Good advice for security with R

August 19, 2015
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Everyone needs to be vigilant about security on the Web today. One particular threat — the man-in-the-middle attack — is a risk anytime you are communicating over the Internet, and an attacker has access to the network between the two endpoints. This is a possibility whenever you are using the Web over an unencrypted channel, or when using an...

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Searching Twitter with ArcGIS Pro Using R

August 18, 2015
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Searching Twitter with ArcGIS Pro Using R

I committed to testing this a long time ago, however, a number of other projects intervened, so I have only just got around to writing up this short tutorial. One of the exciting things from the ESRI Developers Conference this year was the launch of the R-ArcGIS bridge. In simple terms, this enables you to run R...

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Display of Geographic Data in R

August 18, 2015
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This was a deck used in my presentation to the Inland Northwest R user Group this past Friday (August 14, 2015). It relies on the work done by many folks, including Ari Lamstein, Kyle Walker, Erik Erhardt, and the kind folks at R-Studio. Please click o...

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Importing Data Into R – Part Two

August 18, 2015
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Importing Data Into R – Part Two

In this follow-up tutorial of This R Data Import Tutorial Is Everything You Need-Part One, DataCamp continues with its comprehensive, yet easy tutorial to quickly import data into R, going from simple, flat text files to the more advanced SPSS and SAS files. As a lot of our readers noticed correctly from the first post, The post

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Two-Way ANOVA with Repeated Measures

August 18, 2015
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Two-Way ANOVA with Repeated Measures

NOTE: This post only contains information on repeated measures ANOVAs, and not how to conduct a comparable analysis using a linear mixed model. For that, be on the lookout for an upcoming post! When I was studying psychology as an undergraduate, one of my biggest frustrations with R was the lack of quality support for

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Using Azure as an R datasource, Part 4 – Pulling data from SQL Server to Linux

August 18, 2015
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Using Azure as an R datasource, Part 4 – Pulling data from SQL Server to Linux

by Gregory Vandenbrouck Software Engineer, Microsoft This post is the fourth in a series that covers pulling data from Microsoft SQL Server or MySQL/MariaDB on Azure to an R client on Windows or Linux. In the previous posts, we covered pulling data from SQL Server to Windows and from MySQL/MariaDB to both Windows and Linux. This time we’ll be...

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Mango EARL Competition entries

August 18, 2015
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Mango EARL Competition entries

Here is a list of the EARL Competition entries we have received so far: Entry details here “R is the clear choice for quickly prototyping advanced data manipulation and creating analytics dashboards, essential for a fast, informed response to emerging … Continue reading →

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Visualising theoretical distributions of GLMs

August 18, 2015
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Visualising theoretical distributions of GLMs

Two weeks ago I discussed various linear and generalised linear models in R using ice cream sales statistics. The data showed not surprisingly that more ice cream was sold at higher temperatures.icecream temp=c(11.9, 14.2, 15.2, 16.4, 17.2, 18.1, 18.5, 19.4, 22.1, 22.6, 23.4, 25.1), units=c(185L, 215L,...

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RcppArmadillo 0.5.400.2.0

August 17, 2015
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RcppArmadillo 0.5.400.2.0

A new Armadillo release 5.400.2 was just released by Conrad. Consequently, a new RcppArmadillo release 0.5.400.2.0 went out to CRAN and into Debian. Armadillo is a powerful and expressive C++ template library for linear algebra aiming towards a good balance between speed and ease of use with a syntax deliberately...

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Homework during the hiring process…no thanks!

August 17, 2015
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Homework during the hiring process…no thanks!

For the past four months, I’ve been on the job market looking for work as an applied statistician or data scientist within the the online marketing industry. One thing I’ve come to expect with almost every company is some sort of homework assignment or challenge where a spreadsheet would be presented along with some guidelines

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Evaluating Logistic Regression Models

August 17, 2015
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Evaluating Logistic Regression Models

Logistic regression is a technique that is well suited for examining the relationship between a categorical response variable and one or more categorical or continuous predictor variables. The model is generally presented in the following format, where β refers to the parameters and x represents the independent variables. log(odds)=β0+β1∗x1+...+βn∗xn The log(odds), or log-odds ratio, is defined

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Visualising thefts using heatmaps in ggplot2

August 17, 2015
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Visualising thefts using heatmaps in ggplot2

This is a continuation of my previous article, where I gave a basic overview of how to construct heatmaps in R. Here, I will show you how to use R packages to build a heatmap on top of the map of Chicago to see which areas have the most amount of crime. We will require

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T-tests

August 17, 2015
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T-tests

One-Sample T-Tests To conduct a one-sample t-test in R, we use the syntax t.test(y, mu = 0) where x is the name of our variable of interest and mu is set equal to the mean specified by the null hypothesis. So, for example, if we wanted to test whether the volume of a shipment of

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Building Barplots with Error Bars

August 17, 2015
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Building Barplots with Error Bars

Bar charts are a pretty common way to represent data visually, but constructing them isn’t always the most intuitive thing in the world. One way that we can construct these graphs is using R’s default packages. Barplots using base R Let’s start by viewing our dataframe: here we will be finding the mean miles per

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DeployR 7.4.1 released

August 17, 2015
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by Carl Nan, DeployR PM A new version of DeployR, the server-based framework that provides simple and secure R integration for application developers, is now available. (If you're new to DeployR, take a look at the DeployR Overview or download the white paper, Using DeployR to Solve the R Integration Problem.) The following list highlights the changes and improvements...

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Win a FREE ticket to EARL London!

August 17, 2015
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Win a FREE ticket to EARL London!

Competition Time! Mango Solutions are offering an opportunity to win a FREE two day Conference Pass* to the EARL (Effective Application of the R Language) Conference in London on the 14th-16th September.   *Value £545 To Enter: Email [email protected] with … Continue reading →

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Playing with R, Shiny Dashboard and Google Analytics Data

August 17, 2015
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Playing with R, Shiny Dashboard and Google Analytics Data

In this post, I want to share some examples of data visualization I was playing with recently. Like in many other occasions, my field of application is digital analytics data. Precisely, data from Google Analytics.You might remember a previous post whe...

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What’s new in Revolution R Enterprise 7.4.1

August 17, 2015
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by Richard Kittler, Revolution R Enterprise PM, Microsoft Advanced Analytics In its latest release Revolution has added to the platform support of Revolution R Enterprise (RRE) version 7.4. Released August 14, version 7.4.1 extends RRE 7.4 capabilities to the Teradata database, HPC Server cluster, and Windows 10 platforms. With RRE for Teradata customers enjoy the advantage of bringing the...

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RSiteCatalyst Version 1.4.5 Release Notes

August 17, 2015
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It’s only been a month since the last RSiteCatalyst update, and this update is also a pretty minor update in terms of functionality. Set Your Own Endpoint For the overseas users (or companies with weird setups), you can now use the endpoint argument in the SCAuth() function to specify your API endpoint. For the most part, this

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Secure HTTPS Connections for R

August 17, 2015
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Secure HTTPS Connections for R

Traditionally, the mechanisms for obtaining R and related software have used standard HTTP connections. This isn’t ideal though, as without a secure (HTTPS) connection there is less assurance that you are downloading code from a legitimate source rather than from another server posing as one. Recently there have been a number of changes that make it easier to use HTTPS

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3 new R jobs (from R-users.com ; 2015-08-17)

August 17, 2015
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3 new R jobs (from R-users.com ; 2015-08-17)

This is the bimonthly post (for 2015-08-17) for new R Jobs from R-users.com. Employers: you may visit this link to post a new R job to the R community (it’s free and quick). Job seekers: please follow the links below to learn more and apply for your job of interest (or go to R-users.com to see all the R jobs that are currently available) Full-Time Director, Political and Engagement Analytics (@...

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Slides of 10+ excellent tutorials at KDD 2015: Spark, graph mining and many more

August 17, 2015
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Slides of 10+ excellent tutorials at KDD 2015: Spark, graph mining and many more

by Yanchang Zhao RDataMining.com I attended the KDD 2015 conference in Sydney last week. At the conference, there were more than 10 tutorials and I went to two of them, which are 1) Graph-Based User Behavior Modeling: From Prediction to … Continue reading →

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Constructing a network of politicians from newspaper data

Constructing a network of politicians from newspaper data

The following is a guest post by Jana Blahak and Jan Dix (University of Konstanz), with support from Simon Munzert. In the last post, we introduced the rzeit package, an R binding to the Content API at ZEIT Online. This time, we give a little demonstration of what can be done with these media data. The question we...

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Some reflections on teaching frequentist statistics at ESSLLI 2015

August 17, 2015
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Some reflections on teaching frequentist statistics at ESSLLI 2015

I spent the last two weeks teaching frequentist and Bayesian statistics at the European Summer School in Logic, Language, and Information (ESSLLI) in Barcelona, at the beautiful and centrally located Pompeu Fabra University. The course web page for the first week is here, and the web page for the second course is here. (NOTE: Uni Potsdam...

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R, Python, and SAS: Getting Started with Linear Regression

August 16, 2015
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R, Python, and SAS: Getting Started with Linear Regression

Consider the linear regression model, $$ y_i=f_i(boldsymbol{x}|boldsymbol{beta})+varepsilon_i, $$ where $y_i$ is the response or the dependent variable at the $i$th case, $i=1,cdots, N$ and the predictor or the independent variable is the $boldsymbol{x}$ term defined in the mean function $f_i(boldsymbol{x}|boldsymbol{beta})$. For simplicity, consider the following simple linear regression (SLR) model, $$ y_i=beta_0+beta_1x_i+varepsilon_i. $$ To obtain the (best) estimate...

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R 3.2.2 is released

August 16, 2015
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R 3.2.2 is released

R 3.2.2 (codename “Fire Safety”) was released last weekend. You can get the latest binaries version from here. (or the .tar.gz source code from here). The full list of new features and bug fixes is provided below. SOME OF THE CHANGES I personally found two things particularly interesting in this release: setInternet2(TRUE) is now the default for windows (which will … Continue reading...

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Some Considerations of Modeling Severity in Operational Losses

August 16, 2015
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Some Considerations of Modeling Severity in Operational Losses

In the Loss Distributional Approach (LDA) for Operational Risk models, multiple distributions, including Log Normal, Gamma, Burr, Pareto, and so on, can be considered candidates for the distribution of severity measures. However, the challenge remains in the stress testing exercise, e.g. CCAR, to relate operational losses to macro-economic scenarios denoted by a set of macro-economic

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Yet another post on google scholar data analysis

August 16, 2015
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Yet another post on google scholar data analysis

Inspired by this post, I wanted to use Google Scholar data to put nice images on my professional website (girly habit). This post explains how I combined the functions available in the R package scholar with additional analyses (partially inspired from the script available at this link, which in my case results in a cannot

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Managing longitudinal data: Conversion between the wide and the long

August 16, 2015
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Managing longitudinal data: Conversion between the wide and the long

If you measure the same person twice, you have longitudinal data. We all love longitudinal data because we can understand how their health outcomes change with time and this helps answering many interesting research questions. However, newer R users often face a problem in managing longitudinal data because it often comes in two ‘shapes': the

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