Posts Tagged ‘ graphics ’

ggformula: another option for teaching graphics in R to beginners

September 21, 2017
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ggformula: another option for teaching graphics in R to beginners

A previous entry (http://sas-and-r.blogspot.com/2017/07/options-for-teaching-r-to-beginners.html) describes an approach to teaching graphics in R that also “get[s] students doing powerful things quickly”, as David Robinson suggested. In t...

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ggformula: another option for teaching graphics in R to beginners

September 21, 2017
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ggformula: another option for teaching graphics in R to beginners

A previous entry (http://sas-and-r.blogspot.com/2017/07/options-for-teaching-r-to-beginners.html) describes an approach to teaching graphics in R that also “get[s] students doing powerful things quickly”, as David Robinson suggested. In t...

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Options for teaching R to beginners: a false dichotomy?

July 27, 2017
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Options for teaching R to beginners: a false dichotomy?

I've been reading David Robinson's excellent blog entry "Teach the tidyverse to beginners" (http://varianceexplained.org/r/teach-tidyverse), which argues that a tidyverse approach is the best way to teach beginners.  He summarizes two competing cu...

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Options for teaching R to beginners: a false dichotomy?

July 27, 2017
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Options for teaching R to beginners: a false dichotomy?

I've been reading David Robinson's excellent blog entry "Teach the tidyverse to beginners" (http://varianceexplained.org/r/teach-tidyverse), which argues that a tidyverse approach is the best way to teach beginners.  He summarizes two competing cu...

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Quick illustration of Metropolis and Metropolis-in-Gibbs Sampling in R

June 4, 2017
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Quick illustration of Metropolis and Metropolis-in-Gibbs Sampling in R

The code below gives a simple implementation of the Metropolis and Metropolis-in-Gibbs sampling algorithms, which are useful for sampling probability densities for which the normalizing constant is difficult to calculate, are irregular, or have high dimension (Metropolis-in-Gibbs). ## Metropolis sampling ## x - current value of Markov chain (numeric vector) ## targ - target log … Continue reading Quick illustration of Metropolis and Metropolis-in-Gibbs Sampling in R →

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forecast 8.0

February 28, 2017
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forecast 8.0

In what is now a roughly annual event, the forecast package has been updated on CRAN with a new version, this time 8.0. A few of the more important new features are described below. Check residuals A common task when building forecasting models is to check that the residuals satisfy some assumptions (that they are […]

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Reading a Picture

November 30, 2016
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Reading a Picture

Visual storytelling Visualising data helps understanding facts. Sometimes it’s very easy to understand a graph; sometimes it’s necessary to read it and to study it to discover unknown territory. Such graphs are little masterpieces. Here’s one of these and I am sure the authors had more than one iteration and discussion while creating it. The … Continue reading Reading a Picture

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Halloween 2016 count

November 1, 2016
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Halloween 2016 count

Here’s a graph of the numbers of trick-or-treat-ers we saw this evening, by time. 10 of the 25 kids arrived in one big group. (Compare this to our 2011 experience.)

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Eindhoven seminar on time series visualization

September 27, 2016
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Eindhoven seminar on time series visualization

I’m currently in the Netherlands for a few weeks, and I’ll be giving a seminar at the Data Science Centre in Eindhoven next Wednesday afternoon on “Visualization of big time series data”. Details follow. Date: 5 October 2016 Time: 12.30-13.30 Venue: Filmhuis, De Zwarte Doos, 2 Den Dolech, Eindhoven Registration is required but free. Please […]

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R package forecast v7.2 now on CRAN

September 9, 2016
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R package forecast v7.2 now on CRAN

I’ve pushed a minor update to the forecast package to CRAN. Some highlights are listed here. Plotting time series with ggplot2 You can now facet a time series plot like this: lungDeaths <- cbind(mdeaths, fdeaths) autoplot(lungDeaths, facets=TRUE) So autoplot.mts now behaves similarly to plot.mts Multi-step fitted values The fitted function has a new argument h […]

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