Fitbits are increasingly popular amongst the general public as well as health researchers. However, extracting the desired data from a Fitbit has often proved challenging. In this post, we will take through how to use the Fitbit Web API to do this as well as introduce a Shiny Application to simplify the process.
About
I am a 2nd year PhD student at Loughborough University in the School of Sport, Exercise and Health Science. My research focuses on physical activity: the healthful benefits it has, how best it can be quantified and how to encourage the population to do more of it. My PhD is looking specifically at helping to answer these questions regarding short bouts of physical.
During my PhD I have developed a keen interest in all things data science and have recently started to focus on using these skills to develop software including R packages and Shiny applications.
This blog aims to highlight both my research, and data science interests and hopefully can prove useful to you or your colleagues.
TidyModels is a framework for modeling within R. We will use it to answer two key research questions by creating a number of models, then tuning a model and using these models to predict an outcome.
Introduction to Python Before we begin running any analysis, lets import a few libraries that will be useful during our analysis.
Racing bar charts are an interesting visualisation that can show changes in categorical data across time. We will look at how to create he graphs and show a number of examples.
The full slide deck delivered during a presentation given to the CLiMB research group on the 15-06-2021.
An overview of the methodologies and results of the proposed scoping review which will form part of my PhD Thesis.
An overview of the methodology and results of the Individual Participant Data (IPD) meta-analysis which will form part of my PhD Thesis.
This post provides a summary of my PhD Thesis. Each planned study is covered and links to longer form discussions are provided for those that are interested
There are a number of different flavours that can be found in whiskys. In this analysis we analyse which flavours commonly appear together using a network graph. Then we perform cluster analysis and plot these clusters on a map to determine if geogrphic location has an impact on the flavours of whiskys.
Web scraping can be an incredibly useful tool to obtain a dataset from the internet. In this analysis, we scrape some review data from Amazon before performing some basic text analysis on this data.