I like the Economist theme in the latticeExtra package. It produces nice looking charts that mimic the design of the weekly newspaper, such as in this example:
For some time I wondered how I could put the title of my lattice plots into the top left corner as well (by default titles are centred). Reviewing the code of the theEconomist.theme function by Felix Andrews reveals the trick. It is the setting of par.
At the last Cologne R user meeting Holger Zien gave a great introduction to dynamic linear models (dlm). One special case of a dlm is the Kalman filter, which I will discuss in this post in more detail. I kind of used it earlier when I measured the temperature with my Arduino at home. Over the last week I came across the wonderful quantitative economic modelling site quant-econ.net, designed and written by Thomas J.
The example I present here is a little silly, yet it illustrates how to join tables with data.table in R. Mapping old data to new dataCategories in general are never fixed, they always change at some point. And then the trouble starts with the data. For example not that long ago we didn’t distinguish between smartphones and dumbphones, or video on demand and video rental shops. I would like to back track price change data for smartphones and online movie rental shops, assuming that their earlier development can be set to the categories they were formerly part of, namely mobile and video rental shops to create indices.
The other day I had data that showed the development of many products over time. I grouped the products into categories and visualised the data as line graphs in lattice. But instead of adding an extensive legend to the plot I wanted to add labels to each line’s latest point. How do you do that? It turns out that panel.groups is there to help again. Here is my solution: R code
Last Tuesday I attended the LondonR user group meeting, where Rich and Andy from Mango argued about the better package for multivariate graphics with R: lattice vs. ggplot2. As part of their talk they had a little competition in visualising London Underground performance data, see their slides. Both made heavy use of the respective panelling / faceting capabilities. Additionally Rich used the panel.groups argument of xyplot to fine control the content of each panel.
Lattice plots are a great way of displaying multivariate data in R. Deepayan Sarkar, the author of lattice, has written a fantastic book about Multivariate Data Visualization with R . However, I often have to refer back to the help pages to remind myself how to set and change the legend and how to ensure that the legend will use the same colours as my plot. Thus, I thought I write down an example for future reference.
Waterfall charts are sometimes quite helpful to illustrate the various moving parts in financial data, particularly when I have positive and negative values like a profit and loss statement (P&L). However, they can be a bit of a pain to produce in Excel. Not so in R, thanks to the waterfall package by James Howard. In combination with the latticeExtra package it is nearly a one-liner to produce a good looking waterfall chart that mimics the look of The Economist:
The other day I wrote about the R functions by, apply and friends, which allow me to operate on subsets of data. All those functions work nicely, if the data is given in the right format. More often than not it isn’t and I have to reshape the data beforehand. Thus, time to discuss the reshape function. I will focus on the reshape function in base R, and not the package of the same name.