My traditional work flow for embedding R graphics into a blog post has been via a PNG files that I upload online. However, when I created a ‘simple’ graphic with only basic curves and triangles for a recent post, I noticed that the PNG output didn’t look as crisp as I expected it to be. So, eventually I used a SVG (scalable vector graphic) instead. Creating a SVG file with R could’t be easier; e.

Last week I presented visualisations of theoretical distributions that predict ice cream sales statistics based on linear and generalised linear models, which I introduced in an earlier post.
Theoretical distributionsToday I will take a closer look at the log-transformed linear model and use Stan/rstan, not only to model the sales statistics, but also to generate samples from the posterior predictive distribution. The posterior predictive distribution is what I am most interested in.

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 <- data.frame(
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, 332L, 325L, 408L, 421L, 406L, 412L, 522L, 445L, 544L, 614L)
)I used a linear model, a log-transformed linear model, a Poisson and Binomial generalised linear model to predict sales within and outside the range of data available.

I have to admit that I find the plotmath expressions in R a little fiddly to annotate plots with mathematical notation. Apparently I am not the only one, but Stefano Meschiari did actually something about it. A few days ago his package latex2exp appeared on CRAN. The package provides the wonderful function latex2exp that translates LaTeX code into plotmath expressions. Brillant! All I have to remember is to escape the “” character, that is write “\“ instead of “”.

Have I missed unknown pleasures in Python by focusing on R?
A comment on my blog post of last week suggested just that. Reason enough to explore Python a little. Learning another computer language is like learning another human language - it takes time. Often it is helpful to start by translating from the new language back into the old one.
I found a Python script by Ludwig Schwardt that creates a plot like this:

How did I miss the GrapheR package? The author, Maxime Hervé, published an article about the package [1] in the same issue of the R Journal as we did on googleVis. Yet, it took me a package update notification on CRANbeeries to look into GrapheR in more detail - 3 years later! And what a wonderful gem GrapheR is.
The package provides a graphical user interface for creating base charts in R.

Last week’s Cologne R user group meeting was the best attended so far. Well, we had a great line up indeed. Matt Dowle came over from London to give an introduction to the data.table package. He was joined by his collaborator Arun Srinivasan, who is based in Cologne. Their talk was followed by Thomas Rahlf on Datendesign mit R (Data design with R).
data.table
Download slides
Matt’s goal with the data.

For most purposes PDF or other vector graphic formats such as windows metafile and SVG work just fine. However, if I plot lots of points, say 100k, then those files can get quite large and bitmap formats like PNG can be the better option. I just have to be mindful of the resolution.
As an example I create the following plot: x <- rnorm(100000)
plot(x, main=“100,000 points”, col=adjustcolor(“black”, alpha=0.2))
Saving the plot as a PDF creates a 5.

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