Is your organisation’s data as clean as its toilets? This weekend, I spent over two hours deep cleaning our downstairs bathroom. Let’s be clear. I’m not one of those people who loves cleaning. However, we do have three vivacious kids who enjoy playing outside (barefoot) and so a certain amount Read more…
the power of packages in R One of the wonderful things about R / RStudio are the many, many packages which expand the capabilities of R. There are a suite of packages that I use most weeks – these include ggplot2 (for plotting), data.table (for fast data manipulation) and foreign Read more…
Predicting pipe failures with GLMs in R A lot of my time is helping infrastructure companies predict where and when their pipes will fail. One of the approaches I commonly use is to build generalised linear models (GLMs), to predict the location and timing of failures on the basis of Read more…
Is data.table or tidyverse’s dplyr faster at summarising data? When dealing with large datasets, I commonly have to summarise data in order to discover, and then communicate the relevant information. Tabular summaries in R like these are similar to using a pivot table in Excel, but with two differences: 1) Read more…
Chances are that you have started to hear more about R in the last few years, and you’ve wondered if you should be using it in your work. In this post I explore the reasons why I now use R for almost all my work with environmental and infrastructure data. Read more…
Do you ever face a blank script, and not know where to start? This is one reason I find it useful to have a template to follow for my R scripts. The three main reasons I use a template are as follows: a template gets me started, so I rarely Read more…
I always encourage my students (and myself) to try to work in a reproducible manner. It is a bit slower at the start but pays dividends in the end when you have to re-do things (which happens all the time) or if you misplace some data. To work reproducibly, when Read more…