Analytics
For me, analytics is the discpline of getting insights out of data, and these insights are usually delivered in the form of a data product. A data product can be anything from a model, to an automation, to a report of summary statistics, to a web app.
Thus far in my career, I have mostly stuck to making webapps in shiny and automating reporting, so this section will have an emphasis on those skills.
Natural Language Processing
Caitlin Kennedy, Michael Bunting, and myself have been working through a udemy course on NLP in R as part of working group for Booz Allen, and as we have been meandering our way through the class, we have found the materials to be a bit... lacking at times.
Text Mining in R
Like with every other form of analytics, before any real work can be down, the data must exist (obviously) and be in a working format. The following three tutorials explain how to mine text in R:
Web Scrapping with rvest | APIs with jsonlite | Twitter Data with rtweetPreparing and Exploring Text Data
Once the data has been properly gathered and mined, it needs to be put into a usable format. The following tutorials cover how to clean and explore text data.
Tokenizing with tidytext | Exploring Text DataAnalyzing Data
After exploratory data analysis has been performed, we can do further analysis of the relationships and meaning in text.
Diving into Sentiment Analysis |Deck Themer
I have been using Xaringan quite a bit at work to automate weekly decks. One thing that has really impressed me about this technology is that you can easily change the look of a deck by updating a css file.
Check out the below deck explaining how to get started with Xaringan. The theme for this deck is based off of Booz Allen's (where I work) standard theme for decks.