Lately I tried to visualize an hierarchy with Tableau Desktop. The problem was that the hierarchy had a variable depth because it was tree-based. Each row had an id and a parent_id. Normally hierarchies in Tableau are defined by pulling some fields together, such as product category, product group and product id.

Handling tree-based hierarchies seems to be a lot more complex. I found a plugin at https://github.com/tableau/extension-hierarchy-navigator-sandboxed but this only works online.

So I asked myself how I can handle this using R. I found the R-package data.tree at https://github.com/gluc/data.tree. I want to describe how I use this package to preprocess my data.

Sometimes you generate lots of ggplots of a similar kind, e.g. visualizations of different timeseries. Then you want to highlight some dates where something special had happened and you want to show that the value you are plotting changed at these dates.

So it would be nice if we can refactor these geom_vline or geom_hline calls into one single function.

You know it, I’m sure: You are developing a data transformation using lots of pipes. Then you need to remove one or more lines. You do this using comments.

So far so good. But if it’s the last line you want to remove a simple comment will produce an error because of the trailing pipe symbol at the end of the second to last line.

Here’s a solution:

Lately R version 4.1.0 was released on CRAN (see https://cran.r-project.org).

The MacOS version was one day late. But yesterday it was released, too. So I wanted to test the new version without being able to go back. “No problem”, I thought “I’m already using RSwitch”. RSwitch is a tiny Mac program which allows you to switch between installed R versions.

But unfornately RSwitch told me that R version 4.0.5 was incomplete after I’ve installed version 4.1.0.

The main tool for tracking the action on a website is Google Analytics. But more and more websites switch to other tools such as Matomo or PIWIK PRO due to GDPR.

My employer decided to switch to PIWIK PRO, too. So I was looking for a way to access the data PIWIK PRO was collecting to process it with R. When we used Google Analytics as web analytics tool I used RGoogleAnalytics. I added some enhancements such as caching the data and splitting the requests into daily chunks to handle sampling issues with Google Analytcs.

Unfortunately I haven’t found any R package providing access to PIWIK PRO data. So I wrote my own: piwikproR