‘Tis the season! In this article, I’d like to share how I used a smattering of R and some free online services to overcome a surprisingly tricky holiday speedbump. The “Problem” Every year, I, my brother, my sister, and our spouses draw names for Christmas gifts (mostly because we’d all rather buy presents for each others’ kids). This process has just a few requirements: Each person should draw the name of another person in the “gift pool” randomly.
I’ve recently (since the beginning of 2021) been trying my hand learning and using Rust, and so far it has been a really good experience. Rust has a lot to recommend it, including top-notch tooling, inherent memory safety, and blazing speed. That last part comes from the fact that Rust is a compiled, systems programming language and was the inspiration for picking up Rust in the first place. You see, I absolutely love R.
KeepCount was the result of a side project that ended up being used as a work project whose lessons-learned got applied to another side project. Relatively short, strange, trip. The real beginning was my desire to pick up Django, a Python web framework that I would strongly recommend, as a new skill. In doing so, I wanted a project setup that I could easily test, replicate, and deploy into a production environment at need.
Presented May 31st, 2019 @ Memphis NonProfit Data Professionals Meetup The git repository for this talk can be found HERE. The presentation can be viewed HERE. Resources cited in this talk include: SQLCourse: An interactive online training course for SQL, includes good background information about SQL in general. SQLite.org: Website for the SQLite project, houses downloads, documentation, and just about anything else you’d need regarding SQLite.
Presented February 28th, 2019 @ Memphis NonProfit Data Professionals Meetup The git repository for this talk can be found HERE. The presentation can be viewed HERE. Resources cited in this talk include: rvest GitHub Page: GitHub page for the rvest R package for web scraping DataCamp Tutorial: A pretty good tutorial from DataCamp on web scraping with R Analytics Vidhya Tutorial: Another pretty good tutorial from DataCamp on web scraping with R BeautifulSoup Documentation: Official documentation for the BeautifulSoup Python library for web scraping Towards Data Science Tutorial: A pretty good tutorial from Towards Data Science on web scraping with Python Traversy Media Tutorial (YouTube): A video tutorial, for those who learn better by watching that reading Webscraper.
On 09/04/2018, I gave a presentation to a gathering of non-profit and foundation IT and data professionals in Memphis. The git repository for this talk can be found HERE. The presentation can be viewed HERE. Resources cited in this talk include: Tidy Data: A paper by Hadley Wickham providing practical advice on formatting data for programmatic workflows. Data Camp: A really useful site for interactive R training. R for Data Science: An online book by Garrett Grolemund and Hadley Wickham that introduces the basics of data science using R.
During 2017, the state of Mississippi entered a period governed by a court order known as the Stipulated Third Remedial Order, or STRO, pursuant to the ongoing Olivia Y lawsuit. This order included language compelling the state of Mississippi to establish baselines, in partnership with an independent consulting agency, with regards to caseworker contacts with foster children, maltreatment in care occurrence, permanency outcomes for foster children, and statistics related to adoption of foster children.
Repository In the spring of 2017, I prepared a demonstration of an interactive reporting system built on the R/Shiny platform for providing up-to-date and actionable information to MDCPS Social Work Supervisors and Social Workers. The system was built on a philosophy that effective management by data is predicated on providing the right information to the right people in as readily accessible format as possible. For a direct supervisor, this meant providing information about the individual performance of each supervised social worker, highlighting areas needing improvement before a policy requirement had failed to be met.