The answer to this question for most scientists is usually ‘to R’. Which for me, was not the happiest realisation of all time. R* is amazing in its ability to perform nearly any analyses you want it to, but is equally amazing in its ability to frustrate. All analyses are run using computer code - a language I speak with less fluency than French (just read last month’s blog to see my level of French). I recall the first time I used R - it was during my honours and it wasn’t a pretty sight. I spent countless hours just trying load my datasheet in the program only to realise it had some fatal flaws (as mentioned in an earlier blog, this can be as little as a space in one data cell) which took hours to find and rectify. Then I had to reload my data set. Trickier than one might imagine considering I’d done it once already - I had already forgotten how I’d done it in the first place. Fortunately for me, my PhD experiences in R have been more positive. I’ve become a little more fluent at R code (or at least have a bigger vocabulary) and the red error messages that still scream at me from my screen no longer send me into a cowering heap.  I’ve also learnt the power of google - somewhere, sometime, someone else has had a similar problem and often their forum questions have been answered in  English (as opposed to Statistics). So there is a wealth of knowledge out there, as long as you know how to find it. I feel like my statistical abilities have really improved in this PhD but I wanted to improve them more. Hence the collaboration with two academics at l’Université d’Angers in France. These two ecologists, who are well versed in the behavioural ecology of African grazers and the statistical methods commonly used in this field, were interested in my project and keen to help me to analyse the data for one of my papers. So, this seemed like the perfect addition to the conservation biology conference in Montpellier to round out my French working trip.