dplyr

Rolling Average Functions - Part 2

Introduction Wait didn’t you already have one of these tutorial posts? The short answer is yes, in a previous post I reviewed a much more manual way to apply a rolling average function to a set of time series data. I will be leaving that as is since there are still valuable techniques to gain from it (markedly using the floor_date function), but intend for this post to expand and simplify those initial concepts.

Extracting Data from REDCap's API with R

Introduction The REDCap Project was started at Vanderbilt University in 2004 and has a robust following of research institutions and collaborative teams that rely on it for database creation, manipulation, and storage. One of the many key strengths to REDCap is that users don’t have to be computer science or database engineering experts to get started with database creation. The simple GUI system requires very minimal coding (if done at a shallow level a user can get away with zero coding whatsoever).

Handling Time Series Data with Rolling Averages

Introduction When looking at time series data it can be useful to consolidate high frequency data into lower frequency increments. At first this sounds contradictory to common practice, after all isn’t more data always better? It turns out this isn’t always the case and this can be where a rolling average (also known as a moving average) can be helpful. To note, this tutorial will only cover use cases where time series values are of interest.

Analyzing Apple Health Data

Looking at the Data iPhone Gives You