Monthly Archives: February 2014

Merge by City and State in R

February 20, 2014
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Often, you'll need to merge two data frames based on multiple variables. For this example, we'll use the common case of needing to merge by city and state.First, you need to read in both your data sets:# import city coordinate data:coords

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Shapefile Polygons Plotted on Google Maps Using ggmap in R – Throw some, throw some STATS on that map…(Part 2)

February 20, 2014
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Shapefile Polygons Plotted on Google Maps Using ggmap in R – Throw some, throw some STATS on that map…(Part 2)

Well it’s been long enough since my last post. Had a few things on my plate (vacation, holidays, another holiday, some more holidays, and quite a lot of research). March is almost here but the good news is that I have plenty of work stored up to start serving out some intuitive approaches for learning

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Data Analysis for Genomics MOOC

February 20, 2014
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Last month I told you about Coursera's specializations in data science, systems biology, and computing. Today I was reading Jeff Leek's blog post defending p-values and found a link to HarvardX's Data Analysis for Genomics course, taught by Rafael Iriz...

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Using Twitter as a Data Source For Monitoring Password Dumps

February 20, 2014
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Using Twitter as a Data Source For Monitoring Password Dumps

I shot a quick post over at the Data Driven Security blog explaining how to separate Twitter data gathering from R code via the Ruby t (github repo) command. Using t frees R code from having to be a Twitter processor and lets the analyst focus on analysis and visualization, plus you can use t

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RcppArmadillo 0.4.000.4

February 20, 2014
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A new minor release of RcppArmadillo is now on CRAN and in Debian. RcppArmadillo brings the Armadillo templated C++ library for linear algebra to R by means of Rcpp. This release contains both a few minor bugfixes from the 4.000 branch of Armadillo and some enhancements in RcppArmadillo itself that are related to the recent Rcpp 0.11.0 release. Changes in RcppArmadillo version 0.4.000.4 (2014-02-19) Upgraded to Armadillo...

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No need for SPSS – beautiful output in R #rstats

February 20, 2014
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No need for SPSS – beautiful output in R #rstats

About one year ago, I seriously started migrating from SPSS to R. Though I’m still using SPSS (because I have to in some situations), I’m quite comfortable and happy with R now and learnt a lot in the past months. But since SPSS is still very wide spread in social sciences, I get asked every

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dvn – Sharing Reproducible Research from R

February 20, 2014
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Reproducible research involves the careful, annotated preservation of data, analysis code, and associated files, such that statistical procedures, output, and published results can be directly and fully replicated. As the push for reproducible research has grown, the R community has responded with an increasingly large set of tools for engaging in reproducible research practices (see, for example, the ReproducibleResearch...

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Function to Simplify Loading and Installing Packages

February 20, 2014
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One of the more tedious parts of working with R is maintaining my R library. To make my R scripts reproducible and sharable, I will install packages if they are not available. For example, the top of my R scripts tend to look something like this: if(!require(devtools) | !require(ggplot2) | !require(psych) | !require(lme4) | !require(benchmark)) { install.packages(c('devtools','ggplot2','psych','lme4','benchmark')) } This has worked fine for...

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R in Insurance 2014 Conference Poster

February 20, 2014
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R in Insurance 2014 Conference Poster

Here is the poster for the 2nd R in Insurance conference on Monday 14 July 2014 at Cass Business School in London:R in Insurance 2014 conference poster. Download PDF versionImportant dead lines to keep in mind:Abstract submissions: 28 March 2014Early b...

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Backcasting in R

February 19, 2014
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Backcasting in R

Sometimes it is useful to “backcast” a time series — that is, forecast in reverse time. Although there are no in-built R functions to do this, it is very easy to implement. Suppose x is our time series and we want to backcast for periods. Here is some code that should work for most univariate time series. The example...

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