Blog Archives

Desktop DeployR

April 13, 2016
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I'm going to be giving a talk this Thursday at my local R/Data Science Meetupabout my method for deploying self contained desktop R applications. Since my original post on the subject (over 2 years ago!) I've made manyof improvements thanks to the many useful comments I received and my own "dog-fooding".So many in fact that the framework...

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Paging Widget for Shiny Apps

October 21, 2015
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Paging Widget for Shiny Apps

In my last post I described how I built a shiny application called “DFaceR” that used Chernoff Faces to plot multidimensional data. To improve application response time during plotting, I needed to split large datasets into more manageable “pages” to be plotted. Rather than take the path of least resistance and use either numericInput or sliderInput...

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Facing your data

October 10, 2015
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Facing your data

A few years ago, I came across a post on FlowingData about using Chernoff Faces as a fun way to visualize multidimensional data: > The assumption is that we can read people's faces easily in real life, > so we should be able to recognize small differences when they represent data. > Now that's a pretty big...

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Facing your data

October 10, 2015
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Facing your data

A few years ago, I came across a post on FlowingData about using Chernoff Faces as a fun way to visualize multidimensional data: > The assumption is that we can read people's faces easily in real life, > so we should be able to recognize small differences when they represent data. > Now that's a pretty big...

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Easy error propagation in R

January 22, 2015
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In a previous post I demonstrated how to use R’s simple built-in symbolic engine to generate Jacobian and (pseudo)-Hessian matrices that make non-linear optimization perform much more efficiently. Another related application is Gaussian error propagation. Say you have data from a set of measurements in variables x and y where you know the corresponding measurement errors (dx...

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R OOP – a little privacy please?

August 23, 2014
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As of late, I’ve been making heavy use of Reference Classes in R. They are easier for me to wrap my mind around since they adopt a usage style more like “traditional” OOP languages like Java. Primarily, object methods are part of the class definition and accessed via the instantiated object. For instance: With S3/S4 classes, you...

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Optimizing with R expressions

August 21, 2014
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Optimizing with R expressions

I recently discovered a powerful use for R expression()’sSay you are trying to fit some experimental data to the following nonlinear equation: Ky0eu(x−tl)K+y0(eu∗(x−tl)−1)+b1+(b0−b1)e−kx+b2x with the independent variable x using nlminb() as the minimization optimizer.This sort of work is significantly improved (i.e. faster with better convergence) if an analytical gradient vector and a Hessian matrix for the objective function are provided....

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Deploying Desktop Apps with R

April 2, 2014
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(Update) Despite the original publish date (Apr 1), this post was not and April Fools joke. I’ve also shortened the title a bit. As part of my job, I develop utility applications that automate workflows that apply more involved analysis algorithms. When feasible, I deploy web applications as it lowers installation requirements to simply a modern (standards...

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An R flaw: unexpected attribute droppings

February 6, 2014
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Today I was putting some code together that made plots from slices of a 3-dimensional array object aa. A couple of the dimensions in aa had names defined by named vectors. For example: > aa = array(runif(2*3*4), dim=c(2,3,4), ...

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character handling: mean() vs sd()

October 21, 2013
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Here’s a weird R error/bug/nuance I came across today: What would you expect the following lines of code to return? x = c('1', '2', '3')mean(x)sd(x) Well, apparently it is: # mean(x) NA# sd(x) 1 So, sd() silently converts its input to numeric, while mean() does not. More evidence of this is in the source: > meanfunction (x, ...)...

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