Thanks to those of you who were able to join our inaugural Data Charrette Meetup! We had a productive first meeting: we discussed the potential dates for both the fall 2018 and spring 2019 events, we discussed software issues, and we discussed some possible nonprofit organizations with whom we could partner for our next event.
Perhaps our two biggest goals, for right now, are these: First, find a way to make the work at the charrette faster by preparing data analysis templates in R and Python and presentation templates in Google Slides; and second, find a way to do at least some data preparation ahead of the actual event.
As for making templates, we have a tiny amount of preliminary work on that in R that was completed by Barton Poulson. You can download these files (incomplete as they are) and other charrette files from our shared Google Drive folder at https://bit.ly/data-charrette-files. (Please contact Bart at email@example.com if there are any issues with this link or this folder.) The idea is to set up the scripts so that the data can be imported into a data frame called “df” (or maybe a tibble called “tb”) and then split into the outcome variable (called “y”) and the predictor variables/features (called “X”), although it can also be helpful to split those into quantitative and categorical groups (“X_qnt” and “X_cat,” respectively). By using these labels, much of the remaining code can be written generically and easily recycled. (This may be standard practice among programmers, but, as an academic researcher, I only learned about it a few months ago.)
We’ve also created the following web accounts and resources, although there’s not much of anything there right now:
- Slack: https://datacharrette.slack.com (and here’s the invitation link
- LinkedIn page: https://www.linkedin.com/company/datacharrette
- LinkedIn group: https://www.linkedin.com/groups/13609740
- Google Drive (again): https://bit.ly/data-charrette-files
- Wikidot: http://datacharrette.wikidot.com (Maybe that’s a better way to create collaborative documents, but I don’t really know because I’ve never used it before and so it’s totally blank at the moment)
- GitHub: https://github.com/datacharrette (This is probably be the most technically-correct way to collaborate on R and Python code, etc. Again, I have no experience with this, but I imagine may of you do.)
If you need to be granted access, please email firstname.lastname@example.org. Or if you spot any rookie errors, please do the same!
And with that, I look forward to hearing from you, collaborating online, and – hopefully – seeing you at the next Data Charrette Meetup!