Encouraged by this post I had another look at quandl for collecting datasets from different agencies. Right now I need to get data for four countries on a couple of dozen indicators. This graphic is just a quick example with only two indicators of what I am aiming to be able to do. The process
I wrote already about changing figure options mid-chunk in reproducible research. This can be important e.g. if you are looping through a dataset to produce a graphic for each variable but the figure width or height need to depend on properties of the variables, e.g. if you are producing histograms and want the figures to
Knitr is a great tool for doing reproducible research. You can produce all kinds of output inside a single knitr chunk, e.g. you can write a loop to produce lots of figures or tables. The only catch is if you want your figures to have differing captions, heights, etc (and usually you do). The standard
Just now writing a reproducible report in R using RStudio on Ubuntu. So the source is a .Rnw file and I am compiling it with knitr. For the narrative part of the report it is a shame that RStudio doesn’t have autocomplete for latex styles, headings etc. But I just realised that it is possible
Dear R experts, I sent this question to the r-help list but didn’t get much response, probably because it is more of a stats question. But as this blog is syndicated on r-bloggers I thought I would try it again here on this blog. If I am barking up the wrong tree, feel free to
The Timely Portfolio blog via R-bloggers has recently published some interesting entries about the value of horizon plots for visual comparison of a number of time series. Very nice it looks too. You can read more about them here. The trick to understanding them is to imagine that each row was orginally a line chart
I wrote before about heatmap tables as a better way of producing frequency or other tables, with a solution which works nicely in latex. It is possible to do them much more easily in ggplot2, like this library(Hmisc) library(ggplot2) library(reshape) data(HairEyeColor) P=t(HairEyeColor) Pm=melt(P) ggfluctuation(Pm,type="heatmap")+geom_text(aes(label=Pm$value),colour="white")+ opts(axis.text.x=theme_text(size = 15),axis.text.y=theme_text(size = 15)) Note that ggfluctuation will also take
I wrote before about heatmap tables as a better way of producing frequency or other tables, with a solution which works nicely in latex. It is possible to do them much more easily in ggplot2, like this library(Hmisc) library(ggplot2) library(reshape) data(HairEyeColor) P=t(HairEyeColor) Pm=melt(P) ggfluctuation(Pm,type="heatmap")+geom_text(aes(label=Pm$value),colour="white")+ opts(axis.text.x=theme_text(size = 15),axis.text.y=theme_text(size = 15)) Note that ggfluctuation will also take … Continue reading...
The Timely Portfolio blog via R-bloggers has recently published some interesting entries about the value of horizon plots for visual comparison of a number of time series. Very nice it looks too. You can read more about them here. The trick to understanding them is to imagine that each row was orginally a line chart … Continue reading...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
