We managed a little more adventuring in March.
James signed up to run the Heptonstall fell race, near Hebden Bridge. We decided to make a weekend of it stay over for a night at Widdop Reservoir. This meant that although I wasn’t racing, I managed a couple of nice walks in a new area.
On the last weeked of March, I did a short, tile bagging ride around Catterall. I managed to get about 9 new tiles, and this increased my max cluster, linking up Beacon Fell.
February was not quite as lucrative for tiles.
I did one tile hunting ride early in February, which gained me three new all time tiles and increased my max cluster. We also did a really lovely run in the Howgills, however, I ran on paths I had run before meaning - no new tiles.
February has been quite a crazy month for us, with moving house and interviewing for a new job.
January was suprisingly a good start for tiles. We got a sunny ride in on January 1st along Marshaw. Another weekend we drove out to Longridge and did a loop from Longridge, taking in the house which James grew up in.
I went to Tavira to visit my family and managed to get a couple of tiles from beach walks and a coastal run.
Undoubtedly the main focus for January for me was That’s Lyth.
One of the best features of Veloviewer is the explorer score. Essentially, the map is split into a grid of tiles, and if any of your activities cross into a tile then it gets a tick. Since starting recording rides and runs in 2013, I have ticked off a total of 2141 tiles. In 2018, I covered 762 tiles, and 418 of these were “new” tiles. This image below shows the the tiles which I have covered so far.
Last week I attended the UCREL Summer School in corpus-based natural language processing (NLP). The summer school is taught by leading experts in the field both from Lancaster University and other institutions.
Here are a couple of my thoughts and take aways from the week.
Ethics Cambridge Analytical has provided a perfect example of how not to use data ethically. It serves as an important reminder to always think about how you want to use the data before starting any analysis and keep your research questions constantly in your mind.
When people ask me “How do I learn R”, I always point them towards the excellent R for Data Science book. It’s freely available and I love the order the chapters, starting with visualisation and tidy data before delving into the details of programming with R.
Recently a community has developed around this book, when I act as a mentor. One of the regular community events is Tidy Tuesday, a weekly challenge to take a dataset and create a visualisation.