Review: K�lner R Meeting 13 December 2013

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.table package is to reduce times; time to write code and to execute code. His talk illustrated how the syntax of data.table, not unlike SQL, can produce shorter and more readable code that at the same time provides an efficient and fast way to analyse big in memory data sets with R. Arun presented on new developments in data.table 1.8.11, which not only fixes bugs but adds many new features such as melt/cast and further speed gains.

I said early that data.table rocks. For more details see the data.table home page.

Data design with R

Thomas Rahlf: Datendesign mit R

Thomas Rahlf talked about his forthcoming book Datendesign mit R (Data design with R). He shared with us his motivations and aims for the book. In his opinion there are many books that present beautiful charts and concepts (e.g. Tufte's books), but then don't show how they can be reproduced, as there are often done with software such as Adobe Illustrator. Or books explain the graphical functions of a software, yet fail to demonstrate how to create beautiful charts with them. Thus, Thomas' book will contain 100 examples demonstrating that desktop publishing quality charts can be produced with R and in some cases with the help of LaTeX. Indeed, all examples have about 40 lines of code and use the base R graphics system only and not grid or any add-ons such as lattice or ggplot2.

The book's accompanying web site gives you a taster already. The book itself will be published by Open Source Press next month.

The Schnitzel

Of course the evening ended with Schnitzel and K�lsch at the Lux.

The Luxus Schnitzel. Photo by G�nter Faes

Next K�lner R meeting

The next meeting is scheduled for 26 February 2013 (Wednesday before Altweiber), with two talks by Diego de Castillo (Connecting R with databases) and Kim Kuen Tang (R and kdb+).

Please get in touch if you would like to present and share your experience, or indeed if you have a request for a topic you would like to hear more about. For more details see also our Meetup page.

Thanks again to Bernd Wei� for hosting the event and Revolution Analytics for their sponsorship.

Review: K�lner R Meeting 13 December 2013

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.table package is to reduce times; time to write code and to execute code. His talk illustrated how the syntax of data.table, not unlike SQL, can produce shorter and more readable code that at the same time provides an efficient and fast way to analyse big in memory data sets with R. Arun presented on new developments in data.table 1.8.11, which not only fixes bugs but adds many new features such as melt/cast and further speed gains.

I said early that data.table rocks. For more details see the data.table home page.

Data design with R

Thomas Rahlf: Datendesign mit R

Thomas Rahlf talked about his forthcoming book Datendesign mit R (Data design with R). He shared with us his motivations and aims for the book. In his opinion there are many books that present beautiful charts and concepts (e.g. Tufte's books), but then don't show how they can be reproduced, as there are often done with software such as Adobe Illustrator. Or books explain the graphical functions of a software, yet fail to demonstrate how to create beautiful charts with them. Thus, Thomas' book will contain 100 examples demonstrating that desktop publishing quality charts can be produced with R and in some cases with the help of LaTeX. Indeed, all examples have about 40 lines of code and use the base R graphics system only and not grid or any add-ons such as lattice or ggplot2.

The book's accompanying web site gives you a taster already. The book itself will be published by Open Source Press next month.

The Schnitzel

Of course the evening ended with Schnitzel and K�lsch at the Lux.

The Luxus Schnitzel. Photo by G�nter Faes

Next K�lner R meeting

The next meeting is scheduled for 26 February 2013 (Wednesday before Altweiber), with two talks by Diego de Castillo (Connecting R with databases) and Kim Kuen Tang (R and kdb+).

Please get in touch if you would like to present and share your experience, or indeed if you have a request for a topic you would like to hear more about. For more details see also our Meetup page.

Thanks again to Bernd Wei� for hosting the event and Revolution Analytics for their sponsorship.

High resolution graphics with R

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 5.2 MB big file on my computer, while the PNG output is only 62 KB instead. Of course, the PNG doesn't look as crisp as the PDF file.

png("100kPoints72dpi.png", units = "px", width=400, height=400)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
dev.off()

Hence, I increase the resolution to 150 dots per pixel.

png("100kHighRes150dpi.png", units="px", width=400, height=400, res=150)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
dev.off()

This looks a bit odd. The file size is only 29 KB but the annotations look too big. Well, the file has only 400 x 400 pixels and the size of a pixel is fixed. Thus, I have to provide more pixels, or in other words increase the plot size. Doubling the width and height as I double the resolution makes sense.

png("100kHighRes150dpi2.png", units="px", width=800, height=800, res=150)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
dev.off()

Next I increase the resolution further to 300 dpi and the graphic size to 1600 x 1600 pixels. The file is still very crisp. Of course the file size increased. Now it is 654 KB in size, yet sill only about 1/8 of the PDF and I can embed it in LaTeX as well.

png("100kHighRes300dpi.png", units="px", width=1600, height=1600, res=300)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
dev.off()

Note, you can click on the charts to access the original files of this post.

High resolution graphics with R

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 5.2 MB big file on my computer, while the PNG output is only 62 KB instead. Of course, the PNG doesn't look as crisp as the PDF file.

png("100kPoints72dpi.png", units = "px", width=400, height=400)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
dev.off()

Hence, I increase the resolution to 150 dots per pixel.

png("100kHighRes150dpi.png", units="px", width=400, height=400, res=150)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
dev.off()

This looks a bit odd. The file size is only 29 KB but the annotations look too big. Well, the file has only 400 x 400 pixels and the size of a pixel is fixed. Thus, I have to provide more pixels, or in other words increase the plot size. Doubling the width and height as I double the resolution makes sense.

png("100kHighRes150dpi2.png", units="px", width=800, height=800, res=150)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
dev.off()

Next I increase the resolution further to 300 dpi and the graphic size to 1600 x 1600 pixels. The file is still very crisp. Of course the file size increased. Now it is 654 KB in size, yet sill only about 1/8 of the PDF and I can embed it in LaTeX as well.

png("100kHighRes300dpi.png", units="px", width=1600, height=1600, res=300)
plot(x, main="100,000 points", col=adjustcolor("black", alpha=0.2))
dev.off()

Note, you can click on the charts to access the original files of this post.