericVector stl_sort(NumericVector x) { NumericVector y = clone(x); std::sort(y.begin(), y.end()); return y; }library(rbenchmark) set.seed(123) z <- rnorm(100000) x <- rnorm(100)# check that stl_sort is the same as sort stopifnot(all.equal(stl_sort(x), sort(x)))#...

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Using Boost via the new BH package

January 31, 2013
By
Using Boost via the new BH package

Earlier today the new BH package arrived on CRAN. Over the years, Jay Emerson, Michael Kane and I had numerous discussions about a basic Boost infrastructure package providing Boost headers for other CRAN packages. JJ and Romain chipped in as well, and Jay finally took the lead by first creating a repo on...

Read more »

repmis: misc. tools for reproducible research in R

January 30, 2013
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I've started to put together an R package called repmis. It has miscellaneous tools for reproducible research with R. The idea behind the package is to collate commands that simplify some of the common R code used within knitr-type reproducible research papers. It's still very much in the early stages of development and has two commands:LoadandCite:...

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R installation + screenshots

January 30, 2013
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R installation + screenshots

Feeling faint of heart without photos depicting what to do? No worries, here they are. Go to the R website and click “Download R” under “Getting Started” Choose a place to download R. Even though we’re on the limitless and borderless interweb, choosing a location close to you helps speeds things up. Choose which R package to download based

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R users: Be counted in Rexer’s 2013 Data Miner Survey

January 30, 2013
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R users: Be counted in Rexer’s 2013 Data Miner Survey

Since 2007, Rexer Analytics has been conducting periodic surveys to measure the analytic behaviors, views and preferences of data miners and analytic professionals. In the last survey, conducted in 2011, more than 1300 analysts shared information about the data analysis software packages they use. (The results of all Rexer surveys are available free to anyone who requests them.) In...

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RcppArmadillo 0.3.6.2

January 30, 2013
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A new Armadillo version 3.6.2 came out yesterday, and the corresponding RcppArmadillo version is now on CRAN. Changes are mostky incremental:Changes in RcppArmadillo version 0.3.6.2 (2013-01-29)Upgraded to Armadillo release Version 3.6.2 ...

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Maximize Your Expectations!

January 30, 2013
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Maximize Your Expectations!

A ProblemA major problem in secondary data analysis is that you didn't get to decide what data was collected. Lets say you were interested in how many times a student has read the Twilight books). Specifically, you want to know how effective the ads for...

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